Waste assessment model for hot coil spring production using lean approach - R. S., Hasan; Muhammed Nafis, Osman Zahid (Universiti Malaysia Pahang, 2025)
This study develops and validates customized waste assessment methods for the hot coiling spring manufacturing process. This is due to the high demand for springs in the automotive industry and the potential for a significant impact on the overall supply chain of the component. This study uses a case study approach with quantitative methods to assess waste in a hot coiling production line. Data were collected through direct observation (Genba) and documentation, with key waste types identified using a wastage check sheet. Microsoft Excel and VBA macros were used for analysis, enabling the ranking and prioritization of waste based on cost, cycle time and production efficiency. The assessment method successfully identifies and ranks various types of waste in terms of cycle time and cost analysis. Notably, the bar loading process exhibited a significant inefficiency, with an actual cycle time exceeding 3 times higher than the ideal time of 3 seconds. Similarly, the heating process had a 30% deviation from its ideal cycle time, indicating room for optimization. The cost analysis identifies the pre-treatment stage as a major contributor to high expenses, with a high actual cost rate of RM4.29 per piece, which is over 13 times higher than the average actual cost rate of RM0.33 across other processes. The painting process incurs the highest reject cost, which is 102% higher than the average reject cost. Rework analysis highlights the Pre-Treatment stage with a rework cost of 41% above the overall average reject cost. The findings offer critical insights for prioritizing improvements to optimize operations, minimize delays, and reduce production costs, particularly by addressing inefficiencies in the Pre-Treatment process, painting process, and bar loading. Simplifying the method and developing an automated waste assessment can prioritize cost-saving areas, enabling faster decisions, less manual effort, and continuous improvement of the respective processes through lean implementation.