Analysis of Efficiency The Distribution Proces Using The Data Envelopment Analysis (DEA) Method and Lean Thinking Approach
The purpose of this study is to identify the causes of product distribution inefficiencies and determine the appropriate distribution process strategy using the Data Envelopment Analysis (DEA) Method and the Lean Thinking Approach. The DEA method is used to analyze efficiency values with specified input and output and the Lean Thinking Approach with VSM and VALSAT is used to analyze the factors that cause inefficiency and improve the distribution process. Factors that cause inefficiencies include: waiting for processed products from other divisions, the process of moving products, the process of re-inputting data when scanning products and interference with systems. The result of the Lean Thinking approach is to eliminate some processes that have no value added, reduce the lead time in the distribution process and apply the pull system strategy in the distribution process. The calculation result of the DEA Model CCR Model, where the average value of efficiency in the distribution section in 2018 is 97.5% with only 1 month from 2018 which achieves an efficient value is August with recommendations for improvement reducing the input value in the distribution section. Lean Thinking approach uses VALSAT and VSM, for VALSAT with Process Activity Mapping with operations from the initial percentage of 76.62% increasing to 82.86%, while other activities namely transportation decreased from the original 7.79% to 3.83% inspection to by 3.23%, storage by 0% and delay decreased from 13.24% to 10.08%, for VSM the decrease in lead time from 200 minutes to 161 minutes. The Solution of the Lean Thinking Approach is to change layouts, implement pull systems in the distribution process and provide education and information to employees in product processing and product distribution in MPC, so that efficiency improvements can be achieved without having to reduce the input value of products distributed by the MPC distribution section, but the deepening of the application of recommendations for improvement must be explored in depth in terms of costs and broad impact on MPC.