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Implementation of Milk Run Theory for Enhanced Logistics Efficiency: A Case Study in the Plastic Packaging Manufacturing Soesilo, Rahman; Valentin, Adelia Dwi
Krisnadwipayana International Journal of Management Studies Vol 4 No 1 (2024): Krisnadwipayana International Journal of Management Studies
Publisher : Program Studi Magister Manajemen Universitas Krisnadwipayana

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Abstract

This research investigates the impact of implementing the Milk Run method in the plastics industry supply chain, focusing on PT RPI. By reducing the distance by 48 km per day, equivalent to 1,200 km in one month of deliveries, the results are significant in terms of logistics efficiency, cost savings, and reduced carbon emissions. Logistics are improved with more connected travel patterns, allowing trucks to combine pickups or deliveries from multiple sources in a single trip. Distance reduction strengthens transportation cost efficiency by reducing fuel consumption and vehicle maintenance costs. Assuming a fuel consumption rate of 7 km per liter and carbon emissions of 2.68 kg CO2 per liter, a distance reduction of 1,200 km results in a substantial reduction in carbon emissions. This study provides a foundation for future research, including route and scheduling optimization, in-depth environmental impact analysis, and application of the Milk Run to other industries. The results provide a complete picture of the effectiveness of the Milk Run in the context of the plastics industry, highlighting operational excellence, economic impact and the company's commitment to sustainability
An Integrated Markov Chain–Fuzzy AHP Framework for Workforce Planning and Turnover Driver Prioritization: A Case Study at PT X Rahman Soesilo; Adelia Dwi Valentin; Fitriana Hisnainy Latifah; Salsabila Al Ghifari
Integrasi: Jurnal Ilmiah Teknik Industri Vol 11 No 1 (2026): Integrasi : Jurnal Ilmiah Teknik Industri
Publisher : Program Studi Teknik Industri, Fakultas Teknik, Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/integrasi.v11i1.1150

Abstract

This study aims to develop an integrated workforce planning framework by combining the Markov Chain method and Fuzzy Analytical Hierarchy Process (Fuzzy AHP) at PT X, a manufacturing company with relatively high employee turnover. Historical workforce data from 2020–2023 were used to construct inter-grade transition patterns, while assessments from 15 experts were used to prioritize turnover drivers. The Markov Chain method was applied to project workforce distribution and compare it with workforce requirements based on 2024–2025 production targets. The projection results indicate potential workforce shortages of 28 employees in 2024 and 77 employees in 2025 if transition and turnover patterns remain unchanged. The Fuzzy AHP results show that external factors have the highest weight of 0.845, with individual motivation as the dominant sub-criterion, having a local weight of 0.803 and a global weight of 0.679. These findings suggest that integrating Markov Chain and Fuzzy AHP can support decision-making in estimating workforce needs while determining priority areas for retention strategies. The contribution of this study lies in the integrative application of both methods in a case-based manufacturing workforce planning context.