Increased competition has led businesses to compete with each other in streamlining supply chain processes, especially in the manufacturing sector. Supply Chain Management (SCM) determines the success of industrial business processes because it regulates product flow regarding integration, performance, and information. However, several problems have emerged in the supply chain process, such as a lack of coordination in the production queue, difficulties in forecasting trending products, and suboptimal production capacity. To address these issues, the role of information technology is crucial for implementing a Decision Support System (DSS). This study aims to develop a DSS to improve the supply chain processes. The research method used is Extreme Programming (XP) with a qualitative approach through a questionnaire. The research process involves collecting data, defining boundaries and problems, and designing, coding, and testing the system. As a final step, evaluation is carried out by distributing surveys to obtain valid satisfaction results. This research produces a DSS that has applicability in marketing, accounting, and production processes. The application of DSS in the furniture manufacturing industry can help manage the movement of resources, optimize strategic networks, and assist decision-making in the supply chain process.

Supply Chain Management (SCM) is a very important part of the industrial world, especially in the manufacturing sector. The development of the business world affects the complexity of the supply chain due to the lack of logistics infrastructure, quality of materials and components, and much more. Supply chain disruption risk mapping needs to be done due to high uncertainty, which is overcome by implementing a decision support system. Based on the background of the problem, supply chain disruption mapping uses the help of the Six Sigma method, which consists of 5 stages: Define, Measure, Analyze, Improve, and Control (DMAIC). The measurement of disturbance also uses the Failure Mode and Effect Analysis (FMEA) approach to prioritize risk. Risks that have a high assessment and cause failure need to be prioritized for improvement. This study aims to map supply chain disruptions in the current manufacturing industry based on the barriers, resistances, and causes detected for making a decision support system prototype. By implementing a decision support system in the supply chain process, it is

The objective of this research is to minimize product defects based on labor performance and prove the hypothesis on how labor performance affects the quality of a product through a scientific calculation using Overall Labor Effectiveness (OLE). The primary data is obtained by interviewing the supervisor and labor directly. For secondary data is obtained from the company, such as labor working time, machine scheduled downtime, total production, and defective products. The approach to extract the data is using OLE and the continued regression method. Furthermore, it proceeds to Six Sigma using the DMAIC approach since the results show a significant correlation. The result from Failure Mode and Effects Analysis (FMEA) shows four of six potential failures caused by product defects are coming from labor. To prevent failure mode, it is recommended to have the regular machine checked by labor, check the temperature of the machine, and provide Standard Operating Procedures.