Improving Customer Service Efficiency Using Demand Forecasting with Leagile and Lean Six Sigma Concepts: A Case Study

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Background and Objectives: The coffee industry is a highly competitive business, especially for coffee shops that provide fresh coffee. Prolonged service times in such establishments can significantly impact activity-based costing. Although previous research has addressed issues related to extended waiting times, there is a gap in understanding how demand forecasting, combined with Leagile and Lean Six Sigma methodologies, can improve service processes in small coffee shops. This study seeks to identify the maximum increase in beverage demand periods through demand forecasting and implement Leagile and Lean Six Sigma strategies to enhance customer service, decrease activity-based costs, and improve overall efficiency. Methodology: The data on product sales throughout the year 2022 during operating hours were collected from January 1st to December 31st. Python and Autoregressive Integrated Moving Average (ARIMA) were employed for sales forecasting to find the maximum increasing period of demand, and the accuracy of predictions was evaluated using metrics such as Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Mean Absolute Error (MAE). The in-depth interviews were conducted with key informants. The application of Leagile and Lean Six Sigma concepts, with various tools such as service blueprint, process flow chart, activity value analysis, and Cause-and-effect diagram, took place over the period from March 1st to April 31st, 2023 to improve the customer service process. Results: The main findings of the study revealed that peak demand occurred during two specific periods: 07:45-08:00 a.m. and 12:45-01:00 p.m., with increases of 73.2% and 76.8%, respectively. The research identified and addressed five non-value-added activities in the service process which are queuing for ordering, writing down the order on paper, asking for the customer’s name and writing it down on the receipt, folding the tissue paper onto the cup, and choosing the proper straw. Decreasing those processes resulted in a notable 20.71% reduction in lead time (equivalent to 87 seconds per cup). Additionally, four out of five activities (80%) that cause idle time, consist of writing down the order on paper, asking for the customer’s name and writing it down on the receipt, folding the tissue paper onto the cup, and choosing the proper straw, were successfully eliminated. These improvements contributed to an estimated daily reduction of 2,350 baht in activity-based costs, translating to a monthly saving of 61,110 baht. Discussions: Entrepreneurs should consider using demand forecasting to identify peak times of demand, aiming to enhance the efficiency of customer service processes in conjunction with the Leagile and Lean Six Sigma concepts. This involves collecting and analyzing data, identifying the root causes of problems, selecting an appropriate decoupling point to modify the workflow, and utilizing forecast-driven principles in the Lean model. The workflow is then transformed using order-driven principles in the Agile model. Eliminating non-value-added activities is a crucial method to enhance the efficiency of customer service processes, reducing customer waiting times and minimizing unnecessary business activity-based costs. Conclusions: Integrating demand forecasting with Leagile and Lean Six Sigma principles proves beneficial for fresh coffee shops, enhancing operational efficiency and reducing unnecessary activity costs.