Imagine you are responsible for the digitisation journey of your company's manufacturing. You know by embedding digitisation throughout the whole manufacturing value chain will bring success. However, your manufacturing portfolio is diverse e.g. high volume fast moving white goods, novel pharmaceutical biological drugs, automotive components, chemical synthesis and aircraft avionics. You also design, manufacture and operate nuclear facilities. Each of the sectors claim they are unique; however, your experience evidences the underlying challenges are common - although often articulated in different ways! You have learnt lessons from the 1980's where companies adopted automation because 'it was the new shiny technology" but productivity savings were not always realised, and the Made Smarter Review evidenced that in 2017 productivity challenges, still remain - even though the technology is available. You know that people are the critical element. You have seen manufacturing systems fail to deliver because of employee pushback, lack of engagement/skills/leadership as well as poor change management (Made Smarter Review 2017, Vander Luis Da Silva, et al. 2020). You recognise to create value in manufacturing through digitalisation needs investment in people. It is your view that the right combination of current technology, data and people can deliver SMART manufacturing today i.e. if we have the right people, we can be responsive, reactive, make smart decisions to maximise manufacturing value using live data and information. However, to achieve digitally engaged people, especially in manufacturing you believe there needs to be a process for manufacturing companies to follow, regardless of sector and size. The vision of this centre is to enable 'UK Manufacturing to improve their productivity year on year by investing in their biggest assets - people"". This investment will lead to the uptake of digitalisation. As part of our ambitious centre, Theme 4 - "Societal and cultural change: managing the disruptive impact of digital technologies." i.e. achieving digitally engaged people is core and will account for up-to 65% of our activities. Around 56% of UK manufacturing (Q2, 2019) are SMEs which are critical to UK Manufacturing. To meet our goal of digitally engaged people our research will engage the whole manufacturing value chain through Theme 3 - "connected and versatile supply chain" our second core theme However, we recognise investing in people will 'touch' all of the themes and our networking activities will be crucial in leveraging value from the Made Smarter investments. In summary, our hypothesis is that regardless of manufacturing sector and company size a common process leading to digitally engaged people is achievable in practice i.e. in industry. Impacts from embedding our research into aerospace manufacturing demonstrated the data/information engineers believed they needed, was not the data/information they used to make decisions. We were able to increase their productivity by 47% through a combination of manufacturing digitalisation and human factors. This was achieved through a step-by-step process, using data analytics, human factors and observing people in action. Our centre will build on this expertise and create a generic process for use across UK manufacturing - leading to increased productivity.

Imagine you are responsible for the operation of a manufacturing system that is producing the next generation of electric cars. The manufacturing system is streamlined and producing leading-edge innovative cars just in time to meet the consumer needs, has minimum waste, the supply chain providing materials and products to the manufacturing line is green and quality of production is high. Everyone is happy. However, suddenly the supply of a core material used in the manufacture of the car is now quite scarce i.e. there is limited availability. Unfortunately, our manufacturing system is no longer working as it should! The manufacturing system is not producing enough cars and the productivity has hit rock bottom. Sadly, there were indications that the material was becoming scarce - the supplier had been issuing warnings, but the warnings were missed and no-one realised the impact this would have. So, we now have a manufacturing system that is not efficient, the cars can no longer be manufactured at an appropriate rate, and the manufacturer is about to be bankrupt! This could have all been avoided if we had a manufacturing system that was responsive i.e. adapt to change, be sustainable and resilient. The outputs from this research are geared to avoid such occurrences by providing the information to enable the manufacturing system to adapt to both internal and external factors i.e. enable the manufacturing system to be responsive. Our research will use Data, Information and Knowledge, automatically accessed via digital methods to enable the brain (the control centre) of the manufacturing system to continually assess its current status and predict future states. We will facilitate the ability of a manufacturing system to be truly responsive, whilst sustaining its whole life value. Although easy to say - achieving this is extremely challenging. However, with the current impacts of major disruptions such as COVID-19 on manufacturing there is a strong desire and willingness from manufacturers to ensure their systems can be responsive. Hence, the call and our proposed solution is very timely. In parallel to this need, the advancements in the technology and processes, such as digitalisation, 5G and Industry 4.0 have reached the stage that we can create a means by which a manufacturing system can automatically assess whether it needs to change and predict the most appropriate action. Our proposed solution has its foundations in value modelling (a value model is used to assess the impact of any proposed solution in terms of e.g. cost, quality, delivery, environment) to evaluate and assess the impact of any proposed response to changes within/external to the manufacturing system. We will achieve this via the investigation and analysis of a number of real-life manufacturing case studies to identify the level of autonomy that is appropriate in relation to the characteristics of the manufacturing system. We will identify the core Data Information and Knowledge required to create the value model, use data analytic techniques such as clustering/network modelling to automatically analyse the manufacturing system and create a pragmatic and useable step-by-step process to ensure impact from the outputs of the research. In summary, our Vision is to create an automated real-time manufacturing system support toolkit to achieve whole life value from current and future Manufacturing Systems, maximising value through their lifetime i.e. being responsive, sustainable, adaptable and resilient.