Application of Digital Technologies to Innovation in Manufacturing

Application of digital technologies to innovation in manufacturing

FINAL REPORT | 23 SEPTEMBER 2016

Version 1.1

Contents Glossary of key terms ...... 4 1. Summary ...... 5 1.1 Objectives and approach ...... 5 1.2 Definition of digital manufacturing: E-enablement of value chain optimisation...... 5 1.3 Opportunities, benefits, challenges and risks ...... 7 1.4 Role of standards and good practice ...... 8 1.5 Priorities for action ...... 9 1.6 Key stakeholders...... 9 1.7 Recommendations and next steps...... 10 2. Objectives and approach...... 12 3. Definition of digital manufacturing ...... 13 3.1 Competencies for digital manufacturing ...... 13 4. Opportunities, benefits and challenges for manufacturing sectors ...... 18 4.1 General benefits and opportunities ...... 18 4.2 Value chain ...... 19 4.3 Challenges...... 20 4.4 Supply chain ...... 21 5. Role of standards and good practice ...... 26 5.1 Benefits of interoperability ...... 26 5.2 Need for culture change...... 26 5.3 Current practice ...... 26 5.4 Support for common standards ...... 27 6. Priorities for action ...... 29 6.1 Support activities and infrastructure ...... 31 6.2 Interoperability, security and resilience ...... 31 6.3 Requirements for interoperability ...... 31 6.4 Requirements for security and resilience ...... 32 6.5 Benefits of best practice...... 32 6.6 Other needs ...... 32 7. Key stakeholders ...... 34 8. Recommendations and next steps ...... 35 Index of sources...... 35 Annex (PowerPoint file)

Glossary of key terms

Competency: a tool, technique or know-how (whether technical or operational) which may not be immediately visible to the user, but which – when combined with other competencies and resources – enables (one or more) business capability/ies. (Source: IfM HVM Landscape interim refresh report, 2016.)

Digital manufacturing: the collaborative transformation of manufacturing through the exploitation of advances in ICT. For complete definition see section 1.2 (summary) and section 3.

Value chain: The value chain gives all parties involved the ability to create value that exceeds the cost of providing goods and/or services to customers. Maximising the activities in any one of the steps creates a competitive advantage. The steps or activities defined in this study have been: R&D; design; production; supply; sales and marketing; services; reuse/disposal.

Supply chain: The supply chain comprises the flow of all information, products, materials and funds between the different stages of creating and selling a product. Every step in the process, from creating a good or service, manufacturing it, transporting it to a place of sale, and then selling it is a company's supply chain. The supply chain includes all functions involved in receiving and fulfilling a customer request.

Note: The difference between a value chain and a supply chain is that a value chain is a set of interrelated activities all parties involved use to create a competitive advantage, whilst supply chain is the process of all parties involved in fulfilling a customer request.

Digital supply chain scenario: in this report, a set of ten supply chain scenarios have been examined that exhibit enhanced operations capabilities exploiting advances in digital technologies, devices, data analytics, data integration and management across the supply chain. The ten scenarios identified by IfM research comprise: automated e-sourcing; digital factory design; real-time factory scheduling; flexible factory automation; digital production processes; e-commerce fulfilment; extended supply chain (near) real-time monitoring; digital product quality; digital supply network design; product lifecycle management. (Source: IfM Digital supply chain research, 2014–16.)

1. Summary

1.1 Objectives and approach This study’s goal is to establish a working definition of digital manufacturing*, identify the relevant opportunities, benefits, challenges and barriers for manufacturing, and outline the potential areas where standards and good practice can make a difference. The study takes a broad view both across the value chain* and through the supply chain*.

The work was undertaken by the University of Cambridge Institute for Manufacturing (IfM), together with Cranfield University, University of Nottingham and British Standards Institution (BSI), between March and early July 2016, through an extensive web survey1 and a joint workshop2 with industrial, academic and service provider participants.

The approach draws on IfM Digital supply chain research, 2014–163 and IfM’s 2015–16 High Value Manufacturing (HVM) Landscape refresh4 for Innovate UK, notably its definitions of manufacturing competencies*.

Note: terms asterisked* here and below are defined in the glossary of key terms on page 4. Numbered cues refer to data sources listed in the index of sources on page 35.

1.2 Definition of digital manufacturing: E-enablement of value chain optimisation IfM proposes the following definition:

Digital manufacturing is the collaborative transformation of manufacturing through the exploitation of advances in ICT.

This transformation enables new supply chain and operations capabilities (identified in IfM research3 as ‘scenarios’*) to emerge that exploit advances in digital technologies, devices, data analytics, data integration and management across the value chain in many sectors – including food, pharmaceuticals, medical technologies, aerospace, automotive, marine, energy, nuclear and built environment. 1 The ten scenarios identified are shown in the following figure.

1. Automated 2. Digital 3. Real-time Factory 4. Flexible Factory 5. Digital Production e-Sourcing Factory Design Scheduling Automation Processes

… … … … … …

6. e-Commerce 7. Extended Supply 8. Digital 9. Digital Supply 10. Product Lifecycle Fulfilment Chain (near) Product Quality Network Design Management real-time Monitoring

… … … … … …

© Centre for International Manufacturing, IfM 2016 Digital manufacturing requires the development of new systems engineering competencies4 – systems modelling, simulation and interface design – and new skills and attitudes across the manufacturing value chain. Digital manufacturing offers significant national and corporate competitive advantage through affordable flexibility, personalisation and product/service tailoring.1

1.2.1 New competencies required across the value chain Key ICT competencies for digital manufacturing have been identified4 as:

• Software development and management • Big data management and analytics • Autonomy • Internet of things • Cloud computing • Mobile internet.

These drive product and service integration, supply chain and business model innovation, among other manufacturing competencies.1 In summary, the following competencies and technologies have been identified:

• Enhanced operations management/supply chain • Product and service integration • Business model innovation • Product technologies: sensors; autonomous and robotic technologies • Enabling technologies: measurement, metrology and standards • Systems engineering/integration: integrated design and manufacture; systems modelling and simulation; human–machine interface.

1.3 Opportunities, benefits, challenges and risks Digital manufacturing offers important opportunities and benefits. Digitally enabled flexible manufacturing will drive national and corporate productivity. Customers will benefit from greater choice, reduced lifetime costs and improved availability and quality of products and services.1 These opportunities and benefits will be realised by integrating innovation throughout5 the stages of the value chain.1 Design of material, product, manufacturing process, metrology and testing regimes are critical to this in the digital age5:

• General: significant competitive advantage from offering affordable flexibility, personalisation and product/service tailoring • R&D: rapid prototyping and development; digitalisation of early discoveries • Design: quick and low cost design and redesign; using real manufacturing data at the design stage • Supply management: visibility, traceability, synchronisation and collaboration; effective connection to production stage • Production: precision, efficiency, integration; making information from vast amounts of data • Distribution: track and trace, localised demand management, rapid production response to demand changes • Service: full, real-time service condition monitoring and maintenance, with feedback to design and production • Disposal/re-use: in-service data to support remanufacture/re-cycle/disposal decisions.

The prime challenges for the UK are to build vision and collaboration, security, standard and resilient systems, and improve initial costs and data harvesting. Nevertheless, significant opportunity and benefit can stem from digital manufacturing, outweighing risks and challenges.1

Digital transformation can be exploited across the supply chain. All scenarios offer significant opportunities and benefits and have significant challenges and risks, but in each case the challenges and risks are outweighed by the opportunities and benefits.1

The following figures show workshop delegate2 assessments of opportunities, benefits, challenges, risks and gaps for each of the ten scenarios. The percentages relate to the total of delegate ratings of 5 (‘very high’) plus 4 (‘high’) on a five-point scale. Detailed analysis of delegate response is provided in the annex. Opportunities and benefits vs challenges and risks (Size of bubble indicates achievability)

Flexible Factory Automation 92 Real-time Factory Scheduling 90

% Digital Production Processes 5

+ 88

t Product Lifecycle Management

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e 86 e-Commerce Fulfilment

n Digital Product Quality

d 84 n

a Digital Supply Network Design

s 82

i t

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t 80

o p

p 78 Digital Factory Design O Extended Supply Chain 76 (near) real-time Monitoring 0 10 20 30 40 50 60 70 80

Challenges and risks 4+5%

With the possible exception of e-commerce fulfilment skills and technology gaps are significant in all scenarios.2,3 According to delegate assessment of the current situation versus requirements in five years’ time, skills gapsS arekills thegap largers vs te cofhn theolo gtwo,y ga pands (S theize o greaterf bubbl echallenge indicates .s ize of opportunity)

Automated e-Sourcing Extended Supply Chain

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Digital Factory Design

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( 0 10 20 30 40 50 60 70 80 90

Technology gap (= required maturity in five years 4+5 – Current maturity 4+5 %)

Flexible factory automation, digital factory automation, automated e-sourcing and e-commerce fulfilment are most often seen as achievable within five years.

Delegates considered it easier than might be expected to overcome the skills and technology gaps for intra-company digital scenarios compared with inter-company scenarios. In the following figure, scenarios above the red line are considered to have higher achievability than might be suggested by the scale of the capability gaps (the mean of skills and technology gaps).2

Automated e-Sourcing 90 Digital Factory Design

80 Flexible Factory Automation

70 e-Commerce Fulfilment Digital Production

% 60

5 Digital Product Quality Processes

+ 4

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i Real-time Factory Scheduling

b 40

v e

i 30 Product Lifecycle Management

h c

A 20 Digital Supply Network Design 10 Extended Supply Chain (near) real-time Monitoring 0 0 10 20 30 40 50 60 70 80 90

Capability gap = (Skills Gap + Technology gap)/2

Risks5 were also identified in HSI (human–machine interface), new business models (product liability and traceability) and IP protection (retaining originators’ inherent value). The greater apparent relative difficulty with regard to inter-company scenarios is evidence of the need for initiatives and standards to encourage collaboration and sharing.

1.4 Role of standards and good practice There is a clear need for initiatives and standards to encourage collaboration as key to UK success in digital manufacturing.1, 2

New ‘light touch’ standards, where appropriate, and good practice should support interoperability, data security and IP protection5 as well as building of key competencies.1

Workshop delegates supported standards for their role in enabling digital manufacturing through interoperability and for supporting cultural change.2 The study’s survey1 identified that:

• standards vary by sector, but all sectors refer to ISO, ISA and HSE standards, with data security the area of greatest concern • common ‘light touch’ standards can help innovation, particularly through supporting best practice transfer and data security.

Standards might focus on building competencies in business and supply chain modelling, big data management and analytics, software development and systems modelling and simulation.3

1.5 Priorities for action Priorities for action include skills development, collaborative open source applications development, interoperability, resilience and security as well as best practice adoption 1:

• Skills and training are seen as the most important enablers, perhaps focused on business and supply chain modelling, big data management and analytics, software development, and systems modelling and simulation, together with fostering new business models, governance, policies and regulations • Government support for collaborative development of open source applications will create a snowball effect with improved interoperability, security and resilience • Interoperability of both machines and data would be supported by development of artificial intelligence (AI) open systems architecture, common ‘languages’, data sharing platforms and interfaces. However, open systems may be at odds with security concerns • Greater resilience and security of manufacturing systems requires cross-digital-enterprise security standards and increased systems-modelling capability • Wider adoption of best practice in manufacturing business processes can propagate standards, drive continuous improvement and allow real-time ‘management’ • Where appropriate, pressing for new light touch standards in support of the above priorities.

Other issues include developing standards to support commonality (often addressed in the short term by developing bespoke solutions which may be counterproductive in the longer term), and the need for individuals in the work environment to become as familiar with digitisation as they are at home.

1.6 Key stakeholders A wide range of institutions were identified as being able to provide good advice and support. 1,2 These key stakeholders, as well as major industrial partners, should be involved in further development:

Universities • University of Cambridge • Cranfield University • University of Nottingham • Loughborough University • University of Liverpool • Coventry University

Government agencies and trade associations • HVM Catapult • BSI • The Welding Institute (TWI) • Department for Business, Innovation and Skills (now Department for Business, Energy and Industrial Strategy (BEIS)) • Digital Catapult • Innovate UK • Department of Energy and Climate Change (now BEIS) • Research Councils UK (RCUK) • Nuclear Industry Association UK • National Skills Academy • Nuclear Innovation and Research Advisory Board • Energy Technologies Institute (ETI) • National Physical Laboratory (NPL) • The GAMBICA Association • The Institution of Engineering and Technology (IET)

Industry • IBM • Oracle • Hewlett Packard • Siemens • Dassault • Festo • Meggit Plc • Laing O’Rourke • Consultants (such as PwC) • The Open Group

1.7 Recommendations and next steps If the UK does not respond within five years, it will lose out to overseas competitors: a concerted, urgent, action programme is needed.

A national digital manufacturing standards steering arrangement is required – led by key industrial, academic and institutional stakeholders – to ensure one voice, one vision in policy setting. The remit may include the following:

• The ‘Future Cities’ Catapult is a possible model on which the arrangement could be based • The approach needs to be industry led and include input from experts from BSI, HVM Catapult and Research Councils UK • Development of demonstrator(s) which identify and prove frameworks and standards • Ensuring standards can enable wider adoption of digital capabilities, including those that enable data exchange5

• Sharing of knowledge and capability more widely across sectors and up and down the supply chain • Understanding how the academic and standard committees interact • Winning resources for, and overseeing, ‘action’-oriented programmes as mutually agreed.

Further research might focus on international comparisons, including an international benchmark and associated SWOT analysis5, and sector-specific investigation/analysis of opportunities, barriers and enablers (including standards). Other important areas for further study include through-life aspects and design integration.

2. Objectives and approach

The objectives of the study have been to:

• establish a draft domain definition of ‘digital manufacturing’* • identify the opportunities and challenges for manufacturing sectors in the application of digital technologies, the perceived benefits and barriers to companies of innovating in this area, including the application of digital technologies:  across the value chain*  through the supply chain* • understand how standards and good practice can help companies in the manufacturing sector invest in and adopt digital technologies.

The study has been a collaboration between IfM, Cranfield University, University of Nottingham and BSI. Taking place between March and early July 2016, it centred on an extensive web survey 1 and a joint workshop2 with industrial, academic and service provider participants. Table 1 outlines the work plan.

TABLE 1: WORK PLAN Activity Timings Kick off and initial stakeholder mapping and desk review of available H1 March documentation

Online survey of industry stakeholders (reported in the annex) April–June

Review of emerging research findings on digitisation of global supply chains H1 June

Draft interim findings (survey report and ‘digital scenarios’*) H1 June

Stakeholder workshop (reported in the annex) H1 July

Updated report (including stakeholder map) and final presentation End July

Final report Mid-August

Note that terms asterisked* are defined in the glossary of key terms on page 4.

3. Definition of digital manufacturing

The draft domain definition of digital manufacturing formulated by the study is shown in the box below.

Digital manufacturing, enhanced digital supply chains, and new competencies and skills

Digital manufacturing* is the collaborative transformation of manufacturing through the exploitation of advances in ICT.

This transformation enables new supply chain* and operations capabilities (identified in IfM research as ‘scenarios’*) to emerge that exploit advances in digital technologies, devices, data analytics, data integration and management across the value chain* in many sectors.

Digital manufacturing requires the development of new systems engineering competencies* – systems modelling, simulation and interface design – and skills (attitudes) across the manufacturing value chain (defined as R&D, design, production, supply, distribution, in-service, and reuse/disposal).

Digital manufacturing offers significant national and corporate competitive advantage through affordable flexibility, personalisation and product/service tailoring. 1

3.1 Competencies for digital manufacturing Key enabling competencies were identified for digital manufacturing, as shown in table 2. These competencies were extracted from HVM challenges related to digital manufacturing identified in the HVM Landscape refresh conducted by IfM for Innovate UK (2015–16, published September 2016).

TABLE 2: ENABLING TECHNOLOGY COMPETENCY THEMES Enabling technology competency themes Software development New software to achieve or reduce the time to complete a manufacturing task, and management including testing, gauging, monitoring and analysis, supporting integration of software and electronics, augmented reality and human–machine interface Big data management and Collection, storage, management and analysis of large amounts of data to create analytics useful, useable information in support of manufacturing and supply chain management and improvement Autonomy Automated systems with the cognitive ability to react to e.g. changes in consumer demand and product variability. Decision-making software; predictive control Internet of things Networks of physical objects embedded with devices and connectivity, sensing, controlling and exchanging data remotely; the ‘Ethernet of Everything’, including the ability to add objects to this and derive benefits from its use Cloud computing Exploiting ubiquitous network access to a shared pool of configurable computing resources, rather than local storage Mobile internet Accessing internet resources from mobile devices Measurement, metrology, Accurate, fast and efficient measurement and assurance, verification and assurance and standards validation; creation of new and associated standards Source: High Value Manufacturing Landscape refresh, IfM 2015–16 (published September 2016)

These enabling technology competency themes are driving business model innovation, including that related to product and service integration.1 13

Table 3 shows the range of competency themes4 identified by the study as relevant to digital manufacturing.

TABLE 3: OTHER COMPETENCY THEMES RELATED TO DIGITAL MANUFACTURING Management/operational/supply chain competencies Product and service integration Integration of product and service approaches, including through-life capability development, improved monitoring, diagnostics and health management. Supply chain and business model Improving information flow and forecasting, improved and new innovation methods of supply and value chain co-ordination and business model optimisation. Product technology competencies Sensor technologies Sensing and connectivity; on-machine sensors; non-invasive sensing; efficient management of data from sensors for improved control, detection, predictive maintenance, etc. Advanced and autonomous robotic Advanced systems to automate complex manufacturing processes such technologies as: assembly; logistics; materials processing and validation; nano- robotics; haptics. Enabling technology competencies Measurement, metrology, assurance Accurate, fast and efficient measurement and assurance, verification and standards and validation; creation of new and associated standards. Systems engineering/integration competencies Integrated design and manufacture Through-process integration of design and manufacture to reduce waste, improve synergies, reduce the impact of obsolescence and allow structural design. Systems modelling and simulation Advanced analytical modelling and simulation to enable the prediction of system behaviours from a set of initial parameters and conditions. Human–machine interface Optimising the interface between humans and machines, including design for intuitive use, augmented reality, immersive environments and smart robots. Source: High Value Manufacturing Landscape refresh, IfM 2015–16 (published September 2016)

The survey of industry stakeholders was used to identify key competencies influenced by digital manufacturing. Results are shown in figure 1.

Of the competencies selected by respondents, the highest priority was given to integrated design and manufacture, supply chain and business models, sensor technologies, and advanced and autonomous technologies. Measurement, metrology and assurance was widely selected but accorded lesser priority in most cases; human–machine interface was given higher priority due to the concern that even the most sophisticated system needs to be useable in order to deliver benefits.

The survey also confirmed that digital manufacturing is being introduced in many industrial sectors. These include food, pharmaceuticals, medical technologies, aerospace, automotive, marine, energy, nuclear and built environment, as shown in figure 2.

What manufacturing competencies would you consider key in underpinning the different supply chain activities?

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FIGURE 1: RATING OF KEY MANUFACTURING COMPETENCIES FOR UNDERPINNING SUPPLY CHAIN ACTIVITIES BY RESPONDENTS TO UK DIGITAL MANUFACTURING SURVEY, APRIL– JUNE 2016

Within which sector(s) does your business operate?

60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0%

FIGURE 2: MAIN SECTORS OF OPERATION IDENTIFIED BY RESPONDENTS TO UK DIGITAL MANUFACTURING SURVEY, APRIL–JUNE 2016.

As Figure 2 shows, aerospace was the most common area of operation, followed by automotive, electronics and ICT and flexible manufacturing. Some respondents operate in more than one sector.

One-third of respondents cited activity in a wide range of ‘other’ sectors. These were named as: fluid power and mechanical engineering; education; lasers; microscopy; textile products; high performance sport; packaging converting; consumer packaged goods, construction equipment manufacture; heavy industry, utilities and transport; transport systems and intelligentmobility; cost estimation and cost data management; digital research and education.

Digital manufacturing will impact throughout the supply chain3 (figure 3, showing the ten ‘scenarios’) and throughout the value chain1 (figure 4).

1. Automated 2. Digital 3. Real-time Factory 4. Flexible Factory 5. Digital Production e-Sourcing Factory Design Scheduling Automation Processes

… … … … … …

6. e-Commerce 7. Extended Supply 8. Digital 9. Digital Supply 10. Product Lifecycle Fulfilment Chain (near) Product Quality Network Design Management real-time Monitoring

… … … … … …

FIGURE 3: SUPPLY CHAIN AND OPERATIONS CAPABILITIES IDENTIFIED AS ‘SCENARIOS’. SOURCE – IFM DIGITAL SUPPLY CHAIN RESEARCH 2014– 16

FIGURE 4: DIGITAL MANUFACTURING THROUGHOUT THE VALUE CHAIN. SOURCE – UK DIGITAL MANUFACTURING SURVEY, APRIL–JUNE 2016

4. Opportunities, benefits and challenges for manufacturing sectors1

The web survey invited respondents to identify opportunities, benefits and challenges of digital manufacturing. Several general benefits and opportunities were identified, as well as product/service level opportunities and benefits (grouped below by stages of the value chain).

However, respondents also noted that the UK faces a challenge to build vision and collaboration, security, standard and resilient systems, and improve initial costs and data harvesting.

4.1 General benefits and opportunities Digitally enabled flexible manufacturing was seen as potentially driving national and corporate productivity: customers would benefit from greater choice, reduced lifetime costs and approved availability of products and services. Significant benefits also exist, potentially throughout supply chain tiers, in quality assurance and quality control.5

4.1.1 National and corporate productivity According to the survey, the main benefits and opportunities include flexible manufacturing, mass customisation and value chain optimisation, offering customers:

• Cost and lead-time reduction • Value chain optimisation • Personalisation and choice • Improved productivity and availability.

Respondents also agreed that digital manufacturing offers the national economy the opportunity for productivity improvement whilst building a high wage economy.

4.1.2 Greater choice, reduced lifetime costs and improved availability and quality of products and services • Personalisation and choice:  Smart services that customers can configure themselves  Customer issues will be seen and even predicted without the customer raising them  Products and services will be better tailored to customer needs  Greater choice from flexible manufacturing • Agility and reduced costs:  Reduced lifetime costs and better support  Improved delivery, quality and cost  Shorter lead times and cheaper low-production-volume pricing

• Availability and quality5:  Predictive condition monitoring and replacement  Real-time management decision making  Autonomous maintenance and reporting  Better collaboration across the supply chain  Improved quality assurance/quality control throughout the supply chain5

4.2 Value chain Digital manufacturing will impact across the product value chain, particularly in and between R&D, design, supply management and after sales service. 1 Figure 5 shows how respondents assessed benefits across the product lifecycle.

W hich product life cycle stages will most benefit from the use of digital e nabled manufacturing technologies?

90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0%

0.0%

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Attheafter sales Attheproduction Atthe distribution FIGURE 5: DIGITAL MANUFACTURING BENEFITS BY STAGE OF PRODUCT LIFE CYCLE. SOURCE – UK DIGITAL MANUFACTURING SURVEY, APRIL– JUNE 2016

The following benefits were highlighted in respondent replies:

• R&D: rapid prototyping and development; digitalisation of early discoveries • Design: quick and low-cost design and redesign; using real manufacturing data at the design stage • Supply management: visibility, traceability, synchronisation and collaboration; effective connection to production stage • Production: precision, efficiency, integration; making information from vast amounts of data • Distribution: track and trace, localised demand management, rapid production response to demand changes • Service: full, real-time service condition monitoring and maintenance, with feedback to design and production • Disposal/re-use: in-service data to support remanufacture/re-cycle/disposal decisions

• Other: significant sales and marketing benefits from affordable flexibility, personalisation and product/service tailoring.

Design of material, product, manufacturing process, metrology and testing regimes are critical to this in the digital age5.

Asked ‘which opportunities will be realised in the manufacturing sector’ (figure 6), respondents particularly emphasised flexible manufacturing and value chain optimisation. The benefits of these were linked – both relating largely to better control and management through greater visibility and access to information. National productivity was not as widely selected as other options, but scored very highly when chosen.

An interesting submission under other opportunities was ‘the ability to participate in higher-level complex systems where the barriers to entry are currently too high’.

Which opportunities will be realised in the manufacturing sector?

90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0%

FIGURE 6: DIGITAL MANUFACTURING OPPORTUNITIES THAT WILL BE REALISED IN THE MANUFACTURIN G SECTOR. SOURCE – UK DIGITAL MANUFACTURING SURVEY, APRIL–JUNE 2016

4.3 Challenges1, 5 The response identified that the UK needs to build vision and collaboration, security, standards, resilient systems, and improve initial costs and data harvesting. Respondents’ comments divided into cultural and technical challenges, while delegate feedback on the draft report highlighted a further group of challenges concerning risk.

Cultural challenges:

• Overcoming current slowness and cost of adoption of digital technologies, possibly linked to a lack of vision and mind set limitations • Providing adequate skills • Encouraging collaborative working

Technical challenges:

• Addressing security concerns • Developing standard, resilient systems • Overcoming present high initial costs • Improving data-harvesting

Risk5:

• The UK has a world-class reputation in health and safety at work. New digital technology provides new risks (e.g. shared human and robot environments) and potentially new benefits (context- aware technologies). To ensure the UK workforce is safe and preserve its reputation for health and safety the UK needs to explore and exploit the new technologies to protect the workforce which, in turn, will facilitate the uptake of the new working environments. • New manufacturing business models bring risks. Design houses selling to third parties to manufacture against a specification for design and manufacture raises questions of product liability and traceability of specification. No longer is the traceability of product build sufficient, there needs to be the ability to trace the adaptation, modification and variances from the product design and manufacture specification. The UK government needs to take a stance on where liability resides before a test case hits the law courts. • Risks exist around the effective control and ownership of IP: ensuring the value inherent in ownership of complex and valuable product design and service data can be retained • Information and security risks are yet to be established, therefore the appropriateness of existing data encryption methods needs to be better understood.

4.4 Supply chain The ten future digital supply chain scenarios3 (figures 3 and 7) were tested in the workshop of 11th July 2016.2 Delegates assessed multiple factors:

• Opportunities and benefits • Challenges and risks • Technology maturity today • Technology maturity required within five years • Skills today • Skills required in five years • Achievability.

Each factor was assessed using the following scoring method:

1 = Very Low 2 = Low 3 = Moderate 4 = High 5 = Very High.

IfM research3 indicates that digital transformation can be exploited across the supply chain (figure 7).

Digital transformation can be exploited across the supply chain*

2 Digital Factory Design 3 Real-time Factory 4 Flexible Factory 5 Digital Production Digital 3D modelling systems Scheduling Advanced factory Automation Advanced Processes Application of for factory layout design, execution systems with sensor- manufacturing plant/machine digital production processes process and material flow enabled, smart devices, real- reconfiguration, scale flexibility, (e.g. additive manufacturing, simulation time data KPI monitoring, varied levels of human-robot continuous processing) with predictive maintenance collaboration advanced process analytics

Internal

1 Automated e-Sourcing 6 e-Commerce Fulfilment Seamlessly connected Web-based order management automated replenishment from Inbound Outbound (configuration, pricing etc.) and supplier network (multiple tiers) inventory deployment to with real-time KPI monitoring, multiple points of sale, covering predictive disruption analytics last-mile and direct delivery (all tiers through to end users) Suppliers Prime Customers

End-to-end

10 Product Lifecycle 9 Digital Supply Network 8 Digital Product Quality 7 Extended Supply Chain Management Nextgen PLM Design Design tools to Digital product quality (near) real-time Monitoring systems that provide accurate, architect supply network management systems for Extended ‘end-to-end’ supply up-to-date product information configuration – optimisation and connecting ‘traceability islands’ chain visualisation ‘watch accessible throughout the value visualisation of site location, back from customers to towers’ for near real-time chain and product lifecycle capacity, inventory etc. suppliers (root cause analytics) monitoring and decision making

Note (*): Source – IfM Global Digital supply chain research 2014 - 2016 FIGURE 7: EXPLOITATION OF DIGITAL TRANSFORMATION ACROSS THE SUPPLY CHAIN. SOURCE – IFM DIGITAL SUPPLY CHAIN RESEARCH 2014–16. © Centre for International Manufacturing, IfM 2016

4.4.1 Opportunities and benefits versus challenges and risks2, 3 The workshop confirmed that digital transformation can be exploited across the supply chain. All scenarios both offer significant opportunities and benefits and have significant challenges and risks. While the challenges and risks are outweighed by the opportunities and benefits (figure 8), there are significantOpport udeliverynities challengesand bene infi talls scenariosvs challe. nges and risks (Size of bubble indicates achievability)

Flexible Factory Automation 92 Real-time Factory Scheduling 90

% Digital Production Processes 5

+ 88

t Product Lifecycle Management

i f

e 86 e-Commerce Fulfilment

n Digital Product Quality

d 84 n

a Digital Supply Network Design

s 82

i t

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t 80

o p

p 78 Digital Factory Design O Extended Supply Chain 76 (near) real-time Monitoring 0 10 20 30 40 50 60 70 80

Challenges and risks 4+5%

FIGURE 8: SUMMARY OF WORKSHOP DELEGATE ASSESSMEN T OF OPPORTUNITIES, BENEFITS, CHALLENGES AND RISKS: PERCENTAG E OF DELEGATES SCORING FACTORS 5 (VERY HIGH) OR 4 (HIGH). NOTE: DETAILED ANALYSIS OF DELEGATE RESPONSE IS PROVIDED IN THE ANNEX.

4.4.2 Skills and technology gaps2, 3 Skills and technology gaps (the gap between current state and requirements in five years’ time) are significant in almost all scenarios (figure 9). Delegate assessment shows skills gaps the larger of the two and skills maturity is rated the most problematic issue. But enabling technology also needs to matureSkills signifi gapscantly: vs te substantialchnology technologygaps (Size developments of bubble in aredic requiredates siz ine all digital scenarios. of opportunity)

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k 40

e 30 Digital Product Quality

n Digital Production Processes i Digital Supply Network Design

e r

i 10

q e

r 0

( 0 10 20 30 40 50 60 70 80 90

Technology gap (= required maturity in five years 4+5 – Current maturity 4+5 %)

FIGURE 9: SUMMARY OF WORKSHOP DELEGATE ASSESSMEN T OF SKILLS AND TECHNOLOGY GAPS. PERCENTAG E OF DELEGATES SCORING FACTORS 5 (VERY HIGH) OR 4 (HIGH). NOTE: DETAILED ANALYSIS OF DELEGATE RESPONSE IS PROVIDED IN THE ANNEX.

E-commerce fulfilment is the most mature scenario, having the narrowest gap between both current and required technologies and current and required skills. It is also the only scenario where some respondents regard skills maturity as already high today.

4.4.3 Capability gaps versus achievability2 Capability gaps were assessed as the mean of skills and technology gaps. Delegates regarded all scenarios as achievable by 2021, though to varying degrees (figure 10).

The scenarios judged most achievable include intra-factory scenarios – flexible factory automation, digital factory automation – but also those activities that connect the inbound and outbound supply chain, such as automated e-sourcing and e-commerce fulfilment. Delegates considered it easier than might be expected to overcome the skills and technology gaps for intra-company digital scenarios compared with inter-company scenarios. In figure 10, scenarios above the red line are considered to have higher achievability than the capability gaps suggest.

With regard to the digital design scenario, levels of maturity and opportunity scoring vary by respondent type (industry, academia and consultant). This suggests that awareness and understanding of capability and opportunity is variable.

Extended supply chain (near) real-time monitoring is the scenario seen as least achievable (as well as identified as having the greatest challenges, as shown on figure 8).

Automated e-Sourcing 90 Digital Factory Design

80 Flexible Factory Automation

70 e-Commerce Fulfilment Digital Production

% 60

5 Digital Product Quality Processes

+ 4

i l

i Real-time Factory Scheduling

b 40

v e

i 30 Product Lifecycle Management

h c

A 20 Digital Supply Network Design 10 Extended Supply Chain (near) real-time Monitoring 0 0 10 20 30 40 50 60 70 80 90

Capability gap = (Skills Gap + Technology gap)/2

FIGURE 10. SUMMARY OF WORKSHOP DELEGATE ASSESSMEN T ACHIEVABIL ITY VERSUS CAPABILITY GAP. NOTE: DETAILED ANALYSIS OF DELEGATE RESPONSE IS PROVIDED IN THE ANNEX.

Taking together the delegate assessments summarised above, the evidence of the workshop is that skills and technology building should address broad supply chain developments and enablement rather than ‘cherry pick’ specific fields.

Figure 11 gives an overview of delegate assessments: detailed analysis is given in the annex.

Opportunity and benefit are seen as very high across all scenarios.

The required skills maturity is seen as very high across all scenarios, with a significant gap compared with today.

The required technology maturity is seen as reasonably high across all scenarios, with a significant gap versus today. Flexible factory automation, digital factory automation, automated e- sourcing and e- commerce fulfilment are particularly seen as achievable within five years.

5. Role of standards and good practice

As shown in figure 11 (section 4.4.3), workshop delegates judge inter-company scenarios to be more difficult to achieve than intra-company ones. This is evidence of the need for initiatives and standards to encourage collaboration between companies and sharing of data. Delegates confirmed that standards enable digital manufacturing through interoperability and by supporting cultural change. Their area of greatest concern is data security.2

5.1 Benefits of interoperability2 Interoperability offers a number of important opportunities for manufacturers, such as: in-use feedback to design; service innovation; and specialisation in supply/value chain high value services. Some standards exist already, such as RAMI 4.0, BIM level 2/level 3 (Asset Information Modelling), ISO 10303 and the other digital engineering standards from ISO TC184/SC4. Note that there will be a need to decide the approach for standards as the scope of BIM level 3 emerges.5

5.2 Need for culture change2 The attitudes and practices of many companies need to change, in areas such as:

• How to use data • Exploiting data through changed business practice • More collaborative approaches • Trust (for example, regarding ownership and security of data).

5.3 Current practice Standards vary by sector, but all sectors refer to ISO, ISA and HSE standards. 1

The online survey probed the use of standards within digital manufacturing, asking ‘Within your business sector, are there specific standards that need to be adhered to?’ (figure 12).

W ithin your business sector are there specific standards that need to be a d hered to? 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0%

FIGURE 12: RESPONDEN TS’ EXPERIENC E OF STANDAR DS. SOURCE – UK DIGITAL MANUFACTURING SURVEY, APRIL–JUNE 2016

The responses to this question depended largely on the sector of the respondent. (Aerospace in particular has a large number of standards applicable to digital manufacturing.) Data security standards were most common, being reported by 73% of respondents in total. On the other hand, only 55% of respondents cited standards in logistics processes and usage tracking.

The single largest area of concern was data security: almost all respondents referred to in-house practices or standards appropriate only to their own industry, citing a lack of transparency and a need for greater integration.

ISO, ISA and HSE standards were cited in relation to all other areas, but responses also discussed relying either on standards laid down by customers or on ‘common sense’. In summary, while there are necessary standards applicable to each area here, they are often disparate, sector-specific and lacking in commonality.

5.4 Support for common standards1 Respondents favoured common but light touch standards to help innovation, particularly through supporting best practice transfer, data security and IP protection5. Their views are grouped below:

Data • [Common] language and terminology • Processes, procedures, data encryption and data formats • Standards will be vital if the benefits of digital realisation capability are to be exploited • Security of data is crucial for confidence in data sharing. In the absence of assured standards of security, collaborative business models will not be embraced quickly.

Best practice • Providing best practices and guidance for industry • Working towards coordinating and achieving harmony • Good standards will help to stop wastage through reinventing the wheel, enabling actors at all levels to concentrate on optimisation and innovation as digital technologies and processes rapidly evolve.

Light touch • New standards should play a limited role; this is a very complex area where many things need to be considered, such as legacy deployments, technology pyramid (ISA 95 levels), industry sectors, application providers, OEM machine providers, etc. • Where appropriate, there is a need for a common set of standards to ensure stable movement and common standards of quality for data so that each tier can implement their own solution and still exchange information with ease5.

Data governance and IP protection5 The areas that might be addressed to protect UK innovation and minimise risk are: • Appropriateness of the legal protection mechanisms, patents, copyright, watermarks and registered trademarks • Global legal frameworks in which the UK protection laws sit • Skills in the legal protection and defence of IP protection • Education in the value of ideas and innovation.

6. Priorities for action Respondents judged skills and training to be the most important enablers for facilitating digital manufacturing. Fostering new business models, governance, policies, regulations and standards were also seen as key (figure 13). 1

W hat other support activities and infrastructure would you consider key in und erpinning the different supply chain activities? 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% Skills, training Business models Governance and Policy and Other (please and continuous processes regulation specify) professional development

FIGURE 13: KEY ACTIVITIES AND INFRASTRUCTUR E FOR UNDERPINNING SUPPLY CHAIN ACTIVITIES. SOURCE – UK DIGITAL MANUFACTURING SURVEY, APRIL–JUNE 2016

Competency mapping by the project team has highlighted the importance of digital skills, technology and standards in business and supply chain innovation for both skills and standards development (figure 14). IfM’s supply chain research3 leads to the conclusion that the focus should be on business and supply chain innovation skills, together with big data management and analytics, software development and systems modelling and simulation.

t n n g n k y i g n e r it a i n i o l h s ry c m le w a C e ry io r il c t u e D to o t u lf y t e Q y m c g t n c s o u c n N t l i ry a n c o u e S e e c p -t g o F li a ti d s - F if ly n p l in t u F a o s e e L m p u u a r c e r e d c e p ig d S e o a d le m P c r t g s o R it HVM Competencies F im e o l o te e c a u e r d ) l -t h ib t a r a u n S P e r n a l c x u it P m d a l D l d a o t a S e A g m m o a a n e M i e l i o o r M it it e N ig R F D t P g g t ( u -C i i x D A e D D E Key ICT competencies

Adva nc e d di gi tal A dva nc ed S ki lls i n data Advanced

W eb-ba sed tool s for fa c tory a lgori thm s capture, algori thms

de sign / orde r Software development and management layout de sign P rogra m ming analytics a nd based on and l ine ski lls integration network dri vers,

Bi g data Fast data Analytical s kills Holistic / Data analytics /

forecasting to cleansing a nd to m anage coherent view dynamic

custom er / maintenance greater of Big data management and analytics fore casting store / s tock Skills i n data complexity a nd inform ation(e.g.

Web / cloud

enabled quality Autonomy systems l inking customer,

Web / cloud

enabled, re al- Internet of things time i nventory tracking to

Web / cloud

enabled, re al- Cloud computing time i nventory tracking to Mobile internet Management/operational/supply chain competencies

Root cause

analysis a nd Product and service integration solution foc us

Cul tural change Digital t ools to Ability to link Auto-sensing of e-com merce PLM systems Dynamic l oad Holistic Analytical,

to a m ore extend ‘vi sual custom er replenishment conne cted across ‘idea-to- allocation tool s pers pectives deductive

holistic view factory’ requirements, needs by w eb / logistics end’ (in t erms of across va lue thinking, va lue

Supply chain and business model innovation (focus on techniques product da ta cloud enabled Unders tanding PLM system s et- capacity e tc.) chain optimisation Product technology competencies

Sensor ne tworks

that provi de

Sensors real-time data

Knowledge i n Col labora tive

software / robot ics / Autonomous and robotic technologies control system human-robot - architecture s collabora tion Enabling technology competencies

Full system Integration of Int egra tion of

MES / E RP unders tanding platform s eBO M / m BO M

integration Measurement, metrology and standards of a utomation connecting with MES systems, M ES / custom ers / Understanding Systems engineering/integration competencies

Ability to w ork Machine / Integration of Int egra tion of Integration tools

MES / E RP interdis ciplinary device-M ES / platform s eBO M / m BO M from design t o integration (s oftware, ERP integration connecting with MES Integrated design and manufacture equi pment build mechani cs, Full system custom ers / Understanding

Event-driven Digital t ools to Awareness of

simulation extend ‘vi sual system / da ta Systems modelling and simulation software and factory’ capabilities a nd multi-objective techniques cause / effe ct

Smart devices

for m anual / Human machine interface partly automated

Key areas for skills technology and standards development might be: Business and supply chain innovation, Big Data management and analytics, Software development and systems modelling and simulation

FIGURE 14: MAPPING OF COMPETENCIES TO THE TEN SCENARIOS. SOURCE: PROJECT TEAM, POST WORKSHOP OF 11 JULY 2016

6.1 Support activities and infrastructure1 As shown in figure 13, there was general consensus that all of the factors suggested are key, but skills and training were seen in the commentary as the most vital.

Business models which allow flexibility and can adopt service-dominant logic will be key to realising full potential value of digital manufacturing.

Respondents also called for governance and processes which have vision but are also pragmatic and simple, rewarding those who excel as the rate of change increases.

Well-drafted and implemented policies, regulations and standards are required to support and encourage integrated supply chain optimisation and development, especially as there will be more requirements to include digital data management in contracts and procurement.

6.2 Interoperability, security and resilience1 Respondents called for government support for collaborative development of open source applications to create a snowball effect with improved interoperability, security and resilience:

• Interoperability of both machines and data requires open systems architectures and common languages and data platforms so that machines can talk to each other • Greater security of manufacturing systems requires awareness and adoption of best practice security standards and operation, particularly by equipment providers, and the use of closed or encrypted systems to reduce IP infringement • Improved resilience requires greater systems modelling and analysis competence applied to predictive and condition monitoring capabilities • Wider adoption of best practice in manufacturing business processes can provoke a ‘snowball’ effect with collaboration in open sources application development and sharing of best practice supported by government sponsorship and backing.

6.3 Requirements for interoperability1 Interoperability of both machines and data would be supported by artificial intelligence (AI) open systems architecture, common ‘languages’, data sharing platforms and interfaces. Respondents proposed:

• An AI open system architecture for integrating different OEM machines with one another • A fundamental requirement that machines speak the same 'language‘ (Note: This is probably an extension of standard IT systems where well-defined interface standards are essential to achieve proper communication and exchange of meaningful information even where internal languages can be very different) • Common languages and data sharing platforms reduce errors and cost • Automated collection and processing of SPC data will accelerate process improvement and development of predictive process control • By ensuring a common interface through the pipeline, less variation between design and manufactured product could be attained • Automation of processes that currently rely on skilled human expertise.

6.4 Requirements for security and resilience1 It was emphasised that greater resilience and security of manufacturing systems requires cross- enterprise digital security standards and increased systems modelling capability. In more detail the points made included:

• Adoption of security standards across the digital enterprise • Increased awareness of the requirement for security in plants on the part of equipment providers and adoption of best practice security and IT recommendations • Greater systems modelling/simulation capabilities – increasing complexity will require increased resilience • Resilience will come from predictive and conditional monitoring • Copyright/intellectual property rights and patent infringements could be reduced by the use of closed or encrypted systems (in tension with desire for open architectures?).

6.5 Benefits of best practice1 Further, wider adoption of best practice in manufacturing business processes can propagate standards, drive continuous improvement and allow real-time ‘management’. In more detail the points made included:

• Best practice can be a propagator of standardisation/standards of protocols, practices, etc. • Collecting SPC data will be easier, more consistent and reliable in a digital environment; this in turn allows the use of continuous improvement techniques • Robust systems often result in robust products • By collaboration, open sources application development, sharing of best practice, government sponsorship and backing • Processes can be streamed by real-time information and therefore operators can ‘manage’ rather than ‘do’.

6.6 Other needs1 Other issues raised by respondents to the survey include standards and commonality (often addressed in the short term by developing bespoke solutions which may be counterproductive in the longer term), and the need for individuals in the work environment to become as familiar with digitisation as they are at home. In more detail the points made included:

• Standards should be derived by the industries that use them. Bear in mind that the UK is not the force worldwide that it was when mining, steel, shipbuilding, rail, etc. were all very big drivers for standards • Every business and research institute needs to develop secure software platforms and data analysis methods on which to base attractive smart services • Some of the issues we have experienced around digital technologies relate to the application of enterprise security to production devices. A lot of our suppliers come with very limited understanding of the requirement for security of production information, and the limitations this may impose on their devices or equipment or the issues that may be experienced • Standards for data interchange between manufacturing systems are not fully developed, thus it is difficult to interface equipment without bespoke 'bridges' or interfaces which need to be supported through the evolution of two software systems

• Digital manufacturing should be promoted at all levels of a business. It is not the domain of IT, although they will be enablers; all personnel need to become as digitally aware in their working environment as they are in the home • There are two domains that need to be considered, the manufacturers and the technology providers. At the moment the focus is on the manufacturer to create the pull for technology; however, the ICT providers do not offer effective 'out-of-the-box' solutions.

7. Key stakeholders1, 2

Key stakeholders were identified by respondents to the survey and the resulting list was reviewed in the workshop of 11th July 2016.

A wide range of institutions – universities, government agencies and trade associations – were nominated as sources of good advice and support. These key stakeholders, as well as major industrial partners, should be involved in further development (table 4).

TABLE 4: KEY STAKEHOLDERS IDENTIFIED BY SURVEY RESPONDENTS AND WORKSHOP DELEGATES

Universities Government agencies and trade Industry associations University of Cambridge HVM Catapult IBM Cranfield University BSI Oracle University of Nottingham The Welding Institute (TWI) Hewlett Packard5 Loughborough University Department for Business, Dassault Innovation and Skills** University of Liverpool Digital Catapult Festo Coventry University Innovate UK Meggit Plc Department of Energy and Laing O’Rourke Climate Change** Research Councils UK (RCUK) Consultants (such as PwC)

Nuclear Industry Association UK The Open Group

National Skills Academy

Nuclear Innovation and Research Advisory Board Energy Technologies Institute (ETI) National Physical Laboratory (NPL) The GAMBICA Association

The Institution of Engineering and Technology (IET) **Now Department for Business, Energy and Industrial Strategy (BEIS)

8. Recommendations and next steps

If the UK does not respond within five years, it will lose out to overseas competitors, so a concerted, urgent action programme is needed.

Any Digital Council, if established, should have a standards work stream, led by key industrial, academic and institutional stakeholders, to ensure one voice, one vision in policy setting. In the absence of such a council, an alternative route would need to be considered to ensure focus on this important area.

The remit may include the following:

• The ‘Future Cities’ Catapult is a possible model on which a steering arrangement could be based. It needs to be industry led and could be based on the BSI Design for Manufacture Committee and the HVM and Digital Catapults • The approach needs to include input from experts from BSI, HVM Catapult and Research Councils UK • Development of demonstrator(s) which identify and prove frameworks and standards • Ensuring standards can enable information transfer and sharing by users and code sharers • Sharing of knowledge and capability more widely across sectors and up and down the supply chain • Understanding how the academic and standard committees interact • Winning resources for, and overseeing, ‘action’-oriented programmes as mutually agreed • Where appropriate, pressing for new light touch standards5 in support of the above priorities.

Further research might focus on international comparisons, including an international benchmark and associated SWOT analysis5 and sector-specific investigation/analysis of opportunities, barriers and enablers (including standards). Other important areas for further study include through-life aspects and design integration.

The immediate next step for this study is to consult on the results with Innovate UK, HVM Catapult and Digital Catapult prior to finalisation.

Index of sources

1. UK Digital Manufacturing Survey, April–June 2016 2. IfM Digital Manufacturing Workshop, 11 July 2016 3. IfM digital supply chain research, 2014–16 4. IfM High Value Manufacturing Landscape Refresh: Interim Report, September 2016 5. Delegate feedback on first draft report

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