Probabilistic Supply Chain Monitoring

Ongoing trends in the automotive industry toward global supply chains, lean logistics and build-to-order have led to new supply risks. Short order-to-delivery times and low safety stock levels have increased the vulnerability of Original Equipment Manufacturers (OEM) to small delivery delays of components. At the same time, increasing complexity of supply chains with rising numbers of suppliers and long transportation routes raises the frequency of such delays. These developments have created a demand for integrated supply chain monitoring methods. This book introduces a probabilistic concept to monitor supply chain event data. By analyzing historic data regarding the time each component passes specific points within the supply chain, a Bayes network is derived, describing the stochastic behavior of the lead times in each section. This network is applied to a real-time calculation of the availability probability of all necessary components for the assembly at the OEM at any given point in time. This information assists both in monitoring the supply chain for potential disruptions and in rescheduling the assembly process in reaction to predicted delays.

Autorentext

Maximilian Warhanek has completed his studies of mechanical engineering specialising on production technology and logistics. He is continuing his research as assistant at the Institute of Machine Tools and Manufacturing at ETH Zurich.