Multi-objective inventory optimization tool development implementing MOPSO and TOPSIS algorithms : Case EKE-Electronics
Sultanbekov, Amir (2020)
Sultanbekov, Amir
2020
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2020051711877
https://urn.fi/URN:NBN:fi:amk-2020051711877
Tiivistelmä
EKE-Electronics Oy has been significantly growing over the past years and, therefore, it required an improved inventory management system. The main goal of this system was to shorten the production lead times, and thus,
enhance the customer service. To support this mission, a change from a Make to Order to a Make to Stock production system was proposed.
Furthermore, the optimization possibilities of the inventory management system proposed were analyzed. The developed system incorporated the company’s characteristics, management requirements, and room for the future development of the company. The objectives included the selection and setup of the inventory management system and a selection and development of the system’s optimization methods. Therefore, an extensive literature review of existing inventory management and optimization systems was performed.
The objectives were met by implementing an Order Point, Order Quantity (s, Q) inventory management system and coding the MOPSO and TOPSIS algorithms in the Python programming language. The implementation stage included a comprehensive statistical data analysis of historic data for the past 2.5 years and an analysis of the backlogged orders for the following 1.5 years.
The main results consisted of creating a General Bill of Materials (G-BOM) for several selected systems and analyzing data for sixty modules from the portfolio of the company. The G-BOM included an array of stockout probabilities on a 0% to 100% scale, expected total costs of the listed service levels, and the required safety stocks for each module. The outcomes required knowledge of inventory management, coding, statistics, and mathematical optimization to achieve the desired results.
enhance the customer service. To support this mission, a change from a Make to Order to a Make to Stock production system was proposed.
Furthermore, the optimization possibilities of the inventory management system proposed were analyzed. The developed system incorporated the company’s characteristics, management requirements, and room for the future development of the company. The objectives included the selection and setup of the inventory management system and a selection and development of the system’s optimization methods. Therefore, an extensive literature review of existing inventory management and optimization systems was performed.
The objectives were met by implementing an Order Point, Order Quantity (s, Q) inventory management system and coding the MOPSO and TOPSIS algorithms in the Python programming language. The implementation stage included a comprehensive statistical data analysis of historic data for the past 2.5 years and an analysis of the backlogged orders for the following 1.5 years.
The main results consisted of creating a General Bill of Materials (G-BOM) for several selected systems and analyzing data for sixty modules from the portfolio of the company. The G-BOM included an array of stockout probabilities on a 0% to 100% scale, expected total costs of the listed service levels, and the required safety stocks for each module. The outcomes required knowledge of inventory management, coding, statistics, and mathematical optimization to achieve the desired results.