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INFORMATION TECHNOLOGY AND WAREHOUSING

PERFORMANCE OF SUGAR COMPANIES IN WESTERN

KENYA

DORCAS NAFULA WANYAMA

A RESEARCH PROJECT SUBMITTED IN PARTIAL

FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF

MASTER OF BUSINESS ADMINISTRATION, SCHOOL OF

BUSINESS, UNIVERSITY OF NAIROBI

NOVEMBER, 2015

ii

DECLARATION

I declare to the best of my knowledge that this is my original work and has not been

presented for a degree in any university.

WANYAMA DORCAS NAFULA

D61/64578/2010

Signature………………………. Date: 14th

November, 2015

This research project has been submitted for examination with my approval as the

University Supervisor.

MR. GERALD ONDIEK

DEPARTMENT OF MANAGEMENT SCIENCE

UNIVERSITY OF NAIROBI

Signature……………………………… Date: 14th

November, 2015

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DEDICATION

This research project is dedicated to my family, son and friends.

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ACKNOWLEDGEMENTS

I wish to give special acknowledgement to my Supervisor, Mr. Gerald Ondiek for his

guidance and supervision. The Lecturers of the Department of Management Science

who took as through our programme in Kisumu Campus. The Coordinator Kisumu

Campus, Mr. Alex Jaleha and the great staff who made my stay in UON a great

experience.

My appreciation goes to the sugar companies that took the time to assist with data

collection, special mention, Muhoroni Sugar the HR Manager and the warehouse

Supervisor and his team, Mumias Sugar Warehouse supervisor Phylis, and HR

Manager training officers Peter and the Secretary, Trans mara Sugar Company, the

CEO, HR Manager and Mr. Ringui, Nzoia Sugar Company the HR Manager, Training

Officer and the Warehouse Supervisors.

Finally I thank God for seeing me through this whole journey.

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ABSTRACT

The study was conducted to establish the effect of information technology on

warehousing performance of sugar companies in Western Kenya. The study

objectives specifically sought to establish the extent of information technology

adoption in the sugar companies and determine the influence of information

technology on warehousing performance. The study sought to determine if

information technology that has been known to remove fatigue from human to

machine and ensures accuracy and efficiency had any effect on warehouse core assets

like labour, equipment, time and space. The study was conducted in Western Kenya

targeting the eleven listed sugar companies but only four responded. The study was

conducted through cross sectional survey design and data was collected using

questionnaires and interview responses. The findings were measured using a likert

scale of 1-5. Data analysis was done by use of descriptive statistics as the data points

were few. Data presentation was also done by use of tables. The study established that

the sugar companies had not fully adopted the use of information technology systems

in the warehouses with a mean of 3.8 but a performance index of 2.8. On the

influence of IT on warehouse performance the companies averaged a mean of 4 but a

performance index of 3.1. The perfect order index gave a percentage of 43% meaning

orders were not met perfectly. The study concluded that warehousing performance of

the sugar companies was largely affected by use of manual systems. The companies

were unable to meet demand with perennial stock outs and cane shortages whose

visibility could be controlled directly from the warehouse. The sugar industry in

Kenya had the highest costs of production compared to other sugar producing

countries both in Africa and internationally. Some of the costs could be hidden in

warehousing. The study recommended that because success or failure of a company

was largely determined by practices in the warehouses it was important for the

companies to fully implement and integrate the information technology systems with

the existing specialized equipment or invest in technology to reduce costs and ensure

accuracy and visibility of the product in factories, warehouses, in transit and even at

point of sale. The study suggested for further studies in warehouse design of the sugar

companies, use of third party logistic providers, use of cheap labour against

automation and benchmarking of the warehouses.

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TABLE OF CONTENT

DECLARATION........................................................................................................... ii

DEDICATION.............................................................................................................. iii

ACKNOWLEDGEMENTS ........................................................................................ iv

ABSTRACT ................................................................................................................... v

LIST OF TABLES ..................................................................................................... viii

LIST OF FIGURES ...................................................................................................... x

ABBREVIATIONS AND ACRONYMS .................................................................... xi

CHAPTER ONE: INTRODUCTION ......................................................................... 1

1.1 Background of the Study .......................................................................................... 1

1.1.1. The Concept of Information Technology ................................................. 2

1.1.2. The Concept of Warehousing ................................................................... 4

1.1.4. Sugar Industry in Kenya ........................................................................... 6

1.3 Research Objectives ................................................................................................ 10

1.5 Value of the Study .................................................................................................. 10

CHAPTER TWO: LITERATURE REVIEW .......................................................... 12

2.1. Introduction ........................................................................................................... 12

2.2. Theoretical underpinning of the study ................................................................... 12

2.3. Extent of IT adoption in warehousing ................................................................... 13

2.3.1. Information Technology and its influence on warehousing performance14

2.4 The Extent of IT adoption and warehousing performance ..................................... 15

2.4.1 IT and its Influence in Warehousing Performance ....................................... 16

2.4.2 JIT and Modern Concepts on Warehousing............................................ 17

2.5 Conceptual Model ................................................................................................... 19

CHAPTER THREE: RESEARCH METHODOLOGY ......................................... 20

3.1 Introduction ........................................................................................................... 20

3.2 Research Design .................................................................................................... 20

3.3 Population of Study ............................................................................................... 20

3.4 Data collection ....................................................................................................... 21

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3.5 Data analysis and presentation .............................................................................. 21

4.1 Response Level ....................................................................................... 22

4.2 Findings on Contextual Factors ............................................................................. 22

4.2.1 Ownership of the warehouses ................................................................. 22

4.2.2 Specialized equipment ............................................................................ 23

4.2.3 Age of the Companies ............................................................................. 24

4.2.4 Labour ..................................................................................................... 25

4.2.5 Constraints and opportunities ................................................................. 25

4.3. Extent of IT adoption in the Sugar Companies ..................................................... 26

4.3.1 Existence of warehouse management systems ....................................... 26

4.3.2 Level of mechanization ........................................................................... 27

4.3.3 Adoption of IT in the various departments in the companies ................. 30

4.4 Influence of IT on Warehouse Performance ......................................................... 33

4.4.1 IT influence on warehouse metrics ......................................................... 33

4.4.2 Influence of IT on warehouses functions ................................................ 34

4.4.3 IT influence on communication .............................................................. 34

4.4.4: IT influence on forecasting, planning in the sugar value chain ................... 35

4.4.5 IT influence on Order fulfilment............................................................ 36

5.1 Summary of the Study ........................................................................................... 50

5.2 Conclusions ........................................................................................................... 52

5.3 Recommendations ................................................................................................. 54

5.4 Areas for future Research ...................................................................................... 56

5.5 Limitations of the study ......................................................................................... 57

REFERENCES ............................................................................................................ 58

APPENDICES ............................................................................................................. 65

APPENDIX 1: QUESTIONNAIRE ............................................................................. 65

APPENDIX 2: TABLE 1: PERFORMANCE METRICS OF A WAREHOUSE ........ 70

APPENDIX 3: LIST OF SUGAR COMPANIES IN WESTERN KENYA ................ 71

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LIST OF TABLES

Table 1: Type of warehouse ......................................................................................... 22

Table 2: Age of the companies .................................................................................... 24

Table 3: Turnover ........................................................................................................ 25

Table 4: Warehouse Management System ................................................................... 26

Table 5: Manual Systems ............................................................................................. 27

Table 6: Mechanized Systems ..................................................................................... 28

Table 7: Semi-automated systems ............................................................................... 28

Table 8: Automated systems ....................................................................................... 29

Table 9: Extent of Adoption of IT use in the warehouse ............................................. 30

Table 10: Extent IT has been used to link all levels in the organization ..................... 30

Table 11: Employee use of IT systems ........................................................................ 31

Table 12: IT and its adequacy for operations............................................................... 32

Table 13: Use of IT technologies like RFID, Barcodes ............................................... 32

Table 14: Warehouse metrics...................................................................................... 33

Table 15: Warehouse functions ................................................................................... 34

Table 16: Communication between the company and stakeholders ............................ 34

Table 17: IT and decision making ............................................................................... 35

Table 18: Order fulfilment ........................................................................................... 36

Table 19: Order fill rate ............................................................................................... 36

Table 20: Order accuracy ............................................................................................. 37

Table 21: Line accuracy ............................................................................................... 37

Table 22: Order cycle time .......................................................................................... 38

Table 23: Perfect order completion.............................................................................. 38

Table 24: IT influence on inventory management ....................................................... 39

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Table 25: Inventory accuracy ....................................................................................... 39

Table 26: Inventory visibility....................................................................................... 40

Table 27: Damaged inventory ...................................................................................... 40

Table 28: Storage utilization ........................................................................................ 41

Table 29: Dock to stock time ....................................................................................... 42

Table 30: Condition of building, floors, lighting ......................................................... 42

Table 31: Condition of material and storage equipment .............................................. 43

Table 32: Distance material is moved .......................................................................... 43

Table 33: Double handling........................................................................................... 44

Table 34: Safety ........................................................................................................... 44

Table 35: Ideal warehouse ........................................................................................... 45

Table 36: Mean of IT Adoption ................................................................................... 45

Table 37: Mean Warehouse Performance .................................................................... 47

Table 38: Perfect order index ....................................................................................... 48

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LIST OF FIGURES

Figure 1: Conceptual framework ................................................................................. 19

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ABBREVIATIONS AND ACRONYMS

COMESA Common Markets for Eastern and Southern Africa

CBA Collective Bargaining Agreement

ERP Enterprise Resource Planning

ICT Information Communication Technology

IT Information Technology

JIT Just in time

KESREF Kenya Sugar Research Foundation

MHS Material Handling Systems

MHEs Material Handling Equipment

POI Perfect Order Index

RFID Radio Frequency Identification

SKU Storage Keeping Unit

SCE Supply Chain Execution

TMS Transport Management System

XML eXtensible Markup Language

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CHAPTER ONE: INTRODUCTION

1.1 Background of the Study

According to Tompkins (2010) every product has three values: a function value, a

time value and a place value. Manufacturing adds the value of function by producing

product to satisfy customer. Warehousing provides a time and place value by getting

the product to the right location at the right time. Information technology provides

information to support operations that provide the value. Warehousing adds value to

the system by ensuring adequate stock and reducing cost of the total system.

Logistical excellence or the competitive advantage of the warehouse can be distorted

if the firm has frequent stock outs, shipping errors, damage to product, incorrect

documentation, safety and hygiene problems (Otieno, Ondieki & Odera, 2012).

According to Mukopi & Iravo, (2015) sugar companies in Kenya do not manage and

control their inventory holding leading to staying off production and stock outs. It

was reported that the sugar companies had the highest production costs compared to

other sugar producing companies in the COMESA region. Cited problems were

inefficiencies along the whole value chain, high transport costs, high labour turnover,

low space utilization and poor maintenance of equipment.

Research on warehouse performance in other countries was minimal with

concentration mainly being sugar prices and policies (Koo and Taylor, 2012), unique

problems of individual countries (Zimmermann and Zeddies 2002) and (Tarimo and

Takaruma 1998). In India, warehousing was generally a neglected area and therefore

was not used as a strategic area for developing competitive advantage. Compared to

developed countries most material handling systems (MHS) are manual and smaller in

size unlike their counterparts in developed countries who operate economies of scale,

use sophisticated material handling equipment and storage schemes and make use of

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the latest information and communication technology Sople (2007). Manual systems

were also used because of cheaper labour compared to western countries where labour

was scarce and cost of employment was high. Agribusiness is labour intensive and

time consuming therefore there was a lot of time wastage in the manual processes like

loading and packaging leading to errors that translated to billions of shillings lost

(Ombati, 2010) (Yadav and Savant, 2012). Therefore the extent to which the use of

tracking devices and adoption of these systems in the sugar companies in Kenya was

not known and neither were empirical studies available, hence the focus of the study.

There were many theories related to warehousing management. The resource based

view (RBV) theory by Porter stipulated that it was imperative to holistically analyze

all the operations of an organization. RBV focused on the concept of difficult to

imitate attributes of the firm as sources of superior performance and competitive

advantage (Madhani, 2009). Sugar as a commodity was not rare and could be

imitated and substituted. The theory of constraints by Elyahu Goldrat (Unghanse,

2013) stipulated improvements work for warehouse operations. Goldrat evaluated

throughput, inventory and operating expenses and ignored efficiency and utilization

that were core to the study. The study adapted the systems theory by Ludwig von

Bertalanffy. The theory assumed that humans needed help in coping with information

overload and that had been made possible by technology (Caulfied & Maj, 2001).

1.1.1. The Concept of Information Technology

Information technology (IT) and information communication technology (ICT) and

logistics information systems (LIS) were used interchangeably. ICT and IT meant

hardware, software, telecommunications, databases and other technologies which

organizations used to improve their performance but LIS was defined as people,

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equipment, and procedures used to gather, sort, analyze, evaluate and distribute

needed, timely and accurate information to decision makers (Autry, Griffis, Goldsby

& Bobbit, 2005) ; (Nedelko, 2008); (Closs & Xu, 2000); (Porter & Millar, 2001); (Ho,

1996). The study defined IT as warehouse technologies used for tracing and tracking

material or product in the supply chain. . Ho (1996) said that the primary role of IT in

a firm was administrative, operational and competitive. According to Tompkins &

James (1998) companies involved in material handling were under constant pressure

to stay competitive. There was a limit to the extent that one could optimize

productivity in a manual handling environment without jeopardizing safety, precision

and quality levels. Porter and Millar (2001) said that computer controlled machine

tools were faster, more accurate and more flexible compared to manual operated

machines. The logistics network was a coordinated system of organizations, people,

activities information and resources. Planning involved ensuring materials or products

were at the right place at the right time and at the right cost. Execution involved the

physical creation and movement of products and materials and measurement involved

counting of products, resources, materials and activities. Coordination of information

from suppliers, customers and shippers was necessary. Currently, it was not enough to

accurately and efficiently track and account for movement of goods and their related

transactions in a factory. Estimates of demand from customers and availability of

materials from suppliers had to be far more accurate. Information had to flow

seamlessly between a firm, suppliers, customers and extended value chain (Tompkins

and Smith, 2004).

Warehouses impact on the receiving customer in many critical ways. A customer

expects accuracy, right quantity; correct timing of shipment and delivery, accuracy of

documentation and right product condition. The farmers also expected accuracy in

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measurements of their cane and correct payments. Tompkins (2008) says the key to a

company’s success was customer satisfaction. Customer satisfaction was based on the

ability to control the warehouse. An intelligent warehouse integrates computer

systems, material handling equipment, storage equipment and people into a cohesive

working element. The improvement in information quality resulted in fewer errors. It

also minimized unproductive labour hours. Proper space utilization ensures inventory

was more accurate and more locations were available for put away and storage.

Improved inventory accuracy and system directed operations allowed for higher

storage densities. The traditional problem of worker productivity suffering as storage

utilization increases was diminished. The hunting and searching aspects of picking

and put away were also eliminated.

1.1.2. The Concept of Warehousing

Speh (2009) defined warehousing as the management of time and space. Ackerman

(1990) defined it as a place where goods are stored from time of manufacturing until

they were delivered to the customer. That included the general performance of

administrative and physical functions associated with storage of goods and materials.

These functions included identification, inspection, verification, putting away and

retrieval. Warehouses provide the time and place utility necessary for a company to

prosper. Warehousing amounts to 20% of sales cost and if not managed can break or

make a company (Baker & Halim, 2007). De Koster (2008) points out attributes of an

efficient warehouse. A company should clearly know all their customers both internal

and external. There should be quality performance indicators to the workers.

Signboards with shipping errors, customer complaints and returns over time, quality

guidelines indicate sensitivity to the wishes of the customers. Employees should be

5

aware of the consequences of complaints and errors to a firm. If facility was clean it

meant management organizes the processes well. In clean facilities items do not get

lost inventory and order fulfilment accuracy was higher. In a well run facility the air

was clean, noise levels were low, and it was well lit. That concurred with Bernhardt &

Raschke (1998) who believe good manufacturing practice is important in the sugar

industry before even ISO certification. All location codes should be easily readable

and bar-coded to avoid confusion. Worker positions should be designed with attention

to ergonomics since most of the work is repetitive or strenuous. Ill designed work

places leads to high absence rates and labour turnover. Safety is important, and the

layout is important to prevent accidents and collisions. Unsafe conditions can be seen

from damaged racks. Space should not be wasted. Excessively large warehouses leads

to high cost and inefficient processes due to long travel times for storage, order

picking or cross docking; but insufficient space may prevent processes from being

executed efficiently and effectively. If products have to be dropped at temporary

locations because of lack of space, if they have to be dug up because they are stored in

wrong location, or if waiting and delays occurs then the metric receives then the

warehouse was not doing well. On equipment De Koster (2008) argued that material

handling equipment that broke down frequently lead to inefficient operations and

missed deadlines. On storage and order picking, warehouse efficiency depended

largely on methods used for storing and picking products. High labour costs and

larger throughput volumes justify more automated storage, picking systems, higher

level of order picking aids like scanners mobile terminals or voice recognition

equipment.

Ramaa, Subramanya & Rangaswamy (2012); Ginnis et al.,(2002); Baker & Canessa

(2009). Tompkins & James (1998) say that warehouses face challenges that make

6

excellence hard to achieve for that reason warehousing performance has to be

measured. Performance was defined as valued contribution to reach organizational

goals (Melchert & Winter, 2004), while Liviu, Ana-Maria & Emil (2009) said

performance refers to the way work was done. There were two aspects of performance

measurement in warehousing i.e. economic and technical. The study concentrated on

the technical efficiency i.e. inputs and outputs (Cechura & Simon, 2014), (De Koster

2008). Internal issues to be measured in a warehouse were space utilization,

equipment utilization, labour productivity, inventory accuracy, safety and

housekeeping, theft and pilferage and contamination and damage. External issues

were stock outs, fill rate, back order rate, complaints and order accuracy. Ramaa et al.,

(2012) summarised them into order fulfilment, inventory management and warehouse

productivity. In the current supply chain manufacturers and distributors are not only

judged by the quality of their products, but also how quickly and efficiently they

deliver goods to the customers (Won & Olafsson, 2005). Tompkins and James (1998)

point out that if performance cannot be measured then performance cannot be

improved. Measuring the performance of the warehouse is critical for providing

managers with a clear vision of potential issues and opportunities for improvements,

(Abdullabhai & Acosta, 2012).

1.1.4. Sugar Industry in Kenya

Kenya’s sugar industry dates back to the 1900s when Indian labourers put up farms

around Lake Victoria. The first factory was Miwani set up in 1923, followed by

Ramisi in 1927, Muhoroni (1966), Chemelil (1968), Mumias (1973), Sony (1979) and

West Kenya 1986. Sugar is the second most important crop in Kenya providing

essential products and by products to both industrial and household consumers alike.

7

The stakeholders in the industry include farmers, the government, sugar factories, out-

grower institutions like the Kenya Sugarcane Growers Association (KESGA), Kenya

Sugar Board (KSB), Kenya Sugar Research Foundation (KESREF), importers,

financial institutions, transporters, consumers and lobby groups (Omolo, 2005). The

production of sugar in Kenya is 524,000 metric tonnes while consumption is 773, 000

metric tonnes. According to the Kenya Sugar Industry strategic plan 2010-2014 the

industry is facing challenges including capacity underutilization, lack of regular

factory maintenance, poor transport infrastructure and weak corporate governance.

Kenya is not regionally competitive and according to the Lappset report (2012), the

sugar industry in Kenya has the highest costs of production of $415-500 compared to

global average of $263 per metric tonne. In 2014, the COMESA safeguards will

expire and the sugar producers will be forced to come up with cost reduction

strategies and reduce prices, reduce costs and increase productivity. Kenya is going to

suffer stiffer competition from other countries in the region that produce at low cost

and have more efficient production methods. Most of the firms are reported to

implementing strategies to improve efficiency and notable is the use of modern

technologies and equipment. The strategic plan 2010-2014 informs that a reduction of

39% in the costs can greatly improve on capacity and ensure Kenya’s sugar industry

is at par with other producers in the COMESA region. A study by Casaburi, Kremer,

Mullainathan & Ramrattan (2014) shows use of mobile telephony an element of IT by

Mumias Sugar Company. The study points out that there was an increase of 11.5% in

yields because of improved agricultural extension services. Mobile and voice

technology are some of the latest technologies being used to improve warehouse

efficiency in developed countries.

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1.2 Research Problem

Information technology provides a tool to facilitate the automation and optimization

of the material handling process. In warehousing IT can improve inventory accuracy,

facility usage, reduce labour costs and enhance order picking accuracy. Manual

systems in a warehouse increase on costs in terms of equipment, labour, space and

time. Elimination of waste in the sugar value chain and the flow of material, people,

processes and information with the utmost precision, safety and accuracy can only be

done with some of level of IT in form of computer software working in tandem with

the automated and mechanized equipment. Companies are able to develop and

maintain a flexible organization that can respond quickly to changing demands and

conditions. It also enhances information flow and facilitates decision making in

supply chain and logistics operations (Sundarakani, Tan & Over, 2012).

Warehousing core elements are equipment, labour, space and time and this greatly

affects the whole supply chain right from the suppliers to the customers therefore real

time information flow is imperative. Being able to meet customer demand means an

organization has to control all processes in the warehouse. The warehouse has moved

from being the traditional storage structure to a source of logistical excellence and a

node that links material flows between the supplier and the customer. Therefore a

firm’s operational capabilities are brought to the fore if a warehouse is performing to

its optimum (Gray, Karmarkar & Seidmann, 1992). Meaning companies are able to

ensure stock availability without carrying excesses. The companies are able to reduce

costs and control costs through proper labour and storage utilization and time. A

warehouse that ensures that a customer receives right product at the right time in the

right quantity improves a firms view. Being able to meet the competition requires

9

continuous improvement through the use of new information technologies

(Karagiannaki, Papakiriakopoulos & Bardaki, 2011).

The Kenyan sugar industry experiences cane shortages, delays, queuing, low trailer

utilization, high labour costs that point to poor warehouse performance. Cane shortage

or the inventory problem experienced by the sugar companies’ pointed to

warehousing inefficiencies whereby the warehouse being the control point did not

manage stock levels. Mistrust between farmers and the companies pointed to lack of

proper communication and information sharing. The farmers complained about

incorrect payments and this would not happen if the cane was picked by an automated

machine that read the correct tonnage of cane delivered instead of keying in manually

by humans. Wesonga, Kombo, Murumba & Makworo, (2011) pointed out that some

of the factories had poor working conditions and old machines that employees

deemed dangerous. Chullen (2012) concurred in a report on occupational health in

Kenyan sugar factories. The author observed bags of sugar put on the floor of a

warehouse; that was not in conformity with good manufacturing practices and best

practices in warehousing and also brought out the problem of space utilization and

poor warehouse performance. Bula (2012) discussed high labour turnover in some of

the sugar companies, citing problems like poor working conditions, training,

leadership styles, participation in decision making, performance appraisal and a lack

of commitment on the part of staff. In research done by Marco & Mangano (2010) on

the relationship between logistic costs and maintenance of warehouses. They believed

that there was a correlation between the condition of warehouses and the performance

of business which is the main interest of the study. A lot of research in the sugar

industry worldwide was on sugar engineering, natural factors that affect cane

production, regulations and policies, subsidies, taxes but there was very little research

10

on the extent of IT adoption and influence of tracking and tracing devices in the sugar

companies in Kenya. The study was important because apart from the Kenyan sugar

industry having very high costs and was on the verge of collapse because of

mismanagement; the study hoped to highlight to major stakeholders that with

globalization and integration of supply chains, warehousing was a critical factor in

ensuring competitive advantage but this could not be achieved with manual practices.

IT systems were needed to ensure seamless information flow between stakeholders

and ensure visibility of product from the farms to the point of sale. That was

necessary as with the lifting of COMESA tariffs, the country would be open to cheap

sugar imports and competition and this could lead to the collapse of the sugar

industry.

This study therefore sought to address the following question. What was the effect of

information technology on warehousing performance of sugar companies in Western

Kenya?

1.3 Research Objectives

The main objective was to determine the effect of information technology on

warehousing performance of sugar companies in Western Kenya.

Specific objectives were to:

a) Establish the extent of information technology adoption in sugar companies.

b) Determine the influence of IT on warehousing performance.

1.5 Value of the Study

The study was expected to make key significant contributions to both theory and

practice of warehouse management. The study adapted systems theory and hoped to

show that information technology had an effect on warehousing performance through

11

accuracy, relevance and consistency as pointed out in (Delone and Mclean, 2003).

The study also hoped to add to the body of knowledge by forming a basis for further

research in areas related to warehousing like material handling, good manufacturing

practice and maintenance of warehouses.

The study would benefit the management of organizations which operate warehouses

to make decisions based on findings of performance measurements of the warehouses

in order to improve on efficiency and effectiveness and reduce high costs. Case

studies show companies using IT in warehouses have better forecasting and planning,

better inventory management, reduced lead times and fewer manual processes and

procedures, stronger and more focused communication. The stakeholders in the sugar

industry, for example the government, Kenya Sugar Board, Kenya Research

Foundation, the farmers and management of the sugar factories, would find the study

useful because it would point out one bottleneck that most probably has been ignored

along the supply chain and that was the performance of warehouses. The farmers

would also benefit by embracing mobile technology as a way to improve

communication between them and the companies. The government also needed to

look at physical infrastructure as most of the high costs were caused by poor

infrastructure thereby preventing implementation of IT systems. The study noted that

high costs in production were partly on high cost of energy, fuel and farm inputs and

this was transferred to the consumer. The business community needed to protect

against counterfeits by using some of the identification technologies like RFID. The

study could also be used in benchmarking warehouse performance in the

manufacturing industry in Kenya, so that other supply chains can improve on their

logistics strategies.

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CHAPTER TWO: LITERATURE REVIEW

2.1. Introduction

This chapter discussed the literature related to the study i.e. information technology

and warehousing performance of sugar companies in Western Kenya. Speh (2009)

defined warehousing as the management of time and space. Tompkins, White, Bozer

& Tanchoco (2010) argue that in today’s competitive global marketplace, facilities’

planning was a strategy. IT as a resource was an enabler while warehouse as a

resource in the study could only function efficiently, effectively and be productive if

various technologies were used to enhance operational efficiency. Warehouses use

resources (facilities, equipment, inventory, investment and labour) to produce an

economically valuable service. The warehouse could only be efficient and effective if

the following parameters were met. Right product and right quantity could only be

achieved when picking and despatching were done accurately. Delivering to the right

customer at the right time requires correct labelling and loading. In the right condition

meant the product leaves the warehouse clean and damage free. Right price meant

cost efficient operations that delivered value for money. Warehouses need

information technology to reduce errors, locate items, improve efficiency, and create

visibility. The literature focused on the objectives of the study i.e. establishing the

extent of IT adoption in the sugar companies and determining influence of IT on

warehousing performance.

2.2. Theoretical underpinning of the study

Systems theory was introduced by biologist Ludwig von Bertalanffy in 1930s as a

modelling devise that accommodated the interrelationship and overlap between

separate disciplines. It reminded us of the value of integration of parts of a problem.

13

Problems cannot be solved in isolation from interrelated components. Goede (2013)

said for something to be called a system there must be purposeful activity and the

systems included communications, processes, control processes, structures and

emergent properties. The study adapted the hard systems thinking because it dealt

with real world problem solving mechanisms, machines of which IT was an enabler to

equipment and computer software. Sugar industry was largely all about material

handling right from the fields to the consumer and hence the relevance to systems

theory where material handling was a subsystem of the production system; while

material handling was a system that had subsystems type of handling processes like

packing, unpacking, movement and storage involved, maintenance, mode of

transportation by suppliers, distributors, customers and waste.

2.3. Extent of IT adoption in warehousing

Lwiki, Ojera, Mugenda & Wachira (2013) argued that IT was the life blood of all

organizations and that the inventory manager needed IT to succeed in operations.

They also pointed out that to some extent the sugar companies used IT, but the

inaccuracy problems cited by farmers at weighbridges and conditions of some of the

warehouses gave the impression that there could be a lack of adoption of IT in some

of the sugar company warehouses. Warehouses are different in operations and

individual companies decide on the type of IT to use in order to assist in operations.

There were two elements in IT use and that was the computers for directing and

planning and mechanization. De Koster (2008) said that nowadays warehouses do not

run without a sufficient level of information systems. Best in class warehouses use

systems for electronic information exchange with suppliers, customers, carriers,

customs authorities and brokers in the supply chain. They use WMS for managing

14

warehouse processes and use appropriate tools to support important warehouse

processes. The systems come in a great variety varying from simple spreadsheet

applications, to software packages like enterprise resource planning (ERP), radio

frequency identification (RFID). Firms can gain competitive advantage by

operational effectiveness, doing the same as the competitor but doing it better

Azevedo, Ferreira & Leitao (2007).

2.3.1. Information Technology and its influence on warehousing performance

According to Kivinen & Lukka (2003), companies are constantly trying to find ways

of reducing the cost structure, how fixed costs can be transformed to variable costs.

The primary aim of automation was to reduce costs of operations Varila, Seppanen &

Heinonen. Burinskiene (2012) said warehouse productivity could only be achieved by

looking at the processes. The author pointed out that in manual warehouses, the

forklift became the most expensive equipment because of labour, maintenance costs

and equipment. The research suggested reducing duplicative or multiple handling of

pallet, and non-productive movements and construction of routes. Racks in

warehouses were filled by shelving lifts instead of forklifts. Transfers were done

automatically utilizing belt and roller conveyors. Information gathering and

distribution were carried out automatically. The role of labour had changed from

performing the above to monitoring and controlling the system. Labour costs could be

reduced by use of automation. Sugar factories needed to ensure proper maintenance of

existing equipment because a study by Marco and Mangano (2010) showed that was a

relation between high costs in a warehouse and maintenance of equipment.

15

2.4 The Extent of IT adoption and warehousing performance

Warehouses are replacing human or manual work with automation. The primary aim

of automation was reduction of costs of operations. There were two aspects of IT in a

warehouse i.e. computerization and level of mechanization and automation. Hou, Wu

and Yang (2010) said that warehouse activities were in close relationship so that any

decision based upon the previous activities would impact efficiency and cost of

subsequent activities. Initially warehouse managers made decisions using traditional

methods in storage management which Hou et al., (2010) termed as complicated,

inconsistent, labour intensive and time consuming. Manually operated systems

required use of memory and experience on the part of the employee to remember

location of items. Karagiannaki, Papakiriakopoulous and Bardaki (2011) pointed out

that contextual factors affected the performance of a warehouse and also IT

implementation. The most common automation applications in warehouses were

hybrid lift trucks, horizontal transfer systems and automated storage and retrieval

systems. The benefits of automation were reducing labour, increasing speed, accuracy

and reliability, lowering energy costs and better use of space. A fully automated

warehouse worked nonstop and was able to increase throughput capacity. Less

manpower was needed and number of shifts could be reduced and also the dilemma of

demanding high productivity from staff and hiring extra workforce. An automated

warehouse required less handling space. Goods were stored more efficiently and there

was also less paper usage. There was an increased predictability of internal logistics.

There was also less time spent on staff planning and deliveries were handled with

greater efficiency. A company was able to track and trace the location and status of

goods in a supply chain. Effective material handling systems created savings that help

directly improve the bottom line. If an organization suffered from damaged products,

16

slow pick rates, a lack of space or disorganization hiring more labour would not help.

The efficiency of material handling equipments added to the performance level of the

warehouse. The mechanized system shifts the fatigue to machine and brings

effectiveness to human effort Sople (2007). Use of automated storage and retrieval

systems take advantage of overhead space to recover 60%-80% of the floor space

required by shelving and drawer systems. Improved space utilization can also extend

the useful life of existing facilities, eliminating the need for expensive square footage

expansion to meet growth requirements. The small footprint makes vertical systems

valuable for point of use storage and just in time applications (Tompkins 1998,

Tompkins 2010).

2.4.1 IT and its Influence in Warehousing Performance

According to Won and Olafsson (2005), warehouse managers aim to increase

warehouse productivity, reduce costs and fast deliveries. They said the main problems

in a warehouse were batching of orders and order picking. Batching was a method for

reducing how long customer orders have to wait in the system. The picking tasks

contributed to over 65% in warehouse operating costs. Manual operations hamper

efficiency and timeliness. Banker defines manual warehouses as a warehouse where

workers move to pick location, pick the goods and then move to delivery dock. He

defined automation as use of extensive conveyors, sorting equipment; automated

storage and retrieval equipment and other material handling solutions that move

goods to workers. Automation in study was defined as any measure put in place for

operations to perform smarter with less people. Speh (2009) asked how often items

are lost, time spent searching for them, times spent checking the work of order pickers

or receivers, how many shipments were returned , how much was spent in cycle

17

counting. Kivinen and Likka (2004) point out that the only way to control costs was to

identify the relationships between expenditures and relationships that cause them.

Varila, Sepannon & Heinonen, (2005) said that cost efficiency was one of the

cornerstones of logistics business. Speh (2009) pointed out that there were four

categories of costs in a warehouse. The handling cost dealt with all expenses

associated with moving product in or out of the warehouse. The largest component

was labour which was used to handle the products that move through the distribution

centre. It also included all costs associated with equipment used to handle products in

the warehouse. Storage costs were associated with goods at rest. These were related

to the cost of occupying facility and were expressed on monthly basis. Operations and

administration included costs such as line supervision, clerical effort, information

technology, supplies, insurance and taxes. The cost of logistics could not be

controlled without controlling the processes and activities.

2.4.2 JIT and Modern Concepts on Warehousing

JIT is a management concept that aims at eliminating waste associated with time,

labour and storage space. The concept implied that a company produces only that

which was needed by the customer, when it was needed and in the quantity required.

The company only produced only what the customer requested to actual orders and

not forecasting. The problem was compounded by warehouses having excess

inventory and other times having nothing, a case of Kenyan sugar companies.

Inventory in this concept was seen as sign of poor management. JIT ensures better

product quality, higher productivity and lower production costs. According Kuse,

Castro & Takahashi (1995), information systems play an important role in

establishing JIT and shortening lead times. In Kenya a study by Ondiek & Kisombe

18

(2013) on lean manufacturing show some level of adoption of lean practices in some

of the sugar factories but JIT has not been adopted though Muhoroni produces on

demand. Even with the introduction of JIT the role of the warehouse is still important

in the supply chain and cannot be eliminated as it has an effect on the bottom line of a

company (Frazelle, 2001). Use of JIT can improve the overall view of warehousing

with all the seven parameters met i.e. right product, right time, right condition, right

price and right quality if its incorporated with IT systems.

Mumias Sugar Company was reported to be using Agricultural management systems

(AMS) and Enterprise Resource Planning (ERP) in its operations. The operations in

the factory were reported to be efficient because of the level of technology used and

mechanization. According to Keir (2002) South Africa was advanced in sugar

engineering but lagged behind in storage and bagging of sugar. America was known

to use third party providers for its efficient sugar warehousing and distribution.

Tanzania cited lack of storage facilities for their cane and old machines built in the

1960s (Tarimo & Takaruma 1998). According to an article dated march 2012, India is

reported to be using Sugarcane Information System (SIS). This was in response to

communication barriers like survey of farmer’s fields, selling of produce, correct and

timely measurement of product and prompt payment. Initially farmers had to travel

twenty five kilometres for every interaction with mill or society group. They reported

frequent loss or theft of supply tickets and had difficulty coordinating when to bring

their supply to the mills; but with the introduction of SIS there had being an

improvement in communication.

19

2.5 Conceptual Model

Figure 1: Conceptual framework

Independent variable Dependent variable

Source: Author (2013)

This model explained the relationship between information technology and

warehousing performance. It was difficult to optimize on labour productivity, space

and equipment and meet lead times when using manual systems. Computer controlled

equipment which has a million times capability of memory and are able to do things a

human being cannot therefore increasing the productivity of a warehouse. In space

utilization automated equipment ensured that all areas were covered. There was

reduction of high labour costs and extra labour needed. Using highly automated

equipment can assist the sugar companies in ensuring proper space utilization, labour

and time.

Information technology Warehouse Performance

Level of information technology

Radio Frequency

Identification

Enterprise resource

Planning

Warehouse Management

Systems

Bar-codes

Electronic data interchange

(EDI)

Transportation System

Mechanization and

automation

Warehouse assets (equipment,

labour, space, time)

Efficiency

Accuracy

Timeliness

cost

20

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Introduction

The study was conducted through cross sectional research design. The study was

concerned with determining the effect of information technology on warehouse

performance of sugar companies in Western Kenya. It specifically intended to

investigate the relationship between information technology and warehouse

performance. Such issues were best investigated through correlation but because the

data points of the study were few correlation of the independent and dependent

variables was not done. The study instead used descriptive statistics and was able to

determine the effect of IT on warehouse performance of the sugar companies.

3.2 Research Design

The study adopted a cross sectional survey research design. The research design was

preferred because it helped the researcher to explain the causal relationships between

the variables. According to (Kothari, 2010), cross-sectional research design was ideal

where the study sought to investigate relationship between variables in studying a

phenomenon.

3.3 Population of Study

The unit of analysis was the sugar companies. The target population of the study was

all the eleven operational sugar factories situated in Western Kenya as per the Kenya

Sugar Board census report 2013/14/15. The population frame was indicated in

appendix 2. A census study of all the firms was conducted. According to Saunders et

al., (2007), census was suitable where the units of study are not too many and were

concentrated geographically in such a way that accessibility was easy and not

prohibitive in terms of cost, time and other resources. Though the study was meant to

21

be a census study, however only four companies out of the eleven (11) responded to

the questionnaires. The companies were visited according to convenience and

geographic proximity commencing with the nearest to the furthest.

3.4 Data collection

The study utilized primary data as secondary data (document analysis) relevant to the

study were not available. Primary data was collected by use of self administered

questionnaires and interview schedules. The questionnaire was divided into section A

dealing with contextual factors, section B on extent of adoption of IT and Influence of

IT were denoted by (I) and warehouse performance (Wp) see appendix I. The

respondents were warehouse supervisors in the sugar companies but in one company

the Finance Manager handled the questionnaire.

3.5 Data analysis and presentation

Data was analyzed by both quantitative and qualitative techniques. Quantitative data

was analysed by use of descriptive statistics because the sample size was too small.

Qualitative methods were also used and this was achieved by use of means and

standard deviations. Data interview and questionnaire responses were coded

appropriately and were ranked and scored using likert scale on a ratio of 1-5. Results

were presented using tables showing the responses of the respective companies.

22

CHAPTER FOUR: DATA ANALYSIS, RESULTS AND

DISCUSSION

4.1 Response Level

The study was investigating the effect of information technology on warehousing

performance of sugar companies in Western Kenya. Since it was a census study,

questionnaires were administered to all the eleven listed sugar companies in Western

Kenya, however out of eleven companies only four companies gave permission to

collect data two companies were closed and four companies did not give permission

to collect data. Therefore out of eleven questionnaires only four responded giving a

response rate of 36 % which is quite representative and can give an accurate account

of the study as displayed by Visser, Krosnick, Marquette & Curtin (1996).

4.2 Findings on Contextual Factors

Contextual factors have an influence on the performance of the warehouse. The type

of warehouse, equipment, age of the company, labour, constraints or opportunities

experienced by the companies has an effect on operations in the warehouse.

4.2.1 Ownership of the warehouses

Table 1: Type of warehouse

company private contractual percentage

A 1 0 25

B 1 0 25

C 1 0 25

D 1 0 25

4 100

Source: research data, 2015

23

The companies were asked the type of warehouse the company used. The answer was

n=4 confirmed that the companies had private warehouses giving a percentage of

100%. The study observed the warehouses were located next to the factory to allow

the movement of sugar directly from the manufacturing process to the warehouse.

This gives the company the advantage of flexibility of redesign of the warehouses to

meet specific needs and address the constraints raised by some on space and

expansion. The companies are also in control of operations hence can reduce

warehouse costs significantly and improve on efficiency. On the downside the

companies may suffer lower flexibility in investments Ling (2007).

4.2.2 Specialized equipment

On specialized equipment and material handling equipment (MHE) the companies

were asked to list specialized equipment and the study found that the companies used

conveyors and pallets in material movement and stacking. The companies had

mechanized equipment like forklifts and cranes largely used in the main factory

section. Tompkins 1998 says equipment resources expected in a warehouse are data

processing equipment, dock equipment, unit load equipment, material handling

equipment and storage equipment but the findings show a lack of investment in

specialized automated equipment in the warehouses. Automated equipment in

warehousing was a necessity because some of the functions like order picking are

known to be labour intensive taking 50% of operational warehouse costs and are also

cost intensive and time critical (Boysen & Stephan, 2012). This could contribute to

reasons cited by staff for high labour turnover like too much work because of the

manual systems used in material handling. This finding could also give credibility to

24

Ling 2007’s study above that posits lack of flexibility in investing in new

technologies.

4.2.3 Age of the Companies

Table 2: Age of the companies

Frequency Percentage Cumulative percent

Less than ten years old 1 25 25

More than twenty years old 3 75 75

Total 4 100 100


The study found that n=3 of the companies were over twenty years old giving 75%

while n=1 was three years old giving 25%. The company that was three years old

boasted of state of the art technology but the over twenty years showed a lack of

embracing technology as they had the systems but had not implemented for use in the

warehouses and neither had they improved on existing technologies. A survey by

Tompkins International also shows that aged material handling systems affect the

employee productivity by 18.2%. One of the problems bedevilling the sugar industry

was a lack of maintenance of equipment and old equipment that employees deemed

dangerous (Bula, 2012).

25

4.2.4 Labour

Table 3: Turnover

Frequency Percentage Cumulative percentage

No turnover 1 25 25

Turnover 3 75 75

Total 4 100 100


On labour turnover 25% reported that they had nil turnover, while 75% reported

having some turnover, though to them it was insignificant. On staff being part of a

collective bargaining agreement (CBA) 25% reported that none of the employees

were part of a CBA, 25% reported being part of a CBA while 50% reported being

partially part of a CBA. Labour turnover in the sugar industry was largely blamed on

too much work, long working hours, poor pay, poor working conditions, risky

machines, management and lack of growth (Wesonga, Kombo, Murumba &

Makworo, 2011) (Bula, 2012). Labour is known to be the highest cost in the

warehouse therefore reducing amount of labour, pursuing high labour productivity,

good labour relations and worker satisfaction would greatly reduce costs. This could

be done by using modern equipment that that does data entry, picking is made easier

with more efficient routes that require less walk time, packing is more accurate with

scanning therefore happier workforce because of less movement and potential for

mistakes.

4.2.5 Constraints and opportunities

Constraints and opportunities found in warehousing have a moderating effect on

warehouse performance. The companies were asked to give self identified constraints

26

and opportunities in the warehouses. Company A reported that their constraint was

space and opportunity was expansion. Company B felt Company C felt its opportunity

lay in outsourcing and its constraint was manual labour. Company D felt its constraint

was technology, space and storage.

4.3. Extent of IT adoption in the Sugar Companies

The first objective sought to determine the extent of adoption of IT in the sugar

company warehouses and these were the findings.

4.3.1 Existence of warehouse management systems

Table 4: Warehouse Management System

yes No Vendor Frequency Cumulative

percent

A 1 0 SAP 4 100

B 1 0 Oracle

C 1 0 Ebizframe

D 1 0 Sispro 100 %

Source: research data 2015

The companies were asked if they owned any warehouse management system and

n=4, therefore 100 % of the companies had some kind of warehouse management

system. The ERP systems were used in entering information into the computers. One

company pointed out that though information was real time but logistically product

availability and movement to customer would be to a low extent, meaning at times

they are not able to meet customer dates. This shows that there is a lack of inventory

visibility within the companies and this could be blamed on not fully implementing

the IT systems and the various departments not working together.

27

4.3.2 Level of mechanization

The level of mechanization was divided into four parts i.e. manual, mechanized, semi-

automated and automated. The four categories on level of mechanization were

necessary because these companies have specialized equipment which fall in

categories of purely manual, mechanized, semi-automated or automated but findings

show that none were computer controlled.

Table 5: Manual Systems

Frequency Percent Cumulative

percent

To a very low extent

To a moderate extent

To a very large extent

Total

1

1

1

3

25

25

25

75

25

25

25

75


On an ordinal scale the companies were asked to give the level of manual use in the

companies. The companies gave the following answers as concerns manual systems.

The results showed that three of four companies had different answers concerning

manual use in the warehouses. Company A reported using manual systems to a very

low extent. Company B reported using manual systems to a moderate extent.

Company C did not respond to manual systems, while company D agreed that they

used manual systems to a large extent. Though manual systems are the cheapest and

most common they are constrained by low volumes, slow speed, physical

characteristics of product and distance. Manual systems cause equipment to be idle

and be underutilized.

28

Table 6: Mechanized Systems

Frequency Percent Cumulative percent

To a low extent


Total

1

3

4

25

75

100

25

75

100


The results show that three out of the four companies, A, B, C reported using

mechanized equipment in operations leading to a percentage of 75% while company

D reported being mechanized to a low extent leading to a percentage of 25%.

Mechanization reduces fatigue from human to machines. Sople (2007) points out that

mechanised equipment need not be powered equipment like wheeled trolleys that

increase human abilities beyond mental and physical capabilities. Most of the

equipment observed in the companies were forklifts, pallets, trucks, conveyors and

cranes.

Table 7: Semi-automated systems


percent


Total

3

3

75

75

75

75


The findings show that 75% of companies responded to being moderately semi-

automated i.e company A, B, D while company C did not respond because the

company felt that the systems could not be mixed.

29

Table 8: Automated systems


percent



To a great extent

Total

1

1

1

3

25

25

25

75

25

25

25

75


Company A reported using automated systems to a very large extent, B to a moderate

extent and D to a very low extent while company C did not respond. Automation

ensures the human factor is minimised and is restricted to programming and controls.

A warehouse can either have manual systems or automated systems. The primary aim

of automation is to reduce costs of operations Varila, Seppanen & Heinonen.

Burinskiene (2012), says that travel distance of a forklift can be reduced by 27-37%

when radio frequency based process is used compared with when paper process is

used. By gaining control of your warehouse you gain control of your profitability.

Effective material handling systems create savings that help directly improve your

bottom line. If an organization suffers from damaged products, slow pick rates, a lack

of space or disorganization hiring more labour will not help. The efficiency of

material handling equipments adds to the performance level of the warehouse. The

mechanized system shifts the fatigue to machine and brings effectiveness to human

effort (Sople 2007).

30

4.3.3 Adoption of IT in the various departments in the companies

Table 9: Extent of Adoption of IT use in the warehouse


percent


To a great extent

Total

2

2

4

50

50

100

50

50

100


On an ordinal scale the companies were asked to what extent they had adopted the use

of IT in the warehouses. 50% reported adopting IT in the warehouse to a moderate

extent, while 50% reported to a very large extent. The companies used computers to

key in data manually for goods inbound and outbound. They reported having ERP

systems in place but integration of IT systems with equipment to aid in space

utilization; labour or time was not implemented. Operations in the warehouses were

largely manual.

Table 10: Extent IT has been used to link all levels in the organization



To a great extent


Total

1

1

2

4

25

25

50

100

25

25

50

100


On an ordinal scale the companies were asked to what extent IT was used to link all

the levels in the organization. One company 25% reported that IT linkage to all levels

of the organization was to a moderate extent, 25% reported to a great extent and 50%

to a very large extent. Seamless information flow in all departments is important. This

31

can be done through integration of WMS and ERP systems for smooth business flow

and easier tracing of costs and accuracy.

Table 11: Employee use of IT systems



To a great extent


Total

2

1

1

4

50

25

25

100

50

25

25

100


On an ordinal scale the companies were asked to what extent employees use IT

systems in the company. 50% answered to a moderate extent, while 25% to a great

extent, and while 25% to a very large extent. This showed that employees were able

to use computer systems in operations. Warehousing operations in the sugar

companies was largely done by machines that bag the sugar and then move the sugar

to the warehouse and through pallets to the waiting customers. The employees

reconcile the records through entries made in the computers hence the findings. The

first step to improving warehouse operations is to increase labour productivity.

System directed operations are required to reduce errors. Directed operations also

improve labour productivity. Operators no longer have to think about the next

operation. The system does the thinking. Five factors that must be considered to

optimize labour are operator location equipment availability, task prioritization, queue

times and task importance.

32

Table 12: IT and its adequacy for operations


percent


To a low extent


To a great extent

Total

1

1

1

1

4

25

25

25

25

100

25

25

25

25

100


On an ordinal scale 25% responded that IT was adequate for operations to a very low

extent, 25% to a low extent, 25% to a moderate extent and 25% to a great extent. The

systems in the warehouses are not computer controlled nor are they integrated with IT

systems to control space utilization by use of storage systems. Labour was largely

manual where sugar is pushed manually. According to the findings time is affected by

availability of sugar hence the mixed findings.

Table 13: Use of IT technologies like RFID, Barcodes


percent



Total

3

1

4

75

25

100

75

25

100


Despite the fact that all the companies had warehouse management systems 75 % of

the companies do not use RFID or barcodes, but one company reported using

barcodes for bagging of sugar. There are different systems and companies have a

33

choice in type of warehouse management system to use. The above are just examples

of recommended warehouse systems by experts in the field of warehousing.

4.4 Influence of IT on Warehouse Performance

The second objective was establishing the influence of IT on warehouse performance.

The study sought to determine if IT had any influence on information within the

organization and on major functions and elements in warehousing.

4.4.1 IT influence on warehouse metrics

Table 14: Warehouse metrics




Total

1

3

4

25

75

100

25

75

100


Three companies 75% reported that IT had an influence on warehousing key

performance indicators (KPIs) like space, labour, time and equipment to a moderate

extent while one company 25% reported IT having an influence to a very low extent.

The contradiction was brought about by logistics that were not perfect scoring 1 and

information entered into the systems that scored a 5. Since the systems were not

synchronized there was a difference on facts on the ground with customers having to

wait in queue for loading to be done. One company pointed out that it took one hour

to load a trailer while others have to queue awaiting their turn. Despite the findings

here IT had very little influence warehouse KPIs because as shown above IT seemed

to have been adopted in the warehouses.

34

4.4.2 Influence of IT on warehouses functions

Table 15: Warehouse functions


percent


To a low extent

To a great extent


Total

1

1

1

1

4

25

25

25

25

100

25

25

25

25

100


On an ordinal scale the companies were asked if IT had any influence on warehouse

functions like receiving, sorting, storage, picking and transportation or shipping of

products. The answers varied from 25% to a very low extent, 25% to a low extent,

25% to a great extent and 25% to a very large extent. Picking is known to be the most

expensive and laborious task in the warehouse and reducing on costs associated with

the tasks will determine productivity of a company.

4.4.3 IT influence on communication

Table 16: Communication between the company and stakeholders


percent


To a low extent


Total

1

1

2

4

25

25

50

100

25

25

50

100


35

On an ordinal scale companies were asked if IT had any influence on communication

between company and stakeholders like suppliers. Two companies (50%) reported to

a very low extent while two (50%) companies reported to a very large extent.

Communication is important at all levels of the supply chain in order to avoid stock

outs and also work modalities of meeting the deficits. Valuing the farmer the main

supplier of cane needs open communication between company and stakeholders.

4.4.4: IT influence on forecasting, planning in the sugar value chain

Table 17: IT and decision making



To a low extent

To a great extent


Total

1

1

1

1

4

25

25

25

25

100

25

25

25

25

100


On an ordinal scale the companies reported 25% to a very low extent IT having an

influence on forecasting, planning in the sugar value chain, 25% to a low extent, 25%

to a great extent and 25% to a very large extent. This shows that some of the

companies were able to plan and ensure that there are no stock outs using the IT

systems in place but this was also contradictory because at times there was surplus

cane and at times the machines were idle therefore increasing costs. Visibility of

inventory in the warehouses can go a long way in forecasting and ensuring there are

no stock outs or excesses.

36

4.4.5 IT influence on Order fulfilment

Table 18: Order fulfilment



To a great extent


Total

1

2

1

4

25

50

25

100

25

50

25

100


Order fulfilment is a very important aspect in the supply chain. On time delivery

(OTD) means the companies are able to meet deliveries on the date agreed with the

customer or before. This is influenced by production line requirements and cash flow.

This is measured by working days versus calendar days, shipping date versus item

received date, promised date versus required date, commitment date versus needed

date. The companies responded they meet the metrics 25% to a moderate extent, 50%

to a great extent, 25% to a large extent. This information could be accurate

considering one company does not have cane shortage problems and in fact reported

having excesses and been forced to turn away farmers, while one other company

reported production was done on demand.

Table 19: Order fill rate


percent


To a great extent

Total

2

2

4

50

50

100

50

50

100


37

Order fill rate a measure of shipping performance expressed as a percentage of the

total order and the companies had 50% this was done to a moderate extent while 50%

say it was met to a great extent.

Table 20: Order accuracy



To a great extent

Total

1

3

4

25

75

100

25

75

100


Order accuracy a huge contributor to costs is affected by mistakes in keying in orders

or inventory information, misplaced products, incorrect picking and packing or

mismanagement of inventory. The companies gave a response of 25% meet order

accuracy to a moderate extent while 75% reported having order accuracy to a great

extent. If specialized equipment are not used it means staff ensure accuracy is

maintained when picking orders.

Table 21: Line accuracy



To a great extent

Total

2

2

4

50

50

100

50

50

100


Line accuracy shows the total number of lines shipped/transported over all orders.

The companies had 50% for achieving this to a moderate extent while 50% did this to

a great extent.

38

Table 22: Order cycle time


To a great extent

Total

4

4

100 100


Order cycle time shows the actual time to fill a customer’s order. This affects the

business in the sense that inability to meet customer demand means customers will

buy competitors product and in Kenya unfortunately this has lead to smuggling in of

sugar through the wrong channels. The companies reported meeting n=4 100% the

order cycle times.

Table 23: Perfect order completion


To a low extent

To a great extent


Total

1

2

1

4

25

50

25

100

25

50

25


Perfect order completion means the right product, in the right quality, right quantity at

the right time. The companies reported 25% having perfect order completion to a low

extent, 50 % to a great extent and 25% to a very large extent. This shows some

companies are meeting customer demand while some are unable to.

39

Table 24: IT influence on inventory management


To a great extent


Total

3

1

4

75

25

100

75

25

100


The companies reported managing their inventory to a great extent at 75% and to a

very large extent at 25%. Reports from the sugar industry report cane shortages and

idling of machines and customers waiting for days on end is some of the companies

for their sugar. The findings might be contradictory but one company reported having

excess cane while another talked of producing only by order.

Table 25: Inventory accuracy



To a great extent


Total

1

2

1

4

25

50

25

100

25

50

25


Inventory accuracy is the variance between perpetual inventory and physical

inventory. Inaccuracy in inventory leads to companies thinking they more inventory

in stock than they actually do leading to unsatisfied customers. Stock outs interrupt

production and create delivery delays, creates idle time and manufacturing

inefficiency. This has been observed in the sugar industry with closure of companies

because of lack of cane but the companies reported 25% to a moderate extent, 50% to

40

a great extent and 25% to a very large extent Lee (2006). On the hand inventory

accuracy can improve other logistical processes thereby reduce costs.

Table 26: Inventory visibility



To a great extent


Total

1

2

1

4

25

50

25

100

25

50

25


Inventory visibility is the ability of an organization to manage inventory in real time

with visibility into current inventory locations and levels. This visibility enables an

organization to streamline processes related to shipping and delivery. The companies

reported inventory visibility to 25% to a moderate extent, 50% to a great extent and

25% to a very large extent.

Table 27: Damaged inventory



To a low extent


Total

1

2

1

4

25

50

25

100

25

50

25

100


Damaged inventory increases costs in very many aspects. The findings show 25% to a

very low extent, 50% to a low extent and 25% to a moderate extent. Damage is gotten

through material handling. The companies reported that this happened during storage

when bags are caught between the conveyors or fall and open spilling the contents.

41

Unlike other products the damage was corrected there and then, but considering sugar

loss the control cannot be 100%.

Table 28: Storage utilization


To a great extent


Total

2

2

4

50

50

100

50

50

100


The companies pointed out that they fully utilize storage facilities 50% to a great

extent and 50% to a very large extent. Considering some of the constraints were space

and expansion this could explain the findings. The sugar companies are also affected

by excess cane availability and other times shortages leading to idle machinery. Space

utilization is also very important in warehousing. Tompkins and Harmelink (2004)

point out that space is one thing that always runs out in a warehouse. Space is a

primary finite resource. Deficiency in planning of this key factor hinders operating

efficiency of the warehouse. Cited problems are extended travel distances due to poor

layout and poor utilization of space. This is where the control of merchandise is

transferred, and if it is not accomplished safely and accurately it is impossible to

satisfy the customer. Inadequate storage planning i.e. too little or too large will result

in operational problems like lost stock, blocked aisles inaccessible material , poor

housekeeping, safety problems and low productivity. Large spaces or unutilized

spaces result in high space costs in form of land, construction, energy and equipment.

42

Table 29: Dock to stock time


To a great extent

To a very great extent

Total

2

2

4

50

50

100

50

50

100


This is the elapsed time of arrival of material through the receiving process

assignment of location entry into the inventory system and available for order. The

companies reported meeting this 50% to a great extent and 50% to a very large extent.

This is done through keying in information directly to computers.

4.5.7: Condition of the warehouse

The condition of the warehouse and maintenance of equipment also determines

warehouse performance. Excessive handling of material, untidy rooms, poor lighting

also point to poor warehouse performance. After the occupational health report 2012

the companies visited seem to have improved on the conditions of the warehouses.

Table 30: Condition of building, floors, lighting



To a great extent

Total

1

3

4

25

75

100

25

75

100


The companies were asked if the conditions of the buildings, floors was in good

condition a very important aspect as it points to good warehousing practice and is also

a preventive measure for accidents .The answer was 25% to a moderate extent and

75% to a great extent.

43

Table 31: Condition of material and storage equipment



To a great extent

Total

1

3

4

25

75

100

25

75

100


Maintenance of equipment is a major factor in reducing costs in warehousing so the

companies answered 25% this was done to a moderate extent, 75% responded it was

done to a great extent. Tompkins says that preventive maintenance is a best practice in

maintenance. He says a company has the choice of repair strategy a reactive, run to

failure strategy or a strategy that is proactive with a focus on planned maintenance

and preventive maintenance. The first strategy operates with a high level of

uncertainty by running equipment to the point of shutting down, while the second

strategy reduces uncertainty of unplanned downtime and high costs of major failures.

This is cost effective in the long term and De Marco adds that maintenance of

equipment can determine a company’s ability to compete effectively.

Table 32: Distance material is moved


To a great extent


Total

3

1

4

75

25

100

75

25

100


Excessive touching of material in warehousing adds costs in terms of damage loss of

quality and quantity of product. The companies reported avoiding this by 75%

reporting to a great extent while 25% reported to a very large extent. Companies

44

should reduce by selecting equipment that eliminates repetitive and strenuous manual

labour and eliminates unnecessary movement.

Table 33: Double handling



To a great extent

Total

2

2

4

50

50

100

50

50

100


The use of equipment prevents human contact with product and the companies

reported 50% to a moderate extent and 50% to a great extent. The companies have

manual to automated equipment therefore they felt that double handling of material;

was avoided.

Table 34: Safety


To a great extent


Total

2

2

4

50

50

100

50

50

100


Accidents happen in warehouses ranging from falling objects from conveyors, slips

and falls from equipment and the companies said they have ensured safety of

warehouse staff 50% to a great extent and 50% to a very large extent. The

occupational health for 2012 gave a different picture of the warehouses but with the

findings above and observation there was great improvement in most companies. The

findings also show the companies use manual to automated equipment to move

material hence preventing accidents termed ergonomic like pain, pushing or pulling.

45

Table 35: Ideal warehouse



To a great extent


Total

1

1

2

4

25

25

50

100

25

25

50

100


The supervisors were asked if that was the warehouse they would like in and 25%

said to a moderate extent, 25% to a great extent and 50% to a very large extent. Some

companies felt that their warehouses were doing well but others felt there was room

for improvement especially since some had WMS but they were not fully

implemented, others had requested for equipment that had not been received, storage

utilization issues also came up.

Table 36: Mean of IT Adoption

N statistic sum mean * Target

rating

Rating

Score

Manual systems 3 9 3 * 5 15

Mechanized systems 4 11 2.75 * 5 13.75

Semi-automated 3 9 3 * 5 15

Automated 3 8 2.66 * 5 13.3

Extent of IT adoption 4 14 3.5 * 5 17.5

IT and linkage to all

levels of organization

4 17 4.25 * 5 21.25

Extent of employee use 4 15 3.75 * 5 17.5

IT and adequacy for

operations

4 12 3 * 5 15

Use of RFID, barcodes 4 8 2 * 5 10

46

IT and labour 4 8 2 * 5 10

IT and time 4 12 3 * 5 15

IT and space 4 8 2 * 5 10

IT and equipment 4 8 2 * 5 10

Influence on sorting,

storage, picking

4 12 3 * 5 15

Influence on

communication

4 12 3 * 5 15

Influence on sugar value

chain

4 11 2.75 * 5 13.75

80 227.05

The integral performance model by Tompkins 1998 was partially adapted for this

study to measure the performance index because only the likert scale information was

available to test the scores for IT adoption in the warehouses. The performance index

had a rating 2.8 out of 5 and a percentage of 35. The companies reported having

computer systems installed with ERP but integration with the main elements in

warehousing like labour, equipment or space had not been done. The companies

loaded vehicles manually by pushing the sugar on pallets. Space averaged 2 meaning

sugar was stacked without any special consideration to storage systems that

completely utilize space and are able to point out unused spaces. Only one company

reported using a barcode. IT was not used in communication with major stakeholders

though a study by Casaburi, Kremer, Mullainathan & Ramrattan, (2014) showed

company A using mobile telecommunication to communicate with farmers. The

influence of IT was not felt in the companies because farmers still complained of

wrong payments arising from manual picking of the cane and manual keying in of the

information that can be tampered with or have errors. Automatic picking can ensure

accuracy of records and visibility of the same information in real time.

47

Table 37: Mean Warehouse Performance

N statistic sum Mean * Target

rating

Rating

Score

On time delivery 4 14 3.5 * 5 17.5

Order fill rate 4 14 3.5 * 5 17.5

Order accuracy 4 14 3.5 * 5 17.5

Line accuracy 4 16 4 * 5 20

Order cycle time 4 14 3.5 * 5 17.5

Perfect order completion 4 16 4 * 5 20

Inventory accuracy 4 14 3.5 * 5 17.5

Inventory visibility 4 14 3.5 * 5 17.5

Damaged inventory 4 9 2.25 * 5 11.25

Storage utilization 4 16 4 * 5 20

Dock to stock time 4 16 4 * 5 20

60 196.25


The performance index adapted from Tompkins 1998’s integral performance model

showed that the performance index was 3.1. Though the analysis was based on

perception measured on the likert scale the reality on the ground showed that the

sugar companies were struggling with a lot of bottlenecks. On time delivery had a

mean of 3.5 meaning the companies meet customer expectation, but on the ground

one company pointed out that production was done on order while another had a

customer that had waited for sugar for a whole week. Order fill rate had a mean of

48

3.5. Order accuracy had a mean of 3.5 with companies reporting the data entered was

accurate according to customer orders but another pointed out that sometimes when

sugar prices fluctuated customers changed the quantities ordered hence creating

confusion and mistakes because of change in invoicing. Line accuracy averaged a

mean of 3.5 and considering they used semi-automated and mechanized equipment

but differences arose from damages. Order cycle time averaged a mean of 3.5. Perfect

order completion averaged 4 meaning orders were met perfectly. Inventory accuracy

averaged a mean of 3.5, inventory visibility averaged a mean of 3.5, damaged

inventory averaged a mean of 2.25 with companies having damaged inventory to a

low extent and others moderately, but the argument was damaged bags were repaired

immediately hence the loss at warehouse level was corrected immediately. Storage

utilization averaged a 4 with companies believing they utilized space properly but

here lack of sugar was not taken into consideration and the losses it brought in terms

of idle machinery and labour. But this was contradictory in terms of IT having an

influence to a low extent on space, time, labour and equipment. Time averaged three

because the information was entered in the computers and could be assessed in real

time but logistical availability of sugar to meet customer demand was a challenge.

Perfect order index (POI) for the warehouses

This index was done to track events in the output process combining two or more

metrics. The perfect order index averaged 43%, running below 80%.

Table 38: Perfect order index

On time

delivery

* Complete

order

* Damage

free

* Order

accuracy

POI

87.5% * 100% * 56.25% * 87.5% 43%

49

The situation of the Kenyan sugar industry with a deficit of 200 metric tonnes and

frequent cane shortages could explain the POI above. The companies only perfectly

met the orders given to them at an index of 43%. The sugar industry is bedevilled by

cheap sugar imports and smuggling. The companies have been reported to bag sugar

imported from other countries because of cane shortages. Inefficiencies along the

value chain can explain the percentage as there are reported inaccuracies, delays and

queuing. Warehouses in the sugar companies were not fully automated therefore the

fatigue of keying in data was still done manually instead of automatic picking. The

sugar was also manually loaded to pallets and on to waiting vehicles. The companies

reported having ERP systems that were not fully implemented for use in the

warehouses therefore IT was not fully integrated in the warehouses hence the results.

50

CHAPTER FIVE: SUMMARY, CONCLUSION AND

RECOMMENDATIONS

5.1 Summary of the Study

Karaseck (2013) says ‘that a warehouse was performing optimally if each customer

was satisfied completely and when all warehouse and logistic processes were done in

the shortest possible time’ but the sugar industry is bedevilled by sugar shortages,

problems along the value chain and high production costs. The study therefore sought

to establish if the use of IT systems had any effect on warehousing performance and

thereby streamlining the logistical processes. The study was a census study of all the

eleven sugar companies in Western Kenya, but only four responded. The warehouse

in the sugar companies are finished products and materials. All the warehouses are

located within company premises and next to the factory for the flow of sugar from

factory directly to warehouse through the conveyors. Three of the companies were

more than twenty years old. Secondary research shows that old warehouses are slow

to embrace new technology and this was observed in the field. The issue of 3PLs and

efficiency could also be an area or research since the best sugar firms in the world

seem to be embracing 3PLs. Tompkins (1998) says this has been driven by rising

costs of information technology in warehousing and diminishing workforce

availability. The study found that the companies had not fully adopted the use of IT in

the warehouses. This was observed by the performance index of 2.8 on core assets in

warehousing. The influence of IT on warehousing performance also averaged 3.1 but

with perfect order index of 43% meant the companies were still struggling with

accuracy and perfect order completion.

51

The first objective was to find the extent of adoption of IT in the sugar companies.

The extent of IT adoption averaged an overall mean of 3.8 while the performance

index averaged a rating of 2.8 This means that at roughly 76% the companies had

adopted IT in the warehouses mainly through computerization but not automation of

processes. The study found that IT was not fully adopted in the sugar companies. One

company pointed out that despite having the ERP systems in place they were not fully

implemented. IT was not also adopted in the warehouse hence IT had not significant

effect on labour, equipment and space. The companies pointed out that logistics

practice in the companies and use of real time information in the computers conflicted

because work was done manually hence queuing and waiting for orders was

inevitable. The companies also pointed out that IT had not been adopted in forecasting

or planning in the sugar value chain, it was not greatly used between the companies

and the stakeholders. One company pointed out that one of its constraints was manual

labour therefore there was a need to improve on manual processes.

The study also sought to determine the influence of IT on warehouse performance and

overall the factors influencing warehouse performance averaged a mean of 4 and a

performance index of 3.1. Therefore at 80% the companies felt they met warehouse

performance measures order fulfilment and inventory visibility at 80%. Measurement

of warehouse efficiency could not be achieved because data was not availed. All the

variables averaged means of above 3 except for damaged inventory but logistically

that was not the real picture; though some companies felt they met the demand

because they had enough cane and were not involved in cane wars. The perfect order

fulfilment index averaged 43% showing that the companies were not able to meet

customer orders on time.

52

5.2 Conclusions

The study investigated if IT has an effect on warehouse performance of sugar

companies in Western Kenya. This was brought about by studies showing logistical

problems in the whole sugar value chain, high labour turnover, lack of maintenance of

equipment, cane shortages and stock outs, smuggling, pilferage queuing, delays and

wrong payments to farmers. The study specifically sought to determine the extent of

IT adoption in the sugar companies and its influence on warehouse performance. In

view of the findings the study concluded that because IT had not been fully adopted

the sugar companies were still experiencing challenges that would otherwise be

solved by full automation and integration of systems.

The first objective was determining the extent of IT adoption in the sugar companies.

Information technology in the sugar companies did not have a great impact on

warehouse operations or the general sugar value chain. As much as the companies had

some WMS they were not fully implemented hence were not been used. IT had not

been adopted for use in the warehouse with labour, equipment and space averaging

below 3. IT was not used for tracking and tracing of sugar hence the mean of 2. Some

of the high costs experienced in production of sugar could be coming directly from

warehousing in terms of queuing, idleness, space, labour, old equipment. On space

utilization as much as the companies report stacking to the top , because of lack of

racking and stacking systems it could be concluded that there was space wastage

(Ackerman, 1990). Another company pointed out that they lacked storage space hence

they stack until there was no more space. One company pointed out that the insurance

company advised them not to fully utilize space to the roof to avoid accidents. This

showed that operations were not fully automated as with use of IT controlled

equipment receiving, storing and picking would be done by the specialized equipment

53

and not manually hence the issue of accidents would not arise. On equipment it was

reported by one the companies that it took one hour to load one vehicle and it was

observed that there was a queue of vehicles waiting to be loaded. Pushing sugar

manually by way of pallets could be slowing the process and a need to use automated

guided vehicles to load sugar would be an option for accuracy and speed. According

to Kim, Dekker and Heij Econometric Institute Report 2013-2015, warehouse

operations are dependent on labour management. A report on occupational health

depicts high labour turnover in the sugar companies supported by Bula (2012). Cited

reasons were too much work and poor working conditions but with IT use, accuracy

and efficiency was expected because all the work would be done by IT systems

therefore incorrect documentation, re-keying in information fatigue is reduced.

The second objective was determining the influence of IT on warehouse performance.

The perfect order index was 43% meaning the companies were not filling orders

perfectly. The companies reported IT not influencing communication between them

and stakeholders and this can be detrimental to the growth of the industry.

Humphreys, Shiu & Chan, 2001 points out that in transactional relationships or one

time buyer supplier relationships large sums of money are spent in checking quality of

incoming products. There have been complaints by farmers of delays that cause cane

deterioration hence leading to lower payments than expected. The cane received has

been reported to be of lower quality as there is no collaboration between the farmers

and the companies. IT also had no influence on receiving, picking or storage of the

product because the equipment was not synchronized with existing computer systems.

Therefore the study concludes that IT had no influence on warehouse performance in

the sugar companies.

54

5.3 Recommendations

The study found that IT had no significant effect on warehousing performance of the

sugar companies. Some of the constraints raised by the companies were lack of space,

expansion, use of manual labour, storage and this were directly related to some of the

solutions IT controlled equipment could offer to the companies. The first objective

was determining extent of IT adoption and since the study concluded that IT had not

been adopted fully in the warehouses, this study recommended that:

The companies should integrate existing equipment with the WMS systems that are

not been fully utilized. The study posits that, there could be available space that had

been raised as a constraint but the space could not be seen or felt because stacking

was done manually. Therefore with improved storage systems the space and

expansion issues would not arise. Some researchers have pointed out these systems

can be integrated with existing equipment like conveyors to improve operations.

Equipment should be highly improved upon with continuous maintenance. Storage

and stacking equipment should be invested in to improve stacking for those who felt

space was a constraint and needed expansion. The constraints of expansion and lack

of space could be sorted by cube utilization combining IT and specialized equipment.

Movement of human labour should also be minimized and alternative loading systems

applied. IT systems vary, mobile technology is in high use and communication can be

highly improved between the companies and stakeholders. They needed to reconcile

information and logistical practice. There was a need to embrace technology and

automation to reduce on costs improve speed and accuracy. On the condition of the

warehouse, maintenance of equipment to reduce wear and tear using and IT systems

or WMS will aid in efficient routing activity throughout the warehouse. Consolidated

activity, associating equipment to areas of the warehouse and to appropriate work,

55

managing and enabling pickup and delivery and reduced time spent locating product

as a result of accurate inventory.

The study concluded that the companies were not using tracking and tracing systems.

RFID has been known to curb illegal or counterfeit products as in the case of

Walmart. The study would recommend these identification technologies in order to be

able to buy and sell Kenyan sugar on the shelves. A report by biznar 2015 shows 4300

bags of sugar being lost in transit such incidences could be controlled by use of RFID

and barcodes that show location of product in transit and at point of sale.

The study concluded that IT had no influence on warehouse performance. The

companies reported not monitoring cane through IT systems. Tompkins (1998) says

that in order to properly manage inventory, information on demand at all levels of the

supply chain must be maintained in real time. This includes information at point of

sale (POS) down to raw materials deliveries at suppliers. This was supported by

Lwiki, Ojera, Mugenda and Wachira (2013) in capturing of data using different IT

systems for example EDI which compares inventory variables ( stock levels, demand

and delivery dates). Despite the fact that all the companies had ERP systems in place,

it was reported that they were been used in payment of farmers and other departments

but were not been fully utilized in the warehouses. The study recommends fully

implementing these systems to create inventory visibility in the sugar value chain. It

was not proper to keep customers waiting for a product for a whole week or more.

The frustrations experienced by the customers could be the one of the reasons for the

high influx of smuggled cheap sugar. Management could also use information

generated to aid in major decision making both at strategic, tactical and operational

levels. WMS would aid in planning ahead in the warehouse and forecast inventory

demands hence have cost reducing effect. Knowing costs before they occur is a huge

56

factor in cash flow management, budgeting and bottom line profit. The companies

needed to adopt modern supplier relationships with collaborative partnerships

preferred to the old arms length relationships. The issue of farmers uprooting cane for

other food crops would not arise if the companies treated them well and paid them on

time. Forming good supplier relationships would mean cost minimization leading to

efficiency, sharing of risks and rewards, competitive positioning, resource aggregating

and sharing. This has been known to create future savings and innovations. There

would definitely be higher cane quality, improved communication Humphreys, Shiu

and Chan (2001).

5.4 Areas for future Research

i) Companies are moving to 3PLs in the warehouses to improve on efficiency. This

could be an area of further research, to ascertain if this can reduce costs the high

costs in sugar companies in Western Kenya. Studies posit that companies are

unlikely to invest in new technologies compared to 3PLs.

ii) Benchmarking of warehouse performance is another area that needs research for

the companies to improve on best practices and embrace lean practices.

iii) The aspect of cheap labour against IT adoption and automation is also another

area that needs further research because in developing countries cheap labour is

readily available compared to developed nations.

iv) The companies pointed that expansion and space in the warehouses was a

constraint. Research into whether the existing warehouses were built with proper

warehouse design and layouts that allow for flexibility and change were taken into

account.

57

5.5 Limitations of the study

The study faced a lot of challenges:

i) This was a census study covering eleven companies, but some companies were

closed and others have never given permission to collect data to date.

ii) The warehouse supervisor was to answer the questionnaire, but in some

companies the decision rested upon the person in charge, therefore even the

opportunity to use observation was not allowed as the interviews were

conducted away from the warehouse.

iii) Important figures that would have been used for benchmarking were not

availed as the companies were not comfortable in divulging figures.

58

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65

APPENDICES

APPENDIX 1: QUESTIONNAIRE

My name is Dorcas Wanyama, an MBA student at the University of Nairobi, Kisumu

campus. I am undertaking a research project on the Effect of information technology

on performance of warehouses in the sugar factories in Kenya. Being one of the major

sugar factories in Kenya, your contribution to this study will be highly appreciated.

Any information that you will provide will be used for academic purposes only and

will be treated with utmost confidentiality.

SECTION A: INTRODUCTION

1. What is the name of your sugar factory....................................................

2. What type of warehouse do you own? ....................................................

3. Where is your warehouse located?............................................................

4. Give breakdown of specialized storage equipment or material transporting

equipment........................................

5. How long has this warehouse been in operation?...............................................

6. What are the annual direct labour hours?............................................................

7. What are the annual indirect hours?.......................................................................

8. Labour turnover: what is your annual labour turnover for full time employees?

9. What percentage of your direct labour hours are performed by temporary

workers?...........

10. Is your labour force part of collective bargaining unit?......................................

11. What are your self identified constraints to efficiency?......................................

12. What are your self identified opportunities?.......................................................

66

SECTION B: INFORMATION TECHNOLOGY (I) (Establish extent of IT

adoption)

I1. Do you use any warehouse management system package? Name

vendor......................

To a very

low extent

[1]

To a low

extent

[2]

To a

moderate

extent

[3]

To a great

extent

[4]

To a

large

extent

[5]

I2 What is the level of mechanization

Manual

Mechanized

Semi-

automated

Automated

I3 To what

extent have

you adopted

the use of

IT in your

warehouse

I4 To what

extent is IT

used to link

all the levels

in the

organization

I5 To what

extent can

employees

use IT

systems in

the

company

I6 Is the level

of IT and

picking and

storage

technologies

adequate for

operation

I7 Does your

67

warehouse

use any of

these

technologies

RFID,

barcodes,

EDI,

SECTION C: PERFORMANCE OF WAREHOUSE (Wp) (Determine influence of

IT on warehousing performance)

Order fulfilment

To a

very

low

extent

[1]

To a

low

extent

[2]

To a

moderate

extent

[3]

To a

great

extent

[4]

To a

large

extent

[5]

Measure

Wp1 On time

delivery

Do you

always

have

orders

delivered

on time

per

customer

requested

date

Orders on

time

Total

orders

shipped

Wp2 Order fill

rate

Do your

always fill

orders

completely

on first

shipment

Orders

filled

completely

Total

orders

shipped

Wp3 Order

accuracy

Are orders

packed,

picked and

shipped

perfectly

Error free

orders

Total

orders

shipped

Wp4 Line

accuracy

Are lines

picked free

of error

Error free

lines

Total lines

shipped

Wp5 Order cycle

time

Do you

always

meet your

order cycle

times

Actual

ship date –

customer

order date

68

Wp6 Perfect

order

completion

Do you

always

orders

delivered

without

damage or

invoice

errors

Perfect

deliveries

Total

orders

shipped

Inventory management

Wp7 Inventory

accuracy

Do you

always

have

accurate

inventory

quantities

to systems

reported

quantities

Actual

quantity

per SKU

System

reported

quantity

Wp8 Inventory

visibility

Do you

always

have

inventory

visibility

from

physical

receipt to

customer

service

notice of

availability

Total

damage in

KSh.

Inventory

value cost

Wp9 Damaged

inventory

Do you

always

have

damaged

inventory

Total

damage

KSh

Inventory

value cost

Wp10 Storage

utilization

Do you

utilize

space

properly

Average

occupied

square ft

Total

storage

capacity

Wp11 Dock to

stock time

Is order

picking

done on

time

Total dock

to stock

hrs

Total

receipts

Wp12 Are the

buildings ,

floors and

technical

69

installations

in good

quality and

well

maintained

b) material

handling

systems,

racks and

product

carriers

Wp13 Is material

moved over

the shortest

possible

distance

Wp14 Is double

handling

prevented

and

appropriate

carriers

used

Wp15 Is the

facility

clean, safe,

orderly and

well lit

Wp16 Is this the

warehouse

you would

like to work

in

70

APPENDIX 2: TABLE 1: PERFORMANCE METRICS OF A WAREHOUSE

Category Measure Definition

Order

fulfilment

On time delivery = orders on time

Total orders shipped

Orders delivered on time per

customer requested date

Order fill rate = orders filled complete

total orders shipped

Order filled completely on first

shipment

Order accuracy = error free orders


Order picked , packed and

shipped perfectly

Line accuracy = error free lines

total lines shipped

Lines picked

Order cycle time = actual ship date – customer

order date

Time from order placement to

shipment

Perfect order completion = perfect deliveries


Orders delivered without

changes, damage or invoice

errors

Inventory

manageme

nt

Inventory accuracy = actual quantity per SKU

system reported quantity

Actual inventory quantity to

system reported quantity

Damaged inventory = total damage Ksh

inventory value cost

Damage measure as % of

inventory value

Storage utilization = average occupied square

ft

total storage capacity

Occupied space (square

footage) as a % of storage

capacity (square footage)

Dock to stock time = total dock to stock hrs

Total receipts

Average time from carrier

arrival until product is available

for order picking

Inventory visibility = receipt entry time

Physical receipt time

Time from physical receipt to

customer service notice of

availability

Warehouse

productivit

y

Orders per hour = orders picked/packed

total warehouse labour hrs

Average number of orders

picked and packed per person

per hour

Lines per hour Average number of orders lines

picked and packed person per

hour

Items per hour = items picked/ packed

Total warehouse labour hrs

Average number of orders items

picked and packed per hour

Cost per order = total warehouse cost


Total warehousing costs , fixed

space, utilities and depreciation

Variable: labour/supplies

Cost as a % of sales = total warehouse cost

total revenue

Total warehousing cost as a %

of total company sales

Source: Ramaa et al., 2012, Hill & Ginnis

71

APPENDIX 3: LIST OF SUGAR COMPANIES IN WESTERN KENYA

1. Kibos Allied Sugar Factory

2. Chemelil Sugar Factory

3. Muhoroni Sugar Factory

4. Nzoia Sugar Factory

5. Mumias Sugar Factory

6. Sony Sugar Factory

7. West Kenya Sugar Factory

8. Butali Sugar Factory

9. Soin Sugar Factory

10. Transmara Sugar Factory

11. Miwani Sugar factory

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