What are the best ways to use big data in automotive supply chain management?

Indeed, big data can support in increasing forecast accuracy, better demand and supply planing, especially when combined with Machine Learning. One of the challenges though is that in reality the necessary data resides in various data silos and is not harmonized. Therefore, the first step is to identify the data silos, bring data together (usually in a cloud-based data lake) and increase data quality to a point where in can be used for a data science project. This effort should not be underestimated and usually already generates costs without a direct, measurable benefit, which is very often - unfortunately - the reason for companies not to invest into the phase.

My team and I specialize in Supply Chain Internet of Things. IoT sensors on freight create quality data stream captured by cloud platform. Abundant, real-time, versatile, quality data helps better planning and forecasting.

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Over the last years the number of unexpected, unforeseeable events increased, also together with unique events. I think that in a BANI/VUCA environment we’ve faced and that will develop even further, Trying to be able to forecast the really ‘unforecastable’ , is not for me the way. I believe big data, machine learning and artificial intelligence can support automotive supply chain to better react to those events, supporting business processes. So for me the way is to use those AI technologies to identify and measure the impact of events and support the decisions in the supply chain, in a much quicker and automated mode.

The use of Big Data in supplier management is becoming more and more relevant in industries with an OEM-Tier structure, such as Automotive and Aerospace & Defense. The reason is that in n-Tier supply chains end2end visibility/ transparency is usually very low. At thyssenkrupp Aerospace we are currently working on building Digital Twin models of the supply chain as we have data points across various tiers. Obviously, the amount of data generated and collected is huge. Therefore, big data technologies are required. These Digital Twin models will not only help us to steer our own supplier network better and in a more timely manner, but also help us to steer & optimize the supply chain ecosystems of our customers via Global Control Towers.

Timely communication with suppliers is critical for supplier management and ultimately for healthy supplier relationships. Smartly used IoT sensors automatically inform Supply Chain Managers when critical shipments leave suppliers' shipping dock or pass important milestone on its journey. These notifications are captured and distributed by cloud platform. All interested parties, including suppliers themselves are updated at the same time and with same version of truth.

Statistical models have been used in Operations Research for decades. However, the technological possibilities to collect data from various sources have massively increased over the last years and continue to get better, especially when it comes to real-time monitoring. This vast amount of data offers the possibility for more in-depth analytics and the introduction of AI/ ML technology to move from descriptive to predictive analytics. Predictive maintenance concepts have already been in place since a couple of years, but many more use cases can ben envisioned, also the prediction of energy consumption or the creation of CO2 emissions. Sustainability use cases are growing in importance rapidly.

Global supply networks are fragmented and opaque with little visibility across. Supply Chain managers are forced to wait for critical items to arrive before being received into materials system and available for Operations. IoT sensors attached to goods and materials critical for production schedules allow allocation of inventory on the move form virtual "in-transit" warehouse.

IoT truly shines in Logistics Management. With sensors on shipments and on logistics assets we are able to see goods in transit in real-time. We are empowered to resolve issues in timely manner if our shipment is delayed. We take informed, decisive action in case of environmental excursions (for sensitive items) or mechanical shock encountered by our goods in transit. In a pre-COVID study on supply chain ecosystems PwC arrive to conclusion that more than 50% of supply chain cost savings are delivered by smart logistics.

Supply Chain IoT contributes to customer satisfaction. With real-time location and conditions information at our fingertips we keep our customers in the know regarding ETA of the shipments they expect. We proactively notify our customers about potential issues and engage in resolution in timely manner. Our customers love and appreciate the care and attention enabled by Supply Chain IoT systems.