Lean Process Improvement for Rework Performance in Manufacturing

Lean Process Improvement for Rework Performance in Manufacturing

DOI: 10.4018/978-1-5225-4062-5.ch001

Abstract

The primary purpose of this chapter is to identify the factors in the process for performance rework that are causing the most cost to a local manufacturing company to reduce the cost and improve the process. The data comes directly from the facility as historical data from the year 2016. The authors also collected data on low band and high band noise defects. The first method is the value stream map, current state map, and future state map. The second method is to measure noise levels of the bearings using the Anderon meter and Waveometer. The third method is to construct control charts using Minitab software. The findings demonstrated that bearings that fail due to low band noise produce a low final yield after being reworked. On the other hand, bearings that fail due to high band noise had a much higher final yield after being reworked.
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Background Of Lean Manufacturing

The goal of lean manufacturing is to be highly responsive to customer demand by reducing waste. Lean manufacturing aims at producing products and services at the lowest cost and as fast as required by the customer. The lean concept originated in Japan after the second world war when Japanese manufacturers realized that they could not afford the massive investment required to rebuild devastated facilities. Toyota is often credited for the creation of lean manufacturing. Lean manufacturing gives manufacturers a competitive edge by reducing cost and improving productivity and quality. It can include improvement in processing time, lead time, cycle time, set up time, inventory, defects and scrap, and overall equipment effectiveness.

Background of Quality Control

Quality control is the process that ensures customers receive products free from defects and that meet their needs. Some common tools used to support quality control include statistical process control (SPC) and Six Sigma. Statistical process control monitors and controls quality by tracking production metrics. It helps quality managers identify and solve problems before products leave the facility. Six Sigma is a systematic approach for eliminating errors. It uses statistical methods to improve quality by minimizing variability.

Purpose of the Research

The primary purpose of this research is to identify the factors in the process for performance rework that are causing the most cost to the manufacturing company. The goal is to use these factors to reduce the cost and improve the process. The performance rework process is an in-depth process which includes inventory, assembly, test for noise, holding, and potential rework or scrap.

Objectives of the Research

  • To appraise the existing Performance Rework Process at a local manufacturing company.

  • To accurately measure noise levels of parts or parts processed, such as standard cylindrical series, RB (rubber mounted inserts), ER (non-spherical outer diameters), and SK (special order) Inners/Outers for performance ball bearings.

  • To measure and analyze output of noise levels in hz, for variability using control charts in Minitab statistical software.

  • To improve and control the Performance Rework Process.

    • o

      To develop and standardize sorting process to meet customer demand.

    • o

      To develop a more cost-efficient process to meet customer demand.

Significance of Research

The significance of looking into the performance rework process is that many manufacturing facilities have the issue of scrap and rework. This is a topic that can resonate across many different companies. By establishing the areas that can improve the process, companies can then adjust their processes in order to decrease costs and improve customer satisfaction. The results of this research would be of value to organizations who want to find out if it is worth the cost to rework or to send everything nonconforming to scrap.

Assumptions and Limitations

The main limitation of this study is lack of historical data. While the factory has historical data on scrap, there is no current data for rework fallout. This means that parts could still be non-conforming even after being reworked. Also, the data collected by the author used small sample sizes of 4 and a small lot size of 34 for one of the two scrap reasons studied. The limitation of small sample sizes and small lot sizes should be observed.

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