European Data Market SMART 2013/0063 D.3.3 Data-driven innovation in the Manufacturing Industry

November 4th, 2014

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Author(s) Lorenzo Veronesi, Rosanna Lifonti, Giorgio Micheletti, Gabriella

Cattaneo, (IDC)

Deliverable D 3.3 Quarterly Stories – Story 3

Date of delivery November 4th, 2014

Version 1.0

Addressee officer Katalin IMREI

Policy Officer European Commission - DG CONNECT

Unit G3 – Data Value Chain

EUFO 1/178, L-2557 Luxembourg/Gasperich

katalin.imrei@ec.europa.eu

Contract ref. N. 30-CE-0599839/00-39

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TABLE OF CONTENTS

1 SHORT SUMMARY ....................................................................................................... 5

2 OVERVIEW ................................................................................................................... 6

2.1 INTRODUCTION ................................................................................................................ 6

2.2 THE DATA-DRIVEN EVOLUTION OF THE MANUFACTURING INDUSTRY ................................... 7

2.2.1 Manufacturing in the EU: Too big to get unnoticed ..................................................................... 7

Source: IDC, 2014 8

2.2.2 The new challenges of the manufacturing industry ..................................................................... 8

2.2.3 How to tackle these challenges and why: Collecting and making sense of data ......................... 9

2.3 DATA-DRIVEN MANUFACTURING ALREADY AT PLAY: APPLICATION AREAS AND CASE STUDIES10

2.3.1 Quality management .................................................................................................................. 11

2.3.2 Predictive maintenance .............................................................................................................. 12

2.3.3 Operational Intelligence (OI) ..................................................................................................... 13

2.3.4 Sales & Operational planning (S&OP) ........................................................................................ 14

2.4 DATA-DRIVEN INNOVATION BENEFITS AND IMPACTS ......................................................... 18

2.4.1 Quality Management improvements: Estimated Impacts .......................................................... 19

2.4.2 Predictive Maintenance Improvements: Estimated Impacts ..................................................... 19

2.4.3 Operational Intelligence: Estimated Impacts ............................................................................ 20

2.4.4 Sales and Operational Planning: Estimated Impacts ................................................................. 21

2.5 CONCLUSIONS ............................................................................................................... 22

MAIN SOURCES ........................................................................................................................ 24

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1 Short Summary

European manufacturing organizations are facing unique changes and are subject to unprecedented challenges in their marketplace: not only is the current economic environment forcing manufacturers to optimize processes and contain costs, but also the extensive availability of data is in fact empowering the market and making buyers increasingly knowledgeable about products, prices, and other key features.

Today’s data availability, however, is both a potential issue, as well as a great opportunity for manufacturing organizations. In fact, data ubiquity is helping manufacturers to foster an ever integrated decision-making environment supporting real-time and information-based value chains. As a result, manufacturers in Europe are putting in place an ever closer collaboration with technology companies to implement new IT architectures enabled by Big Data, Internet of Things, Cloud computing and other technologies sustaining a fast changing business environment with common real-time data, workflow and alerting capabilities. Manufacturers are therefore turning their attention to the potential of data at their disposal and to the ways of analyzing and interpret the information they have in terms of the potential impacts and risks to their business.

Recent evidence from the Economist Intelligent Unit (EIU) has revealed that 86% of manufacturers have significantly increased the amount of production and quality-control data stored for analysis over the past two years. Also, nearly two-thirds of manufacturers use sensor-generated data from networked machines and 20% plan to do so in the near future. But why are manufacturers increasingly collecting and using digital data? Our research suggests that manufacturers are turning to data to realize a wide variety of savings and gains across a number of manufacturing’s application areas:

- In quality management processes, manufactures can dramatically reduce quality-related costs and, in particular, those costs associated with scrap and rework: our estimates indicate that if scrap and rework were reduced to zero levels, an overall impact of 160 billion Euro would be achieved among the top 100 Europe’s manufacturers alone.

- By adopting data-driven preventive management processes, manufacturers can significantly reduce total equipment downtime and increase production (50% of downtime reduction and 20% of production increase according to our estimates).

- Operations would also benefit from advanced data gathering and analysis: if only 10% increase in production efficiency were obtained across the top 100 European manufacturers, a consolidated gain of 265 billion Euro would theoretically impact the European industry.

- A constant and advanced application of Sales & Operations Planning IT tools would bring double-digit (and even triple-digit) percentage improvements to a series of fundamental supply-chain metrics such as the response time to unforeseen events affecting orders (300% improvement), order delivery time (approximately 120% improvement), time to market (approximately 70% improvement), monthly inventory turns (50% improvement) and share of new product launch failures (30% improvement).

Yet, collecting and making sense of data does not automatically yield benefits: EIU research, coupled with additional IDC studies, shows that not all European manufacturers are actually able to keep up with the large volumes of data they collect and generate; also, while monitoring processes seem to be relatively widespread in Europe, a still somewhat small number of manufacturers regularly use predictive analytics to generate useful future insights and find solutions to upcoming problems: an extra effort is therefore needed to make sure that the European manufacturing industry fully takes advantage of the emerging data-driven innovation.

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2 Overview

2.1 Introduction

This document represents the deliverable D3.3 of the Study “European Data market SMART 2013/0063” entrusted to IDC and Open Evidence by the European Commission, DG Connect.

The present story focuses on the data technologies’ impacts on the European manufacturing industry with specific reference to operational improvements. The table below outlines the story’s general information and the key elements of the story’s description.

The document is structured in three main parts:

• In the first paragraph an overview of the technology-driven evolution of the European manufacturing industry will be provided;

• In the second paragraph a few case studies from the European manufacturing industry will be presented: the stakeholders involved in those case studies, the technology used and the impacts obtained by the innovative use of this technologies will also be investigated;

• The third paragraph will outline the key data-driven benefits, effects and impacts (where available) for Europe’s manufacturing industry and summarizes the key messages and conclusions to be taken from the case studies.

Table 1: The Story at a Glance

GENERAL INFORMATION

Title The Impact of Data Technologies on the Manufacturing Industry – Real-Time Operations and Intelligent Value-Chains through Innovative Uses of Data.

Link with information /Sources

- IDC and IDC Manufacturing Insights’ research reports under:

http://www.idc.com/prodserv/insights/manufacturing/ps/innovating.jsp

http://www.idc.com/getdoc.jsp?containerId=MIOT02V

- The Economist Intelligence Unit

http://www.economistinsights.com/technology-innovation/analysis/manufacturing-and-data-conundrum

- McKinsey and Company, Insights & Publications:

http://www.mckinsey.com/insights/operations/how_big_data_can_improve_manufacturing

- Volvo Group Global:

http://www.volvogroup.com/group/global/en-gb/newsmedia/pressreleases/Pages/pressreleases.aspx

- SAS Institute, 2013: Examples and Case Studies; SAS®PREDICTIVE ASSET MAINTENANCE, SAS Institute, 2013

Interviews IDC Manufacturing Insights’ Survey on the Next Generation of Sales & Operational Planning (S&OP); Number of valid respondents: 360; Base: All sample. Source: IDC Manufacturing Insights, 2013

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STORY DESCRIPTION

Topic/object of story

How Europe’s manufacturing industry is putting (new) data to use to achieve real-time operations and intelligent value-chains and, ultimately, face the current industry challenges through increased competitiveness.

Main examples

A series of success cases featuring both European manufacturers and technology companies.

Main impacts identified in this story

- Reduction of downtime and related cost; - Lower maintenance costs; - Improved root-cause analysis; - Increased asset longevity; - Reduced order delivery time; - Increased output.

Main stakeholders

Advanced predictive analytics companies, European Manufacturers

Key words Real-Time Operations, Intelligent Value-chain, Quality management, Predictive maintenance, Operational Intelligence, Sales and Operational Planning (S&OP).

2.2 The Data-Driven Evolution of the Manufacturing Industry

2.2.1 Manufacturing in the EU: Too big to get unnoticed

The manufacturing industry lies at the core of the European economy: according to Eurostat1, the manufacturing sector contributes to approximately 18% of the overall EU GDP and represents more than 2.1 million companies over a total of 22 million companies in the EU 282. The centrality of manufacturing is confirmed when looking specifically at its size in terms of Information Technology’s spending in Europe: according to IDC3, the manufacturing industry (including both discrete and process manufacturing segments) spent a total of more than 90 billion US$ (71.8 billion Euro) in IT in 2013 in Western Europe, well ahead of the banking, the communications or the business services sectors. In relative terms, this represents a good 24% of the total IT spending market in Western Europe, while the financial services industry (including banking, insurance, and other finance) accounts for 20% of the market (see Figure 1).

1 http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/National_accounts_-

_main_GDP_aggregates_and_related_indicators 2 ibidem

3 Source: IDC, Western Europe Vertical Markets IT Spending 2014–2018 Forecast: Utilities, Retail, and Services Driving Growth, #M09W, IDC 2014

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Figure 1Western Europe IT Spending by Vertical Market, 2013 ($B)

Source: IDC, 2014

2.2.2 The new challenges of the manufacturing industry

While the role of the manufacturing industry within the EU economy remains unquestioned, European manufacturers are increasingly faced with unprecedented changes in their marketplace. Consumer lifestyles and manufacturing clients' purchasing patterns have been completely redefined by the extensive availability of information through social networks and its rapid transmission via a vast range of new mobile devices. The extensive availability of information is empowering the market and making

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buyers increasingly knowledgeable about products, prices, and key features. They now use ubiquitous access to information to make informed decisions very rapidly. They can compare, select, or discard a product with just a tap on their tablet.

The pace of change and the speed in today's purchasing patterns is resulting in significant demand variability. The ongoing challenging economic environment is making the issue even more difficult. Market variability and velocity are becoming critical issues for manufacturers that need to respond rapidly to these varying demands. Not being able to understand changing purchasing behavior and rapidly incorporating it into supply chain management processes leads to higher inventories of unsold products and sudden product shortages. But volatile market demand is the "new normal" and the problem is only likely to worsen in the near future.

2.2.3 How to tackle these challenges and why: Collecting and making sense of data

The widespread diffusion of data and new technologies allows for a greater number of manufacturers to not only collect, but also make sense of newly created and available data. This, in turn, is helping the industry to become increasingly data-driven and reap the benefits of this new data availability.

This is confirmed by a recent survey conducted by the Economist Intelligence Unit (EIU) among manufacturers at worldwide level (50% of which in the EU)4. The survey has revealed that 86% of the respondents have significantly increased the amount of production and quality-control data stored for analysis over the past two years. Also, nearly two-thirds of the manufacturers that took part to the survey said that they use sensor-generated data from networked machines and 20% plan to do so in the near future.

But why would manufacturers rely more and more on collecting and using digitalized data? Because, so the survey, insights gathered from production-data analysis appear to produce considerable gains in terms of cost of quality (net losses incurred due to defects) and production efficiencies. And when asked in which manufacturing areas greater volumes of data actually yield the biggest gain, the manufacturers surveyed by the EIU responded by pointing to a handful of application area: product quality management, predictive maintenance/process design, operations management/process control, and supply chain management (see Figure 2 below). In the following sections, we look into the above mentioned gains and in the manufacturing application areas in more detail.

4 Manufacturing and the Data Conundrum: Too Much? Too Little? Or Just Right. A report by the

Economist Intelligence Unit. The Economist Intelligence Unit, 2014

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Figure 2: Areas where greater volumes of data yield the biggest gains

Source: The Economist Intelligence Unit Limited, 2014 (re-adapted by IDC, 2014)

2.3 Data-Driven Manufacturing already at Play: Application Areas and Case Studies

Powered by the rapid evolution of IT architectures supporting Big Data technologies and Internet of Things (and further sustained by mobility technologies, ubiquitous smart sensors, widespread presence of RFID tags and diffusion of Cloud Computing), European manufacturers are swiftly enhancing their capabilities of assembling, utilizing and interpreting an increasing amount of data. While certainly pertaining to the overall manufacturing cycle, IDC believes that the segment where Europe’s manufacturing industry seems to be affected the most by the creation and utilization of data is the operation part of the manufacturing cycle, namely the activities that are related to production facilities for the creation or assembly of semi-finished goods or finished products. Four application areas stand out as particularly relevant in this respect:

• Quality management; • Predictive maintenance; • Operational intelligence; • Sales & Operations Planning (S&OP).

Quality management, predictive maintenance and operational intelligence refer to the inner part of the cycle – as such they have a direct and immediate influence on products at plant and shop floor level. Sales & operational planning, on the other hand, extends its influence to the outer part of the cycle and affects elements such as distribution, outbound logistics and, ultimately, the relationships with customers. A graphic representation of the identified four application areas is presented below.

72

66

44

42

30

20

12

10

Products quality management

Predictive maintenance/Process design and improvements

Process control

Operations management

Supply chain management (including S&OP)

Safety & facility management

Throughput improvement

Targeted capital spending

% of respondents

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Figure 3: The manufacturing cycle and the four application areas

Source: IDC, 2014

2.3.1 Quality management

Manufacturers in Europe and around the world are under increasing pressure because of huge demand swings and the need to improve product quality and yield. By applying advanced analytics and extracting quality KPIs in real time, manufacturers can now make sure that their products’ quality is kept at the highest levels during the very early stages of productions and well before their products are finalized, stocked and, eventually, distributed. In particular, this “intelligent quality management” features allow manufactures to provide continuous quality management with real-time error detection and correction, thus reducing the need for traditional external quality controls and improving control in remote testing locations

The cases of Grundfos and ABB / SANDVIK

With an annual production of more than 16 million pump units, Grundfos (head-quartered in Denmark) is one of the world’s leading pump manufacturers. To maintain its leading position, Grundfos attaches great importance to Research and Development. A four-man group in the organization was therefore established to control the total quality management with production facilities and a-18.000workforce spread out all over the world. Most of the quality control was done through time-consuming, manual processes. Grundfos implemented an advanced analytics solutions provided by SAS Institute (a global developer of analytics software) to obtain an overview of all quality related data throughout the organization and deliver quality KPI's and analysis of errors. The SAS solution deployment at Grundfos has improved the internal quality yield and increased the transparency of quality management processes across the overall group. The company estimates that the usage of advanced analytics solutions applied to its quality management process entails a reduction in the rate of defect products return from the market of 20%-30% in 5 year.

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ABB, based in Zurich, Switzerland, produces power, automation and electrical products and provides a range of industrial control services. One of its recent successes came from helping Sandvik Materials Technology in Sandviken, Sweden (about 190 km north of Stockholm), which makes specialty stainless steel, titanium and alloys for specialized usages. In 2013, as part of a long-term process improvement effort, Sandvik’s attention turned to an important component of the production system—a bidirectional rolling mill, or Steckel mill, used to make metal strips thinner and thinner with each pass through the rollers. Sandvik had no plan to replace the equipment. Instead, it opted to improve how it used the mill with a few new sensors feeding digital controls. Based on a tightly defined model of perfection, these would continually adjust rolling speeds, pressures and the number of passes through the mill to compensate for variation. First, Sandvik installed additional sensors to measure precisely the width of the rolled metal and its temperature, which changes during rolling as the metal interacts with the machinery. In some factories, dozens or even hundreds of sensors could have been required, but Sandvik made do with just nine. To control the process, the company needed more data, but not a flood of it.

Thanks to the new sensors and related process controls, Sandvik can now roll specialty metal to thinner tolerances while maintaining the metallic properties, such as strength and formability, required for the final use. Compared with its old process, the new controls have reduced the degree of deviation from perfection by 35%, and the average volume of imperfections has dropped by 80%.

2.3.2 Predictive maintenance

Traditional maintenance practices are changing, particularly as modern equipment utilize intelligent sensors and the widespread application of advanced mathematics is increasingly used to automate the analysis of data. As a result, predictive and condition-based maintenance practices are rapidly being deployed at the expense of preventive maintenance. Predictive maintenance practices analyze sensor, condition, asset, and maintenance data in order to generate alerts on assets that are likely to fail and accelerate root cause definition through problem resolution.

By applying a condition-based or predictive approach to maintenance two goals are simultaneously achieved:

• A reduction in the probability to face “dangerous” situations, since the equipment under consideration is likely to be replaced before a possible failure or an all-out breakdown occur;

• An increase in the chance of using the equipment longer and more effectively than traditional preventive maintenance mechanisms might suggest: the latter are based on average usage conditions pre-defined by the equipment vendors, whereas the actual usage conditions within a specific manufacturing process may be different. Predictive maintenance can easily adapt to these “real-life” conditions, rather than rely on theoretical usage assumptions.

This approach is finding more and more concrete applications through advanced analytics solutions providing proactive prevention through the analysis of an increasing amount of data stemming from manufacturing equipment and machines.

The case of Volvo and additional cases

After leaving the passenger car market, the Volvo Group refocused its offerings and redesigned its business model as well as its relationship with customers. Instead of simply selling trucks, Volvo now offers “transport solutions” within a wider range of logistical services. To enhance the quality of its services, Volvo launched QRAFT (Quality Report and Follow-Up Tool), a tool for monitoring its quality and product warranties. Based on SAS, the tool analyzes more than 100 parameters to predict the wear on a component, identify abnormal events and speed up the diagnostics of incidents affecting a vehicle. The information is transmitted from a network of warranty repairers and is stored in a shared data warehouse. With SAS Field Quality Analytics, Volvo is now in a position to run in-depth analyses

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to understand the root cause of an incident and help the engineers make decisions about how to resolve it and prevent it from recurring in the future. The overall idea is therefore to anticipate events and being proactive with customers and ensure continuous improvement.

The global implementation of QRAFT took a huge effort by combined information systems and business teams, with the support of SAS consultants in Sweden, France, the United States and Japan. Nearly 500 people – across functions, divisions and business units – conduct analyses with SAS Field Quality Analytics. The tool has been so successful that the server size needed to be increased in less than one year after its launch. The first months of the tool’s use have already demonstrated its value in diagnosis. With the widespread use of electronic boxes, Volvo is now able to track dozens of indicators while the truck is operating and define alert thresholds to allow its maintenance teams to act proactively before an incident happens.

According to the SAS Institute5, the application of predictive maintenance analytics solutions has produced significant benefits to number of manufacturers, as an example:

• It can significantly reduce the occurrences of malfunctioning: for example, an oil/water separating equipment had malfunctioned 22 times in 24 months. It was repaired with analytical guidance to run without issue for 36 continuous months.

• It prolongs the advance notice period to predict when certain equipment may incur in future failures: for example, a hi-pressure gas turbine operator is provided 75 day advanced notice of pending turbine failure with 95% reliability.

• It helps detecting new possible causes of failures that were previously ignored: for instance, the usage of predictive maintenance analytics contributed to identify pump failures caused by temperature variations that were previously acceptable to the engineering specifications of the pump.

2.3.3 Operational Intelligence (OI)

The complex and iterative nature of today’s high-level and collaborative decision-making in the manufacturing industry necessitates an interrupted stream of information and workflows. What stands out as a key issue is that the higher the information gets, the less timely it becomes. To cover these gaps, manufacturers are adopting a new generation of analytics applications that can deliver operational intelligence (OI). OI can cover the existing gaps and create an intelligence layer connecting the factory’s “shop floor” with boardrooms, providing real-time analytics to key decision makers across the entire plant network and not limited to a single production line or equipment. In fact, manufacturers are increasingly turning to a combination of collaboration technology and analytics applications that are specific to multi-plant information management.

The case of Syngenta

Syngenta is one of the world's leading agro-industry companies, with more than 28,000 employees in 90 countries. One of its key production facilities is the Huddersfield Manufacturing Centre (UK), which employs over 400 people. Like all companies with multiple manufacturing locations, Syngenta keeps a close eye on production performance across the board. The challenge for Syngenta in managing this vital shop floor was that the site’s various production processes were capable of producing huge amounts of valuable data that were not being stored for analysis, leaving the Syngenta team to react to issues rather than proactively solve them.

The company started its operational intelligence journey by working with Solutions PT and implementing Wonderware Historian, a large-volume plant data, real-time database with high

5 SAS®PREDICTIVE ASSET MAINTENANCE, SAS Institute, 2013

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scalability and performance potential. This allowed for the elimination of data silos and empowered technical and manufacturing people to use data to drive efficiency.

Although a hugely positive step forward, easy access to production data enabled by Wonderware Historian also highlighted a number of new challenges for Syngenta. Firstly, the company’s complex product range and various process control systems produced masses of data and over the years, in the absence of a best practice framework, many employees had developed different ways of working with it. The first challenge was for the data to be presented in a way that was accessible and intuitive to all. Secondly, due to the increased level of insight into each manufacturing process, Syngenta was now seeing that there were further opportunities to improve in terms of key performance indicators such as product quality, downtime, and yield.

Wonderware Historian proved to be so successful that the team was encouraged to share its methods as "best practice" with other Syngenta sites. One of the Huddersfield production facilities was able to increase production from three batches to eight after analysis of the data being produced, and production of another product was increased by 50%.

The historian, and the complementary tools built by Syngenta’s in-house team, has been used to conduct detailed process investigations, review process performance, and monitor production rates and performance. Syngenta’s Technical Improvement Teams use the dashboards to study production delays and associated reasons and to steer focus for improvement priorities. All of this has enabled Syngenta to increase output, reduce downtime, and dramatically speed up cycle times. As an example, improved data analysis allowed operators to reduce the bottleneck cycle time in part of the process: according to Syngenta, in gross margin terms, this reduction was worth £2.5 million to the business.

2.3.4 Sales & Operational planning (S&OP)

Today, most manufacturers are moving ahead with their vision to create demand-driven supply chains. As such, they have already some sort of sales & operational planning (S&OP) processes in place. But in most cases, these processes are still basic and managed via rigid spreadsheets. The critical touch point meeting between sales and operations functions is still called the "war room," while the level of collaboration across functional silos is still very poor. The process clock (i.e. the average frequency of war room meetings) is monthly or quarterly, and the level of involvement of trading partners is nearly zero. All in all, the typical S&OP process in place today is not adequate at all to cope with today's speed of business and demand variability. However, triggered by real-time demand sensing and supply changes (and with a tighter integration of tactical processes such as demand forecasting, inventory optimization, and supply chain planning), today’s S&OP applications are rapidly updating and improving S&OP processes. Users can directly feed the S&OP process and assess the impact of their input on the new consensus plan in just one screen. This is possible because advanced databases are also supporting manufacturing applications to speed computational capabilities and develop new operational scenarios. On top of that, social software functionality allows line of business people to track all updates and keep relevant stakeholders informed.

The Sales & Operational planning’s maturity model and its associated benefits

In 2013, IDC conducted a survey on a total of 360 businesses in Western Europe, Asia/Pacific, and the Americas across a series of manufacturing segments such as food and beverages, chemicals and pharmaceuticals, industrial machinery and equipment, and high-tech. 120 interviews were carried out among European manufacturers in several EU Member States including France, Germany and the U.K.6

6 The Journey towards Next-Generation S&OP in the Manufacturing Industry. IDC, 2013

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By clustering the survey results around a number of specific issues (such as the business performance, the frequency and the schedule of S&OP processes - “S&OP clock”- , the type of IT tools deployed to support the S&OP processes), IDC was able to determine an S&OP maturity model based on the following five maturity levels: Table 2 S&OP Maturity Levels

Maturity Level Process Definition (Table 1)

Impact on Business Performance

Process Clock Supporting IT Tools

Level 5 Advanced 5 Excellent Monthly or weekly S&OP IT application

Level 4 Extended 4 Very good Monthly S&OP IT application

Level 3 Integrated 3 Good Monthly A number of separated applications

Level 2 Basic 2 Limited Quarterly Only spreadsheets

Level 1 No S&OP 1 Inadequate Quarterly or yearly No support of IT. Purely manual process

Source: IDC Manufacturing Insights, 2013

One of the key results of the study was that manufacturers in Europe and around the globe are gradually and consistently moving their S&OP processes up the maturity level, so to improve the impact of their sales & operations on their overall business performance.

The figure below shows the level of the S&OP maturity today at worldwide level (excluding Europe). Figure 4: S&OP Maturity Model – The level of Maturity Today in the World

Source: IDC Manufacturing Insights, 2013

Level 1 Level 2 Level 3 Level 4 Level 5

No S&OP

Integrated

Extended

Time

S&O

P Ex

celle

nce

Advanced

Basic

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The vast majority of the interviewed companies at worldwide level appeared to have a basic level of maturity with limited support from IT tools. In contrast, Europe’s situation seemed to be ahead in terms of maturity with a higher number of manufacturers featuring integrated, extended and advanced levels of S&OP maturity (see Figure 5):

Figure 5: S&OP Maturity Model – The level of Maturity Today in Europe

Source: IDC Manufacturing Insights, 2013

IDC data analysis showed that manufacturers can achieve hard benefits by improving their S&OP process and transitioning along the maturity model from level 1 (lowest maturity) to level 5 (highest maturity). Most of the metrics displayed a relatively weak performance in lower S&OP maturity levels, with a general progression of metric value moving up from level 1, 2, and 3. With S&OP level 4 (where an integrated S&OP IT application is in place) and particularly with S&OP level 5 (where extended collaboration outside the four walls of the enterprise is enforced) those metrics significantly improved. Table 3 and Figure 6 below provide an overall picture of the improvements that are generated by transitioning from a level 1 of S&OP maturity to a level 5 in the seven metrics under consideration.

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Table 3 Supply Chain Metrics and Improvements from Level 1 to Level 5

Metric Definition of metric Improvement from Level 1 to Level 5

Order delivery time The average time to deliver an order. 3.5 to 0.8 days

Response time Delay (in days) in responding to an internal or external unforeseen event impacting customer orders (e.g., late production).

9.6 to 4.4 days

Inventory turns Number of times inventory is sold or used in a month. 1.4 to 3

Time to market Time to market is defined as time required developing a product to the point it can be sold to customers. This varies by sector. We considered average values.

144 to 87 days

New product launch failures

Share of new product launch failure out of total new products launches.

12.5% to 9.7%

Source: IDC Manufacturing Insights, 2013

Figure 6: Percentage of Metrics’ Improvement at Maturity Level 5 if compared to Maturity Level 1

Source: IDC Manufacturing Insights, 2013

In particular, we can observe that:

• Response time obtained a 300%-improvement by passing from level 1 to level 5 of S&OP maturity. As an example, the average delay in responding to an internal or external unforeseen event impacting customer orders was of 3.5 days at level 1 and of 0.8 days at level 5. With S&OP at level 5 of maturity manufacturers can thus achieve a much more rapid and accurate reaction to unexpected operation-related issues.

0%

50%

100%

150%

200%

250%

300%

Response Time Delivery time Time-to-market Monthly Inv. Turns

% Product failures

% Forecast accuracy

% Perfect Orders

Metric (% of improvement)

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• Order delivery time achieved an overall improvement of more than 120% from level 1 to level 5 of the S&OP maturity passing from 9.6 days of average time to deliver an order to 4.4 days. At higher maturity levels, the availability of an integrated S&OP IT application enhances the planning of production capacities against demand and enables a more accurate consensus plan. At level 5, the extended collaboration with suppliers and customers makes the time to deliver a customer order less than half of level 1.

• Time to market. Time to market can be as long as 144 days adopting a lower level of S&OP maturity. The metric starts to improve with S&OP level 4, where the availability of an integrated S&OP IT application improves the planning stage for new product design and introduction. With the addition of S&OP level 5 collaborative capability, this metric performance further improves to an average of 87 days – approximately a 70% improvement in this metric.

• Inventory turns. This metric improves with the introduction of IT applications at S&OP level 3 and particularly at S&OP level 4, where this metric value rises to 1.8 inventory turns per month. This metric value further improves to 3 inventory turns per month when it comes to S&OP level 5 representing an overall improvement of slightly more than 50% if compared to level 1.The better match among demand and capacity gained through the integrated and collaborative planning process at level 5 makes it possible to reduce inventory, better serve the client, and increase inventory turns.

• New-product launch failures. The percentage of new product failures is reduced with S&OP level 5 – from a share of 12.5% at level 1 to a share of 9.7% at level 5, i.e. a good 30% of improvement. The ability to extend collaboration externally to the enterprise enables better understanding of market trends and more accurate planning for new product introductions that in turn reduces the failure rate.

• Forecast accuracy and Perfect order seem to be the only two metrics that are not particularly affected by the evolution of the S&OP maturity level. As a matter of fact, the demand forecast is an input (and not an output) to the S&OP process, whose goal is to build a consensus around a supply plan to match that forecast. As for perfect orders, they are primarily associated with plant-floor processes such as quality control, shipping, and returns and do not have significant impact on processes affecting sales and operational planning.

2.4 Data-driven Innovation Benefits and Impacts

The main benefits to be derived from a data-driven manufacturing industry, by key application areas, are summarized in table 4 below. Table 4 Key benefits of data exploitation in the manufacturing industry subdivided by manufacturing application area

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Manufacturing Area Effects Description Benefits for Manufacturing Companies

Quality management

Enhanced Predictive Quality: Data Mining, Root Cause Analysis, Advanced Process Control, Early Warning.

• Reduce complexity and shorten the time to solutions;

• Optimal set-up of process flows and settings through predictive modeling;

• Complex interdependencies between process deconstructed through root cause analysis;

• Early Warning of impending Quality issues

Predictive maintenance

Better processing of data from intelligent sensors to optimize equipment utilization and predict the failure of the equipment before it happens.

• Reduce downtime and related cost; • Lower maintenance costs; • Reduce unscheduled maintenance; • Improve root cause analysis; • Increase the longevity of assets; • Facilitate compliance.

Operational Intelligence

A combination of collaboration technology and analytics applications that is specific to multi-plant information management.

• Intelligence layer connecting the shop floor with boardrooms, providing real-time analytics to key decision makers.

• Increase output, reduce downtime, and speed up cycle times.

Real-time S&OP Connect sales and operations planning processes in real-time instead of lengthy war-rooms.

• Reduce order delivery time, response time, inventory turns, time to market,

• Reduce new product launch failures. • Improve customer satisfaction.

2.4.1 Quality Management improvements: Estimated Impacts

The cases of Grundfos and Sandvik demonstrate that the deployment of advanced analytics and other big-data related solutions to the quality management process can provide immediate benefits in terms of internal quality yield, reduction in rate of return from the market (20% to 30% less returns every five years ad Grundfos) and a considerably lower degree of deviation from quality standards (average volume imperfections has dropped by 80% in one year by Sandvik). More generally, we can argue that the cost of poor quality is one of the most significant "hidden" profit killers. This cost is indeed a combination of underutilization (deriving from the planned under-capacity to anticipate low yield), scrap and rework, warranty costs (including reverse supply chain management, and of course lost sales, poor market reputation and customer churns. Among Lean / six sigma experts, this cost is typically computed at about 20% of total sales for an average manufacturing company. According to SAS Institute, about 6% of sales revenue is used to pay for Scrap and Rework only. This implies that a company with yearly revenues of 10 billion Euro would pay 600 million Euro just for these two elements of poor quality management. A theoretical reduction of scrap and rework to zero levels thanks to data-driven quality management improvements could therefore represent a huge impact for the whole of Europe’s manufacturing industry. If we consider only the top 100 European manufacturers, for example, the impact could sum up to more than 160 billion Euro.

2.4.2 Predictive Maintenance Improvements: Estimated Impacts

Predictive maintenance technology is not cheap and, as such, its implementation requires a significant corporate commitment. However, the benefits can considerably outpace costs, especially in businesses running very expensive, mission-critical assets.

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Reduction in maintenance costs is obviously the first direct benefit. Being able to predict exactly when the equipment will fail can save significant costs thanks to a significant minimization of scheduled maintenance activities. IDC estimates that the introduction of predictive maintenance practices in an average small to medium European manufacturing business would produce a reduction between 25% and 33% of the total maintenance cost (i.e. culling one out of four/three maintenance cycles).

The bigger effect resides however in the complete elimination of unexpected machine breakdowns. By definition, the system will "know" when it will fail and, as such, it will send appropriate signals and information to the owner. This makes easier for a technician to service equipment just before it fails. This could lead up to 75% of total breakdowns. Combined reduction of maintenance activities (requiring often forced downtime) and overall breakdowns can lead up to a reduction in total downtime up to 50% and in an increase in production of almost 20% on average (IDC estimates).

2.4.3 Operational Intelligence: Estimated Impacts

Analytics solutions are also helping processing data-intensive manufacturing processes to improve operational intelligence, connect the factory’s “shop floor” with boardrooms, and provide real-time data to key decision makers across the entire plant network. The application of Wonderware Historian allowed Syngenta to increase production from three batches to eight after analysis of the data being produced, and production of another product was increased by 50%.

IDC does not have a consolidated estimate of existing or potential impacts in the operational intelligence area. The Economist Intelligent Unit’s survey, however, offers a few telling results (see Figure 7). Interestingly, all respondents affirmed to have realized production efficiency gains or savings from the collection and usage of data connecting the “shop floor” with decision-makers in the boardrooms. Approximately, two thirds of the respondents have obtained production efficiency impacts up to 25%, a bit more than one fourth has attained between 26% and 50% of production efficiency impacts and a tiny (but not negligible 8%) has reached impacts of more than 50%. A systematic introduction of operational intelligence processes across the whole of Europe’s manufacturing industry could theoretically determine huge production efficiency gains: if we considered only the top 100 European manufacturers, as in the quality management case for example, and assuming a level of impact of only 10% in production efficiency, we could easily derive an overall impact of more than 265 billion Euro (IDC estimates) at European level. 7

7 This indicative estimate holds true assuming that a 10% of gain in production efficiency would

translate in a similar percentage of additional revenues on average, per company.

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Figure 7: Savings from Data: Operational Intelligence and Production Efficiency

Source: The Economist Intelligence Unit Limited, 2014 (re-adapted by IDC, 2014)

2.4.4 Sales and Operational Planning: Estimated Impacts

IDC primary research has demonstrated that the use of appropriate and advanced technology has a direct impact on the manufacturers’ sales & operational planning maturity levels and that, the higher the maturity level, the strongest the impacts on key supply-chain metrics. IDC’s survey among manufacturing companies in Europe and Worldwide has shown that companies adopting an extended level of S&OP based on the use of advanced S&OP IT applications benefit from double-digit percentage improvements in a series of fundamental sales and operations aspects such as the response time to unforeseen events affecting orders (300% improvement), order delivery time (approximately 120% improvement), time to market (approximately 70% improvement), monthly inventory turns (50% improvement) and share of new product launch failures (30% improvement).

Further IDC research among European companies shows that the key driver for manufacturers investing in new value chain and S&OP applications is not cost control per se: improved visibility, enhanced risk control and improved responsiveness all rank higher than cost control (in particular, 65.4% of business leaders rank value chain visibility as "very important" and 54.8% rank it higher than simply cost control): this represents a major change in perspective for European manufacturers, that where in the space only focused on cost controlling.

A summary of the effects and impacts of data exploitation in the manufacturing industry subdivided by manufacturing application area is presented in table 5 below.

0

30

36

26

8

No impact 1 - 10% 11 - 25% 26 - 50% > 50%

% of respondents

Production Efficiency

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Table 5 Summary of effects and impacts of data exploitation in the manufacturing industry subdivided by manufacturing application area

Manufacturing function

Case Study / Example

Effect (referred to Case Study / Example) Overall impact (estimated)

Quality management

• Grundfos • ABB/Sandvik

• 20% to 30% reduction of product return in 5 years

• Deviation from perfection reduced by 35%; volume of imperfections dropped by 80%

• 160 B Euro among 100 top Europe’s manufacturers if scrap and rework reduced to zero levels

Predictive maintenance

Volvo and other manufacturers

• 36 months of uninterrupted equipment functioning (previously 22 malfunctioning occurrences in 24 months)

• Reduction in total equipment downtime up to 50% (per manufacturing company, on average)

• Production increase of almost 20% (per manufacturing company, on average)

Operational Intelligence Syngenta • Increased production

from 3 to 8 batches; • Increased production

of another product by 50%;

• Reduced bottleneck cycle time worth £2.5 M to the business

• 265 B Euro among 100 top Europe’s manufacturers if 10% increase in production efficiency is achieved

Real-time S&OP IDC S&OP Survey • Reduction in Order delivery time (3.5 to 0.8 days)

• Reduction in Response time (9.6 to 4.4 days)

• Improvement in Inventory turns (1.4 to 3 times)

• Reduction in Time to market (144 to 87 days)

• Reduction in New product launches failures (12.5% to 9.7%)

• Order delivery time: 120% improvement;

• Response time to unforeseen events affecting orders: 300% improvement;

• Monthly inventory turns: 50% improvement;

• Time to market: approximately 70% improvement;

• Share of new product launch failures: 30% improvement.

2.5 Conclusions

The adoption of data-intensive solutions across Europe’s manufacturers is a reality in many manufacturing segments and is rapidly helping the industry to evolve towards data-based processes such as “real-time operations” and “intelligent value chains”. As a matter of fact, the widespread diffusion of data and new technologies allowing manufacturers not only to collect but also to make sense of these data is helping the industry to become increasingly data-driven and reap the benefits of these new data availability.

This focused on one specific aspect of the manufacturing cycle – operations. Yet, even with this limited perspective in mind, we have been able to highlight a series of benefits and positive impacts

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that European manufacturers are drawing from data exploitation. We have identified four manufacturing application areas – Quality management, Predictive maintenance, Operational intelligence and Sales and operational planning – and, for each area, we have made out and investigated a series of benefits positively impacting operations’ activities.

Our research has shown that the systematic usage of insights gathered from quality management processes can dramatically reduce quality-related costs and, in particular, those costs associated with scrap and rework: our estimates indicate that if scrap and rework were reduced to zero levels, an overall impact of 160 billion Euro would be achieved among the top 100 Europe’s manufacturers alone. The use of advanced analytics to implement predictive maintenance systems would also bring a significant reduction in total equipment downtime: we estimate approximately that 50% of downtime and 20% of production increase would be achieved per each European manufacturing company (on average) if digital data were used to turn the current preventive maintenance systems into predictive maintenance systems. Operations would also benefit from advanced data gathering and analysis: if only 10% increase in production efficiency were obtained across the top 100 European manufacturers, a consolidated gain of 265 billion Euro would theoretically impact the European industry. Finally, data-driven supply chains would have a positive effect on the current sales & operations planning (S&OP) processes among European manufacturers: a constant and advanced application of S&OP IT tools (corresponding to an advanced maturity level in our S&OP maturity model) would bring double-digit (and even triple-digit) percentage improvements to a series of fundamental supply-chain metrics such as the response time to unforeseen events affecting orders (300% improvement), order delivery time (approximately 120% improvement), time to market (approximately 70% improvement), monthly inventory turns (50% improvement) and share of new product launch failures (30% improvement).

All in all, our maturity model has shown that Europe is well positioned vis-à-vis the manufacturing industries of other World regions when it comes to some specific aspects of the manufacturing processes - particularly in terms for sales and operational planning processes. This means that appropriate IT tools are effectively supporting many European manufacturers to enhance their supply chain and improve their business performance. Our model, however, suggests that even greater benefits could be achieved if manufacturers would be adequately sustained to reach higher maturity levels with a stronger involvement of IT tools and new technologies.

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Main Sources

• Business Strategy: The Journey Toward the People-Intensive Factory of the Future, May 2013 | Doc #MIOT02V | Business Strategy, Pierfrancesco Manenti, Pasquale Paolone, Lorenzo Veronesi

• It's All About Return on Assets: Modern Enterprise Asset Management (EAM) Practices, May 2012 | Doc #MIOT01U | Vendor Assessment, Pierfrancesco Manenti, Lorenzo Veronesi

• The Journey Towards Next-Generation S&OP in the Manufacturing industry, February 2013, Doc #IDCWP02V Custom Report, Pierfrancesco Manenti, Lorenzo Veronesi

• From Data to Actionable Information: Improving Operational Processes by Deploying Operational Intelligence, August 2014, Doc #IDCWP25W, Lorenzo Veronesi

• EMEA Manufacturing Industry 2014 Top 10 Predictions, Feb 2014 | Doc #MIVC02W | Top 10 Predictions, Lorenzo Veronesi, Pierfrancesco Manenti, Martin Kuban, Kimberly Knickle, Robert Parker

• Manufacturing and the data conundrum. Too much? Too little? Or just right. A report by The Economist Intelligence Unit, September 2014

• How Big Data can improve Manufacturing, McKinsey and Company, Insights & Publications, July 2014

• When Big Data goes lean, McKinsey and Company, Insights & Publicatinos, • Volvo Group Global: http://www.volvogroup.com/group/global/en-

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