Interaction based measure of manufacturing systems complexity and supply chain systems vulnerability using information entropy
Authors: Sohyung Cho
Venue:  (2008)

The first primary objective of this dissertation is to develop a framework that can quantitatively measure complexity of manufacturing systems in various configurations, including conjoined and disjoined systems. In this dissertation, an analytical model for manufacturing systems complexity that employs information entropy theory is proposed and verified. The model uses probability distribution of information regarding resource allocations that are described in terms of interactions among resources for part processing and part processing requirements (e.g. part mix ratios and process plans or routings). In the proposed framework, both direct and indirect interactions among resources are modeled using a matrix, called interaction matrix, which accounts for part processing and waiting times. The proposed complexity model identifies a manufacturing system that has evenly distributed interactions among resources as being more complex, because under disruption situation more information is required to identify source of the disruption. In addition, implicit relationships between the system complexity and performance in terms of resource utilizations, waiting time, cycle time and throughput of the system are studied in this dissertation by developing a computer program for simulating general job shop environment. The result from the computational experiments using the program for different problem instances show that increasing manufacturing complexity results in an improvement in system performances. The result of this dissertation can be used by a manufacturing system planner or shop floor manager while making decisions about the selection of parts and part mix ratios. The result can also be used to reduce decision making complexity or increase manufacturing complexity for improving systems performance. The second primary objective of this dissertation is to develop a mathematical model for measuring the vulnerability of the supply chain systems. Global supply chains are exposed to different kinds of disruptions. This has promoted the issue of supply chain resilience higher than ever before in business as well as supporting agendas. In this dissertation, an extension of the proposed measure for manufacturing system complexity is used to measure the vulnerability of the supply chain systems using information entropy theory and influence matrix. We define the vulnerability of supply chain systems based on required information that describes the system in terms of topology and interrelationship among components. In the proposed framework, both direct and indirect interactions among resources are modeled using a matrix, called interaction matrix, which are characterized by the form of influences. The proposed vulnerability model identifies a supply chain system that has evenly distributed interactions among components as being more vulnerable, because under a disruption situation more information is required to identify the source of the disruption. The proposed framework for vulnerability modeling in this dissertation focus on disruptive events such as natural disasters, terrorist attacks, or industrial disputes, rather than deviations such as variations in demand, procurement and transportation.