SBIR Phase I: Dynamic Risk-Based Planning and Scheduling

This Small Business Innovation Research (SBIR) Phase I research project will determine the feasibility of new software tools that utilize innovative principles to provide supply chain managers with more effective planning and execution tools than are currently available. These new tools emanating form this research will offer a comprehensive methodology that can interface to an existing ERP/SCM system (for data only) and then, utilizing newly developed stochastic models of the supply chain, provide a supply chain planner with a small number of key operational measures and controls. Additionally, these new models address critical mistakes that have been perpetuated in existing SCM offerings. This new tool, Dynamic Risk-based Planning and Scheduling (DRPS) anticipates to simplify the choices that can made at the planning level such as adding or reducing capacity on various days, or pushing out due dates). Consequently, these can be enumerated generating a solution that is both robust and optimal. If successful, the proposed approach will have solved the optimal scheduling problem that has eluded researchers for more than 40 years. The DRSP provides a Product Flow Dashboard that enables company planners to see critical variables in real time as well as provide useful and robust controls. This innovation has the potential to revolutionize the industry by providing a meaningful direct link between planning and execution. Moreover, it provides a means to use a few key measures (total inventory, probability of missed shipments) to predict future performance and as well as using a few key controls (add capacity, work ahead, remove capacity). Such a system would have several distinct advantages in that it: 1) it is dynamic in that it is self correcting to random fluctuations in both demand and supply and signals when a significant change occurs that requires attention; 2) Is more accurate than models used today requiring less inventory for the same service; 3) The system explicitly considers risk and the stochastic nature of the supply chain; 4) Provides planning that is intuitive without burdening the planner with unnecessary details; and 5) Extends the range of manufacturing environments that can benefit from pull production.