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Optimizing Equipment Layout with ARC and 5S in a Metal Casting Molding Division Arinda Soraya Putri; Pujji Hastuti; Afiqoh Akmalia Fahmi; Eko Setiawan; Fadhilla Tri Nugrahaini; Much Djunaidi; Bayu Permana; Riva'i Arva Irianto
Performa: Media Ilmiah Teknik Industri Vol. 25 No. 1 (2026): Performa: Media Ilmiah Teknik Industri
Publisher : Industrial Engineering Study Program, Faculty of Engineering, Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/performa.v25i1.2907

Abstract

A metal casting manufacturing company operating in Ceper, Klaten. One of the key areas in its production process is the molding division, where casting molds are made. Several issues were identified in this area, including poor organization, inadequate cleanliness, and insufficient attention to workplace safety. This study aims to evaluate the implementation of the 5S culture (Seiri, Seiton, Seiso, Seiketsu, and Shitsuke) in the molding division and to propose improvements to enhance four key principles: effectiveness, efficiency, productivity, and occupational safety. Based on the results of the 5S questionnaire data analysis, the overall implementation level was relatively low at 52.78%, with Seiso scoring the highest (66%), followed by Shitsuke (57%), Seiri (54%), Seiton (51%), and Seiketsu (35%). To address these issues, several improvements were proposed, including procedures for item sorting, equipment labeling, implementation of workplace signage, development of Standard Operating Procedures (SOP) for machines, scheduling of machine/tool maintenance, and the establishment of a reward and punishment system. These efforts were further supported by optimizing the workplace layout using the Activity Relationship Chart (ARC) method. The implementation of these improvements resulted in a significant increase in the utilization of the production area, from 33% to 66%.
The selection of sago flour suppliers using Multi Criteria Group Decision Making approach with different sets of criteria Usamah Afiq Nuruddin; Indah Pratiwi; Afiqoh Akmalia Fahmi; Eko Setiawan
AGROINTEK Vol 20, No 2 (2026)
Publisher : Agroindustrial Technology, University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/agrointek.v20i2.29902

Abstract

Tiga Hati Mutiara, a producer of pearl sago, has only one supplier of raw material for sago flour for the production of sago pearls. Based on data from the Indonesian Ministry of Agriculture, there are 13 sago-producing provinces. These 13 sago-producing provinces can become alternative sago suppliers for Tiga Hati Mutiara. The purpose of this research is to find the best supplier of sago flour for Tiga Hati Mutiara, in accordance with its needs and goals. To address the problem under concern, this research uses the MCGDM approach with a different set of criteria. This method requires expert data, such as decision makers, supplier alternatives, and supplier criteria. The study found that supplier alternative 2, namely Terong Mas, was chosen as the main supplier of sago flour out of six supplier alternatives and fifteen sub-criteria used. This research is expected to be a suggestion for improvement in supplier selection decision-making for the company, as well as material for further research literacy.
INTEGRATING LEAN WASTE ANALYSIS INTO ROBOTIC INDUSTRIES: INSIGHTS FROM THE WASTE ASSESSMENT MODEL Satriyono, Raden Danang Aryo Putro; Faozi, Ekan; Fahmi, Afiqoh Akmalia; Wibowo, Ari Mukti
JEMIS (Journal of Engineering & Management in Industrial System) Vol. 14 No. 1 (2026): In Process
Publisher : Industrial Engineering Department, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

The rapid growth of high‑tech manufacturing demands rigorous waste‑reduction strategies to sustain competitiveness. This research applies the Waste Assessment Model (WAM), a systematic tool for identifying and ranking the seven classic lean wastes, to PT Stechoq Robotika Indonesia, a research and development driven robotics firm. Using the three‑step WAM process (Seven Waste Relationship, Waste Relationship Matrix, and Waste Assessment Questionnaire), data were collected from production‑line operators and analyzed through matrix scoring and questionnaire weighting. Quantitative results reveal that Defect (19.9 %), Overproduction (17.1 %), and Process (17.1 %) are the most influential waste sources, while the WAQ highlights Motion (24.4 %), Transportation (22.4 %), and Process (17.1 %) as the dominant waste types to target. The study demonstrates that waste from defects, overproduction, and process steps significantly propagates secondary wastes, with inventory waste emerging as the most affected (17.8 %). These insights provide a data‑driven roadmap for lean‑focused interventions, aligning with broader industry practices on waste prioritization.