Safety Stock & Order Quantities : A study on the optimization of safety stock and order quantities in the manufacturing industry

Sammanfattning

Manufacturing companies often need to balance high material availability with low capital tied up in inventory. High stock levels can increase costs, while low stock levels may cause production stops. Safety stock and order quantities are affected by factors such as demand, lead time, service level, and inventory-related costs. ERP systems can support this process by collecting relevant data, but there is lack of the models that take various important factors into consideration for effective decision making. The purpose of this study is to develop a formula-based model that can be used as a decision support tool for calculating optimal safety stock and order quantities. The study is conducted as a qualitative case study with quantitative elements at a global manufacturing company. The empirical material is based on observations, semi-structured interviews, and system data from the company’s systems and forecasting tools. The study shows that the case company’s current approach is characterized by high material availability and low tolerance for stockouts, but also by high inventory levels, long storage times for certain items, long external lead times, and limited system support for automatically calculating safety stock and order quantities. It is also evident that information is currently managed across multiple systems and that inventory control partly relies on manual assessments and established routines, which makes a consistent and data-driven approach more difficult. The results show that a formula-based model should be based on demand, lead time, variation, service level, and costs. The model in this study is based partly on company data, but also on assumed values and reasonable complementary data in cases where the company currently lacks sufficiently developed system support. Furthermore, the model should be standardized in its structure while still being adaptable to the conditions of different items. Future studies should focus on testing the model with more reliable data, examining the possibility of implementation in ERP systems, and analysing the model’s long-term impact on capital tied up in inventory and inventory turnover.