Integrating a cost-reduction shipment plan into a single-producer multi-retailer system with rework

This study integrates a cost-reduction shipment plan into a single-producer, multi-retailer system with rework process. In a recent article, Chiu et al. [1] have examined a single-producer, multi-retailer integrated inventory model with a rework process. For the purpose of reducing the inventory holding cost, this study combines an alternative n+1 product distribution policy into their model. Under the proposed shipment plan, an extra (initial) delivery of finished items takes place during the production uptime to meet the retailers’ product demands for the periods of the producer’s uptime and reworking time. Upon the completion of rework, multiple shipments will be delivered synchronously to m different retailers. The objectives are to find an optimal production-shipment policy that minimizes the expected system cost for such a supply chain system, and to demonstrate that the result of this study gives significant holding cost savings in comparison with Chiu et al.’s model [1]. With the help of mathematical modelling and Hessian matrix equations, the optimal operating policy for the proposed model is derived. Through a numerical example, we demonstrate our model gives significant savings in stock holding cost for both the producer and retailers

Inventory management in the electricity industry in South Africa : a case study.

Master of Commerce in Management. University of KwaZulu-Natal, Pietermaritzburg 2017.Electricity remains one of the most important technological innovations in human history, because of its importance to modern daily life, both domestically and industrially. In South Africa, Eskom Holdings is the state-owned power company responsible for generating, transmitting and distributing electricity. Eskom’s material management department deals with the planning and ordering of materials and their transportation to the regional distribution centres (warehouses). This department is expected to contribute to the company’s business goal of providing sustainable electricity for a better future. However, inept decision-making processes at Eskom have led to a number of problems associated with inventory. These costly problems diminish the material management department’s efficiency and hence the company's ability to reach its goals. This study used qualitative research to investigate the inventory management in Eskom’s KwaZulu-Natal (KZN) cluster with a view to identifying those decisions and actions responsible for such inventory anomalies. A conceptual model of inventory management was used to structure this research. This model emphasises the way in which managers’ decisions are influenced by the context in which the supply chain operates. The research objectives were to examine the impact of demand, the supply chain structure, information availability and Eskom's business goals on how inventory decisions are made, and to understand the effects of this decision making processes. The major finding was that the department has a significant problem of unbalanced stock, with an excess of certain items and shortages of others in all its six regional distribution centres in KZN. This inventory challenge facing the company was found to be caused by inadequate forecasting, poor information sharing, poor housekeeping, large quantities of inventory returns from projects and the disorganised scheduling of deliveries. The study also found that there is a gap in the system of classification of inventory in Eskom which adversely affects the management of inventory. Recommendations include replacing the economic order quantity system with a periodic order quantity system and incorporating elements of lean into the management of inventory. Furthermore, improving the information available to material requirement planners so that purchasing is responsive to customer demands will reduce the burden of inventory that is not required and ensure the availability of stock as it is needed

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry