Waste Identification in Production Process Using Lean Manufacturing: A Case Study

All manufacturing industries need to perform a continuous efforts to survive in the current competitive and challenging market. In order to manage the critical situation, the stakeholders are trying to implement the most recent and innovative technique to the production system. The aim is to attain effective and efficient. One important step to reach efficient working process is by understanding wastes occurred along the production line and try to reducing their presence. Lean manufacturing approach is one of the powerful philosophies to guide a company to improve their quality, by providing varieties of tools and strategies. This approach is able to help company in many sectors, such as identification and elimination of waste, better quality of product and service, lowering the production cost, reducing the human effort and the production process time. In this study, a small industry was chosen as the object of the research. The aim of the research is to identify waste that occurs in the production using waste assessment model then solve the problem and determine the value added activity in the whole production process of cake using value stream mapping. Based on the analysis, it can be concluded that based on the current state value stream mapping, the value of the process cycle efficiency is only 28.49% which is still relatively un-lean. From the overview is also identified the departments that were subjected to the identification are the emergence of goods WIP (work in process), the amount of finished goods inventory, and long lead time. After using PAM, it is known that the whole process of making cake consisted of 28.49% of time spent on value-added activities, 3.35% non-value-added activities, and the rest for necessary but non value added activity. This study has proposed schedule for the Green-bean-filling`s department. This proposed schedule is able to increase the process cycle efficiency to 41.36%. Keyword: lean manufacturing, waste identification, waste assessment model, value stream mapping