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Optimizing the last time buy decision at the IBM Service Part Operation organization

Koopman, C.W. (2011) Optimizing the last time buy decision at the IBM Service Part Operation organization.

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Abstract:Introduction: Within Service Parts Operation (SPO) of International Business Machines (IBM), the Product life cycle management (PLCM) is responsible for executing an last time buy (LTB). An LTB has the goal to obtain as many spare parts needed to mitigate the risk of running out of spare parts during the remaining service period (RSP). An LTB is initiated when a supplier stops supplying the spare part. The LTB is a decision that balances between buying too few spare parts and buying too many spare parts. Motivation & Approach: Significant improvement possibilities were discovered by a study in the Lenovo laptop division. This study and pressure on cost trigged management to investigate other divisions as well. The objective is to research what the current LTB performance is and which improvements are possible. This is done by studying the LTB process, the LTB model, and interviewing the PLCM team and others who are involved in the LTB calculations. Conclusions & Results: Based on our research we found that the LTB process was unnecessary complicated. The collection of information did involve many people and departments in order to generate accurate and good forecasts. This lead to an information overload and made the LTB decision unnecessary complicated and time consuming. Much of the information was not defined properly, not accurate, and was different used by analysts in the model. This leads to discussion and room for interpretation by the analysts. We showed with numerical analysis that the demand forecast procedure performs better with a simple approach than the currently used complex approach. The new proposed model is based on a demand forecast and a safety stock. It is tested on a dataset of the Power division which is chosen after an initial analysis of all divisions. This initial analysis showed that the Power, Storage and Mainframe divisions are the most promising divisions in terms of financial improvement. The new model is capable of delivering the same service level, defined as the stock out probability, as the original model with 16% less investment. The fill rate will only drop with 0,03 %. The new model is implemented in an Excel sheet and is used by the Power analyst. The safety stock is based on the standard deviation and the length of the RSP. To forecast demand on a standard decline/factor, and the average demand of the last 12 months is used. The new model uses the parameters of the reutilization department to forecast repair, which are process yield, verification yield, and return rate. In total there are now 6 parameter automatically determined by a fixed process. The analyst can focus on exception management and discussion about the service level in stead focus on the parameter values. The model must been seen as a first step, it is only applied to a specific group and more testing is needed to check if the model will be valid for larger/other groups. We think the framework still will be valid for larger groups only the values for the factor and the relation between goal and safety stock may change. The model can be optimized when more data becomes available and extended by including more dependencies between demand, repair, dismantling, and including costs such as carrying cost. Next to the new model and the delivered result we also showed that current inventory levels are rather high. Many LTBs do not need additional supply, and the forecast generated for stock level setting is structural too high. More research should be done on this subject. Another observation was that many LTBs are about cheap common items, such as keyboards and cables. We challenge if an LTB was really necessary. More research is needed to extend this model to the full product and project range of IBM. Better forecasting based on more information, such as commodities, and global risk sharing will be an interesting topic to research in more detail. As last we have the following recommendations to IBM. • Make a global SPO calculation to reduce the LTB investment. The forecasts can be more accurate, and risk can be spread amongst the geographical areas (GEOs). • Mitigate an LTB when possible; avoid an LTB on easy replaceable items such as keyboards because alternatives can be easily found. • Monitor the LTB spare parts to timely avoid expensive stock out solutions. Time is essential in the LTB, when a stock out situations can be foreseen IBM can act proactively. • Use the new Excel sheet, with the new model for the LTB process and calculation, and use to storage function to be able to analyze decisions taken. • Put more effort in the data management, and use correct information. Much time is lost by just checking if data is correct. • Store all information about, demand, repair, dismantling, demand plans, supply plans, assumptions accurate and for a long period in as structured format. When this is done IBM is able to improve forecasting and the LTB decision.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Business Administration MSc (60644)
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