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Is next generation AI-powered digital twin technology set to unleash a new era of supply chain productivity?

Jonathan Barrett
CEO, Kallikor

The UK faces a critical mission to improve its supply chain productivity. Recent reports from the UK Parliament found the nation’s economic growth has been sluggish since 2008, with UK labour productivity slowing from an annual average of 1.9% between 1993 and 2008 to 0.4% between 2008 and 2023.

The culprits? Low investment and policy uncertainty has been a significant hindrance. With global competitors accelerating their adoption of digital technologies, robotics and automation to boost productivity, the UK is at risk of falling behind. @The Economist’s observation that digital twins are on the rise in the motor industry caught my eye – could a new generation of simulation technology play an important role in transforming productivity within the supply chain?

For many businesses, transforming supply chain performance requires significant capital outlay often in new technologies such as robotics and automation, alongside considerable investment in process, people and culture change. This requires the Board to be confident in the projected return on those investments, convinced that the transformed operation will be able to support the strategic future business ambition, and secure in the ability of the organisation to implement and operate the new technology to deliver on that promise. Confidence in future outcomes is key to unlocking the investment needed to close the productivity gap, and this is where the next generation of AI-powered synthetic environments could play a part.

The productivity challenge: Treading automation water

According to the Office for National Statistics’ productivity flash, the value of each worker’s hour to the UK economy is, on average, lower than in similar countries, such as France and Germany. And although overall productivity is improving in the UK, it is rising slowly. The UK is far from alone in this. The issue is central to Mario Draghi’s recent report on competitiveness for the EU Commission: “If the EU were to maintain its average productivity growth rate since 2015, it would only be enough to keep GDP constant until 2050”. One of the main reasons for stunted productivity so far is a lack of investment in innovative technologies that can help the workforce to work smarter, not harder.

The value of each worker’s hour to the UK economy is, on average, lower than in similar countries, such as France and Germany
Office for National Statistics

Automation has long promised to increase efficiency, reduce costs, and improve productivity. However, despite fast-growing UK tech businesses securing an impressive £24 billion in funding in 2022, surpassing both France and Germany, the UK still lags behind its competitors in implementing automation and other advanced technologies in the supply chain. For example, a 2022 report from the International Federation of Robotics found that new robot installations in the UK were down 7% year-on-year, giving the nation an average manufacturing robot density of 111 robots for every 10,000 employees. This is in contrast to Germany with 415 robots per 10,000 workers. What can the UK expect as a result? Stagnant productivity growth year after year. 

While there is a clear need and enthusiasm for automation to power productivity and advance competitiveness, the complexity and pace of change in the business environment creates uncertainty and holds back investment. The traditional supply chain objectives of cost, quality and speed have now been joined by sustainability and resilience. Throw into the mix a generational labour skills/cost challenge, unprecedented geopolitical uncertainty, rapidly changing customer demand and competitive pressure – the result is a complex and rapidly changing landscape for boards to navigate. Supply chain strategy is very much a CEO issue – as David Garfield wrote in HBR recently, the risk cannot be left to procurement and operations teams to deal with alone.

Layer in what for some is a history of automation false starts and unrealised value, and it should be no surprise that many business leaders feel at a crossroads: play Defence, minimise risk, but allow productivity to continue to stagnate, or – like Amazon’s recently announced supply chain service – seize the moment and go on the Offence by accelerating AI, robotics and automation deployment to get ahead of the competition. In truth however, the strategy for competitive advantage and long-term success must be to play both simultaneously

Could a new breed of simulation technology powered by AI deliver more confidence to robotics and automation investment decisions?

The concept of the digital twin is not new, especially for those in engineering-led disciplines, or those of us that were involved in the first wave of applying the concept into business early in the last decade. Since then simulation has been applied with success in specialist areas of engineering, and certain process and tactical planning related areas of the enterprise – as outlined by the Economist. Digital twins have most often found success at these tactical levels and in technical environments where they answer a single important question really well, within a contained environment where the relevant context and required datasets don’t change a great deal over time. 

Where digital twins have gained less traction and success has been in answering a wider array of business questions at the ever-evolving strategic level of the enterprise. The same approaches and technologies that work to create single use digital twins at the tactical and technical level have proven unsuited to representing an up to date picture at the strategic level – often resulting in time consuming implementations and eventual solutions that are unwieldy to adapt as the situation and relevant factors continually evolve and new questions from business users arise.

That said, the technology landscape in enterprise and wider society has developed considerably since that initial flush of digital twin promise. Have things changed that may allow them to deliver for business users at the strategic level now?

What has changed to mean that now may be the right time for widespread adoption of digital twins at the strategic level in enterprise?

An approach where AI enhances digital-twin capabilities is gaining traction, providing business users with an up to date synthetic environment to answer strategic questions.

Advancements in the practical application of AI mean that many of the previous challenges can now be overcome, unlocking new opportunities for AI-powered synthetic environments at the strategic level in the enterprise.

  • Increased explainability and confidence in the results
    Widespread adoption also comes with concerns about transparency and hallucination. AI does not need to be ‘black box’ with little insight into how it is deriving its answers. New and novel approaches to causal reasoning now allow decision-makers to understand the logic behind AI’s conclusions.
  • The ability for AI to foresee and consider unprecedented future events
    – combining simulation with AI means that the simulation can generate new training data covering rare and unprecedented future events to inform AI , meaning the AI does not need to guess about situations that are unlike anything it has seen in historic training data.
  • Rapid solution creation and ongoing update
    – by applying AI tools it is now possible to characterise business processes and environment at the strategic level into one or more models. Moreover, on an ongoing basis we can now identify contextual changes in the operating environment and update solutions with updated or new datasets far more rapidly than ever before, all while retaining control. This makes the initial time to value and ongoing costs considerably more attractive.
  • Improved adoption and enabling flexibility to answer freeform business questions
    AI technologies are becoming well known for making advanced capability easily accessible, with large language models such as ChatGPT giving huge power to large numbers of people, paving the way for widespread acceptance in the enterprise.

Strategic supply chain transformation and planning is the place to start

Flexibility, cost, resilience and sustainability are critical issues that have brought supply chain performance to the boardroom for many businesses. The supply chain, and within that often warehousing and distribution centres, are the fulcrum where supply meets customer demand. The strategic embrace of robotics, automation and AI beyond trials and point solutions has the potential to take supply chain performance to the next level. Confidence in the future is key to making the required investment and accelerating the value of these advanced technologies. The time is right and the technology is now available for AI-powered synthetic environments to play a part in unlocking these strategic decisions.

Leap ahead with Kallikor: https://kallikor.ai/