Simulation-Based Production Planning Based on Logistic Monitoring and Risk Management Aspects

Abstract

For companies which are part of complex supply chains structures, risk management is gelling increasingly important. This development is the result of actual trends that can be observed, especially in the automotive industry. Companies reduce their work in progress to cut down the cost level of stock; certainly the delivery performance is expected to be excellent. However, the reduction of work in progress frequently results in missing parts or raw material. Moreover, the logistic environment is continuously gelling more turbulent by continuously fluctuating demands and in some cases - like steel for example - increased delivery times for raw materials. Combined with the mentioned reduction of work in progress, achieving the agreed logistic performance is not ensured without a significant additional effort. An approach, developed by IPH, is the use of discrete-event simulation tools for the combination of production planning and control methods with logistic monitoring tools. Based on the periodic analyses of the logistic supply chain performance and the simulation of different alternative scenarios, the relevant planning parameters are identified and adopted to the planning processes. For this feedback loop, logistic risk management aspects need to be considered as well. By identifying potential risky and developing counter measures, the logistic performance is increased tremendously and consequently the performance of the complete supply chain is improved. In this paper, the specific challenges of forging companies that are part of an automotive supply chain are described and lined out. The forging industry is subject to the implementation of new forging technologies for being able to battle the competition from low cost countries. Those new technologies are not only benefcial; they bear a lot of risks for the companies as well. For some technologies, the tool life cannot be forecasted accurately; hence the lot sizes are only estimated roughly and not calculated with a well proofed algorithm.