Spare Parts Inventory Management (Al Sarrawi Automobile Spare Parts Industry)

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Mahdi Attieh
Haneen Saymeh
Hisham Jaber
Aya Abu Zant
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Project Summary: In this Project we present a case study in inventory management of spare parts in a company called Al Sarawi for Mercedes Spare parts. The companys core business is selling spare parts for Mercedes Cars to consumers. The company does not give adequate importance to inventory management due to various reasons including the disorganization of spare parts and the non availability of any inventory modeling system or ordering policy combined with inexperienced manpower. As a result, there is an inefficient deployment of inventory. This study focuses on the inventory management of spare parts for a specific model of Mercedes Cars called 416. After the forecasting of the demand, collecting the relevant costs and building the models the results were satisfying and a good amount of savings were achieved with considering the different ordering criteria that is being used by the company.   Introduction:       Effective inventory management of spare parts is essential to many companies, from capital-intensive manufacturers to service organizations, such as car manufacturers, chemical plants, telecom companies and airlines. Different from work-in-process (WIP) and finished product inventories, which are driven by production processes and customer demands, spare parts are kept in stock to support maintenance operations and to protect against equipment failures. Although this function is well understood by maintenance managers, many companies face the challenge of keeping on stock large inventories of spares with excessive associated holding and obsolescence costs. Thus, effective cost analysis can be an important tool to evaluate the effects of stock control decisions related to spare parts. However, the difficulty in assessing good strategies for the management of spare parts lies in their specific nature, normally very slow-moving parts with highly stochastic and erratic demands. For example, typical industrial data sets comprise limited demand history with long streams of zero demand values and a few large demands. This makes the estimation of the lead time demand (LTD) distributions very difficult, which is essential to obtain the control parameters of most inventory policies. Although different inventory models have been proposed in the literature to tackle this problem (see next chapter), This project concerns a study on spare parts at a local car industry in the Palestine, which is consisted of many phases, in the first phase we determine and classify the spare part items by using ABC analysis, The second phase was focused on the analysis of the demand data provided by the company, then building a model for each type of the spare parts, then evaluate the previous conditions and compare them empirically with the new results based on our inventory models. One major difficulty of the study was the limited demand history available, and no real inventory model systems were applied. By describing the case, we make general observations about the practical aspects of inventory control. Moreover, our aim is to compare various policies with real demand data from the case to see which one is best under what circumstances. Common methods presented in the literature rather use given statistical demand distributions to assess the performance of inventory models. Consequently, with our methodology we can better identify the real limitations of industrial data sets.   Recommendation: The limitation in this project has been the amount of data available. Clearly the data obtained does not cover a long enough time-frame to provide accurate forecast so in a more few years of stored data will give better results and accurate assumptions, and the need to the system to be continually updated.