PROZIMA (Productivity, Optimization and Manufacturing System Engineering)

Clustering-Allocation Model Under Risk, and Emissions Factors: Evidence from an Indonesian Region

2025-02-27T07:38:26+00:00

This study contributes to risk-based location-allocation problems by constraining time and emergency medical services (EMS) carbon emissions. During the COVID-19 pandemic, this study develops a location set covering the problem of implementing ambulance allocation to optimize opening new facilities and the cluster with the highest emission value in heritage cities. This study also presents an integer linear program considering risk, time, and carbon emissions at three facilities with demand locations. The model was also validated using two cluster methods, K-means clustering and Agglomerative Hierarchical Clustering, with Python software and Google Collaboratory machine learning (GCC). The findings revealed the opening of three facilities and clusters with potential points, with the highest emission values at M₃ (0.575% (kg). M_2. potential point, with a value of 5832 represents the highest risk. Furthermore, the validation results indicate that the distance significantly total energy consumption (BTU) and carbon emissions (kg). This study ignores the vehicle category. It can be used as a reference by decision-makers by considering these parameters and making a clear contract with a third party in ambulance procurement for humanitarian logistics. The model will help provide insight into another region's relevant emergency medical center. Furthermore, research can anticipate strategies to deal with pandemic outbreaks.

Design and Build of Broom Handle Cutter and Shaper to Improve Production Efficiency and Working Time

2025-03-03T07:13:22+00:00

This study designs a cutting and shaping tool for broom handles to improve production efficiency and reduce operator workload at Victory 2 Singa. The main issues include slow manual production processes, high costs due to the use of nails, and non-ergonomic tool designs. The proposed solution involves designing a new tool using Quality Function Deployment (QFD). Through QFD, customer requirements such as comfort, safety, durability, affordability, and ease of use are analyzed and translated into technical attributes for the tool. The results of the research show that the designed tool can meet the primary priorities, namely providing precise cutting results, ergonomic handle shapes, and an efficient working process.

Lean Six Sigma Project Selection to Improve College Business Processes

2025-03-03T04:57:12+00:00

Higher education institutions are required to continuously improve the quality of their services to ensure institutional sustainability. Lean Six Sigma (LSS) offers a framework that integrates efficiency and quality improvement. This study aims to determine the priorities of LSS projects in higher education, considering the limited resources and the risk of project failure. The Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods were used to determine priorities. The results showed that improving human resources (HR) and financial information systems is a strategic step. An integrated HR system supports the effective management of lecturers and education personnel, while an accountable financial system increases the transparency and efficiency of financial management. Both systems contribute to improving the quality of services to students. This research provides a basis for developing a digital transformation roadmap for higher education institutions.