An investigation into the improvement of the inventory management process, in the automotive OEM company's supply chain

Abstract

The automotive industry tends to have high volumes of accumulation of inventory. Likely reasons for this is that the automotive industry aim to have high customer service levels, which implies that they need to stock service-, crash- and maintenance parts which are prone to become Slow Moving and Obsolete (SLOB) inventory (inventory that did not sell in the last four years). Inventory can be divided into three types, namely: fast moving-, medium movingand slow-moving inventory. Inventory management is vital to minimise the accumulation of all three types of inventory in an automotive supply chain. Previous research has mostly focussed on fast moving inventory, as its revenue generating ability is higher than SLOB inventory, however the pro t impact of resource wastage on SLOB inventory is likely much higher than assumed in the industry and better management of this type of inventory can change bottom line returns to investors. Therefore, the project investigates the improvement of inventory management on all three types of inventory identi ed, however the project makes speci c reference to SLOB inventory as it is particularly relevant in the automotive industry. To obtain the necessary data for the analysis both qualitative data collected from interviews and quantitative data were obtained from the automotive Original Equipment Manufacturer (OEM) company's Rosslyn warehouse's Warehouse Management System (WMS). To answer the research question, numerous inventory management techniques were researched. Two particular inventory management techniques stood out for this speci c problem. Business Process Modelling Notation (BPMN) charts are used to map current inventory management process and a linear programming model is developed to optimise the automotive OEM company's inventory levels across the supply chain. The current inventory management process was mapped to identify "problem areas" within the process that could be solved by developing unique solutions for each "problem area". In the preliminary mapping it seems that more regular scrapping processes are clearly needed to minimise storage costs and improve the supply chain's efficiency. Furthermore, minor changes such as doing activities simultaneously instead of sequentially, can improve the inventory management process's efficiency, by drastically reducing idle time. The aim of the linear programming model is to minimise transportation- and storage costs across the automotive OEM company's supply chain. Therefore the model used various input data (as discussed in the quantitative data analysis chapter) to generate estimated optimal quantity of parts that the warehouse should order. If the automotive acOEM company implemented the suggested warehouse ordering quantities derived from the Linear Programming (LP) model, the automotive OEM company's supply chain can save an estimated R16 472 807 within two years, which is about a third of their current expenditure on medium- and fast moving parts in the supply chain. Furthermore the SLOB inventory, obtained from the sample data provided by the automotive OEM company's WMS costs the automotive supply chain about R 28 000 000 to store these parts for two years. If these parts can be reduced by implementing regular scrapping processes the inventory management process will operate more effectively and save vast amounts of money.