Articles

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.

Proposed Design of Performance Management Framework for 3PL (Third-Party Logistics) Aggregator

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This white paper proposes a performance management framework for third-party logistics aggregators (3PLs) that connect e-commerce companies and logistics providers. The logistics industry has grown rapidly in recent years, and with the emergence of aggregators, performance records must be updated. Using a case study of an Indonesian startup PT P, we create a framework using qualitative and quantitative interviews with the company’s C-levels. The framework is based on KPBMS, a simple and knowledge-based performance management system available in Indonesia. Traditional financial-based performance management systems have proven limited in their ability to adapt to modern organizational operating systems. A new-generation performance management system is based on the company’s strategy and values, is customer-centric, long-term, and emphasizes continuous improvement. The proposed framework includes strategic objectives, key performance indicators (KPIs), objectives, initiatives and reviews. KPIs are categorized into financial, customer, internal process, and learning and growth perspectives. Goal setting is based on broader goals, and initiatives are defined on the basis of rapid outcomes and long-term projects. The review process includes monthly, quarterly, and annual reviews focused on identifying areas for improvement. The proposed framework will help 3PL aggregators like PT P to set up a performance management system to monitor the performance of logistics providers and provide recommendations to e-commerce companies.

Proposed Improvement of Logistic Operations to Increase Service Level Agreement (SLA)

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In the digital era, e-Commerce or online shopping is a big breakthrough in the world of buying and selling services on the Internet, the success of e-Commerce is inseparable from the success of its shipping services or logistics partners. The level of customer satisfaction must be balanced with the delivery performance since the customer buys until the item received. SEI is a company engaged in delivering package. SEI is one of the delivery services which provide the delivery service end to end from the First mile to the Last mile. Thus, SEI should be able to control the performance of their shipment from pick up until successfully delivered to the customer. The most significant volume of SEI comes from the marketplace (e-Commerce) or sellers. Therefore, the customer satisfaction level also determines the shipping company’s performance or they called service level agreement (SLA). SEI recorded to have untargeted SLA within this current 3 months. This research aims to find the problems and the suitable solutions of the shipping company operations that affect service level agreement scored. The methodology used for this research is both qualitative and quantitative data. Data collection is primarily based on an interview with the internal stakeholder of the company to find the root causes of the problem. The root cause analysis evaluation is done by interviewing several stakeholders about the performance and quality of the shipping company. The secondary data comes from historical data from SEI used to know the current and previous performances. The historical data has been taken during 2022. The output of the root cause analysis illustrated in the Cause-Effect

Diagram or usually called Fishbone Diagram. After find the root causes, the suitable solutions for this SLA’s problem proposed by DMAIC method. DMAIC is part of Six Sigma method, one of the quality management tools that aimed to manage quality improvement activities throughout an organization/company.