Innovation: Artificial intelligence algorithms and discrete event simulation models can be efficiently used to find optimal inventory policies as well as analyse inventory policy changes without physically altering the supply chain. The models developed are innovative and novel in the sense that by using them, manufacturing companies do not need to make costly changes to their supply chains because the intelligent models can suggest optimal changes which can be implemented on computer and their effects known, before risky and costly physical implementation and supply chain alteration.

Sector/Industry Application: Production Engineering, Manufacturing Engineering, Industrial Engineering, Oil and Gas Distribution.

Description: The aim of my research is to create a multi-echelon multi-product hybrid intelligent discrete event simulation modelling framework consisting of machine learning and computer simulation models, for use in obtaining the optimal inventory policy and analysing the effects of inventory policy changes on manufacturing supply chains under study. Supply chains are predominantly responsible for the efficient delivery of products and services from the point of origin to the point of consumption. The supply chain term refers to a network that links a producer, its suppliers and consumers, and facilitates the production and distribution of specific products to the consumer. Many manufacturing companies experience limited capacity such as inadequate warehouse space or inadequate number of delivery trucks within the supply chain and this problem adversely affects the supply chain performance of these companies, especially when the companies do not manage inventory properly across their supply chains. Supply chain optimisation is a major aspect of manufacturing company management around the world. My research combines the use of artificial intelligence algorithms with discrete event simulation modelling to manage inventory within and between warehouses across the supply chain. Therefore, my research will provide a generic hybrid discrete event simulation modelling framework that can serve as an inventory management tool for optimising limited capacity supply chains in the manufacturing industry, thereby helping the companies improve profitability, improve service levels and reduce costs. Moreover, manufacturing companies require a cost effective means of studying and evaluating the outcome of making changes to their supply chains, without incurring the risk and cost of physically altering the supply chain. Therefore, for visualising the effects of inventory policy changes, it has become necessary to develop models that can aid in improving the flow of materials between the individual echelons of manufacturing supply chains, in order to achieve reduction in costs.

Problem: Supply chain inventory management problem. Many manufacturing companies experience limited capacity such as inadequate warehouse space or inadequate number of delivery trucks within the supply chain and this problem adversely affects the supply chain performance of these companies, especially when the companies do not manage inventory properly across their supply chains.