Collaborative Research: A Design Methodology for Operational Flexibility

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This grant provides funding for the creation of a methodology for modeling and mathematical computation to quantify operational flexibility in service and manufacturing supply chains. It emphasizes the flexibility attained when sources of production (e.g., workers, machines, plants, etc.) are multi-functional. Examples of this include workers with multiple skills, machines with flexible or reconfigurable capabilities, and supply chains with flexible sourcing and distribution options. By having multi-functional servers (sources), capacity can be dynamically allocated in response to the realization of uncertain demand. Taking queueing networks as models of the production systems, approximate deterministic metrics of flexibility are used to accurately predict the best design from various alternatives. Mathematical programming, queueing theory, Markov Decision Processes, and discrete event systems simulation are employed. The research illuminates the connections between general queueing networks with flexible servers and the properties found in approximate, deterministic network models.If successful, the results of this research will lead to improvements in the design of service and manufacturing operations and supply chains that deliver high-performance efficiencies and service levels despite uncertain changes in technology, heightened consumer expectations, decreased vertical integration of the supply chain, and intensified global competition. The primary objectives of this research are to (1) develop effective methods for quantifying flexibility, (2) develop principles and methodology to better select a roster of flexible workers for a shift (or work period) that results in a robustly productive team, and (3) invent a methodology to assist in the design of robust multi-echelon supply chains. The anticipated results of this research are: (1) managerial insights that deepen the understanding of how to design production systems and worker/staffing schedules that are truly flexible and robust and (2) tractable analytical/numerical models and methods for the analysis and design of multi-echelon supply chains. Upon implementation, the results will help ordinary people through increased quality of service in everyday transactions, workers through increased career development and training, and businesses through improved management practices and competitive operations.