eThesis - Kauppakorkeakoulun sähköiset gradut

Increasing importance of maintenance services for equipment manufacturing companies have also raised a need for more and more accurate just-in-time deliveries of spare parts. As a result of unique service part characteristics, an effective inventory control plays an important role for the maintenance companies’ service performance and cost effectiveness. Inventory optimization aims to find right inventory levels for different materials in every location. Optimization target is that not too much items are stocked in one location and after all acceptable service level for the overall distribution network is achieved.

Objective of this thesis is to test how inventory optimization model, based on e.g. Muckstadt’s (2004) researches, operates in the case company’s inventory environment. Study considers a single-echelon inventory system with three distribution centers. The aim of my study is to test how reorder point costs and open procurements can be decreased with the new optimization model. Study forms in two: first the casecompany’s inventory environment is evaluated and the second part of the research is dedicated to the actual single-echelon, multi-item optimization.

Thesis’ inventory network analysis discovered that several environment characteristics and policies should be noted in the optimization. Environment analysis pointed out also e.g. different levels of distribution centers’ importance to the system. Finally,environment analysis generated several cost factors for optimization and lateral transshipment effectiveness evaluation.

Study’s optimization research found out that model enables case company to achieve holding cost savings from overall network inventory level reductions. I evaluate inventory level changes via reorder point cost changes with five most important segments for the case company. Moreover, study showed that model enables company to simulate how changes in inventory parameters for segments affect company’s service performance, holding costs, backorder costs and ordering costs. Finally, research proved that single-echelon optimization enables decrease in case company’s future procurements and thus derivates for example ordering cost savings.