Implementation of Lead Time Improvement in the Cutting Production Process using Clustering Data Mining and Lean Manufacturing

Abstract

In steel companies, there are some processes in the production such as cutting, machining and heat treatment. The production process begins with cutting process and then machining process and the last one is the heat treatment process. It becomes a background that gives the challenge and needs continuous improvement on all aspects, mainly in the cutting process. The research is conducted at one of steel companies located in Pulogadung Industrial Estate. The purpose of the research is to know the factors which cause process lead time cannot be achieved. The methods used are clustering data mining and lean manufacturing. Clustering method can be used to focus on big data and find out clusters or the same pattern. Those clusters are processed with Weka software and using K-means algorithm. Improvement ideas will be implemented to the formed clusters using lean manufacturing such as Single Minute Exchange of Dies (SMED) which have been mapped through value stream mapping, 5S, and Kanban beforehand. The materials’ dimension on the production process is affecting the cutting process lead time. The thicker material diameters will need a longer time to process. With the methods used, lead time of cutting process increased from 3449 minutes to 2165 minutes (3 days to be 2 days). Meanwhile if the “SMED” activities are implemented, the cutting process lead time increased from 187 minutes to 136 minutes.
 Keywords: Clustering; SMED; Lean Manufacturing; Process Lead Time; Value Stream Mapping