i-PICS: Integration of Advance Demand Information with Pull-Type Production Control Systems

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

The increase in product variety and sharing of advanced demand information (ADI) across supply chains are re-defining the fundamental tradeoffs in factory operations. To obtain quantum improvements in factory efficiencies, the information available across supply chains must be integrated with the production inventory control systems (PICS) that determine the production schedules and inventory quantities for the different products within the factory. These integrated PICS (i-PICS) can have a significant impact on factory efficiency (production flow times, capacity utilizations and delivery performance). This research is to develop new queuing network models for performance evaluation of PICS in manufacturing systems with product variety and ADI. These models will be used to (i) quantify the operational improvements obtained from the integration of ADI with pull-type PICS, (ii) investigate the impact of quality of ADI on potential benefits of integrated PICS, and (iii) obtain insights into information sharing strategies (collaboration versus competition) in supply chains in a game theoretic setting. The research will lead to new scalable and computationally efficient approaches to analyze an important class of closed queuing networks with synchronization stations. The methodology is based on node decomposition and traffic process approximations that do not require the restrictive assumptions on probabilistic distributions, thereby enabling their use to analyze realistic models of manufacturing systems. This research will form the basis of the multiple doctoral theses and will provide undergraduate student research opportunities at Rensselaer Polytechnic Institute. A new graduate/undergraduate course will be designed and additional teaching modules will be developed to enhance existing curriculum. This research will also provide team-based project experience to students at local industry. Industry data and implementations will validate this research and the insights will be disseminated in collaboration with the Center for Economic Growth in New York and its industry affiliates.