With increased competition, logistics has become a crucial part important and today it is an important competitive tool. An effective logistics setup means increased revenues and lower costs are obtained. Examples of costs items that are affected by the logistics are inventory carrying cost and warehousing cost. One way to of reducing these costs is to reduce the inventory levels. Another cost item is lost revenues which also can be reduced by effective logistics. Bosch Thermotechnology AB is a company that manufactures and sells heat pumps. To achieve cost savings, they want to work towards effective logistics. Bosch experience problems with their inventory control as the demand for heat pumps varies widely in the European market. The high season falls in August to November and the rest of the year the demand is evenly distributed. This is because the end customers need a heat source for winter and the heat pumps break more frequently when the load increases. The problem Bosch is experiencing with the seasonal variation is the safety stock, which the order point is based on, is not regulated by season which means that the inventory levels and the storage costs during low season are assumed to be unnecessarily high. Bosch wishes to review their inventory control, with the aim of reducing inventory costs, which can be done by reviewing articles that ties up capital. The reference framework covers inventory, forecasting, delivery service, inventory control, ABC-classification, total cost of logistics and model building and simulation. The four first areas aim of helping to build new inventory control models. The aim of the fifth area is to help with article classification. Furthermore, to evaluate the new inventory control models, total cost analysis were treated since the new inventory control has been implemented on the articles that has been selected with the help of the information from the preceding section. The final section of the reference framework covers model building and simulation since the models must be simulated before they can be evaluated. With the help from the reference framework and the empirical findings, main questions and sub questions were established to answer the purpose of this study. Methodology part in this study presents four phases, Planning phase, Selection phase, Implementation phase and Final phase. In the first phase, Planning phase, was about preparing the work by identifying the problem area, current situation, and then defining a purpose and specifying the task. Based on purpose, a literature search could be done. In the Selection phase, the literature from the previous phase then came to be compared and analyzed based on what emerged from the collection of empirical data. The next phase, Implementation phase, data was collected and simulation was performed. Based on the results from the simulations, an evaluation could then be carried out in terms of total cost and stock availability. The Final phase was the final phase of the study and here the results of the study will be presented and discussed. From eight developed models, two models were chosen, one for articles with long lead times and one for articles with short lead times. When to order and how much to order are the same for both models. When to order is determined like before, which is an ordering point system that consider demand during lead time. The ordering point system will therefore vary over time and depends on safety stock level and demand during lead time. The order quantity will be decided as current. This means there is a minimum order quantity but if this does not cover the demand for the next incoming delivery, a number of multiples to cover the need can also be purchased. What distinguishes the models is determination of the safety stocks. For articles with short lead times, this will be calculated with SERV1. SERV1 is also recommended for articles with long lead time, but the difference is the standard deviation which will be calculated for two different periods instead of an average standard deviation over one year. The models are summarized in the table below. Lead time When Quantity Uncertainty Long Ordering point – Adjusted demand during lead time MOQ and MLT SERV1 Short Ordering point – Adjusted demand during lead time MOQ and MLT SERV1 – standard deviation per period There were several models that resulted in reduced total costs and an approved service level. However, the models mentioned above gave the best results with respect to the two assessment parameters. These models were implemented to six articles, where two of them are products in Bosch and four of them are articles where the lead time has changed for the two previously mentioned articles. The results are shown in the table below: Lead time Current situation (SEK) Alt. models (SEK) Cost saving (SEK) Cost saving (%) Stock availablity (%) Long 15 130 736 14 323 803 806 933 5 99,96 Short 21 003 352 9 170 918 11 832 434 56 99,56 Total 36 134 088 23 494 721 12 639 367 35 99,76 A sensitivity analysis was performed for the chosen models and showed that the models were robust with respect to ordering costs, inventory carrying charge, seasonal variation and changed safety stock for current situation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-157662 |
| Date | January 2019 |
| Creators | Wenner, Mathilda, Troung, Atimmy |
| Publisher | Linköpings universitet, Logistik- och kvalitetsutveckling, Linköpings universitet, Logistik- och kvalitetsutveckling |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0026 seconds