- 學位論文
Integrated Quality Controlled Monitoring of Inventory Accuracy – combining cycle counting and statistical process control
Integrated Quality Controlled Monitoring of Inventory Accuracy – combining cycle counting and statistical process control
摘要
Inventory accuracy is vital in many industrial environments, particularly large distribution and warehouse environments, especially when implementing computerized systems that relay on accurate inventory. Accurate inventory results from the agreement of the recorded stock keeping unit (SKU) level with the actual SKU level, where inaccuracy affects the performance of the supply operation and realization of benefits form inventory control and procurement. Cycle counting is a technique used to combat inventory inaccuracy, but due to its excessive time and resource requirements this technique fails to ensure inventory accuracy, unaided. Therefore, this research develops an approach to control and measure inventory accuracy promoting the techniques of statistical process control (SPC) that combines resource efficiency and practicality. Specifically, the technique of a c chart is utilized, for the evaluation and ongoing monitoring of inventory management, integrated in implementation by sample selection with only cycle counting methods that are used for measuring accuracy. Also, to aid in the search to find special causes, a method for identifying a change point in the data is presented. The tolerance level setting measure of the proposed method is developed to be utilized in setting the standard for the c chart to provide a useful mechanism for analyzing the performance of the inventory management, coupled with a prescribed inventory accuracy measure. For this research, in an inaccurate inventory the number of adjustments for each SKU is the number of non-conformities. The objective of this research is to develop and analyze a SPC outlook for cycle counting by employing the use of the c chart to examine large populations of SKUs modeled by Poisson counting processes. In the implementation, the focus is on creating a workable integration through sample selection with SPC and cycle counting. In analyzing the proposed approach the use of both analytical as well as simulation results are set forth to demonstrate its effectiveness.
並列摘要
Inventory accuracy is vital in many industrial environments, particularly large distribution and warehouse environments, especially when implementing computerized systems that relay on accurate inventory. Accurate inventory results from the agreement of the recorded stock keeping unit (SKU) level with the actual SKU level, where inaccuracy affects the performance of the supply operation and realization of benefits form inventory control and procurement. Cycle counting is a technique used to combat inventory inaccuracy, but due to its excessive time and resource requirements this technique fails to ensure inventory accuracy, unaided. Therefore, this research develops an approach to control and measure inventory accuracy promoting the techniques of statistical process control (SPC) that combines resource efficiency and practicality. Specifically, the technique of a c chart is utilized, for the evaluation and ongoing monitoring of inventory management, integrated in implementation by sample selection with only cycle counting methods that are used for measuring accuracy. Also, to aid in the search to find special causes, a method for identifying a change point in the data is presented. The tolerance level setting measure of the proposed method is developed to be utilized in setting the standard for the c chart to provide a useful mechanism for analyzing the performance of the inventory management, coupled with a prescribed inventory accuracy measure. For this research, in an inaccurate inventory the number of adjustments for each SKU is the number of non-conformities. The objective of this research is to develop and analyze a SPC outlook for cycle counting by employing the use of the c chart to examine large populations of SKUs modeled by Poisson counting processes. In the implementation, the focus is on creating a workable integration through sample selection with SPC and cycle counting. In analyzing the proposed approach the use of both analytical as well as simulation results are set forth to demonstrate its effectiveness.