STRATEGIC PLANNING FOR DEMAND FORECASTING, CAPACITY PLANNING, AND ORDER FULFILLMENT TO ENHANCE OPERATIONAL PERFORMANCE AT PT RAS

• Main Author: Azzahra, Nafisah
• Format: Theses
• Language: Indonesia
• Subjects: Manajemen umum
• Online Access: https://digilib.itb.ac.id/gdl/view/86975
• Institution: Institut Teknologi Bandung

This study investigates strategies to enhance operational performance at PT Runesha Artha Sejahtera (PT RAS) by addressing inefficiencies in demand forecasting, capacity planning, and order fulfillment. PT RAS, an SME specializing in nanotechnology-based consumer goods, faces challenges in meeting demand due to reactive production strategies and resource constraints. Using historical sales data, this research evaluates five forecasting methods: moving average, single exponential smoothing, double exponential smoothing, linear trend line analysis, and time series decomposition to develop accurate demand predictions. For capacity planning, the Level Production method is employed for the Master Production Schedule (MPS), while Capacity Bills are utilized in Rough-Cut Capacity Planning (RCCP) to align resources with projected production needs. The integration of these strategies not only optimizes resource allocation but also ensures a proactive approach to production planning. By synchronizing demand forecasts with production capabilities, PT RAS can prevent bottlenecks, reduce delays, and maintain consistent order fulfillment performance. This approach ensures the company can fulfill customer orders on time by maintaining sufficient production capacity to meet varying demand levels. The findings demonstrate significant improvements in operational efficiency, production scheduling, and customer satisfaction. Moreover, this research directly addresses order fulfillment challenges by creating a structured framework that balances demand variability with resource availability. Recommendations emphasize the adoption of advanced forecasting models, streamlined production processes, and enhanced resource allocation strategies to support sustained business growth and competitiveness. This research contributes to SME operations management literature by addressing demand variability and capacity constraints in dynamic market conditions.