InterAct Hub

Dr. Robert Stewart (University of Strathclyde)

A host of publications in the grey and academic literatures herald the emergence of smart manufacturing and the digital transformations brought on by new waves of technological innovation. In manufacturing the Fourth Industrial Revolution (or 4IR) is the overarching socio-technical term, sometimes used to describe the changes arising from these emerging digital technologies and practices.

Smart manufacturing combines the automation of tasks, the digitisation of processes and co-ordinating platforms of digital networks alongside technologies like artificial intelligence (AI). Like the previous three Industrial Revolutions that used steam to mechanise production, electricity to power mass production, and electronic advances in information technology to automate production, smart manufacturing represents a systematic step change. This is because of the scale and complexity of integrating various advanced digital technologies (ADTs) and digitised data in manufacturing production and distribution systems.

Some of the most cited technologies include the Internet of Things (IoT), cyber-physical systems, big data analytics, cloud (and quantum) computing, additive manufacturing, 3-D printing, advanced robotics, augmented reality and blockchain technology: a landscape that is emerging and evolving through innovation, synergy and combination.

For manufacturers there are strong digitalisation incentives: longer product lifecycles, increased operational visibility, reduced costs, optimised production cycles and direct relationships with consumers. Digital servitization is the process of providing bundles of products, services and tools (e.g. big data) for greater customer value. Fuelled by cloud computing, IoT allows the multiple interconnections of (unique) ADTs, while sensors and rendering devices provide a digital intelligence to generate real-time data and actions. These along with big data analytics and platforming enable a shift to Business-to-Customer (B2C) models: greater market reach, faster product to market cycles, more brand control, opportunities to give added-value digital intelligence to products and the collection of customer usage data to inform design and product improvements interfaced with e-commerce platforming.

Digitalisation is depicted as an (yet) uncharted blue-sky horizon of technological development whose haze blurs the lines between the physical, digital and biological worlds: a fusion of technologies that will shape manufacturing sectors, workforces, jobs (their content, design, regulation and protection) and business models in the coming years. For manufacturers, the future script is one of opportunity and uncertainty: continuing and rapid product innovation as companies vie to integrate and develop their digital toy boxes of tools, products and markets.

Smart manufacturers will have to blend ADTs with the digital skills and unique attributes of people to lever their productivity gains. Typical of publications in the grey literatures, Deloitte offer an idealised vision of the types of high-skill, high-tech white-collar graduate-level jobs and roles that smart manufacturing will generate in the very near future. These stand in stark contrast to the often-stereotypical images of older, dirty, risky, and repetitive blue-collar factory jobs that can sometimes haunt images of the sector.

Deloitte outline six prototype jobs and three of these are described as examples: the Digital Twin Engineer who can ‘virtually’ look inside physical assets, systems and structures to optimise design, performance, predict maintenance and improve customer experience; the Robot Teaming Co-ordinator who trains people and bots to work collaboratively; and a popular example often cited in the literature, the Smart Factory Manager, with responsibilities for production, quality control, inventory, connectivity and analytics. Common to all these roles is the use of sophisticated co-ordinated ADT systems, AI and the support of co-bot teams working alongside people at different levels of production and distribution.

Against the backdrop of this digital toolbox meze there is no shortage of some very familiar challenges for work and employment. Automation is strongly associated with job polarisation and the decline in the share of mid-pay mid-skill jobs observed throughout leading western economies in recent decades. Digital production processes and platforming are usually negatively linked (especially in the retail and hospitality sectors) with job (and role) fragmentation and an increase in (the sometimes precarious) work that differs from the standard relationship of permanent, full-time and secure employment (i.e. ‘gig economies’).

Some of these issues may seem very distant from Deloitte’s clean, slick and precise white-collar smart manufacturing settings outlined above, in job design, smart manufacturers will still have to be savvy enough to address the issue of how to attract talent and create meaningful work: higher pay, access to employee benefits and work that is interesting. There are still several broad workforce challenges to be faced: work architecture, workforce skills and (re)training (attracting and retaining digital talent), and what this means for human resources, organisational and job/task design (and processes), strategic organisational leadership, capabilities and cultures. Working in smart manufacturing will also have the potential to raise concerns about greater data-driven employee monitoring and exposure to physical and psychosocial risks arising from constant connectivity and the overlap of work and non-work time.

A strong theme in the literature are the demands for high-skill smart manufacturing workforces with digital and ‘soft’ skills. A recent World Economic Forum study highlighted that the top 10 skills for the next decade include what we think of as essential human skills – critical thinking, creativity and people management – alongside technical digital skills. ‘Essential’ human skills are the things that cannot be automated (yet). This puts the emphasis on retraining existing manufacturing workforces, building ‘soft’ skilled workforce capacities and more widely, being able to attract and retain ‘digital people’ talent. To support the latter, there may be markedly better opportunities created in high-tech smart manufacturing settings for equality, diversity and inclusion initiatives around working time flexibility and the labour market integration of specific groups, such as women and those with more domestic care responsibilities and health issues. Also, as products, services, workforces and supply chains become increasingly connected, this results in new chains of value creation increasing the potential for more employee-led innovations, and distributed leadership to create more opportunities for change and building capacities for improvements to products and customer services.

A major workforce challenge in transitions to smart manufacturing will be fears about job substitution and the destruction of jobs (automageddon) by the rise of the bots (and their new co-bot allies). Automation has always been the harbinger of apprehension about its consequences for jobs and aggregate levels of employment: the jobs that people can do, the tasks they will perform and the new key skills that workers will have to acquire. This is a popular image of automation even though technology also creates many new jobs.

Historically, labour markets have always accommodated and absorbed automation. Technology complements workers skills and increases their task performance, while eliminating those manual and repetitive tasks that are easier to automate. Although technology has historically had little adverse impact on aggregate employment, this still masks some underlying processes of job destruction and creation in particular sectors and groups of workers. Initially, automation complemented low-skill work. Later, it substituted for it while complementing middle and high-skill work. Today it complements high-skill work but often substitutes for middle-skill work. It is reasonable to expect that these technology-related effects in smart manufacturing will rhythmically evolve, emerge and once again change in the future.

A significant limitation of current debates in automation and smart manufacturing is that it is very difficult to predict future levels of employment given the relative recency and quality of existing studies that are trying to map events that are still emerging in ‘real time’. There is still a great deal of speculation and uncertainty about what will happen as smart manufacturing sectors emerge, grow and develop. The Babbage Repot makes it clear that smart manufacturing is largely concentrated in larger sized companies in ten developed economies who tend to be those who make the greater share of new technology patents, investments, adoptions and implementations. Equally important but relatively understudied, are questions about how smart manufacturers influence the design of work and jobs, and which tasks/skills can be substituted, complemented or augmented. The mechanisms by which smart manufacturing technology complements/ arguments jobs and tasks are not well understood, nor the strategic decisions that companies’ make around technology investments. In this sense, there are interesting issues about workforce engagement and dialogue: and how smart manufacturers anticipate, plan and strategically balance decisions about technological investment alongside workforce issues.

Professor Vania Sena (University of Sheffield)

The Future of the Economy is one of the three substantive research streams of the InterAct programme.

Digitalisation is central to the new generation of manufacturing systems and processes. The diffusion of new digital technologies creates distinctive challenges to firms and organizations, in terms of the adoption of new technologies as well as the management of transition.

Digitalisation may disrupt existing firms while accelerating new venture creation and change the existing industrial structure. Eventually, this will affect the structure of the wider economy and it will be shaped according to the needs and benefits of digitilsation across industry.

It is the aim of our Future of the Economy workgroup to investigate these impacts and generate actionable insights that will allow businesses to prepare for major changes to the way the manufacturing sector operates.

Our focus is distributed between three key areas:

Digitalisation, diffusion, and high growth

Crucially, digital technologies can help reconfigure capabilities within companies and across industries. Eventually this may alter all aspects of the UK’s industrial fabric by reshaping the drivers of high growth. However, so far, there is limited understanding of how this will happen. There is an expectation that the digitalisation will alter the structure of the knowledge networks of the UK economy, and this will change the nature of linkages among sectors. If so, what new linkages will emerge and how can we map them? How does knowledge diffuse across industries? What are the new channels of diffusion?

Digitalisation, industrial structure, and social mobility

We know digital technologies may lead to the emergence of ‘superstar’ manufacturing firms. However, this not the only consequence of digital transformation. Crucially, the combination of new technologies and trade openness has led to rising job polarisation, wage dispersion, and regional divergence. It is argued that new digital technologies may have a similar negative impact, in particular for women and marginalised groups.

What is the impact of digitalisation on social mobility and how can the existing trends be offset? Importantly, what institutions need to be in place to maximise social mobility and inclusivity? In this respect education and training are key factors that can offset these trends.

Evidence from many countries suggests that dedicated centres of innovation linking industry stakeholders can help develop new programmes for re-skilling. The UK already has some of these types of facilities which could be better exploited to galvanise industry-wide change. This is essential so that digitalisation may lead to local technological diversification. As such, if marshalled within a broader coordinated national policy response involving all stakeholders, digital technologies have the potential to help society make significant progress on societal challenges such as inclusivity as well as social mobility. 

Digitalisation, the ‘Levelling Up’ agenda, and trade

Digital transformation creates tensions on the shape and the geography of the economy. Although often overlooked, these changes can be highly disruptive, as new generations of digital technologies replace and reshape industries, altering the geography of production as a result. Enhanced manufacturing performance can potentially help the ‘Levelling Up’ of many regions and indeed UK evidence already suggests that there are long-term local productivity advantages to the early adoption of digital technologies.

The fact that the levels of patenting activity, and particularly ICT-patenting activity, of the UK’s major manufacturing regions tends to be no more than ‘moderate’ by European standards further emphasises how important the rapid take-up of these digitalisation technologies is for the ‘Levelling Up’ Challenge.

SMEs are often at a financial as well as a technical disadvantage to deploy new capital-intensive technologies; at the same time, UK manufacturing is also more exposed to Brexit than other sectors, a trade shock which will impose significant costs to UK industry over those in other countries.

We want to address these two research questions:

• How can the diffusion of digital technologies in UK manufacturing contribute to the ‘Levelling Up’ agenda?
• How will the UK’s trade structures change in response to the manufacturing sector’s uptake of digital technologies?

The projects under the Future of the Economy workstream will examine the likely effects of digitalisation in manufacturing the context of the wider inter-industry, inter-regional, and international linkages evident in UK manufacturing. This will allow us to measure the impacts of digital transformations at the sectoral and regional scale and to consider the impacts of different digital-adoption scenarios across industries.

Professor Colin Lindsay (University of Strathclyde)

The COVID-19 crisis and its impacts have required organisations to demonstrate dynamism in innovating their business models, developing new product and service offers, and transforming methods of work organisation. Business stakeholders and researchers are accordingly interested in tools that can help them to understand why organisations have been more, or less able to demonstrate agility and innovation in the face of the crisis.

It seems to me that there is considerable value in revisiting the already highly influential ‘dynamic capabilities’ framework developed by US scholar David Teece and colleagues from the mid-1990s onwards. For David Teece:

“Dynamic capabilities are the firm’s ability to integrate, build and reconfigure internal and external resources to address and shape rapidly changing business environments.”

For Teece and colleagues, dynamic capabilities can be understood in terms of organisations’ capacity to sense and articulate opportunities and threats, seize opportunities, and dynamically reconfigure tangible and intangible assets to transform the organisation and its operations.

Sensing involves searching and exploring across networks, technologies and markets, identifying latent or emerging demand, spotting changes in sector and market structures and business models, and processing threats from competitors and insights from supply chain partners and customers.

Seizing involves responding through the development of new products or services and/or innovating the business model or ways of working (facilitated by investments in new technologies, systems, and capabilities). Reconfiguring and transforming requires the modification and re-alignment of product or service offerings in line with the organisation’s (renewed) systems, processes, ways of working and capabilities.

Examining dynamic capabilities is a helpful approach to understanding organisations’ handling of COVID-19 challenges, given the theory’s focus on responsiveness to change and crisis. Indeed, several reports have used the framework to assess the agility demonstrated by some organisations in pivoting and innovating in response to COVID-19. For example, Gloria Puliga and Linda Ponta’s recent research with Italian firms notes the importance of external networking to dynamic capabilities in organisations responding effectively to COVID-19 challenges.

However, perhaps a key weakness in much of the existing dynamic capabilities research is its relative failure to factor in the role of people management (or indeed just people at different levels and roles within organisations) in promoting dynamism and innovation. This isn’t to say that HRM and people management is completely absent from dynamic capabilities research. The original research by leading dynamic capabilities scholars makes some (albeit limited) reference to HR investments to promote a flexible workforce that is willing to buy-in to transformative change processes.

Recent work by Paula Apascaritei and Marta Elvira in Human Resource Management Review journal has been helpful in scoping out what ‘HRM dynamic capabilities’ might look like, focusing on:

• Knowledge building capabilities (strategies to support workforce diversity to contribute to innovation; the valuing of relational ties brought to the organisation that can lead to knowledge, talent and/or customer acquisition).
• Social integration capabilities (enabling relationship-building with customers and partners; building social capital within and across teams).
• Reconfiguration-enhancing capabilities (a focus on upskilling employees for ‘functional flexibility’; but also practices helping employees to manage change and mitigate stressors associated with organisational transformation).   

There remains a need for further research on how these sorts of capabilities are made real by people managers in different organisational contexts. We also think that there may be important lessons for dynamic capabilities researchers that we can draw from the broader evidence base on how targeted HRM strategies can support ‘workplace innovation’.

Our previous research in this space warns against pursuing a single, de-contextualised best practice, but identifies a number of recurring themes around HR practices that have supported employees to innovate in different organisational contexts: job design that values high levels of autonomy; consistent and good quality feedback; strong communications and peer support networks within teams; and sufficiently challenging job content that nonetheless guards against employees becoming burnt out.

Manufacturing can be a challenging context within which to redesign work to maximise autonomy and innovation at the individual-level. However, even here there are examples where thinking creatively about people management has empowered employees at all levels to participate in innovation and drive change.

What more can we learn about the combination of workplace and people management practices that work well in tapping the innovative potential of employees to drive dynamic capabilities? What challenges do manufacturing employers face in aligning people management with their priorities for sensing and seizing opportunities and transforming their operations to build back better post-COVID-19?

These are some of the issues that our Strathclyde Business School InterAct team will be exploring with UK manufacturers in the coming months. If you are a manufacturing sector stakeholder or an employer interested in maximising the innovative potential of employees at all levels, we would be excited to hear your views and to discuss our research.