University of Twente Student Theses
Stock control in the aftermarket
Nijland, L. (2016) Stock control in the aftermarket.
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| Abstract: | Currently Company X thinks that they are keeping too many excess stock levels and obsolete inventory in stock. This research aimed to provide a method to assess whether keeping an item in stock is justified at all, and if so, how many should be in stock. The research was carried out for the product line ‘Aftermarket’, which is an umbrella group for all products that are not manufactured anymore, but still being serviced and therefore need spare parts in stock. The presence of excess stock level and obsolete inventory is measured with Company X’s Excess and Obsolete (E&O) guideline, providing calculation methods for the financial reservation on the firm’s profit that is made for excess and obsolete stock. The aftermarket product line contains a total value of € xxx on E&O, which Company X wishes to reduce. We found that the aftermarket product line is split into three different phases: First aftermarket stage: providing refurbishments and extensions, spare parts and generic maintenance Second stage: providing spare parts and generic maintenance Third stage: only obliged to provide generic maintenance And that E&O occurs most in the first aftermarket stage. We continued to conduct a root cause analysis and found four root causes, responsible 20.18% of aftermarket obsolescence, which we decided to further investigate on how they can be improved: overestimated demand in last time buys, responsible for about €xxx, deliberately high amounts stocked to guarantee availability (€xxx) or reduce start-up costs (€xxx) and too high minimum order sizes (€xxx). The research continued to examine how a method could be provided to assess whether the stocking of an item is justified, and found three key factors that justify (E&O) service spare parts to be kept in inventory if either of them is fulfilled: non-reproducibility, criticality and items of which the replenishment lead time is longer than the allowed maximum response time by the customer (LLTs). We then analysed Company X’s demand data to determine what inventory models or forecasting methods are suitable, and discovered that the vast majority 98% of demand was not suitable for a regular inventory model, as leads time occur without demand due to intermittent demand. Therefore we searched literature to find a forecasting method suitable for intermittent demand, and found Croston’s method. For the four root causes we also did a literature study and found Moore’s method for forecasting demand in last time buys, and derived a method from the Economic Order Quantity (EOQ) method to compare what service level is established if Company X would reduce the order size for items put in stock in high quantities deliberately to guarantee availability, and how costs can be compared by ordering a larger amount for a lower unit price, to ordering a smaller amount for a higher unit price. We then continued to validate the proposed methods, starting with the developed decision model. We started with presenting the decision model’s outcomes about stocking an item or not to Company X’s service supply chain analyst (SCA), and found that the model accurately determines the right items to stock. The model also identified that the high E&O value in the first stage of aftermarket is partly due to the stocking of items that Company X should not keep in stock. Moore’s method for forecasting the necessary size for last time buys could not be validated due to a lack of data. More historical demand is needed to successfully determine the size of a last time buy. Also Croston’s method was also not validated properly, as we were only able to test it for two items due to the limited time for this research. The method should be tested on more items to fully test its usefulness. At last we tested the method for comparing order sizes and prices on an item with a high minimum order size and an item that was stocked deliberately to reduce start-up costs. For the minimum order size we found that Company X could still establish a 96.3% service level by ordering 23 units instead of the minimum order size of 100. The unit price is then allowed to rise from €40.50 to €183.91, so renegotiating the unit price for 23 units to below €183.91 will provide the company a profit. For the item put in stock deliberately at an amount of 40 we found that ordering 7 units still establishes a 94.88% service level, but would have saved the company €11,286.46 over three years time, minus three times the start-up costs, which we were unable to retrieve. We recommend Company X to put both the decision model and the method for comparing order sizes into operation, as the first has proven to be valid and the latter gives at least a good indication on the consequences of ordering a certain amount for a certain order price. To fully operate the decision model, data needs to be gathered on each item on its non-reproducibility, criticality and lead time related to customer expectations of replenishment. |
| Item Type: | Essay (Bachelor) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 85 business administration, organizational science |
| Programme: | Industrial Engineering and Management BSc (56994) |
| Link to this item: | https://purl.utwente.nl/essays/71119 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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