Integrating the Objective Matrix Model and Traffic Light System for Productivity Assessment in the Tuna Canning Industry
DOI:
https://doi.org/10.64268/josce.v1i2.43Keywords:
Objective Matrix, Operational Efficiency, Productivity, Traffic Light System, Tuna CanningAbstract
ABSTRACT
Background: Productivity is a critical indicator of operational performance in the food processing sector, particularly in the tuna canning industry, where production fluctuations directly affect competitiveness. PT Bali Maya Permai, a major tuna canning company, has experienced inconsistent production outputs, necessitating a systematic approach to performance evaluation and improvement.
Aims: This study aims to measure the productivity of the tuna canning production department, identify the lowest-performing productivity ratios, and propose targeted improvement strategies to enhance operational efficiency.
Methods: A descriptive-quantitative approach was employed, applying the Objective Matrix (OMAX) method to measure partial productivity indices. The Traffic Light System was integrated to prioritize underperforming ratios, and root cause analysis was conducted using the Ishikawa diagram. Five productivity criteria were evaluated: raw material productivity, labor utilization, working hours, production target achievement, and product release percentage.
Results: The highest productivity index was recorded in June 2024 at 148%, while the lowest occurred in September 2024 at –78%. The Traffic Light System identified raw material productivity as the top priority for corrective action. Ishikawa analysis revealed factors related to material quality, process control, and labor efficiency as major contributors to low performance.
Conclusion: The integration of OMAX and the Traffic Light System offers a comprehensive framework for measuring and prioritizing productivity improvements in tuna canning operations. The findings underscore that sustained productivity growth requires systematic monitoring, data-driven decision-making, and continuous process optimization. For industry practitioners, this approach not only pinpoints inefficiencies but also provides actionable insights for resource allocation, workforce training, and quality control. The methodology demonstrated in this study can be adapted across diverse manufacturing sectors to establish a culture of continuous improvement, strengthen competitive positioning, and support long-term operational sustainability in the global food processing industry.
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