การวางแผนกำลังคนในการผลิตส้มสายน้ำผึ้งบนพื้นฐานของความต้องการลูกค้าและฤดูกาลผลิต: กรณีศึกษา โรงแว็กซ์ส้มสายน้ำผึ้ง อำเภอฝาง จังหวัดเชียงใหม่

Abstract

This study investigates manpower capacity planning for tangerine processing after post-harvest, based on customer demand and seasonal production factors. The research began with sales data analysis to forecast customer demand for the upcoming sales year, which is crucial for accurate manpower planning in the tangerine process after post-harvest. The study compares three forecasting methods: Exponential Smoothing, Winter's Method and Time Series Decomposition, using Mean Absolute Percentage Error (MAPE). The first dataset forecasts monthly sales over 36 months, identifying Time Series Decomposition Multiplicative as the optimal model (Yt = 4,662 + 136 x t, MAPE: 16%). In the second dataset, consisting of daily sales 10 days before the Chinese New Year, an event period is forecasted using a Time Series Decomposition Additive (Yt = 1414.5 - 11.77 x t, MAPE: 12.4%). Using these forecasts, the thesis plans post-harvest process manpower rates, starting with process flowcharts and Maytag's time study theory. The performance of each process step is evaluated by calculating the average time, normal time, rating factor based on the Westinghouse method, and allowance time in accordance with the standards of the International Labour Organization. These calculations determine the standard time for each operation, These standards facilitate systematic planning for planners and operators, providing a clear framework. This will assist the Sorting facility in allocating labor effectively in response to the variable demands of customers. Effective planning mitigates manpower shortages or surpluses, optimizes time and volume assessments for product delivery, and enhances production efficiency. This study has been applied to increase efficiency by reducing waste caused by employees waiting for work through the combination of workstations based on takt time calculations, along with evaluating efficiency by analyzing utilization The results indicate that utilization can be maximized by as much as 23.13%, depending on customer demand and the production timeframe