Improving inventory accuracy through order management processes with DMAIC
Seredyuk, Victoria (2024)
Pro gradu -tutkielma
2024
School of Business and Management, Kauppatieteet
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20241213102544
https://urn.fi/URN:NBN:fi-fe20241213102544
Tiivistelmä
Inventory discrepancies result from various causes and often lead to operational disruptions. This thesis investigates ways to improve inventory accuracy by optimizing inventory-related order management processes for finished goods, based on the case of a medium-sized technology company.
The literature review covers key topics such as inventory management and control principles, including inventory accuracy and common reasons for discrepancies causing inaccuracies. It also discusses best practices in academic research to optimize inventoryrelated processes and provides a few success examples from leading companies. The review also explains the data-driven approach to optimization and later applies the structured optimization methodology DMAIC, which is supported by the Ishikawa cause diagram.
The empirical study assesses the case company's current situation by analyzing inventory data for six months for end-of-month counts. The study evaluates the current situation using several quantitative methods, including exploratory analysis, distribution analysis, data visualizations, a normality test, and correlation analysis. The main conclusion of the quantitative part of the research is that inventory discrepancies exist in 62% of data entries. Then, the study reviews four current finished goods inventory-related processes within the order management scope selected and investigates possible causes of inventory inaccuracy based on professional observations. The results of the Ishikawa root cause analysis reveal that the key causes contributing to the inventory discrepancies are related to data management, process efficiency, systems effectiveness, and human factors, including communication and collaboration between actors in the processes. After that, several ideas for improvement are proposed to address the identified causes with control measures to ensure sustainability. In short, the measures are to standardize and modernize the workflows, upgrade the ERP system's functions, and train all staff in all locations involved with inventory management tasks, aiming to reduce errors in inventory transactions, thereby increasing inventory accuracy.
This study contributes to the field by showing how data-driven structured methodologies can effectively improve inventory accuracy via order management processes within similar organizational contexts.
The literature review covers key topics such as inventory management and control principles, including inventory accuracy and common reasons for discrepancies causing inaccuracies. It also discusses best practices in academic research to optimize inventoryrelated processes and provides a few success examples from leading companies. The review also explains the data-driven approach to optimization and later applies the structured optimization methodology DMAIC, which is supported by the Ishikawa cause diagram.
The empirical study assesses the case company's current situation by analyzing inventory data for six months for end-of-month counts. The study evaluates the current situation using several quantitative methods, including exploratory analysis, distribution analysis, data visualizations, a normality test, and correlation analysis. The main conclusion of the quantitative part of the research is that inventory discrepancies exist in 62% of data entries. Then, the study reviews four current finished goods inventory-related processes within the order management scope selected and investigates possible causes of inventory inaccuracy based on professional observations. The results of the Ishikawa root cause analysis reveal that the key causes contributing to the inventory discrepancies are related to data management, process efficiency, systems effectiveness, and human factors, including communication and collaboration between actors in the processes. After that, several ideas for improvement are proposed to address the identified causes with control measures to ensure sustainability. In short, the measures are to standardize and modernize the workflows, upgrade the ERP system's functions, and train all staff in all locations involved with inventory management tasks, aiming to reduce errors in inventory transactions, thereby increasing inventory accuracy.
This study contributes to the field by showing how data-driven structured methodologies can effectively improve inventory accuracy via order management processes within similar organizational contexts.