Decision strategies and design of agent interactions in hierarchical manufacturing systems

Decisions in manufacturing planning environments involve various interdependent hierarchical levels, ranging from shop-floor operations over production planning to enterprise coordination. Distributed decision making across these hierarchies reduces the complexity compared to an otherwise monolithic planning approach. We develop a stochastic model with two decision makers (agents) and analyze their strategic interaction using game theory. The mutual influence between agents depends on organizational parameters, which, in a first case, agents themselves can regulate, and, in a second case, an organizational designer can control. The model provides managerial insights on how to achieve cooperation among self-interested agents and how decisions and preferences of agents can be aligned with the goals of the organization. Moreover, we investigate the degree and fidelity of information necessary for optimal decision making. The model is applied to a manufacturing enterprise context.