2011International Journal of Industrial and Systems EngineeringRequires access

Application of value stream mapping using simulation to decrease production lead time: a Malaysian manufacturing case

Alireza Esfandyari, Mohd Rasid Osman, Napsiah Ismail, Farzad Tahriri

Open publisher page 34 citations

Abstract

Lean concept has been applied across many companies which offer value and eliminate wastes. Value stream map (VSM) as one of the fundamental tools in lean concept outlines the material and information flows for a product family to reduce wastes at discrete event production routine. In this paper, the improvement of the production lead time using VSM as a technique in a Malaysian supplier, with a job shop production system, is investigated. The main contribution of this paper is reducing production lead time when the Takt Time is much higher than the highest station’s cycle time, and reducing unplanned released orders. This paper evaluates the present routing events using current state map and the future state is created answering the eight standard questions. Then, a detailed simulation model was developed to verify the result from future state map and answering the questions that are unable to be addressed by VSM.

About this research paper

What this paper is about

Lean concept has been applied across many companies which offer value and eliminate wastes. Value stream map (VSM) as one of the fundamental tools in lean concept outlines the material and information flows for a product family to reduce wastes at discrete event production routine. In this paper, the improvement of the production lead time using VSM as a technique in a Malaysian supplier, with a job shop production system, is investigated. The main contribution of this paper is reducing production lead time when the Takt Time is much higher than the highest station’s cycle time, and reducing unplanned released orders. This paper evaluates the present routing events using current state map and the future state is created answering the eight standard questions. Then, a detailed simulation model was developed to verify the result from future state map and answering the questions that are unable to be addressed by VSM.

Why it matters

OpenAlex reports 34 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Lean concept has been applied across many companies which offer value and eliminate wastes. Value stream map (VSM) as one of the fundamental tools in lean concept outlines the material and information flows for a product family to reduce wastes at discrete event production routine. In this paper, the improvement of the production lead time using VSM as a technique in a Malaysian supplier, with a job shop production system, is investigated. The main contribution of this paper is reducing production lead time when the Takt Time is much higher than the highest station’s cycle time, and reducing unplanned released orders. This paper evaluates the present routing events using current state map and the future state is created answering the eight standard questions. Then, a detailed simulation model was developed to verify the result from future state map and answering the questions that are unable to be addressed by VSM.

Key concepts: Value stream mapping, Lead time, Lean manufacturing, Production (economics), Discrete event simulation, Event (particle physics), Job shop, Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
Application of value stream mapping using simulation to decrease production lead time: a Malaysian manufacturing case — Research Paper | ScholarLens