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Abstract
This thesis presents a study of disturbance management in production-inventory and supply chain systems. The study focuses on generating and analysing the recovery decision after the occurrence of a disturbance on a real-time basis. In this thesis, the developed approach was divided into several steps. At first, a plan was developed without considering any disturbance. Then a mathematical model was formulated to obtain a revised plan after the occurrence of a disturbance in the system. An efficient heuristic approach was proposed for solving the mathematical model in order to obtain the recovery plan. The mathematical model and heuristic approach were also extended to consider multiple disturbances, one after another as a series, on a real-time basis. Finally, the experimentation was conducted and the heuristic results were compared with other standard solution approaches to judge and validate the results. The framework was applied for managing production disruption: (1) in a single-stage imperfect production-inventory system, (2) in a two-stage production-inventory system, (3) in a three-stage mixed production-inventory system, and (4) in a supply chain network with multiple entities in each stage. The framework was also applied to two other models: (1) for managing demand fluctuation in a supplier-retailer coordinated system, and (2) for managing supply disruption in a three-tier supply chain system. In this thesis, three different types of disturbances were explored, namely (1) production disruption, (2) raw material supply disruption, and (3) demand fluctuation.
In real-life situations, multiple disturbances, as a series, can happen at any time and at any stage of the system. This thesis considered multiple disturbances, one after another in a series, that may or may not affect the plans revised after previous disturbances. If a new disturbance occurs during the recovery time window of another, a new revised plan which considers the effects of both disturbances must be derived. Accordingly, as this is a continuous process, an extended mathematical model and heuristic approach was developed to deal with a series of disturbances on a real-time basis, by incorporating a modified version of those developed for a single disturbance.
The results of the experimental analysis showed that the optimal recovery plan is highly dependent on the shortage cost parameters such as, back orders and lost sales costs, and to the disturbance duration. For a certain range of disturbance duration and cost values, it was found that back orders were more attractive, and in such cases, back orders cost was less than the lost sales cost. On the other hand, when back orders cost were more than the lost sales cost, the solution had lost sales in their recovery plan.
In the final work of the thesis, a simulation model was developed to analyse the effects of different types of randomly generated disturbance events that were not known in advance. The simulation model considered all three types of disturbances, namely (1) production disruption, (2) raw material supply disruption, and (3) demand fluctuation. A good number of random experiments were conducted to judge the simulation model, and to make the simulation model closer to real-world processes.
The developed approaches were tested by solving a significant number of randomly generated test problems. The sensitivity analysis was carried out for the model parameters. Two of the models, developed in two-stage and three-stage production-inventory systems, were also tested using real-life cases from a pharmaceutical company. It was found that the developed approaches were more beneficial than the company’s existing practice.