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OULU 2012

C 418

Hanna Kropsu-Vehkaperä

ENHANCING UNDERSTANDING OF COMPANY-WIDE PRODUCT DATA MANAGEMENT IN ICT COMPANIES

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU, FACULTY OF TECHNOLOGY,DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT

C 418

ACTA

Hanna K

ropsu-Vehkaperä

C418etukansi.kesken.fm Page 1 Tuesday, March 27, 2012 11:34 AM

A C T A U N I V E R S I T A T I S O U L U E N S I SC Te c h n i c a 4 1 8

HANNA KROPSU-VEHKAPERÄ

ENHANCING UNDERSTANDING OF COMPANY-WIDE PRODUCT DATA MANAGEMENT IN ICT COMPANIES

Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of Technology andNatural Sciences of the University of Oulu for publicdefence in OP-sali (Auditorium L10), Linnanmaa, on 4 May2012, at 12 noon

UNIVERSITY OF OULU, OULU 2012

Copyright © 2012Acta Univ. Oul. C 418, 2012

Supervised byProfessor Harri Haapasalo

Reviewed byProfessor Hannu KärkkäinenDoctor Jari Collin

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ISSN 0355-3213 (Printed)ISSN 1796-2226 (Online)

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JUVENES PRINTTAMPERE 2012

Kropsu-Vehkaperä, Hanna, Enhancing understanding of company-wide productdata management in ICT companies. University of Oulu Graduate School; University of Oulu, Faculty of Technology, Department ofIndustrial Engineering and Management, P.O. Box 4610, FI-90014 University of Oulu, FinlandActa Univ. Oul. C 418, 2012Oulu, Finland

Abstract

Data is becoming more critical success factor as business processes rely increasingly oninformation systems. Product data is required to produce, sell, deliver, and invoice a product ininformation systems. Traditionally, product data and product data management (PDM) studieshave focused on product development and related activities, with less attention being paid to PDMin other lifecycle phases.

The purpose of this doctoral dissertation is to clarify challenges and prerequisites for company-wide PDM. The study covers the entire product lifecycle and provides potential solutions fordeveloping company-wide PDM and enhancing PDM understanding as a company-wide action.

The study was realised by collecting and analysing data from those ICT companies that areseeking for better ways to manage a wide product-range, technologically complex products andcomprehensive solutions by enhancing their data management practices. The empiricalpractitioner’s experiences and perceptions are seen to have increased the knowledge in company-wide PDM. This study adopted a case study approach and utilises interviews as the main datacollection method.

This study indicates that company managers have already realised that successful businessoperations require a higher-level understanding of products and related product data. In practice,however, several challenges hinder the ability to achieve the goal of higher-level business-drivenPDM. These challenges include product harmonisation, PDM process development requirementsand information systems development requirements.

The results of this research indicate that product harmonisation is required to better supportefficient product data management. Understanding the true nature of product data, that iscombination of product master data and other general product data, and the content of product datafrom different stakeholder perspectives are prerequisites for functional company-wide PDM.Higher-level product decisions have a significant impact on product data management. Extensiveproduct ranges require general guidelines in order to be manageable, especially as even singleproducts are complex. The results of this study indicate that companies should follow a top-downapproach when developing their PDM practices. The results also indicate that companies requirea generic product structure in order to support unified product management. The main implicationof this dissertation is the support it provides for managers in terms of developing true company-wide product data management practices.

Keywords: ICT, PDM, PLM, product data, product data management, product structure,stakeholders

Kropsu-Vehkaperä, Hanna, Edellytyksiä yrityksen laajuiselle tuotetiedonhallinnalle ICT-yrityksissä. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Teknillinen tiedekunta, Tuotantotalouden osasto,PL 4610, 90014 Oulun yliopistoActa Univ. Oul. C 418, 2012Oulu

Tiivistelmä

Tiedosta on tullut tärkeä liiketoiminnan menestystekijä liiketoimintaprosessien hyödyntäessäyhä vahvemmin tietojärjestelmiä. Tuotteisiin liittyvä tieto on olennaista, jotta tuote voidaan val-mistaa, myydä, toimittaa ja laskuttaa. Tuotetietoa ja sen hallintaa on perinteisesti tarkastelu tuo-tekehityslähtöisesti kun tämä tutkimus pyrkii ymmärtämään tuotetiedon hallintaa kattaen myösedellä mainitut yrityksen toiminnot. Tämän tutkimuksen tavoitteena on tunnistaa haasteita japerusedellytyksiä yrityksenlaajuisten tuotetiedonhallinnan käytäntöjen kehittämiseksi.

Tuotetiedon hallinta yrityksen laajuisena toimintona vaatii ymmärrystä eri toimijoista, jotkakäyttävät tuotetietoa; tiedon luonteesta sekä tiedon hyödyntämisestä eri prosesseissa. Tutkimustoteutettiin ICT yrityksissä, joissa tuotetiedon käytäntöjä tehostamalla haetaan keinoja hallitalaajaa tuotteistoa, teknologisesti monimutkaisia tuotteita sekä kokonaisratkaisuja. Käytännöntoimijoiden kokemukset ja käsitykset ovat ensiarvoisen tärkeitä lisätessä tietoa yrityksen laajui-sesta tuotetiedonhallinnasta. Tutkimus toteutettiin tapaustutkimuksen menetelmin, joissa pääa-siallisena tiedonkeruu menetelmänä hyödynnettiin haastatteluja.

Tämä tutkimus osoittaa, että liiketoimintalähtöisen tuotetiedon hallinnan kehittäminen onajankohtaista yrityksissä. Tutkimuksessa tunnistetaan lukuisia haasteita, jotka ovat estäneet lii-ketoimintalähtöisen tuotetiedonhallinnan saavuttamisen. Näitä haasteita ovat: tuotteen harmoni-sointi yrityksen eri toiminnoissa, tuotetiedon hallinnan prosessien kehittämisen vaatimukset sekätietojärjestelmien kehittämisen vaatimukset.

Tutkimustulosten mukaan tuotteiston harmonisointi on yksi perusedellityksistä tehokkaalletuotetiedon hallinnalle. Yrityksen kattava tuotetiedon hallinta vaatii myös tuotetiedon todellisenluonteen ymmärtämistä, joka koostuu tuotteen master datasta sekä muusta tuotetiedosta. Lisäksion olennaista ymmärtää tuotetiedon sisältö sen todellisten käyttäjien näkökulmasta käsin. Tä-män tutkimuksen tulokset osoittavat myös, että tuotetiedon hallinnan kehittäminen pitäisi edetä”top-down” eli ylhäältä-alas periaatteen mukaan. Tulokset myös viittaavat siihen, että geneeri-nen tuoterakenne tukee yhdenmukaisia tuotehallinta käytäntöjä. Nämä tulokset tarjoavat työssäesitettyjen kuvausten ja mallien ohella tukea tuotetiedon hallinnan käytäntöjen kehittämiseenyrityksen laajuisesti.

Asiasanat: ICT, PDM, PLM, tuotetiedot, tuotteet - elinkaari, tuotteet - hallinta, tuotteet- rakenne

7

Acknowledgements

In the autumn of 2007, when I started my work at the Department of Industrial

Engineering and Management (DIEM) at the University of Oulu, it was not clear

to me what the work could involve, other than working as a teaching assistant.

The intervening years have introduced me to a wide variety of academic work.

Specifically, producing this doctoral thesis has been a learning journey for a

researcher’s work. This journey has offered a lot of different kinds of experiences

and has not always moved forward in a straight line. Some parts of the journey

have been delightful and easily passable, but I have also learned to deal with

strong feelings of frustration and uncertainty. Certain special people have made

the journey easier and more pleasant and I owe them my thanks for enabling me

to complete this work.

First of all, I would to express my great gratitude to my supervisor, Professor

Harri Haapasalo, without whom this research project would not have been

possible. Thank you, Harri, for your constant support and guidance. Your

encouragement along the way has kept me on track when I doubted whether I

could complete this research. I also appreciate that, from the very beginning, you

familiarised me with the different sides of a researcher’s work, which will make

my next steps somewhat easier. It has been joy to work with you.

I also would like to thank the head of DIEM, Professor Pekka Kess, who

offered me the possibility to work at DIEM. Pekka, you have given me the

opportunity to see other views of academic work and your wide network has

enabled my research exchange during my thesis work. You also always had time

for discussions when I had my many questions of doing research.

Special thanks also go to Dr Pekka Belt, Dr Janne Härkönen and Dr Matti

Möttönen. You have showed by your example the new ways of doing research

and encouraged also others to do so – to work as a team. It took some time for me

to be ready for that but, in the end, the review sessions we held were full of

difficult but effective work that resulted in the completion of this thesis summary.

I certainly needed the positive push that you gave me in order to finalise my

thesis. Also, several people have offered their support as co-authors of the journal

articles. Many thanks to Professor Harri Haapasalo, Associate Professor Kongkiti

Phusavat, Dr Janne Härkönen, Mr Risto Silvola, Mrs Suvi Lokkinen and Mr Olli

Jääskeläinen for their valuable support and contributions to the articles.

I am very grateful to TEKES, the University of Oulu and DIEM, and the

partner companies that funded the original project (PLMD2). I would like to

8

thank the project organisation all the support for my research. The discussions in

steering group were very valuable and gave me confidence regarding the practical

applicability of the research. I would like to especially thank Mr Risto Silvola for

his commitment to this project and support for the research. You, Risto, have a

very special innovative attitude towards developing data management and

business effectiveness. Your help and wide network was also priceless when

organising the data collection in companies. Your support was also valuable and

encouraging in my moments of frustration. I would also like to thank the other

PLMD2 project researchers, Mr Olli Jääskeläinen, Mr Tapani Kemppainen and

Mr Mikko Vahtiala, for their work efforts during the research. I also want to thank

the almost 20 students who participated in the autumn of 2008 in the product data

management course and took part in the research assignment. Warm thanks for

your work in carrying out and documenting the interviews. I am also grateful to

all the company representatives who took part in the interviews for providing

their time and knowledge. You have offered me a great learning opportunity for

product data management in practice. I have been privileged to meet and discuss

with people who have strong professional know-how.

One special path on my journey led me to the wonderful country of Thailand.

I am very grateful to Associate Professor Kongkiti Phusavat for your support in

making my research exchange possible at Kasetsart University in Bangkok. I

would like to thank you your time for discussions, guidance in making research,

and your special kind of hospitality during my stay.

I also want to thank my former and present colleagues at the DIEM for your

co-operation. Special thanks are due to Dr Mirja Väänänen, Dr Maila Herrala, Ms

Anyanitha Distanont and Mr Tuomo Kinnunen for your valuable peer support.

I would also like to thank the pre-examiners Professor Hannu Kärkkäinen and

Dr Jari Collin for their constructive critique of the thesis.

I would like to express my gratitude to the following foundations that gave

me financial support during my doctoral studies and thesis work: Tauno

Tönningin säätiö, Nokia Foundation, TeliaSonera Finland Oyj:n tutkimus- ja

koulutussäätiö, Riitta ja Jorma J. Takasen Säätiö, and RADMA (Research and

Development Management).

The latest year has brought dramatic change in my life and I am really

leaving the busiest year of my life. My husband and I welcomed our first child,

Vilma, in the spring of 2011, while we were building a house and I had this thesis

project to finalise. One can feel insane finalising a thesis under such

circumstances, but I did it for three reasons. First, the incomplete thesis was on

9

my mind too much, making me unable to even make other things and, secondly, it

was not so badly incomplete after all. Thirdly, and most importantly, I received

great encouragement and support from my family. My parents’ support has being

priceless for surviving and concluding this project successfully. Without your

altruistic support, Mum and Dad, I could not have completed this thesis yet. You

have done so much for my sake, more than you can ever believe. I also appreciate

the encouragement that the rest of the family, in-laws, and friends have showed to

me during this project.

I am indescribably grateful for all the support and love of my husband, Janne.

This project has not always been easy to me but your positive grasp on life and

way of handling issues enabled me to calm my storms of frustrations sooner. Your

own research experience has been valuable to me and I have received practical

advice when needed. I am grateful for your patience and support when I was

questioning the sanity of this research. Thank you for believing me during this

project. Last but not least, Vilma, you have been the reason and given me the

power to complete the thesis sooner rather than later.

Oulu, Finland, March 2012 Hanna Kropsu-Vehkaperä

10

11

List of abbreviations and definitions

BOM Bill-of-Material

CAD Computer-Aided Design

CAM Computer-Aided Manufacturing

ECM Engineering Change Management

EOL End of Life

ERP Enterprise Resource Planning

FEM Finite Element Method

HW Hardware

ICT Information and Communications Technology

IT Information Technology

OEM Original Equipment Manufacturer

PC Personal Computer

PDM Product Data Management

PLC Product Lifecycle

PLM Product Lifecycle Management

STEP Standard for the Exchange of Product model data

SW Software

Data

Data is digitally stored information (Encyclopædia Britannica 2009).

Data occurs in forms such as symbols, images, and texts (Lawrence

1999, Williamson 1982).

Product

A product is an item that has been made to be sold. It can be

hardware, software, service or a combination thereof. Product may

also contain documentation.

Product Data

Product data broadly covers all data related to a product. Product

data ensures that a company manufactures, delivers, sells and

maintains correct products.

12

13

List of original publications

This dissertation is based on the following publications:

I Kropsu-Vehkapera H, Haapasalo H, Harkonen J & Silvola R (2009) Product data management practices in high-tech companies. Industrial Management & Data Systems 109(6): 758–774.

II Kropsu-Vehkapera H, Haapasalo H, Lokkinen S & Phusavat K (2011) The influence of product complexity on order handling process. International Journal of Innovation and Learning 10(2): 123–143.

III Kropsu-Vehkapera H & Haapasalo H (2012) Defining product data views for different stakeholders. Journal of Computer Information Systems 52(2): 61–72.

IV Kropsu-Vehkapera H, Haapasalo H, Jaaskelainen O & Phusavat K (2011) Product Configuration Management in ICT Companies: The Practitioners’ Perspective. Technology and Investment 2(4): 273–285.

The author of this dissertation was the primary author of all of the original

publications. The role of the researcher is described detailed in Chapter 1.3. The

role of the co-authors included reviewing and commenting on the article

manuscripts of the first author. In addition, the author of this dissertation has

participated as a team member in broader research on product development in the

department of industrial engineering and management at the University of Oulu,

which resulted in several other publications as a co-author.

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15

Contents

Abstract

Tiivistelmä

Acknowledgements 7 List of abbreviations and definitions 11 List of original publications 13 Contents 15 1 Introduction 17

1.1 Background ............................................................................................. 17 1.2 Objectives and scope ............................................................................... 21 1.3 Research process and dissertation structure ............................................ 23

2 Literature review 29 2.1 Theoretical framework ............................................................................ 29 2.2 Multidimensionality of a product ............................................................ 31

2.2.1 Defining the product ..................................................................... 31 2.2.2 Different iterations of a product ................................................... 32 2.2.3 Product complexity ....................................................................... 33

2.3 Managing product data ............................................................................ 34 2.3.1 Product data management ............................................................. 34 2.3.2 Defining product data ................................................................... 36 2.3.3 Identifying the content of product data ......................................... 37 2.3.4 Product structure as a way to model a product and product

data ............................................................................................... 39 2.4 Internal stakeholders – creators and users of product data ...................... 42

2.4.1 Product lifecycle ........................................................................... 42 2.4.2 Business processes and key stakeholders ..................................... 43

2.5 Theoretical outline for analysing company-wide product data

management ............................................................................................ 44 3 Research contribution 47

3.1 Key challenges on managing PDM in ICT companies ........................... 47 3.2 Internal stakeholders and their view to product data ............................... 51 3.3 Enhancing the management of product data ........................................... 55 3.4 Results summary ..................................................................................... 59

4 Discussion 63 4.1 Theoretical implications .......................................................................... 63 4.2 Practical implications .............................................................................. 65

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4.3 Reliability and validity ............................................................................ 67 4.4 Recommendations for further research ................................................... 71

References 73 Original publications 83

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1 Introduction

1.1 Background

The current business environment, with its fierce competition, requires companies

to continuously seek for new ways to reduce costs and improve the effectiveness

of their activities. In particular, the operational environment of information and

communications technology (ICT) companies has changed dramatically over the

past few decades (e.g. Rao et al. 2004). Frequent technology development,

increased product complexity, constant time-to-market pressures, and heavy price

erosion have contributed to the current situation (Lehto et al. 2011, Belt et al.

2009, Helo 2004, Moore 1999, D’Aveni 1995). Technological development has

had a dramatic effect on the ICT industry, which now includes the sectors of

information technology and telecommunications and has recently even converged

with the media industry (Hallikas et al. 2008, Palmberg et al. 2006). The PC and

Internet revolution, as well as digital communication networks, has created new

business opportunities. The nature of ICT products is changing from traditional

electronic devices to offerings that include a great amount of internal integration,

as well as converging technological products and services (Pynnönen et al. 2008,

Mikkonen et al. 2008, Palmberg & Martikainen 2006, Rao et al. 2004). Even

traditional manufacturing companies are moving towards service orientation (e.g.

Baines et al. 2009). Consequently, the overall variety of ICT products 1 is

immense.

The heterogeneous nature of ICT products, combined with wide product

portfolios, increasing product variation, internal product complexity, and

continuous product changes, makes product data management (PDM) challenging.

Product management and, in particular, efficient product data sharing through

product lifecycle, has become a necessity for fast product introductions

(Buffington 2011, Ouertani et al. 2011), for process functionality with

collaborative partners in supply chain (Gerritsen et al. 2008), and for ensuring the

1 The OECD (2003) defines ICT goods as “those that are either intended to fulfil the function of information processing and communication by electronic means, including transmission and display, OR which use electronic processing to detect, measure and/or record physical phenomena, or to control a physical process”. The EU Commission (2006) adds services to the definition of ICT products: “any communication device or application … as well as the various services and applications associated with them”.

18

quality of product, service and support. All of these factors contribute to the

overall efficiency of a company’s value-creation chain (Terzi et al. 2010).

Modern digitalisation has made it possible to organise work in multiple sites

while enabling tight collaboration with business partners (e.g. suppliers and

subcontractors). One of the major challenges in this operational environment is to

ensure adequate data between different sites and stakeholders. Data has become

an important asset that makes data quality a topical issue for ensuring daily

operations (e.g. Redman 2008). The increasing amount of data makes data

management challenging, which leads to data quality problems that are very

common in today’s companies (Breuer 2009, Lee et al. 2006, Knolmayer &

Röthlin 2006). Data errors and inconsistencies cause data quality issues, which

leads to mistakes, lost opportunities, failed deliveries and invoicing problems (for

more examples, see Redman (2008)). Experts have estimated the costs of poor

information quality to equate to 15–25 percent of operating profits (Olson 2003).

According to Russom (2006), product data causes 43 percent of all data problems

in organisations. Product data-related challenges are topical today, with the vast

amount of data created and handled throughout a product’s lifecycle, and they

continue to increase as companies are required to keep track of products longer in

their IT systems (Saaksvuori & Immonen 2008, Ameri & Dutta 2005, Crnkovic et

al. 2003). However, most managers believe that IT systems actually ensure the

quality of data (Redman 2008). Figure 1 illustrates changes in the business

environment that influence product data management in ICT companies.

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Fig. 1. Changes in the business environment increasing the importance of product

data management in ICT companies.

Since the 1980s, engineering companies have taken steps to electronically

manage engineering data (also known as engineering drawings) (Huang et al.

2004, CIMdata 2002, Sulaiman 2000). In the mid-1990s, the concept of product

data management (PDM) was introduced to the business world (e.g. Rangan et al.

2005, Sulaiman 2000, Halttunen & Hokkanen 1995). This caused a shift in focus

towards integrated and managed product data processes and applications in order

to manage all kind of information that define products2 across multiple systems

and media (e.g. Saaksvuori & Immonen, 2008, Stark 2005, Philpotts 1996). Today,

companies are looking for company-wide solutions to improve their business

performance through better product data management (CIMdata 2002). The

current trend in PDM discussion is to cover the entire product lifecycle in data

management. Consequently, the product lifecycle management (PLM) concept

has emerged recently, placing greater emphasis on full, integrated lifecycle

management (e.g. Giménez et al. 2008, Sudarsan et al. 2005). Today, PLM is the

predominant concept and also covers PDM activities (see, e.g. Stark 2005,

Sudarsan et al. 2005, CIMdata 2002).

2 When considering PDM, a “product” can be understood as a product type with possible revisions and variants, not as an individual serial-numbered item delivered to the customer. PDM is not intended to manage individual products that are delivered to customers in the first place. Enterprise resource planning (ERP) and the like are typically used for these individual product records (e.g. Philpotts 1996, Rangan et al. 2005)

Quick product development- more products in shorter time- more product variations and versions

Cost reduction- process perspective

-reusing the existing design and documentation- tackle the lack of product data in processes

- product perspective- increase effectiveness and volume by productconfiguration

New business models- life extended services- e-commerce- collaborative operations

Amount of product data is increasingNeeds for product configuration and change management

Reuse of product strucureImprove the quality and availability of product data

Need for product data model

Product data needed over longer timeProduct need to be clearly definedNeed for product data model

Drivers Needs for PDM

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PLM promises to tackle the challenges of managing all product-related data,

from cradle to grave (e.g. Saaksvuori & Immonen 2008, Sudarsan et al. 2005).

However, the promise of full lifecycle-wide product data management has not yet

been reached in most organisations (Rangan et al. 2005). In fact, product

development and manufacturing views of product data have dominated the

discussion and solutions on PDM (e.g. Stark 2005, Abramovici & Sieg 2002, Liu

& Xu 2001). Only individual studies (e.g. Johansson and Medbo (2004) study on

material supply) have tried to raise discussions on other viewpoints. In addition,

according to Ala-Risku (2009), surprisingly few academic studies have actually

covered, even shortly, product data in operations and maintenance phases in PLM

discussion. It has also been argued that the methods used to collect product data

from middle and end-of-life phases are incomplete in practice (Yang et al. 2007,

Jun et al. 2007, Abramovici 2007). Silo thinking lives on, promoting functional

orientation, and the organisational processes and distinct applications all too often

fail to support the idea of smooth data flow through the entire order-delivery

process (Anderson et al. 2006). Stakeholders across the product lifecycle, such as

sales, delivery and after-sales, should be included in order to reach a true

company-wide PDM (Gerritsen et al. 2008). The few PLM practical experts place

greater emphasis on different operations and stakeholders when discussing

product data management (see Saaksvuori (2011), Saaksvuori & Immonen (2008)

and Stark (2005)). It seems that there is very limited knowledge about the role of

product data in other operations than product development and manufacturing.

True organisation-wide product data management cannot be achieved without

understanding the product and related data from the business perspective. In the

networks of diverse stakeholders, the way product data is understood becomes

especially challenging. Product data is complex in nature and is scattered over

organisation and managed separately, having different meanings (Snow 2008,

Saaksvuori & Immonen 2008, Boyd 2006, Sudarsan et al. 2005). Product data

exists in different forms in various applications (Feng et al. 2009, Liu & Xu 2001),

often in conflicting formats due to the lack of standardisation (Baïna et al. 2009,

Lee et al. 2006). All these variations have led to variance in the business concepts

and object definitions (Moss 2007). Consequently, integration of applications,

data and processes across companies is challenging (e.g. Qu & Wang 2011,

Silcher et al. 2010, Shu & Wang 2005).

In order to be competitive, companies require a single, company-wide

presentation of product data that can be utilised across an entire organisation

(Saaksvuori & Immonen 2008). According to Abramovici (2007), this kind of

21

company-wide product presentation is not yet to be fully realised, which leaves

room for further studies. In order to improve product data management that

covers company’s product related processes and integrates diverse organisational

product data requirements and product concepts, it is necessary to have a holistic

product data model that unifies product terminology across a company (Baïna et

al. 2009, Hvam et al. 2002, Sudarsan et al. 2005, Svensson & Malmqvist 2002).

The need to share information across the company and supply chains requires that

data is exposed and shared from silos and presented in a unified format (Boyd

2006, Dumas et al. 2005).

Overall, the benefits of PLM are widely recognised (e.g. Terzi et al. 2010,

Saaksvuori & Immonen 2008, Alemanni et al. 2008, Stark 2005, CIMdata 2002),

although the implementation of full scope PLM appears to remain a challenge.

The focus of PLM is still certain special activities rather than the entire lifecycle

of a product (Saaksvuori 2011, Siller et al. 2008, Ming et al. 2005). Also, the

academic literature has not met the challenge of recognising the relevant product

data for diverse business processes (e.g. Terzi et al. 2010) and cannot explain

what product data is by noting all the different stakeholders that use the product

data, even though product data harmonisation and standardization remains a

topical challenge for practitioners (e.g. Saaksvuori 2011). The focus of the present

thesis is on product data, how different stakeholders perceive it, and how to

expand the understanding and utilisation of product data throughout the company.

In order to tackle this challenge and manage the heterogeneous set of information,

it is necessary to have a model that helps recognise all relevant information

related to the product from diverse stakeholders’ viewpoints. This kind of

approach could also help companies create common terminology for their

products that have been pointed out as an important issue for practitioners. This

dissertation aims to identify the elements that are needed in order to develop a

company-wide product data representation.

1.2 Objectives and scope

Success in business today relies on information about markets and competitors, as

well as internal information on one’s own products and processes. The

significance of product data has increased due to product complexity and the need

to provide product variations. Companies face challenges related to the effective

use of product data as the required information is scattered and is typically

located in different parts of organisations and in multiple data bases with different

22

formats. In addition, product data needs vary depending on which stakeholder

group’s needs are to be addressed. Consequently, there is a clear need to improve

product data management across company functions.

This study explores the role of product data in modern, global ICT business.

The research objective that this doctoral dissertation attempts to address can be

formulated as:

To recognise challenges and prerequisites for company-wide product data

management covering the product’s entire lifecycle, and to provide potential

solutions on how to approach company-wide product data management.

The research objective is further divided into the following research questions:

RQ1. What are the key product data management challenges in ICT

companies?

RQ2. Who are the relevant key stakeholders using product data and how is

product data understood in different parts of a company?

RQ3. How can the company-wide management of product data be enhanced?

RQ1 aims to map the fundamental PDM challenges that apply for true ICT

companies. RQ2 clarifies what product data is throughout a company and which

actors utilise it. RQ3 takes a more holistic approach towards improving product

data management in a company-wide context.

The research questions are answered by four research articles covering

different viewpoints, all of which provide a partial answer for the research

objective. RQ1 is answered by a combination of articles I, II and IV. RQ2 is

answered by article III and RQ3 by articles III and IV.

This dissertation understands PDM as a part of the PLM concept. PLM can

be approached by numerous product management entities that are often studied

from this particular managerial viewpoint. The following product management

entities can be considered as part, but not all, of PLM: product data management,

product change management, configuration management, product structure

management, product program management, product portfolio management,

workflow and process management, and product development management (e.g.

Liu et al. 2009, Ebert & De Man 2008, Saaksvuori & Immonen 2008, Stark 2005).

However, the present study does not intend to discuss all of the different sub-areas

of PLM in great detail.

23

Product data studies are mainly focused on engineering-related data and its

management on PLM context (e.g. Siller et al. 2008, Kiritsis et al. 2003).

Therefore, the present study focuses on understanding the nature of product data

experienced by company internal stakeholders and how the product data should

be modelled in order to reach the company-wide perception of product data. This

dissertation is managerial in nature, without particularly focusing on data systems

while discussing product data. The data systems viewpoint has dominated

previous research, which leaves room for complementary viewpoints.

The focus of this dissertation is limited to large and medium-sized ICT

companies. As the ICT sector is broad in nature, the study is particularly

interested companies whose products combine hardware, software or services

(such as maintenance or consultancy), thereby making their products

heterogeneous in nature. The products are physical goods with services or

services with heavy physical infrastructure, which excludes pure software or

media products. Technology and knowledge intensity is high in the products

studied.

In order to avoid confusion, it is important to clarify the terminology for

using data and information. Data is often defined as facts (Alter 1999, Pollock

and Hodgson 2004) that occur in the form of symbols, images or text, for

example (Lawrence 1999, Williamson 1982), whereas information is defined as

data that has been processed in some manner (Lawrence 1999, Wang 1998,

Williamson 1982). The terms data and information are often used synonymously

and these two terms are separated intuitively in practice (Wang 1998). In the

context of PLM, the term product data is often used interchangeably with the

term product information when they actually mean the same thing (Fensel et al.

2001, Liu & Xu 2001). The present study uses the term product data since it is

more commonly used when discussed product data management and related

issues. Therefore, data in this case is not only unprocessed data but also

understood to include the wider definition of product data to cover all product-

related data that is used for communication, interpretation, or processing, either

by human beings or by computers.

1.3 Research process and dissertation structure

According to Lancaster (2005), researchers considering scientific research from a

philosophical viewpoint face epistemological, ontological and ethical questions.

Namely, how can one believe and know of reality based on scientific research,

24

how is scientific knowledge obtained and when is this knowledge scientific; when

does the researcher abuse his research object or act unethically against the

scientific community.

Epistemology considers what is understood as appropriate knowledge

regarding the social world. An important aspect is the question of whether a

natural science model of research process is suitable for studying the social world.

(Bryman & Bell 2003). Ontology can be seen as a reality in which studied

phenomena are understood to exist and how the studied phenomena relate to this

reality. Ontological pre-conceptions on the nature of studied topics are typical for

scientific research. Ontology determines whether the reality is objective or

subjective. Ontology is understood to influence the selection of theory and

concepts. (Harisalo 2008, Anttila 2005). Figure 2 illustrates the epistemological

and ontological basis for this research.

Fig. 2. Epistemological and ontological basis.

Epistemology can be roughly divided into positivism and interpretivism.

Positivism reflects a view in which a phenomenon is sought to explain causal

relationships or regularities, whereas interpretivism emphasises the understanding

of a phenomenon through those who are involved in it. (Saunders et al. 2009).

The present is closer to interpretivism than positivism. The research approaches

product data management as a company-wide issue, which is not a standardised

one. In general, academic PLM research is relatively young (e.g. Kärkkäinen et al.

2009), which means that the related concepts are vague. Consequently, this study

aims to increase the understanding of the subject; in order to understand this

phenomenon, it is necessary to first understand the involved persons and then

how they perceive product data and its management.

Ontology can be roughly divided into objectivism and subjectivism.

Objectivism is an ontological position that implies that research is based on facts

rather than subjective analysis. Objectivism sees phenomena as being independent

of social actors. In objectivism, a company or an organisation is seen as a

machine-like entity that functions based on standards, guidelines, rules and

Epistemology

Ontology

Positivism Interpretivism

Objectivism Subjectivism Pragmatism

25

legislation. People are operators and realise processes and values. According to

subjectivism, social phenomena are created by social actors and their observations,

which highlights individual’s experiences. In companies, operations are based on

interaction by people. (Saunders et al. 2009, Bryman & Bell 2003). The present

study is somewhere in the middle of the above ontological scale and can be seen

to follow pragmatism. Created knowledge requires a contextual understanding

and is valuable when used in practice.

Since the research topic, which approaches PDM as a company-wide issue, is

not standardised, the qualitative method was seen as providing the best support

for this study. The aim of this study is to create in-depth understanding of product

data and its company-wide management when the experiences of industry

managers and practitioners were found to be the key issue in order to obtain the

research goals.

This study has utilised inductive logic of reasoning. Inductive logic can also

benefit the dialogue of theory and observations and is not necessarily a one-way

approach from observations to theories (Bryman & Bell 2003). The present study

uses earlier studies as background of qualitative investigations in order to ensure

that relevant concepts are clearly defined for the research phase. However, the

core of the research is intended to create new knowledge based on the findings.

The interviews form the foundation for the study and utilises the understanding of

the researcher and other relevant actors.

Qualitative research is often seen as a way of gaining a deeper understanding

of a phenomenon, which makes it fitting for the purpose of this research. The case

study method was selected for data collection for several reasons. Case studies are

often used in order to increase the understanding of a fuzzy phenomenon. The

case study method makes it possible to utilise different techniques on data

collection and have a strong empirical emphasis. (e.g. Yin 2003).

In order to ensure its quality, research must meet the criteria of reliability and

validity. Research design, data collection, and data analysis are the key to

establishing the quality in case studies. (Yin 2003). The validity and reliability of

the research in the present study is designed to be increased in the following ways:

validating the case study reports by the informants; data triangulation such as

using multiple sources of evidence, as well as different perspectives and different

researchers; describing the research process; and establishing a database for

research data. These are discussed further in the following description of the

research process.

26

This research was conducted across four separate studies involving industrial

companies and university researchers. Table 1 describes the researcher’s role in

each of the studies. The researcher was the main planner of individual studies I,

III and IV. For the research in study II which covers a larger study area, the

researcher planned the sub-research part focusing on product data management

issues. In this context, planning denotes defining the research problem, relevant

interview structure and questionnaire, and the subsequent method of analysis. The

researcher was responsible for selecting the relevant informants in studies I, III,

and IV, but also participated in the selection of informants for study II. The

researcher was the major data collector in studies III and IV; study I utilised

research assistants and colleagues collected the data for study II. The contribution

of other researchers in the data collection stage reduced the level of bias in the

data. Importantly, the researcher was responsible for analysing and drawing

conclusions in all of the studies that comprise this dissertation.

Table 1. The researcher’s roles in each sub-study.

Article Planning Selecting

informants

Collecting data Analysing Drawing

conclusions

I X X X X

II X X X

III X X X X X

IV X X X X X

The research process for each study phase was almost identical. Figure 3

describes the typical research process for all four articles upon which this

dissertation is founded. The key steps of each individual study include a literature

review, followed by creation of the interview structure and, in some cases, a

framework for study, data collection, analysis, and conclusions.

Fig. 3. Research process.

The individual studies were mainly carried out inductively. Each study started

with a literature review in order to gain an understanding of the key concepts and

Literature review on

studied issuesData collection Analysis Conclusions

Framework for study

Interview structure

27

key results of previous studies related to the research topic. The literature review

was a foundation upon which to formulate interview structure and questionnaire.

Articles I and III also utilised the literature review in order to create a frame with

which to support the results analysis phase.

The research material is a set of case studies that were selected to support the

research target described in detail in each article. In article III, for example, the

case company selection is based on gaining versatile viewpoints of studied issues.

Cases from the ICT sector were chosen since product data management issues are

topical in this sector. The ICT sector includes a lot of variety, which means there

are vast possibilities for different case selections. The intention was to select

representative cases for different sub-research purposes that, together, support the

main research objective. Basically, in order to get companies interested in this

study, they must all have met product data management to be topical development

issue on their organisations.

The data collection phase in each study included interviews conducted in the

ICT sector. The interviews followed a semi-structured thematic interview

approach and were conducted informally, in a qualitative manner, which enabled

the interviewees to explain and clarify the topics as entities. All of the interviews

were recorded and transcribed in order to ensure the full utilisation of the research

data in the analysis phase. In addition to the interviews, article III included a wide

range of secondary data sources (relevant company documentation) as research

data. Table 2 summarises the industrial involvement and materials used in the

research phases. The table presents the number of companies and the number of

industrial interviews for each research article, as well as the other relevant

material utilised in the study. More than eighty interviews were conducted.

Table 2. Industrial involvement and materials used in research.

Article Nr. of companies Nr. of interviews Others

I 4 12 Workshop with 7 company representatives

II 1 12

III 3 46 16 documents related to companies’ PDM

IV 6 42

In each sub-study, all of the individual interview results were analysed according

to the chosen focus. Next, the general company-wide viewpoint on the focused

issue was drawn in order to understand the meaning of the issue in focus, not

from each interviewee’s viewpoint but from the wider company-wide or other

28

relevant sub-organisational unit viewpoint. In sub-studies II and III, the

informants validated the case reports. When appropriate, the overall results were

compared with the literature. Finally, implications and conclusions were drawn

based on the analysis. Industrial PDM expert group also participated in the study

in order to validate the final conclusions so that the business relevance of the

research reports was ensured. In each of the articles (see the appendices of the

original articles), the research process of each sub-study and conclusions of the

results are presented and discussed in more detail.

The dissertation is comprised of four individual journal articles, plus this

summary. The summary is organised as follows. Chapter 2 presents the theoretical

foundation for the research. Chapter 3 summarises the research contribution.

Chapter 4 discusses the implications of the study and presents the study’s overall

conclusions.

29

2 Literature review

2.1 Theoretical framework

Business can be viewed from different viewpoints, such as the customer, the

process, organisational functions, etc. Product lifecycle management (PLM) takes

a product focus on business (Stark 2005). These days, PLM is a concept that

provides full support for managing products from their early phases until

retirement (e.g. Terzi et al. 2010, Sudarsan et al. 2005). It has shifted from

traditional engineering-focused PDM towards an integrated business management

approach that covers people, processes, and information systems that deal with

products (e.g. Terzi et al. 2010, CIMdata 2002). PLM covers the complete

lifecycle, including several processes and stakeholders, which makes it

challenging to implement (Batenburg et al. 2006). Consequently, when a

company takes this kind of general viewpoint for a matter that is traditionally

handled in a limited manner, it can even be difficult to define the basic issues.

This theoretical review focuses on covering the key concept in order to study

company-wide product data management. Furthermore, this review takes different

viewpoints of the concepts of product, product data and its management, and also

how internal stakeholders utilise the former. The context of the theoretical study

is presented in Figure 4.

Fig. 4. Theoretical framework.

30

This theoretical review discusses products and their multidimensionality in the

context of product data management. The review includes an examination of the

nature of products and their lifecycles, but also products as informational objects.

The multiple meanings of a product and product data complicate the discussion

on product data management, which has not been systematically examined in

earlier studies.

A number of studies have discussed product data management (e.g.

Saaksvuori & Immonen 2008, Forza & Salvador 2007, Crnkovic et al. 2003, Liu

& Xu 2001). The focus of this theoretical review is limited to the basic principles

of PDM, with the intention of understanding the nature of product data and its

role during a product’s lifecycle and related processes. The emphasis is on

gaining knowledge of how product data can be presented in such a way that it can

be better utilised throughout a company. Information systems have an important

role as the main enablers of PLM (e.g. Lee & Suh 2009, Rangan et al. 2005).

However, this literature review does not cover the diverse information systems, as

such. Systems alone cannot solve the organisational challenges; they only support

operational and managerial execution. Information systems are the basis of this

theoretical foundation as they relate to the product and product data management,

but are not handled as a separate section in the theoretical review.

This dissertation aims to clarify the concept of company-wide product data

management. The company-wide aspect is understood here through different

internal stakeholders. Here, the stakeholder approach covers the business

processes and lifecycle aspect. This involves examining the organisational players

that handle product and related data in diverse business processes throughout the

products’ lifecycle. This dissertation does not include the external (extended

company) stakeholders under the review or customer viewpoint to a product and

related data.

The limited focus on engineering data handled in the product development

and manufacturing phases does not make it possible to achieve the objectives of

PLM. To better discuss PLM-related systems, practices, and organisation, it is

necessary to understand the basic concepts from a company-wide perspective.

Developing the company-wide understanding of PLM and related product data

management issues accelerates companies’ learning curve and PLM adaption.

31

2.2 Multidimensionality of a product

2.2.1 Defining the product

The term product is commonly defined as a thing that is made to be sold

(Cambridge Online Dictionary 2011, PDMA 2006). It can be goods, services or

knowledge that are sold. Products can be tangible, physical goods, or intangible,

such as services, or a combination of the two (e.g. Cooper 2001, Ulrich and

Eppinger 2000, Urban and Hauser 1980.) Some scholars separate the concepts of

product and services (e.g. Bebko 2000, Zeithaml & Bitner 1996) whereas others

consider services to only be one type of product (e.g. Saaksvuori & Immonen

2008, Stark 2005). The business environment has changed recently and even

traditional manufacturing companies are providing a product type called solutions;

that is, products that combine physical goods and services (Baines et al. 2009,

Gebauer et al. 2005).

The term product has various meanings. STEP (Standard for the Exchange of

Product model data) defines a product as both the deliverable product for a

customer as well as the sub-parts of the deliverable item (ISO 1994). The term

product also refers the product type as well as the individual serial-numbered item

delivered for a customer (Peltonen 2000). Product can be also understood from

various stakeholder viewpoints. The customer experience of a product is different

for a module manufacturer or than for a maintenance provider (e.g. Stark 2005).

As seen, the term product can be understood in multiple ways (Peltonen

2000). Therefore, this study does not aim to provide the “correct” definition for a

product; instead, the term is used here with the following meaning:

Product is defined as hardware, software, services or some combination of

these elements. Product also contains documents any of earlier defined cases.

Product is understood as a portfolio item and not an individual serial-

numbered item.

Products also have an informational aspect that is becoming more critical as

business processes rely increasingly on information systems. Informational

products represent a deliverable product in applications (Liu et al. 2009). The

product data model has an important role in the building of an informational

product (Vanderfeesten et al. 2011). The informational aspect of a product is

covered more in Chapter 2.3 as part of the discussion of product data.

32

2.2.2 Different iterations of a product

Companies today face the continuous need to provide new products to the market.

This is often handled by developing new version of existing products or creating

new variants that satisfy various customer needs (e.g. Pine 1999, McKay et al.

1996). In order to avoid confusion, it is important to understand the difference

between a product version and a product variant.

New product versions are created to improve some part of an original product

– often to reduce costs, improve quality or product performance. A new product

version replaces a previous older one (Westfechtel et al. 2001, Wilson & Norton

1998), which means that the amount of deliverable products is replaced rather

than increased.

Products are often revised several times during their lifecycle after product

release, which generates a new version to be managed in information systems

(Stark 2005). This creates an extra challenge in the form of keeping track of all

the different types of versions related to deliverable product, sub-modules and

components. Therefore, version management, and especially version

compatibility, can be a challenging task especially when the amount of product is

high (Stark 2005, Crnkovic et al. 2003).

Different product variants satisfy different sets of requirements (McKay et al.

1996). Variants effectively enable product customisation when it is not viable to

produce a unique product for each customer (Lim et al. 2011, Aldanondo &

Vareilles 2008, Pine 1999). Product variants extend the amount of deliverable

items and those that exist in parallel (Wilson & Norton 1998). A variant is a group

of alternatives of which only one is selected for the final deliverable (Pulkkinen

2007).

Product configuration is widely seen as a quick and inexpensive response to

customisation needs (Hvam et al. 2008, Zhu et al. 2008). Product configuration

can be understood as a way of reacting to customer requirements by compiling a

fixed set of pre-defined components that are defined on product design phase (e.g.

Saaksvuori & Immonen 2008, Forza & Salvador 2007, Sabin & Weigel 1998,

Tiihonen et al. 1998b). These pre-defined components are variants, options, and

rules and constraints to configure a product (Cheng & Wang 2009, Tiihonen et al.

1998a). Variants and options guide the customer’s choice.

33

2.2.3 Product complexity

ICT products are typically complex in nature (Lehto et al. 2011, Möttönen 2009

Steinmueller 2001). This complexity can be seen as a consequence of rapid

technological development that enables embedded systems, a short technological

lifecycle that drives redesigning existing product, and services that keep products

alive and active longer. (Saaksvuori & Immonen 2008, McGrath 2001, Meyer &

Zack 1996). Products that combine hardware, software and services are making

production and maintenance more complex (Crnkovic et al. 2003). This study

does not discuss customer-perceived product complexity, which has been widely

studied in the field of industrial marketing (see the review by Zhang & Reichgelt

2006). The focus of this part is to discuss product complexity and how it affects

operational processes.

Product complexity is often considered to mean the technical complexity of a

single product. The technical product complexity includes the number of parts,

the number of different types of parts, the number of interconnections and

interfaces, and the number of the performed functions of an end product (e.g.

Vesterby 2008, Closs et al. 2008, Crnkovic et al. 2003, Harter et al. 2000, Novak

& Eppinger 2001, Pugh 1991). The diverging customer requirements are realised

by customised components, embedded software, and client specific integrations

(see Saaksvuori & Immonen 2008, Hobday 1998) that makes the product even

more complex in nature. In order to manage the complexity of customised

products, the hierarchical level of customisation or special integrations and

installations should be carefully studied and defined (Vesterby 2008, Hobday

1998).

Companies that only handle single products are rare. Therefore, product

complexity can also be approached from a logistical complexity viewpoint

(Khurana 1999). Companies eagerly try to meet diverging customer needs by

offering a large range of product variants and customised solutions, which results

in a wide range of deliverable products (Vesterby 2008, Lamothe et al. 2006,

Kratochvíl & Carson 2005). This leads to wide product portfolios. Product

portfolio complexity is composed of single product complexity elements such as

end product technical complexity, the level of single product variation, and end

product maturity, together with production volume (Choe et al. 1997, Kotha &

Orne 1989). Handling multiple complex products means that the impact of

complexity is greater in operational processes than in product development or

sales (Hoole 2006). High complexity may also require variation in operations,

34

which increases costs (Anderson et al. 2006). However, the potential implications

of product portfolio complexity are not fully understood or recognised (Closs et al.

2008). In addition, frequent product versioning increases the amount of

administrative work required to keep every product and sub-module and part

version up to date (Stark 2005). Since the effects of product complexity are often

obscured by other difficulties (Smith & Reinertsen 1992), it is challenging to see

how product complexity actually affects operational processes. As a whole,

complexity increases on a new level when product complexity is multiplied

exponentially, since operational processes no longer handle a single product as

they do in the product development phase.

Products also have an informational aspect. The “physical” nature of a

product and its complexity is also faced from an informational viewpoint. Product

proliferation and customisation, together with increased service types of products,

have increased the volume and diversity of product data (Jin et al. 2007, Crnkovic

et al. 2003, Forza & Salvador 2002). Services such as maintenance and

consultancy require companies to keep track of products in information systems

for longer, which further increases the amount of product data in applications

(Saaksvuori & Immonen 2008). The role of product data in operational processes

increases when business relies on information systems. Huang et al. (2003, 2005)

and Johansson and Medbo (2004), for example, have stated that supply chain

performance is heavily dependent on product data and its handling. The complex

nature of products, together with vast amount of data, makes effective product

data management throughout the lifecycle a challenging task (Jin et al. 2007,

Anderson et al. 2006).

2.3 Managing product data

2.3.1 Product data management

This dissertation approaches PDM as part of the PLM concept. Several studies

have offered definitions for product data management (PDM), each with their

own emphasis. For example, CIMdata (2009) defined PDM as a business

approach to manage the complete set of product data – its creation, control,

dissemination and usage throughout the product’s lifecycle. Saaksvuori and

Immonen (2008), on the other hand, viewed PDM as including a set of tools and

methods with which to effectively manage product data. Liu and Xu (2001)

35

argued that PDM aims to manage all product data in an integrated manner,

sharing it with those who need it. The present study understands PDM as a system

perspective and defines it as follows: PDM integrates and manages processes,

applications and all kind of data that define products across multiple systems and

media (e.g. Saaksvuori & Immonen 2008, Stark 2005, Philpotts 1996).

The PDM system connects product data related to a product and process

management, providing an infrastructure for controlling and sharing data for users,

together with a related user interface (Rueckel et al. 2005, Stark 2005). PDM

systems contain at least the following basic modules (e.g. Kumar & Midha 2006,

Stark 2005):

– Information warehouse or data vault

– Information warehouse management: tracing any data-related actions

– Document management

– Configuration management

– Product structure management

– Product and workflow structure: definition modules

– Workflow and process management

– System administration management.

According to the above list, compatibility with other IT systems is an essential

element of a PDM system (e.g. Wei et al. 2009, Maletz et al. 2007).

PDM applications are used to gather data from specific software, such as

CAD, CAM and FEM, and to store and administrate data centrally (Stark, 2005).

The standard definition of product-related data is that it is key to integrating

activities in a value chain and to making application integration possible and more

functional (e.g. Giménez et al. 2008). The common definition of product data

includes creating an understanding of data collation needs in later PLC phases,

covering the end-to-end view of a product, as requested by Yang et al. (2007) and

Jun et al. (2007). According to these two authors, PDM and PLM technologies

also involve other challenges. For example, current technologies are not capable

of providing reasonable solutions to support the increasing need to deliver

tailored products (Ming et al. 2007) and adequately support the integration of

products’ mechanic, electronic and software components (Abramovici 2007). In a

complex product environment, the diversity of data formats, such as free text,

structured and semi-structured data and combinations of these, together with

variety of data sources, creates a challenge for data management within integrated

systems (Feng et al. 2009, Yang et al. 2009).

36

2.3.2 Defining product data

The definition of product data in the literature is somewhat ambiguous. In the

PLM context, the term is widely understood to cover all product-related

information that is required to design, produce, sell, deliver and maintain a

product (Fensel et al. 2001, Liu & Xu 2001). Some researchers have specified

that product data can be further divided into product definition data, product

lifecycle data and metadata (Saaksvuori & Immonen 2008, Halttunen &

Hokkanen 1995). Here, product definition data includes exact technical data as

well as abstract and conceptual information about the product, whereas product

lifecycle data identifies the product’s stage in the order-delivery process.

Metadata is information about information used for data management purpose: it

describes the type of product data, its location, and practicalities related to

recording and accessing it (Saaksvuori & Immonen 2008). Since the present study

aims to clarify the content of company-wide product data management, the focus

is on product definition and product lifecycle data; metadata is not further

discussed here.

Product data can be also classified as static and dynamic data (Scheidt &

Zong 1994). Static data is related to the product definition and provides details

about factors such as materials, components and suppliers, product configuration

options, and operational instructions. Generally speaking, static data is created

during product development and usually remains unmodified throughout the

product’s lifecycle. Dynamic data, on the other hand, occurs in different order-

delivery processes, covering the patterns of use, environmental conditions, and

service actions such as part replacements, for example. (Yang et al. 2007,

Hribernik et al. 2006, Simon et al. 2001). Dynamic data includes transactional

data of an individual product item, which is not typically managed under PDM

but in ERP, and similar kind of systems (e.g. Saaksvuori & Immonen 2008).

A new viewpoint for product data is presented under the master data

management discussion. These studies represent a term product master data that

includes (but is not limited to) data such as product descriptions, product item

codes, weight, price and supplier information, as well as product configuration

options (e.g. Snow 2008, Zhang et al. 2004, Nagi 2001). In addition, the master

data is static in nature (White 2007) and remains fixed over a specific period of

time (Loser et al. 2004). These characteristics are similar to the previous

representation of static product data. The master data is typically reference data

37

for transactional data, which means it is used widely across an organisation (e.g.

Otto & Hüner 2009).

However, there are other types of product data that cannot be called master

data, even if they are static in nature. Typical examples include product

specifications such as BOM, technical drawings, and functional models of a

product (e.g. Zhang et al. 2004, Vroom 1996). Some studies have expanded the

contents of product-related data to cover logistic data such as lists of packing

materials or packaging instructions, user guides and engineering change

information (Saaksvuori & Immonen 2008, Crnkovic et al. 2003, Liu & Xu 2001,

Vroom 1996). Some of this data is often informal in nature and is managed by

single disciplines, resulting in unnecessary complexity (Lee et al. 2006).

Based on earlier viewpoints, the term product data is used here with the

following meaning:

Product data is data that is broadly related to a product. Product data

ensures that a company produces, delivers, sells and maintains the correct

products. Product data compounds product master data and product-related

data. Product data is static and does not contain any transactional data (such

as a product’s sales record).

2.3.3 Identifying the content of product data

A lot of product data is created, changed, transferred, stored and converted during

the product lifecycle and is used in daily operations (Lee & Suh 2009). The

definitions of a product data do not describe the actual content of product data.

Few studies have discussed the content of product data. It is challenging to define

the content of product data due to the fact that it is not a standardised concept, is

scattered across an organisation and utilised and created in different business

functions (White 2007, Sudarsan et al. 2005).

Literature reviews of product data contents have been unfruitful and a

systematic review is somewhat impossible due to the fragmented field of studies.

Vroom (1996) is one of the few researchers to have determined and concluded the

different aspects on product data, including the following content:

38

– Product identification code and product name

– List of parts, materials, drawings, testing manual, technical design data

– Parts of product prototype

– Conception of production process design, tooling, inspection guidelines

– Logistics data, such as the number of products per box, transportation

package, description of packing materials, packaging instructions

– Product target price.

One way to obtain ideas about the content of product data is to analyse other

studies from a product data content viewpoint. However, such an approach is

laborious and actually results in a reviewer’s perception of what product data

content might be, rather than being articulated by the original authors. For

example, an analysis of Ulrich and Eppinger’s (2000) description of product

development and related data contents revealed the following product data

elements: product specification, technical drawings and 3D models, functional

model, BOM, guidance for manufacturing and layout of the modules, packaging

design, detail information on individual components, parts, and descriptions of

sub-modules and assemblies. However, this analysis is limited to the product

development phase only. An analysis of Crnkovic et al. (2003) enables the above

list of product data content to be complemented with user manuals and brochures,

SW documentation and source code. However, this kind of analysis should make

for multiple studies in order to cover the full lifecycle perspective.

In addition, only a few case studies also list some examples of product data

contents. Lee and Suh (2009), in particular, represent the EOL data that has been

defined to contain information on different EOL activities (remanufacturing,

repair, recycling, and dispose), legislative information, corporate policies for EOL

activities, as well as market analysis (repair cost vs. market price). Liu and Xu

(2001) took an engineering-oriented viewpoint of product data. They defined

product-related data as typically containing engineering change orders, product

geometry, engineering drawings, project plans, part files, assembly diagrams,

product specifications, analysis results, and BOM, among other things. Johansson

and Johansson (2004) provide examples of what product data is required to design

a material supply system. Most critical data for that purpose is component-related

data (how components should be handled and basic data about components) as

well as product structure data (which product components are linked to). However,

other research covering the other order-delivery process related actions and their

data needs is rare. According to Lee et al. (2006), it is difficult to articulate the

39

specific product data needed to fulfil certain operational tasks. When defining

product data, organisations encounter many problems because the product data

has different forms based on the traditional silo thinking, data is fragmented, and

there are no standards for product data (Feng et al. 2009, Russom 2006, Sudarsan

et al. 2005).

Earlier studies provide some examples of the actual content of product data.

Aforementioned examples describe some product data contents, such as product

identification, construction of a product, procurement, logistics and EOL

activities. However, it is very difficult to create a holistic view of product data

content throughout the lifecycle based on the literature.

2.3.4 Product structure as a way to model a product and product data

The informality and tacitness of product data complicates the creation of a formal

and structured presentation of it (Lee et al. 2006). There is a clear need to have a

common product data model throughout a company in order to efficiently manage

product data in information systems (Sudarsan et al. 2005, Svensson &

Malmqvist 2002, McKay et al. 1996). However, it is difficult to create a product

model that fulfils the need for an understandable product model for both humans

and computers (Crnkovic et al. 2003) that could support the formalised

presentation of a product over a company.

Some earlier studies (Saaksvuori & Immonen 2008, Hvam et al. 2003,

Andreasen et al. 1996) have presented the idea of utilising product structure as a

product data model. Product structure is kept in a way that can be utilised

throughout a company in order to standardise the understanding of a product (e.g.

Sudarsan et al. 2005, Svensson & Malmqvist 2002, Pikosz & Malmqvist 1996,

McKay et al. 1996). Product structure is also utilised in some information

systems as a product model (Janardanan et al. 2008, Forza & Salvador 2002). In

order to manage products in applications that support product handling, the

information related to a product should be structured appropriately (e.g. Jørgensen

2006, Crnkovic et al. 2003). For example, in the case of configurable product, a

general product structure is represented as being a typical way to describe the

product and its arrangement of ready-defined parts (Cheng & Wang 2009).

Product structure represents the product, data linked to the product, as well

as the relationship between product components (Saaksvuori & Immonen 2008,

Zhang et al. 2004, Svensson & Malmqvist 2002). The present study visualises

40

product structure as a triangle, including its components (rectangles) and their

simplified relationships (Fig. 5).

Fig. 5. Visualisation of product structure.

Product structure is more complex than BOM, so they are not synonyms. BOM

presents a single product breakdown structure, while a product can have several

common structures for different purposes (e.g. manufacturing or maintenance)

(Svensson & Malmqvist 2002, van der Hamer & Lepoeter 1996). These structures

can be also referred to as views of a product for different functions, such as

product development, sales, production or maintenance (e.g. Hilletofth &

Eriksson 2011, Saaksvuori & Immonen 2008, Shu & Wang 2005, Svensson &

Malmqvist 2002, Andreasen et al. 1996). Johnston and Clark (2008) argued that

different views are needed in order to define, for example, how customers, sales

and production perceive the product that can be either tangible or intangible, and

how these views are actually connected.

Examples of these types of views of product structure are listed in Table 3.

Consequently, the product structure, as a product representation, is seen as

standardising the understanding of a product between different functions.

However, product structuring work is seen to be challenging to realise (Andreasen

et al. 1996), even though it is seen to be beneficial in order to improve developed

solutions and knowledge reuse, experience-based database creation, or supporting

engineering change management (ECM) (Svensson & Malmqvist 2002,

Andreasen et al. 1996).

Product

Components

41

Table 3. General product structure views (Kropsu-Vehkapera & Haapasalo 2012

published by permission of IACIS).

Author Product structure views

Andreasen et al. (1996) Production, Sales, Use, Maintenance, Disposal

Svensson and Malmqvist (2002) Master structure including following views:

Design, Manufacturing, Purchasing, Sales, Service

Shu and Wang (2005) Conceptual design: Product function and product module view

Product engineering design

Product manufacturing: process planning, procurement, manufacturing

and assembly models

Service and support: sales, recycling, and service models

Saaksvuori and Immonen (2008) Definition, Design, Sales, Manufacturing, Service

Product structure can also be examined through different product item types for

different purposes. A sales item defines the product marketed to a customer.

Deliverable items describe the product to be delivered to a customer, including

user manuals, end products and accessories. Source items define sub-modules,

source codes and the like. (Jansen et al. 2005). Figure 6 demonstrates the different

potential views derived from product structure. Stakeholder specific views can be

drawn (A); alternatively, different item category views can be drawn (B) (Jansen

et al. 2005, Svensson & Malmqvist 2002, Andreasen et al. 1996). However, there

is a lack of practical examples in the literature, which has not discussed the actual

content of the specific views.

Fig. 6. Views of product structure: (A) Stakeholder-specific views, (B) Item-type-

specific views (Kropsu-Vehkapera & Haapasalo 2012 published by permission of

IACIS).

Product

Sales

Manufacturing

ServiceProduct

Sales item

Deliverable item

Source items/components

A B

42

2.4 Internal stakeholders – creators and users of product data

2.4.1 Product lifecycle

Product lifecycle (PLC) is often used to refer the phases that a product goes

through during its lifecycle. The most typically used PLC model is an S-shaped

curve with the function of sales and time in the phases of product development,

growth, maturity, and decline (e.g. Levitt 1965). This traditional PLC model has

long supported marketing executives’ product strategy decisions (Stark 2005,

Birou et al. 1998). There are other PLC models as well. Stark (2005) represents a

five-phased PLC model that varies by the actor perceiving PLC. The first three

phases – imagine, define and realise – are always the same when two last phases

differ depending on the viewpoint. From the manufacturer perspective, for

example, those phases are referred to as the support/service and retire phases,

whereas customers examine them as use and dispose phases. Across these phases

are activities such as product design, sourcing, testing, manufacturing or

maintenance. According to CIMdata (2002), the overall PLC is made up for three

interacting lifecycles: product definition, production definition, and operational

support. In the PLM context, the product definition lifecycle is the primary

lifecycle containing the full set of product data that defines how a product is

handled in different processes, such as design, manufacturing, or service. Kiritsis

et al. (2003) studied the data gathered throughout PLC. They characterised a

systems lifecycle for three phases: beginning of life (design and production),

middle-of-life (use, service and maintenance) and end-of-life (recycling).

Product data management applied during the PLC is the means by which the

product is controlled through the processes where the product is handled.

Consequently, many studies that discuss product data management and PLC (e.g.

Lee & Suh 2009, Saaksvuori & Immonen 2008, Sudarsan et al. 2005, Thimm et

al. 2006) follow the process-based view for PLC. However, they do not use

exactly the same terminology for the processes included in their PLC view. For

example, Thimm et al. (2006) highlighted the manufacturer’s viewpoint to

identify the PLC stages, whereas Kiritsis et al. (2003) and Saaksvuori and

Immonen (2008) represent PLC processes on a more general level.

In a modern company, product data has become an important resource,

especially when business shifts from product delivery to product support (e.g.

Baines et al. 2009, Gebauer et al. 2005, CIMdata 2002). The majority of product

data is created during the product development phase (Sudarsan et al. 2005);

43

during the other phases of PLC, a lot of product data is used, changed, transferred,

stored and converted by a diverse range of stakeholders (Lee & Suh 2009).

PDM has long focused on engineering data management, which has led to the

fact that PDM mainly supports product development actions (Abramovici & Sieg

2002) and there is still a lack of coverage of the whole PLC-wide PDM (Lee et al.

2008, Ameri & Dutta 2005). It has also been argued that the current applications

that should cover the whole PLC are not mature enough (e.g. Stark 2005). PDM

solutions are often based on the desires of one discipline (most commonly product

development) (Svensson & Malmqvist 2002), which means they do not meet the

requirements of other functions that create and use product data in different ways

(Sudarsan et al. 2005). Therefore, companies are limiting the use of applications

mainly to product development purposes (Stark 2005, Abramovici & Sieg 2002).

Consequently, the product-related data flow is less complete during the product’s

middle and end-of-life phases (Kiritsis et al. 2003).

2.4.2 Business processes and key stakeholders

In order to obtain the benefits promised by PLM, companies must integrate all

relevant data and processes that handle products (Shu & Wang 2005). In practice,

this means recognising all the stakeholders that create or use product data

throughout PLC or the processes in which the product is handled (see Figure 7)

(e.g. Sudarsan et al. 2005). The key business processes in which a product is

handled are product process and order-delivery process (see, e.g. Saaksvuori &

Immonen 2008). In order to recognise the relevant stakeholders, various aspects

for product structure can be utilised (see Table 3). In sum, handling product and

related data includes product development and product maintenance, whereas in

order-delivery functions include sales, production, and after sales services. The

product process generates and maintains the portfolio product whereas deliverable

product individuals are handled in the order-delivery processes. (e.g. Terzi et al.

2010, Saaksvuori & Immonen 2008).

44

Fig. 7. Business processes and stakeholders handling products.

Technically, product development can design and manage one complex product.

However, product and related data management becomes more challenging in

order-delivery processes in which a series of products are handled simultaneously.

(Ebert & de Man 2008, Kratochvíl & Carson 2005, Forza & Salvador 2002). In

order to effectively handle order-delivery process and manage products digitally,

product data needs to be correct and in the correct place (Lee & Suh 2009,

Saaksvuori & Immonen, 2008). However, organisational barriers that promote

functional orientation are still very much alive. Consequently, operations and

applications do not support the idea of smooth data flow through the order-

delivery process (Anderson et al. 2006).

2.5 Theoretical outline for analysing company-wide product data

management

The theoretical study provides an outline for the analysis of company-wide

product data management. The outline includes a definition of the key

terminology used later in the study, as well as representing a framework for

analysing product data from different stakeholder perspectives. Therefore, the key

concepts will be defined as follows:

Product is defined as hardware, software, services or a combination of these

elements. Product also contains documents related to any of earlier defined cases.

Product is understood as a portfolio item rather than an individual serial-

numbered item.

Product data is data that is broadly related to a product. Product data ensures

that a company produces, delivers, sells and maintains the correct products.

Product data compounds product master data and product-related data. Product

Product processProduct development

and product maintenance

Order-delivery process

Sales, Production, After sales services

Portfolioproduct

Deliverable products

45

data is static and does not contain any transactional data (such as a product sales

record).

Product structure is a product model that represents the product, data linked

to the product and the relationship between product components.

Stakeholders are the parties that create and utilise product data. In this study,

the key stakeholders are product development, product maintenance, sales,

production and after-sales services.

Traditionally, PDM studies have focused on product development and related

activities, with less attention being paid to PDM in other lifecycle phases, even

though it has been stated that poor PDM practices have a clear influence on

operational processes as the number of deliverable products has dramatically

increased, further increasing product data (Jin et al. 2007, Anderson et al. 2006).

In order to achieve the true management of product data across a company, all the

relevant stakeholders and their product data requirements must be addressed. It

must be taken into account that the views for product data can be divergent. This

divergence can be modelled via a product structure, which is a natural way to

model a product. The product structure may also help formalise a product from

diverse stakeholders’ viewpoints. As defined above, product structure also

represents the relationships between the product components. The applications

that store the data on product are covered in this context since that is where

product data exists. Figure 8 summarises these elements.

Fig. 8. Key components of company-wide product data management.

This dissertation focuses on analysing relevant product data for different

stakeholders. Figure 9 represents the framework used for that purpose. The

stakeholder analysis for PDM purpose is steady construction. Once the

stakeholders are identified, the actual product data content can be analysed.

Product structure is a way to model product and related product data, but can also

Applications that manage product data

Stakeholdersthat use product data

Product dataProduct

Sales

Manufacturing

Service

Product structure

Productobject that is handled

Real world

Information world

Form of appearance Conceptual model

Hierarchy

46

be utilised as a model to develop diverse stakeholder views since not all product

data is relevant for all stakeholders.

Fig. 9. Creating a stakeholder-specific view of product data (Kropsu-Vehkapera &

Haapasalo 2012 published by permission of IACIS).

Stakeholders

Product data

Product structure

Utilise Gives hierarchical organisation

View

Taking stakeholder specific view to product data

47

3 Research contribution

3.1 Key challenges on managing PDM in ICT companies

Articles I, II and IV address research question 1, with all of those articles

discussing the product data management challenges encountered in ICT

companies. Article I contains general challenges related to product data

management and related practices. Article II analyses the practical PDM

challenges experienced by operational actors, while Article IV discusses

challenges relevant for product configurations required to address the varying

needs of different customer groups.

Developing a PDM system is a topical issue in ICT companies. The results of

this dissertation indicate numerous challenges and Table 4 summarises key

product data management challenges experienced in studied ICT companies. The

identified challenges can be categorised as product harmonisation, processes for

PDM and information systems for managing product data.

Table 4. Key product data management challenges in ICT companies.

Challenge

Product

harmonisation

Fuzzy offering

Lack of clarity related to possible end-product combinations due to end-

products not being pre-designed and fixed and variable parts not being

properly defined

Inadequate planning of offering: designing single products instead of defining

the offering, resulting in a vast number of end-products

Operations having difficulties to handle the expanded offering

Configuration strategy and mechanisms

Case-by-case configuration instead of configuration strategy

Configuration strategy fails as unclear to which product level variation should

be designed

Difficulties in defining fixed and variable parts

Product structure

Integrating business goals and product structure

Challenges defining products

Challenges in perceiving product structure uniformly across a company

Rules for product definition

Managing product variations and product changes

Optimal number of configuration options

Realisation of modularisation

48

Challenge

Processes for PDM Defining product data ownership

Realisation mechanisms to ensure product data quality and availability

Standardising operations for product data maintenance and workflows

Ramp-up and ramp-down phases

Product changes

Workflows for version control and product coding, product definition etc.

End-to-end view in all product data processes

Defining relevant stakeholders

Information systems System-related challenges

Multiple non-integrated data systems for product data causing manual work

Connections to customer and suppliers

Support for managing product portfolio

Product data

Understanding the real value of information

Recognising key stakeholders and their data needs

Common company-wide understanding of product data

Product harmonisation

According to the results of this dissertation, harmonising a company’s product

range is one of the key issues in developing a PDM system. The main challenges

in product harmonisation are seen to be fuzzy offering, configuration strategy and

mechanisms, and developing general product structure.

Due to the multiplicity of customer needs on and product complexity, there is

a vast number of possible product variations. Consequently, customers are being

offered products that have unambiguous realisation to internal stakeholders.

Companies are not pre-designing their product variants adequately, and it is

unclear which elements are fixed and what can be configured. This results in fuzzy

offering; that is, different understandings within a company about what can be

sold. Also, the consultants interviewed for this study felt that a company with a

common view of products across the entire organisation would be seen as an

exception, even though the common view should be the starting point of defining

offered products, and only after that should attention turn to the composition of

products.

It is practically impossible to manage an overly extensive product portfolio in

operational processes. Sales, product development and production might not have

a common understanding of a complex product portfolio and its management. At

the point of sale, sales personnel are not able to perceive all of the information

49

relevant to a vast product portfolio, which results in customer offers being

prepared without considering what is optimal for the business. Also, after-sales

services may encounter problems related to software updates, spare parts and the

like. In addition, it can be challenging to test the functionality of all possible

options.

Defining configuration strategy and mechanisms is a key challenge for the

management of configurable products. Companies find the current situation to be

confusing, as numerous configuration needs are addressed on a case-by-case basis.

It is also unclear for companies how to optimally manage configuration

parameters and their interdependencies. All the interviewees appreciate the

potential benefits of systematic configurations; that is, improved quality of

products and services and operating efficiency. Nevertheless, it is unclear to what

level in the product hierarchy the configurability should be built in and which

hierarchy level is optimal for the company to realise customers’ wishes for

variation. Currently, products are typically configured using different mechanisms,

which makes them laborious to manage. In addition, the interviewed companies

almost exclusively ignore services and configuration, covering only the physical

product. This results in service provision not being fully repeatable and,

consequently, the situation not being economically optimal.

Product structure is seen as a model that unifies and helps the general

definition of a product to be perceived. The results of this study indicate that

companies do not have a clear enough understanding of what a product structure

could mean in their specific case. However, some of the companies have already

seen some of the potential of product structure, and some interviewees considered

it as a prerequisite for realising configurations. Companies have been unable to

define optimal product modularisation, mechanisms for realising variations, and

variation restrictions. Current product structure solutions are seen as inadequate to

simultaneously support different type of products. The interviewed consultants

felt that it typically takes several years for companies to appropriately model their

products and relevant realisation mechanisms. Companies tend to consider

product structure from a technical perspective, overlooking the business aspects.

Processes for PDM

According to the results of this dissertation, well functioning processes are

another key issue in developing a PDM system. In many of the studied cases,

there are ongoing efforts to develop product data management at a practical level,

50

including PDM processes and information systems. A key driver for developing

PDM processes is to ensure better product data quality and availability. The

bigger an organisation is, the more laborious it is to keep product data up-to-date,

even with automated actions. Inevitable manual work includes data entry into

systems and content checking, which runs the risk of human error. Practical

product data challenges at the operational level indicate practical weak points as

data being incorrectly entered into the systems or systems not replicating the data

to different applications. Especially challenging is to manage data entry during

product ramp-ups and ramp-downs. Complex products typically change many

times after their first release, and it is critical to establish new versions and have

compatibility between different versions in order to ensure smooth product

management in information systems. The interviewed companies have attempted

to prevent this type of product data challenge by defining data ownership. Each of

the studied companies has defined the basic principles for data ownership. Data

responsibilities are typically allocated according to organisational units and

related business responsibilities. However, despite efforts to define data

ownership, this is seen as being very challenging in practice. Typically,

responsibility for correcting incorrect data in the system is unclear. The results

indicate that centralised PDM solutions are considered necessary to support

business operations in a multi-site global environment when the speed of data

transfer and on-time data availability is essential.

Information systems

Information systems are the third key issue identified here as being significant for

developing a PDM system. Information systems play a key role in managing

product data, which makes them central to supporting PDM processes. Multiple

non-compatible information systems are typically used for PDM purposes, which

makes data transfer laborious, requiring unproductive work with several data

entries. Apart from the major PDM applications, several tools such as MS Office

and CAD, are used to create product data. In fact, product data is also stored and

utilised by systems such as sales configurators, ERP and other operational tools

throughout the product lifecycle. Inadequate application integration causes

manual data entry and transfer, which results in information being stored in

different forms in different data systems, thereby blurring the correctness of data.

Tool integration varies among the interviewed companies but is generally seen to

be somewhat lacking. An additional challenge is staff creating their own solutions

51

instead of following created processes and applications; this increases the

probability of errors.

The true value of product data is considered to be inadequately understood,

leading to a situation in which there are no uniform practices across companies

and the needs of all stakeholders are not addressed. It is considered vital that staff

who create product data fully understand the importance of entering the data into

systems in a manner that acknowledges the needs of others. Ensuring the correct

format of data is vital to avoid unproductive work. Interviewees emphasised the

fact that personnel should understand that product data has an impact on

profitability.

3.2 Internal stakeholders and their view to product data

Article III, which addresses research question 2, analyses how product data is

understood by different internal stakeholders in different parts of a company. The

article analyses three company cases and describes their product data in a way

that is relevant for diverse internal stakeholders. One of the companies represents

a typical consumer electronics provider with rather stable PDM traditions. The

second company provides service products, that utilise heavy infrastructure, and

takes the first steps towards developing PDM practices. The third company has

complex products, which makes product data management difficult. The third

company is currently moving towards solution business.

The results indicate that product data is typically observed and defined from

the product development and product data maintenance viewpoints. The studied

cases show that product development creates the basic product data content, while

specialised product data teams are responsible for maintaining product data in

later phases of the product lifecycle. These specialised teams are responsible for

product change and compatibility management in organisations where product

data practices are more advanced. Product data is widely needed in operational

processes across companies, even though it is mainly created by product

development. Different stakeholder groups require different product data to

complete their duties.

Key stakeholder groups using product data typically include sales, production

and after-sales. These organisational key units that utilise product data are similar

in all the studied cases. It is noteworthy that the sub-division of these main groups

into smaller entities may not be identical in different companies. For example, in

a company that only provides services, after-sales is a part of production. The

52

results indicate that production is typically sub-divided into sourcing,

manufacturing and delivery when considering product data.

To some extent, product type influences the stakeholders. In the case of

hardware products, demand/supply planning is one of the key production

stakeholders. The nature of the production process and relevant stakeholders are

slightly different in the case of service products. For example, in the case of

service products, the after-sales services are usually experienced as part of service

provision, not as a separate process as is the case with hardware products. Billing

was introduced by one key stakeholder in a studied service case whereas in other

cases it is not commonly linked to be main product data consumers.

In summary, the key internal stakeholders needing product data in their

operations include:

– Sales & Marketing

– Sales (customer interface)

– Sales support

– Production (Service provision)

– Demand/supply planning (HW)

– Sourcing

– Manufacturing (HW)

– Delivery

– Billing

– After-sales services (HW).

Even though the main stakeholder groups are nearly identical in different

companies, it was noticed that every stakeholder group contains different sub-

organisational units that handle product data. Also, not all stakeholders are

necessarily relevant in all organisations.

In this study, product data is studied from operational stakeholders’

perspective to recognise product data content that is relevant for them. The results

indicate that practitioners see product data as a combination of business-oriented

product master data and other general product data that has a more practical role.

Product master data is also seen to include the data system information required

to run the business. Consequently, any flaws in this information have a direct

business impact. Product master data is generally created during product

development. Other general product data ensures that operations are carried out in

53

a predictable and efficient manner, as opposed to staff creating their own

solutions. Other general data is more versatile in nature and is currently not

systematically defined and managed.

Product master data contains information relevant for business, required for

data systems. Table 5 summarises the content of product master data.

Table 5. Basic product master data.

Class Content

Product definition Unit code, Item price

Delivery time, Country of Origin, Customs information codes, Delivering

plants, Physical dimensions (e.g. net mass and volume, material group/type)

Item type specific definitions, such as:

List of spare parts and accessories (service item)

Licence information (SW)

Product structure Decomposition of a product: parts, product structure on module, circuit board

and components level (BOM)

Component information: lead times, lot size, order frequency, prices,

hazardous material content, part manufacturer information

Product classification Searching in data systems: Object type, product group, etc.

Product change data Item revision information

Compatibility information

Configuration rules In case of configurable products, define the rules to configure a product

Product lifecycle status Released, ramp-up, volume production, ramp-down, end of life, termination,

etc.

Basic product master data includes the core product data, such as item name and

code, product lifecycle status, and product definitions. The basic product master

data is defined for every product that is created and managed in a company.

However, it should be noted that every organisation defines its own product

master data and this compilation is only an example of it potential contents. Each

stakeholder group will only have access to master data relevant for its purposes.

This stakeholder-specific master data is derived from the basic product master

data. The exact content of the stakeholder specific master data set differs between

companies based on how they define the relevant data required for business

purposes.

Other general product data defines how a product is to be sold, produced and

maintained. Other general product data may contain different types of work

guidelines and instructions for sales, pricing and product-specific troubleshooting.

The results indicate that product data is understood as a wide set of different data

54

for different stakeholder groups. Table 6 summarises the contents of other general

product data. The summary includes different company-specific views, which

have been obtained from interviews and other data sources and which cover

general product data from diverse stakeholder perspectives.

Table 6. Stakeholders and examples of their general product data requirements

(modified Kropsu-Vehkapera & Haapasalo 2012).

Stakeholder Product data

Sales & Marketing

Sales & marketing

(Customer

accounts

Configuration

personnel)

Sales package description defining sales item combinations

Commercial and technical marketing and sales material

Service pricing and customer deal instructions

Configuration guidelines: recommended configurations, marketing/sales

limitations, customer specific configurations

Delivery times and sales channels for saleable products

Specific customer discounts (customer contracts as a reference)

Customer service descriptions

Sales Support Internal work instructions for product-specific questions

Internal product guidelines, system and realisation for reconfiguring products

Production

Demand/

supply planning

Information on planning frames and possible product allocation preferences

Prioritisation instructions (work instruction)

Information on planned volume ramp-up/ramp-down

Sourcing Purchaser of an item, vendor, lead times, lot size, order frequency, purchasing

price

Mode of purchasing if original equipment manufacturer (OEM)

Delivering plant with outsourced services: service descriptions and local costs

Manufacturing Instructions: manufacturing process guidelines, item testing, etc.

Production BOM with supplier/OEM information

Delivery Packing instructions: compliance and document requirements, package

identification marking

Information on shipping lot size and guidance for picking

(Service delivery/

Project execution)

Guidance for service implementation: roles and responsibilities

Pricing reference material for profitability calculations

Billing Price list, price item and product catalogue

After-sales

After-sales

services

Product error correction plans and roadmaps

Instructions for troubleshooting, testing and system set-ups

SW correction packages and information on new SW releases

Change information for products and supporting infrastructure

55

Unfortunately, general product data is often undervalued and, therefore, not

optimally utilised. The studied cases show how the role of stakeholder-specific

general data is particularly important for service provision. Services are often

poorly defined and managed, which results in services (in particular) having the

most room for improvement.

A versatile set of general product data, such as work instructions, guidelines

and specific product descriptions, are seen to be required for successful operations.

However, based on the results, it cannot be said that the creation of this type of

data is either systematic or is even defined in the actual contents of general

product data.

3.3 Enhancing the management of product data

Articles III and IV address research question 3. Both of these articles provide

means for enhancing product data management. Article III analyses product

structure for organising product data, while article IV discusses ways to organise

product data management at a general level.

Modern ICT companies face a diverse set of challenges. Companies have to

meet competition by providing new products faster and must answer widely

varying customer needs. From a product data management perspective, the

complexity of products is increasing and, at the same time, there is pressure to

address not only single products but also product portfolios. As a consequence, it

is becoming more difficult to manage product data and the role of information

systems increases. However, one should not fall for the illusion that selecting and

implementing a data system will rectify problems. According to interviewed

consultants, companies tend to use data systems as a starting point when

developing their product configuration management. The present study indicates

that companies should follow a top-down approach when developing their

product data management practices.

The results indicate that managers of studied ICT companies considering

their configuration and product data management ought to start with business and

strategy, not by solving the challenges of single products. The results of this study

indicate that more advanced companies are already striving for this type of

approach. In particular, when handling product portfolios that include a high

number of variations, product harmonisation should start from general, upper-

level product design. Unlike more advanced companies, companies that are

unable to manage their product portfolios and product configurations have started

56

to develop their product configuration systems on a product basis. The top-down

approach is a way to clarify fuzzy offering.

Harmonising products and their management should start with product

strategy. In the case of a configurable product, for example, the results indicate

that companies should start by formulating their general configuration strategy,

followed by configuration mechanisms and rules. In order to achieve a systematic

product portfolio, logic for handling and defining products must be clear. Too

often, the starting point is challenges encountered by a single product, resulting in

different products being realised via different mechanisms. Overall, it is very

important to understand the big picture – how the selected mechanisms, such as

configuration, affect operational processes. The logic for handling and defining

products also influence information systems. If products are configured using

different logic, diversifying applications, such as configurators, are required.

The results indicate that companies need a generic product structure to

support unified product management. A general product structure is seen as

helping formalise products and how they are understood across a company. A

general product structure is seen as a way to minimise the number of new

products and related data. General product structure also helps define all of the

different variants, thereby tackling the fuzziness of the final offering.

Since the companies are already familiar with the concept of product

structure and utilise it to define their products, product structure can also be

utilised to support product data management. General product structure can be

utilised to build separate views for different stakeholder groups. Product structure

also helps create a common understanding of a company’s products and product

data.

Figure 10 illustrates a general product structure and stakeholder-specific

views for a product. This is a compilation of three company-specific product

structures. It is built to represent a product that includes all different product types,

hardware, software, service and documents.

57

Fig. 10. Example of product structure and stakeholder specific views (modified

Kropsu-Vehkapera & Haapasalo 2012.

58

The stakeholder-specific views in Figure 10 show product levels for each

stakeholder group’s practical work. These stakeholder-specific views match with

the product data requirements presented in Table 6. The sales view covers

descriptions of saleable parts of the product, while the production view covers the

technical product aspects, including detailed description of SW and HW items.

Since the practice seems to be that service items are not versioned and their

composition is different, service items are represented separately from SW and

HW items.

The production view can be further divided into delivery, sourcing and

manufacturing views. The manufacturing view differs from the total production

view in that it does not include any raw materials such as SW components that

can be included in total production view. In the case of services, manufacturing

means producing the service. The after-sales service view consists of HW parts

that are maintained or updated with software. In this case, the demand/supply

planning view is also included. Within service products, the after-sales service

view includes maintaining the sold services and therefore understanding the

connection of sold sales items and ensuring the availability of required resources.

However, the example figure does not include all these stakeholder views (such as

demand/supply planning or billing) since the content of those views remains

unclear.

All product types, including hardware, software, service and documentation

elements, can be illustrated using this framework. However, the model is also

adequate when the final product only contains HW, SW or service. It should be

noted that the requirements for product structure are strongly company-specific.

In order to define stakeholder-specific views and related data contents, a general

product structure is required.

Defining stakeholder specific views on relevant product data for different

stakeholder groups helps improve product data management and product handling

practices. Figure 11 illustrates the common denominator for all the cases and

stakeholder groups. This illustration provides a framework for companies for

defining stakeholder-specific views. All product types, including hardware,

software, service and documentation elements, can be illustrated using this

framework.

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Fig. 11. Stakeholder-specific view content (Kropsu-Vehkapera & Haapasalo 2012

published by permission of IACIS).

In order to utilise this framework, a company must first define a general product

structure. Structural illustrations are direct stakeholder specific subsets derived

from the general product structure. The stakeholder-specific product master data

and other general data are discussed in more detail in chapter 3.2.

3.4 Results summary

The research for this dissertation was conducted in three phases. First, key

product data management challenges encountered in ICT companies were

clarified. Second, relevant internal stakeholders dealing with product data within

companies were identified. Finally, conclusions were reached in order to find

ways to enhance product data management. Table 7 summarises the research

contributions.

Table 7. Research contribution.

RQ Results

Key PDM challenges Identification of key PDM challenges

Product harmonisation

Process development requirements

Information systems development requirements

Stakeholders & product

data

Identification of key stakeholders

Defining product data content for different stakeholders

Product data = product master data + other general product data

Enhancing product data

management

Top-down approach

Business- and strategy-driven PDM

General product view vs. single product

General product structure

Stakeholder views

Stakeholder specific view content

60

The results indicate that developing a PDM system is a topical issue in ICT

companies. PDM system development is moving towards company-wide

approaches that cover different operations. Company managers have already

realised that a higher-level understanding of products and related product data is

required for successful business operations. However, in practice, numerous

challenges hinder achieving the goal of higher-level business-driven PDM.

Product harmonisation is required to better support efficient product data

management. Fuzzy offering and deficiencies relating to configuration strategy

and mechanisms cause problems for operational-level activities. According to the

results of this dissertation, a key for product harmonisation is to model products

using a general product structure. Unfortunately, defining product structures has

proved rather complicated, as it required both general product modelling

decisions and detailed product design rules.

Developing PDM also includes considering processes for PDM and related

information systems. Key PDM process challenges include understanding product

data management as a company-wide activity and ensuring product data quality

and availability by developing product data ownership. Processes are tightly

linked to information systems that tend to suffer from integration deficiencies.

Also, it is challenging to define product data uniformly for different operations

and related systems.

The content of product data from different stakeholder perspectives is a pre-

requisite for functional company-wide PDM. Product data consists of product

master data that is relevant for business, as well as more practically-oriented

general product data. It is vital to combine business and other data that have

traditionally been considered separately.

This study indicates that higher-level product decisions have a significant

impact on product data management. Extensive product ranges require general

guidelines in order to be manageable, especially as even single products are

complex. The results of this study indicate that companies should follow a top-

down approach when developing their product data management practices.

Managers of ICT companies considering their configuration and product data

management ought to start with business and strategy, not by solving the

challenges of single products in order to harmonise their product ranges.

Harmonising products and their management should start with product strategy.

In the case of a configurable product, for example, the results indicate that

companies should start by formulating their general configuration strategy before

61

configuring mechanisms and rules. In order to achieve a systematic product

portfolio, the logic for managing and defining products must be clear.

The results indicate that companies need a generic product structure to

support unified product management. A general product structure is seen as

helping to formalise products and how they are understood across a company.

This dissertation highlights how a general product structure can be utilised to

build separate views for different stakeholder groups. Using a generic product

structure makes it possible to create a presentation in which product structure and

stakeholder-specific views for a product are interlinked. This dissertation also

presents a framework with which to define stakeholder-specific views on relevant

product data for different groups. All product types, including hardware, software,

service and documentation elements, can be covered using a general product

structure and a stakeholder-specific view framework.

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63

4 Discussion

4.1 Theoretical implications

This study is related to the field of product lifecycle management, contributing to

the research stream of lifecycle lasting product data management. Few studies

have conducted research concerning product data management as a company-

wide action that is outside the realm of product development. Concluding the

results of this dissertation, prerequisites for company-wide PDM can be

summarised as:

– defining a harmonised product data concept over a company, potentially

exploiting product structure modelling

– understanding the true nature of product data that is a combination of product

master data and other general product data

– recognising the key product data stakeholders and their actual data needs, and

– approaching product and product data management from a top-down

perspective.

In addition, the results include different descriptions and models in order to

harmonise product data representation through a company. This includes a theory-

base model to create a stakeholder-specific view of product data as well as the

key definitions for company-wide product data management that can used to

harmonise product data representation in companies. Empirical evidence is used

as a base upon which to define the aspects of stakeholder-specific view content

and product structure and related stakeholder views.

This dissertation complements previous PDM literature (e.g. Terzi et al. 2010,

Liu et al. 2009, Saaksvuori & Immonen 2008, Stark 2005, Sudarsan et al. 2005)

by describing product data management challenges specifically in the ICT

industry (see Table 4). In particular, the results PDM challenges in cases where

products are a combination of electronics, software or services; this has recently

emerged as a topical issue in practice (see Saaksvuori 2011).

This dissertation highlights the importance of addressing and serving the

needs of different stakeholders requiring product data in a coordinated manner. In

other words, product data practices must be harmonised throughout the

organisation. Another important challenge is to approach product and product

data from a higher level rather than to solve single product-related problems, in

order to develop a long-lasting solution for PDM. Therefore, the findings of this

64

study enhance the previous, more theoretical studies (e.g. Svensson & Malmqvist

2002, Andreasen et al. 1996). This dissertation creates new theoretical knowledge

by describing the potential content of product data for different stakeholders;

however, it cannot define an all-inclusive solution. Understanding the content of

product data for each stakeholder enables efficient product data management.

Previous PDM literature has mainly viewed product data as technical data

(e.g. Liu & Xu 2001, Vroom 1996) or some other type product data (e.g. Lee &

Suh 2009, Lee et al. 2006, Johansson & Johansson 2004), but has not included

the master data aspect in definitions of product data. Product master data is

discussed separately in the master data literature (e.g. Snow 2008, Zhang et al.

2004, Nagi 2001). This dissertation creates new knowledge about defining the

content of product data by combining master data and other general product data

into product data as practitioners define the product data. Product master data

contains data that is used in data systems and is relevant for business. Product

master data contains the core product data, such as item name and code, product

lifecycle status, and product definitions. Other product data defines how a product

is to be sold, produced and maintained. Other product data may contain different

types of work guidelines and instructions for sales, pricing, and product-specific

troubleshooting. Different work instructions and guidelines relevant to specific

stakeholders are generally not discussed in the product data and management

literature; unfortunately, this data is often undervalued and consequently not

utilised optimally.

Previous PDM/PLM studies have focused on technical product data issues

(e.g. Kiritsis et al. 2003, Liu & Xu 2001). Stakeholder-specific general product

data ensures that operations are carried out in a predictable manner, which

prevents personnel from creating their own personal solutions. In contrast to

previous literature, this dissertation has shown how the role of stakeholder-

specific general data is particularly important for service provision. It is common

for services to be poorly defined and managed, which results in services and

related data management, in particular, having the most room for improvement.

Creating stakeholder-specific views requires that processes be properly analysed

and the required product data identified.

This dissertation proposes a way to utilise product structure to describe

stakeholder-specific views on product data. The previous literature presents

product structure models (e.g. Saaksvuori & Immonen 2008, Shu & Wang 2005,

Svensson & Malmqvist 2002, Andreasen et al. 1996), but does not provide

tangible instructions on how to utilise them. The present study complements the

65

previous literature by presenting possible ways to utilise product structure models

in company operations. In particular, product structure models are utilised to

create stakeholder-specific views. Stakeholder-specific views make it possible to

identify what type of product data is relevant for each actor. Therefore, this study

combines the findings of previous literature (e.g. Saaksvuori & Immonen 2008,

Shu & Wang 2005, Svensson & Malmqvist 2002, Andreasen et al. 1996) and

provides more tangible examples of how stakeholder-specific views can be

realised.

Overall, this doctoral dissertation presents a clarification for defining product

data and what type of model can be created to improve company-wide

management of product data. In so doing, the study helps consolidate a

fragmented field of product data. This study shows that companies can potentially

benefit from a more business-oriented approach to analysing and managing

product data.

4.2 Practical implications

The purpose of this dissertation is to examine the challenges and prerequisites for

company-wide product data management, especially in the ICT sector, where

products are a combination of hardware, software and/or services. These ICT

products are typically complex and there are a vast number of products and

product variants as well as different customer segments. As a result, the amount

of product-related information is greater than ever, which further complicates

PDM.

The practical implications of this dissertation include providing ICT

companies that have complex products with an overall view of PDM-related

challenges and potential solutions, as well as the necessary prerequisites for

functional company-wide product data management. Also other industries can

benefit the results especially when harmonising their products and product data

management.

This study provides new information for managers requiring new viewpoints,

as the traditional way of managing only technical product data is not sufficient to

support true organisation-wide PDM. It also requires PDM managers to widen

their scope towards the full product lifecycle, rather than just product

development and manufacturing. This dissertation shows that it is not possible to

provide guidelines that are directly suitable for every company due to the

diversity of products, processes and organisational structures. However, a

66

collective set of prerequisites can be found that better ensures product data quality

and availability during a product’s lifecycle. This dissertation offers support for

managers concerning the development of true company-wide product data

management practices.

This dissertation also highlights how product data management must be seen

to combine product management, data management, and organisational functions

dealing with product information. Consequently, solutions for managing product

data are not developed within single organisational units; instead, a functional

solution requires company-wide cooperation. Senior decision-making managers

must understand the need for cooperation.

The challenges are versatile in nature and many cannot be tackled only by

implementing a PDM/PLM application. Therefore, company managers should

abandon their traditional way of understanding product data management as an IT

issue. The IT solution should only be included once the prerequisites for

company-wide PDM have been defined. An optimal starting point for realising IT

projects would be that a company has a common company-wide understanding of

its products. This understanding acts as a requirement list for IT solutions.

It is typical for the ICT sector to tackle a number of varying customer

requests by configuring their products, which results in wide product ranges. This

study points out to product managers that a wide and complex product portfolio

also influences operational processes. The managers of ICT companies ought to

see the product portfolio as a whole in order to develop efficient product data

management practices. In practice, this requires a top-down approach, starting

from business and strategy issues related the whole product portfolio, rather than

solving the challenges of single products. The top-down approach is also a way to

clarify a fuzzy offering that has become a major challenge for ICT companies

with high product variety. Overall, it is very important to understand the big

picture – how configuration is executed in practice and how the selected

mechanisms affect operational processes. This cannot be achieved or managed

from the viewpoint of a single product.

There are many internal stakeholders within companies, all of which require

product data, even though they may have different perspectives. Companies strive

for a common company-wide understanding of their products and related data.

This study suggests utilising product structure as a base for this harmonisation.

The general product structure helps harmonise products and how they are

understood across the company. The general product structure also minimises the

number of new products and related data. In addition, a general product structure

67

helps define different product variants, which clarifies the final offering. However,

managers must note that the requirements for product structure are strongly

company-specific.

Product structure is traditionally used to describe mechanical products. This

study shows that product structure is also applicable for describing all product

types or combinations thereof. Product structure must cover all different product

types in order to systematically manage them in data systems. This dissertation

shows that a properly considered product structure also improves product data

management efficiency and makes it possible to integrate different data systems.

Managers can utilise a general product structure to enable different stakeholder

views over product data.

This dissertation indicates that companies do not adequately acknowledge

how different stakeholders have varying needs in relation to product data

management. The study presents a tangible way of building separate views for

different stakeholder groups. Managers need to recognise that company-wide

product data management requires that non-technical individuals also be

recognised as product data users and providers. Without this understanding,

functional product data management throughout product lifecycles will not be

possible. PDM managers have typically recognised the needs of product

development and production; they should also realise that other stakeholders

should be offered tailored views on product data.

This dissertation helps PLM and PDM system developers by clarifying the

true challenges encountered in current systems and proposing the use of product

structure and stakeholder-specific view approaches. System developers could

improve their systems to better serve companies wishing to use the solutions

proposed in this dissertation.

Managers need to realise that any development process will take time, as it is

not wise to jeopardise existing business relationships, current product portfolios

or functioning operations. This is also true when improving product data

management practices.

4.3 Reliability and validity

This study investigates company-wide product data management and seeks a

more in-depth understanding of this phenomenon. The empirical practitioner’s

experiences and perceptions are seen to have increased the knowledge in this field.

Therefore, the qualitative research method is justified in this study. The case study

68

method was selected since it can potentially provide a broad view of the

phenomena. As Bryman and Bell (2003) stated, the reliability and validity of

qualitative research can be assessed by answering the following four questions:

1. How trustworthy are the results?

2. Are the results valid in another environment?

3. Are the findings likely to occur at other times (repeatability)?

4. To what extent have the researcher’s own values influenced the results?

The trustworthiness of the results can be understood as the degree to which

research results correspond with the real world. In order to ensure reliability, this

research has utilised several data collection techniques and interviewed multiple

people in order to triangulate the results. The interviews were primarily made

following a semi-structured method, which enabled flexible interaction between

the researcher and interviewee and left room for interviewees to explain the issues

as they felt best. Although the interviewers did not attempt to influence the

answers of the interviewees, their impact is still worth questioning since the

discussed topics are somewhat blurry and needed to be clarified in some

interviews. Several researchers collected the research data, which decreases the

degree of researcher bias.

The representativeness of the cases is one of the main concerns in case

studies (Yin 2003). The studied cases represent the Finnish ICT sector, although

the companies act globally. The selection of the cases was based on the fact that

the case companies were developing their PDM practices. They can also be seen

to be a step ahead in terms of developing their practices in this area, which

offered a fruitful view for the topic. This means that they do not necessarily

highlight all of the types of challenges that ICT companies with complex products

may face in their PDM. Cases could always be selected differently when

resources are often the most limiting factor in the case selection.

Within each case study, the trustworthiness of each case is increased by

selecting people with different backgrounds, rather than just reaching conclusions

based on a single person’s opinion. Although the actual number of interviews in

this study was adequate, the number of interviewees in each company is quite

small compared to the company sizes. Even with this sample size, the PDM

challenges can be described within a reasonable level when the descriptions and

examples of product data are considered as the first views on what contents this

data set includes. In practice, defining the company-wide product data requires a

69

wider analysis that must be completed at the company level. Also, the overall

quantity of companies analysed in individual studies is low and the general

product structure with stakeholder-specific views was not tested on a wider

audience. Even though the results have not been tested, the trustworthiness of the

given solution can be considered relatively good, strengthening the earlier

research results as well as utilising them as a foundation for new solution. In

addition, all the results are validated by industrial experts, which means they can

be seen to correspond well with the real world.

Another issue to asses when evaluating the qualitative research is the validity

of the results in another environment. The studied phenomenon and solutions for

company-wide product data management are more relevant in companies that are

medium-sized or large. The communication of product data becomes problematic

when the organisation size and number of products increases or work is organised

in multiple sites. Therefore, PDM solutions in the studied scale are not suitable

for micro-companies since, for example, a single person can represent almost all

stakeholders using product data. However, companies that drive for growth can

use the results in a proactive way. The challenges recognised are general in nature

and are faced widely. Smaller companies can learn from the experiences of others

and avoid repeating others’ mistakes. They can also use the results as a way to

harmonise their product data.

Case studies always face the challenge of generalisability (e.g. Bryman &

Bell 2003, Yin 2003). This study includes an examination of complex ICT

products that are heterogeneous in nature, including hardware, software and

services. The solutions and preconditions are created from that sense and based

on few cases. This study does not intend to provide a full solution for

practitioners, not even of the ICT industry; instead, it describes how ICT

practitioners and other companies can approach the challenge of company-wide

product data management. The generalisability is more theoretical, providing

descriptions and models that can be applied especially in companies with

heterogeneous products. These tools may be more valuable companies that are at

least moderate in size, but not make them useless in smaller companies. The

findings do not provide a single proposition that fits all companies. Defining the

content of product data and recognising the key stakeholders and their needs

towards product data are usable in many contexts. Arguably, the suggested

product structure model with stakeholder views will be relevant in other

industries, where the convergence of different product types, especially the

addition of services with manufactured products, is topical challenge. However,

70

one must understand that the needs of each company’s product management must

be addressed by analysing their specific business realities. Even though different

types of companies were deliberately chosen, the generalisation of the results

requires further research.

Repeatability is understood in that another researcher would reach similar

findings in a similar research setting. This means that another researcher would be

able to repeat the research logic, having same data and end up same conclusions

(Yin 2003). However, it is unlikely that another researcher would be able to

obtain exactly the same research setting. The findings reflect the reality at the

time when the data was collected, which means that the ability to replicate the

research is limited (Saunders et al. 2009). One would like to believe that

companies develop their practices further, which would make the research non-

repeatable. Also, all the interviews have been unique situations, which means that

complete replication would not be possible, even when the same questions were

asked. Utilisation of case study protocol, establishment of a case database, and

multiple source of evidence can be utilised to improve reliability (Yin 2003). In

this research, the data collected has been recorded, transcribed and stored,

ensuring that the relevant evidence could be retrieved if necessary. Regardless of

this database, it is questionable whether another researcher could reach with same

conclusion, given that the role of the researcher is remarkable in analysis at the

end, despite the fact that different steps of data analysis are also retrievable. As a

researcher, one would hope that the research is objective. However, qualitative

research often has subjective connotations (Yin 2003). Qualitative research

emphasises worlds and the interpretation thereof, especially when research

utilises interviews as the main data collection method (as this study did). In order

to reduce single-researcher bias, a number of researchers collected the data for

this study. However, the data analysis is highly dependent on the researcher and

her way of thinking about the phenomena. The researcher’s previous work

experience has given her a certain mind set for the studied issue, but mostly the

presumptions and how things are valued are based on the literature study and

experience gained when preparing the research project. Therefore, in this research

project environment, the direction to the analysis would probably be rather similar.

Still, it would have been possible to select the topics of the sub-studies with a

slightly different emphasis and reaching a somewhat different conclusion.

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4.4 Recommendations for further research

This study aims to enhance the understanding of company-wide product data

management as a way of improving PDM practices in ICT companies. In general,

few studies have sought to understand product data comprehensively throughout a

company. Therefore, this qualitative research serves to describe this research area,

as well as proposing a solution to organise product data in order to support

company-wide PDM. This study presents the preconditions for company-wide

product data management, together with more practical descriptions and models

to use in order to harmonise the representation of product data throughout the

company. The findings are preliminary in nature, presenting the aspects that

should be noticed in order to approach product data and its management holistic.

Further research is suggested in order to validate and supplement the findings.

This study suggests using the product structure with specific stakeholder

views to harmonise the product concept within a company. Since this solution has

not been tested on a wider audience and in other industries, future research could

include testing the proposed solution. The general product structure model need to

be validated in other cases and also needs more detailed extraction so that

companies can apply it more easily. Potential future research could also include

expanding the stakeholder analyses to cover external stakeholders.

This study includes recognising the stakeholder-specific product data.

However, these findings are not all inclusive. Further research is required to

create a holistic picture of product data as it is experienced by a certain

stakeholder group. This is needed in order to develop PLM to meet is full scope.

This information would also help application developers cover the required parts

of organisation and ensure that the tools really support the full organisation.

Companies often seek proven practices that could be used to save time in

order to implement new things. Therefore, it would be valuable to conduct a

longitudinal study to follow companies that have applied this type of product

structure and stakeholder-specific views approach over a longer period of time.

Future research may also include descriptive case studies on constructing the set

of stakeholders and their views. More in-depth studies are also required to show

how product data occurs in daily operational activities and how important

efficient PDM practices are also in the order-delivery process point of view.

This study is made in medium-sized and large companies, which means that

the applicability of the results for small companies is unclear in this format.

However, defining products and related product data properly is a valid issue for

72

every company. In order to prevent the product data management challenges,

more knowledge is needed regarding what is the critical point for a company to

implement a more structured approach to product data management.

Product data is more often linked to the technical, physical products.

However, current products are increasingly becoming a combination of products

and services (Baines et al. 2009, Gebauer et al. 2005). This study provides first

views that service products can also be modelled as more traditional products and

that the stakeholder-specific product data can be even more valuable for service

products. However, examples of solution product data and its management are

limited, which means that solution types of products are fruitful field of study.

In order to succeed in modern complex business, it is necessary for several

internal and external functions to have an adequate understanding of product data.

However, this dissertation focuses on PDM-related issues within a single

company and does not cover the supplier or other partner’s viewpoints regarding

product data. There would be room for further research to figure out how the

definition and practices of product data are matched when acting with external

partners and how product structuring could help such a form of collaboration.

As this section indicates, there are several appealing avenues for future

research in order to further enhance company-wide product data management.

Even though this study was conducted in the ICT industry, there are no limitations

to conducting the further research suggested above in other industry sectors,

especially where better product data has an important role in ensuring and

improving efficiency in operations.

73

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Original publications

I Kropsu-Vehkapera H, Haapasalo H, Harkonen J & Silvola R (2009) Product data management practices in high-tech companies. Industrial Management & Data Systems 109(6): 758–774.

II Kropsu-Vehkapera H, Haapasalo H, Lokkinen S & Phusavat K. (2011) The influence of product complexity on order handling process. International Journal of Innovation and Learning 10(2): 123–143.

III Kropsu-Vehkapera H & Haapasalo H (2012) Defining product data views for different stakeholders. Journal of Computer Information Systems 52(2): 61–72.

IV Kropsu-Vehkapera H, Haapasalo H, Jaaskelainen O & Phusavat K (2011) Product Configuration Management in ICT Companies: The Practitioners’ Perspective. Technology and Investment 2(4): 273–285.

Reprinted with the permission of the copyright holders. The above-named

journals are the original sources of publication for the four articles above.

Original publications are not included in the electronic version of the dissertation.

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