• Fraser Harper

Manufacturing the Future Workforce

High Value Manufacturing Catapult and Gatsby Foundation. January 2020. 68 pages.

Understanding the processes by which skills are defined and come to be mastered by an occupation is a key activity when it comes to keeping a workforce up-to-date and current. In the recent report on Manufacturing the Future Workforce a series of recommendations are made which seek to pull together the fragmented UK skills system around the well-established concept of value chain analysis1 to map out emerging and future skills requirements. It also lays out the process which moves from future skills identification through to curriculum development and education and training delivery.

The whole report is well informed by drawing upon a series of best practice case studies drawn from the Singapore, United States, Germany, Switzerland and Ireland. The case studies focus on “Centres of Innovation” and Training Factories and how they have been harnessed in a systematic and long-term way to inform and shape workforce development plans.

While the recommendations are very practical and requires a degree of good well across many potential stakeholders to build a skills value chain, the following points needs to be considered and incorporated into the recommendations:

  1. Technology development and diffusion: as technology is developed and then applied across multiple workplaces, it is important to be able to understand the rate of technology uptake (its diffusion) and its further refinement. Evidence suggests that the truly leading companies make the most of the latest technologies and have far superior productivity levels2. Their success prejudices the future of their lesser able competitors. Any forecasting approach requires some understanding of the rate of diffusion of a technology and its impact on the nature of labour demand. In addition to this, it is useful to categorise innovation in terms of scale ranging from break through and platform to a form of adaptation and improvement.

  2. Quantification of magnitude: linking the forecasting and definition of new and modified (and displaced) skills, it is important to be able to relate them to the actual tasks people undertake at work and how these might be impacted3. Then trace this on to the impact of specific occupations and employment levels.

  3. Skills as “management know how”: while stakeholders within a sector will often be prepared to share information for the common good of all companies, there is a boundary quite quickly reached where the skills being proposed reveal aspects of how a process is defined and managed, and how a technology is being mastered and improved4.

  4. Definition of manufacturing: the report quite rightly focuses on the core of manufacturing but it needs to also include the skills value chain process all of manufacturing and so include the chemicals and pharmaceutical sectors amongst others, and recognise how others sectors overlap with manufacturing e.g. construction as it adopts more factory-based modular build practices5.

  5. Balance of innovation types: within manufacturing there are two prime forms of innovation: process and product. It is important to recognise the type of innovation being examined in the skills value chain as they two main forms of innovation have different impacts on skills and employment levels6.

  6. Build on proven methods: the skills value chain concept dates to the mid-1980s and around much the same time there was a very substantial body of work which developed a range of methods for skills forecasting and definition which were highly effective7. It would be useful to draw upon on this legacy much of which was funded by the predecessor to SEMTA8.

  7. Technology versus technologies: it is very easy to talk about a technology, but we are increasingly seeing a greater convergence of technologies and this creates quite unpredictable skills requirements and is where we often see the greatest gains to be made in a sector. Those companies which have to master multiple technologies, like the auto-industry, might be one place to view in detail as they bring together new power systems, new power storage systems, different frame and body materials and combinations, sensors for vehicle management, etc.9

  8. Database the information captured: in creating the skills value chains during the proposed process, it is vital to capture the information and database it to allow on-going analysis and linkage to current occupational information e.g. from ONS, from NOS, from O*NET, from ESCO, from Euro Skillsfund, etc.10

  9. Digital learning and “Edutainment”: while digital learning is recognised as being a key approach of capturing and sharing new skills to future practitioners, the potential here is huge and we should build on the UK’s expertise in gaming and MOOCs. This would create a major way of raising education and training productivity as well.11

  10. Micro-credentialization: progress has been relatively recent on the creation of very small units of education and training which can then be meaningfully aggregated to meet the requirements of an undergraduate degree.12 This might also call for a revision of the very well established practice of degree classification.13

  11. UK labour market in a global context: scan across many emerging sectors and there are parts of an industry spread across the world. For example, in the space sector (ranging from rockets and satellite manufacture to downstream data analytics and providers), many businesses are innately global due to the products and services they provide, and the labour market is similarly global.14 So, when skills value chains are developed for a sector like this, it needs to have an international dimensions and not just view the UK.

  12. Skills and technology diffusion: when a new set of technologies have been embedded into pieces of plant and equipment, the manufactures will seek to supply through: on-line, real-time operational support; virtual reality-based education and training programmes; on-site support staff for the commissioning and later operation of the equipment, etc. In fact, some capital equipment is leased and is priced based on output achieved not on the cost of the capital item. This changes the suppliers of technology increasingly becoming suppliers of services. The aerospace sector in the supplier of aircraft engines in a prime example of this approach which has profound impacts upon the purchaser of the equipment to develop the ability to improve and maintain the equipment.15

  13. Public space to explore: throughout the development of universities in the UK, there has been an important role it has played in the undertaking of fundamental science and the sharing of the results in very open and generous way. This approach is vital at the outset of a new technology prior to the phase when the technology can be developed with early adopters willing to take the risks of charting new territory.16 It is therefore important that as the skills value chain is developed that practitioners are the focus rather than universities.

  14. Sector critical mass: when developing the skills value chain, the likely size and location of the industry is worth considering. Is the industry likely to develop a critical mass to support formal education and training programmes on a mass scale along with any relevant qualifications and standards? At the outset this is a very difficult task but to rate the scale of a new industry or application area in a series of ranges: 10,000+; 50,000+; 100,000+ etc.17

  15. Technology showcasing: the role of Centres of Innovation and education and training centres have long been used to showcase the latest technologies using equipment loaned by a manufacturer (and supplier). This practice is referred to in the report and is one which can be used both to help develop awareness of a new technology and begin the process of skills development. In some cases, this will not be possible as the demand for the technology is so great that showcasing serves no commercial benefit to the originator. It is also possible that the technology is not available for public display as the initial adopters enforce some form of restrictions.18

  16. Skills flow and the family tree: when a technology emerges and starts to be applied there is often a following flow of talent who join the early adopters and in some cases set-up their own companies. This flow of people is the true value chain of the skills required to develop, apply and then extend the technology. We have seen this pattern repeat itself across micro-electronics and semi-conductors, and from leading technology firms like Hewlett Packard. Understanding this form of skill flow might be one way of seeing how the skills might emerge in a sector or with a range of applications will help inform the form the skills value chain might take.19

  17. Facilitating and enabling occupations: in mapping out the skills value chain for a technology, it is important to identify those occupations (and skillsets) which are the truly facilitating and enabling ones and which are most likely to form the core of a hybrid set of occupations. Both new and hybrid occupations form the building blocks for the derivation of new standards and qualifications.20

So, the HVM Catapult and Gatsby report marks a good step forward in proposing a process and outline method for ensuring we have up-to-date occupations, but it is one which can be further improved upon and learn much from previous applied research.


  1. Porter, M.E. (1985) Competitive Advantage: Creating and Sustaining Superior Performance. New York: The Free Press. See Chapter 1, pages 11-15. We see this process-based approach developed elsewhere e.g. S.C. Wheelwright and K.B. Clark (1992) Revolutionising Product Development. Quantum Leaps in Speed, Efficiency and Quality. New York: The Free Press. 364 pages and R.S. Kaplan and D.P. Norton (2004) Strategy Maps. Converting Intangible Assets into Tangible Outcomes. Harvard Business School Press. 454 pages.

  2. Andrews, D.; Criscuolo, C. and Gal, P.N. (2015) Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries. The Future of Productivity: Main Background Papers. OECD, Paris. 39 pages. “Firms at the global productivity frontier – defined as the most productive firms in each two-digit industry across 23 countries – are typically larger, more profitable, younger and more likely to patent and be part of a multinational group than other firms. … the rising productivity gap between the global frontier and other firms raises questions about why seemingly non-rival technologies do not diffuse to all firms.”

  3. McLoughlin, I. and Clark, J. (1988) Technological Change at Work. Open University Press, Milton Keynes. 202 pages. See: Chapter 5, New technology, work tasks and skills, pages 99-117 and Chapter 6, New technology, job content and work organisation, pages 118-141. See also: Goodstein, L.P.; Andersen, H.B. and Olsen, S.E. (1988) Tasks, Errors and Mental Models. Taylor and Francis, London. 342 pages. See: Part II. Complexity and Cognitive Tasks, pages 105-190.

  4. Beyers, J. and Berman, B. (2009) “Measuring and conveying IP value the HP way”, Intellectual Asset Management, March/April, 43-48

  5. Building Magazine (2009) “Construction methods modular”, July 27th, 46-50

  6. Christensen, C.M. and Raynor, M.E. (2003) The Innovator’s Solution. Creating and Sustaining Successful Growth. Harvard Business Review Press. 320 pages

  7. Work was undertaken by Industrial Training Services in London, and Industrial Training Research Unit, Cambridge. In addition to these two bodies survey approach using skills inventories can also be used e.g. the John Annett inventory.

  8. The Science Policy Research Unit, University of Sussex undertook a programme of work for the Engineering ITB (predecessor to SEMTA) which covered the application of semi-conductors within manufacturing and followed a robust methodology for technology and skills fore sighting e.g. R.M. Bell (1972) Changing technology and manpower requirements in the Engineering Industry. Research Report No 3. Sussex University Press in association with the EITB. 101 pages

  9. Simoncini, M. (2017) Convergence of auto and tech. Centre for Automotive Research. Presentation at MBS 2017. 16 pages

  10. e.g. Occupational Information Network (O*NET) and University of Amsterdam (2019) Measuring job tasks by ISCO-08 occupational groups. SERISS

  11. See for example the Carnegie Mellon University’s Entertainment Technology Centre

  12. Future Learn has certainly gone down this path.

  13. Degrees have been classified as 1st, 2.1, 2.2 etc. for 100 years and more but there is perhaps a changing logic to adopt a different approach as we see different degrees being offered and different assessment profiles.

  14. See for example the Seraphim Spacetech Map 2020 which splits the sector in two: upstream and downstream and then into three sub-sectors in both major categories. www.seraphimcapital.com

  15. Rosenberg. N. (1982) Inside the Black Box: Technology and Economics. Cambridge University Press. 304 pages. See: Chapter 6, Learning by doing, 120-140. Book uses aerospace and VLSI as examples.

  16. Greenberg, D.S. (2007) Science for Sale. The Perils, Rewards, and Delusions of Campus Capitalism. University of Chicago Press, Chicago. 324 pages. Plus, S. Marginson (2016) Public/private in higher education: a synthesis of economic and political approaches. Centre for Global Higher Education Working Paper Series. Working Paper No. 1. 26 pages

  17. Muro, M. and Katz, B. (2010) The new ‘cluster moment’: how regional innovation clusters can foster the next economy. Brookings Institution. 59 pages

  18. UNCTAD (2018) Technology and Innovation 2018. Harnessing frontier technology for sustainable development. UN. 134 pages.

  19. Laws, D. (2016) “Fairchild, fairchildren and the family of Silicon valley”, Computer History Museum, December 20th. www.computerhistorymuseum.org

  20. BMI analysis of O*NET data for Gatsby in support of the review of the IfATE engineering and manufacturing occupational map. November 2019.

#Futureskills #Manufacturing #Skillsvaluechain

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