DEMAND FORECASTING ANALYSIS AND INVENTORY MANAGEMENT AT PT. BERKARYA MAJU MANDIRI

• Main Author: Nudiya Hurin, Khalisa
• Format: Theses
• Language: Indonesia
• Subjects: Manajemen umum
• Online Access: https://digilib.itb.ac.id/gdl/view/67326
• Institution: Institut Teknologi Bandung

PT. Berkarya Maju Mandiri offers high-quality wood furniture. Seeing the growing expansion of furniture in Indonesia motivates companies to maintain quality and perfection to compete in the market, therefore they face numerous hurdles, including fulfilling demand and satisfying consumers effectively and efficiently. PT BMM's supply chain depends on manufacturing and inventory management. The company buys plenty of raw materials every month to suit market demand. Warehouse overstock hinders companies’ productivity and operations because PT. BMM lacks forecasting and inventory management, causing high inventory costs. Lack of forecasting and inventory management will damage the company's output, especially if it can't match sales forecasts with real demand, producing excess material stock that may not be utilized in the next production cycle. Therefore, this study aims to offer several solutions related to the problems faced, includes applying forecasting methods to the company's products using previous demand data helps estimate raw material needs, determining production capacity, demand, and personnel may help organizations predict production costs and prepare financially, and define safety stock and reorder points to save inventory costs. This study uses quantitative research approaches to acquire data from companies to assist them solve problems. Reviewing forecast, production planning, and inventory management literature to assemble business solutions. According to the Pareto diagram and corporate data, this research will concentrate on fabric, foam, and wood. This study's findings may assist the organization improve sales, production, and inventory. Suggestions concern: 1) Use 9-month moving average forecasting and linear regression to predict future product demand; 2) The chase strategy can be used to plan production activities and costs; 3) The FIFO method can be used to increase raw material use based on warehouse conditions; 4) The fixed-order quantity method can be used to control inventory. This study's limitations include its emphasis on PT BMM's operational and production divisions and its use of current data from January 2021 to March 2022. The firm limits data for privacy concerns. So, part of the data that may be acquired is average or broad. This business solution should be implemented from August to December this year so the company may adjust to the proposed methods and arrange production, inventory, and costs in the following year.