The Record - Issue 18: Autumn 2020

106 www. t e c h n o l o g y r e c o r d . c om F E ATUR E Intelligent production As supply chains become ever more complex, automotive manufacturers are making use of new cloud-based AI and mixed reality solutions for optimising their operations BY A L E X SM I TH A utomotive manufacturers are known for their commitment to continuous improve- ment, and success at achieving it. However, they must navigate the challenges caused by a complex web of historically disconnected systems and processes. While now semi-connected, there is not necessarily the level of transparency and immediacy that can really drive next-generation optimisation and performance. One of the most valuable tools to help automo- tive companies face these challenges is the huge amount of valuable data they now have access to. Every machine, system, process and action creates data, all of which can be used to produce valuable insights if effectively analysed. “The challenge is first to collect the data and then storing it coherently in a harmonised data- base, which can then be used to truly get into advanced data analytics,” says Darren Coil, direc- tor of business strategy for automotive supply chain, connected factory, and IoT automotive industries at Microsoft. “Once you do this, there is the potential to find some real gems of informa- tion. This expands exponentially when you gain access to similar data from suppliers feeding the inbound and outbound operations of a company. For example, if an original equipment manufac- turer (OEM) knew the current work-in-progress, finished goods inventory and raw inventory positions, coupled with the suppliers capabilities, capacities and order commits, it could rapidly adjust its build plans to meet what is continually evolving on the customer demand side.” Another important tool is artificial intelligence (AI), which can help optimise supply chains in a wide variety of ways. For example, AI enables the prediction of future demand for products by learning from patterns in previous demand and