Articles

Simple Multi-Attribute Rating Technique for Warehouse Location Selection (Case Study: PT. PPT)

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Indonesia’s Manufacturing Industry, encompassing processing and non-oil and gas processing, grew in Q3 of 2022, contributing 17.88% and 16.10% to GDP. Transportation and storage played a significant part, with a 15.93% year-on-year growth in Q1 2023, highlighting the sector’s importance. PT. PPT is a sub-holding of one of Indonesia’s companies. The company manages to trade PP/PE significantly from more sources (import, local producer, or local trader). This research will determine and analyze the criteria for the selection of warehouse locations for PT. PPT analyzes the weight of each criterion at each location in the warehouse location selection process for PT. PPT and obtain the priority location for PT. PPT to build a new warehouse.  This research aims to guide and help the decision makers to choose and find a suitable location based on the Centre of Gravity (CoG) analysis conducted by a third-party consultant. The five locations resulting from the Centre of Gravity (CoG) as alternatives will be analysed with Kepner-Tregoe Analysis in the decision-making process, using Situation Appraisal defined in Business Issues. The author then employs a Rich Picture and Fishbone Diagram to assess the scenario or the process of select the new warehouse. The decision analysis was carried out in eight stages using the one of Multi-Criteria Decision Making (MCDM) method, which is the Simple Multi-Attribute Rating Technique (SMART). Lastly, using the preceding analysis, undertake a Potential Problem Analysis. The major criterion is the distance to the customer, which aligns with the company’s emphasis on customer satisfaction. Demand growth affects warehouse capacity, with more growth necessitating a larger facility. Distance from suppliers reduces delivery lead time, and the size of the land must accommodate demand growth and product capacity for PT. PPT.

Warehouse Location Optimization with Clustering Analysis to Minimize Shipping Costs in Indonesia’s E-Commerce Case

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Due to the growth of the Internet economy, the popularity of online shopping has escalated in recent years. One of the largest e-commerce enterprises in Indonesia, PT. S, is the subject of the research in this article. Instead of typical e-commerce, where anybody may start a store, PT. S is concentrating on social commerce, which makes use of several resellers to offer hand-picked SME brand partners. PT. S must expand the market for inter-island or non-java-to-non-java transactions to fulfill its vision. However, PT. S will have logistical difficulty completing this job. The business used performance indicators to keep track of the logistics process’ vision and mission. Gross merchandise value, pickup time service level, and shipping time service level are a few of the performance indicators that pertain to logistics. The process of managing the supply chain will become more complex as a result of the opening of the new warehouse, and the business will need to maximize its use of various selling channels, logistical services, and supply chain management. With the aid of clustering analysis, which assesses demand similarity and proximity, the enterprise can locate a new warehouse. Durairaj and Kasinathan developed the framework template for this study in 2015. Based on the case study, literature review, and clustering method framework, the framework will be modified in several ways, particularly clustering analysis. The alteration concerns framework-integrated theories as an input and as a data source. According to the simulation’s findings, shipping costs per kilogram decreased by about 35% for five clusters. But if the corporation does not have a problem with the number of warehouses, according to the simulation’s findings, because the cost of transportation will go down as the number of clusters increases, the number of warehouses can be expanded to more than five.