Intelligent networking of forecast, planning and optimization Designing sustainable transport chains
Original Language Title: Intelligente Vernetzung von Prognose, Planung und Optimierung zur Gestaltung nachhaltiger Transportketten
The prevailing freight transportation sector underlies wide variations between the planned order of the constituent and the actual use of transport capacity. This continuing trend of highly volatizing goods is attributed to cyclical economic fluctuations. In addition, freight forwarders increasingly encounter low planning security, which is insufficiently incorporated by existing IT-systems. The consequence is short-term decision-making and Controlling alongside unforeseen and unpredicted demand fluctuations and causes ecological and economic inefficiencies.
The overall target is the specification and expansion of the planning horizon by the development of a system-integrated method within the framework of this research project: The IPPO-Demonstrator, which connects forecasts, planning and optimization within one model. The core elements of this demonstrator are regarded to be:
- Developed forecasting logic that is based on the manufacturer`s forecasts and actors of influence (e.g. seasonality, economic growth, plant capacity planning, contracts, strategies etc.) in order to determine higher planning security and more specific and finer granularity of planning.
- Planning of transportation by connecting generated transportation demand with the capacity available in order to predict discrepancies at an early stage
- Sustainable optimization of transportation planning by using adequate alternatives of action in order to support sustainable transportation modes (rail, waterway) and thereby ecological and economic potential.
The purpose of the IPPO-Demonstrator is to improve capacity planning by using predetermined forecast parameters, to increase the use of sustainable and multimodal transport chains with larger capacities (train or vessels) as well as to reduce energy and resource consumption. The forecast-based planning algorithm is projected for a case study in the automotive industry but can be applied on several areas within the Transportation logistic.