Optimization Methods in Continuous Improvement Models: A Relational Review

Optimization Methods in Continuous Improvement Models: A Relational Review

Brian J. Galli (Assistant Professor and Graduate Program Director, Master of Science in Engineering Management Industrial Engineering, Hofstra University, USA)
Copyright: © 2019 |Pages: 14
DOI: 10.4018/IJAIE.2019010103

Abstract

There are numerous processes used to implement quality, such as TQM, 6 Sigma, and Lean. For these quality processes to remain effective, a continuous improvement model is required and implemented from time to time. Some of these models include Define, Measure, Analyse, Improve and Control (DMAIC); Plan, Do, Check, and Act (PDCA); Identify, Measure, Problem Analysis, Remedy, Operationalize, Validate, and Evaluate (IMPROVE); and Theory of Constraint (TOC). Furthermore, continuous improvement tools need to remain effective through the use of optimization techniques to produce the best possible outcomes. This article discusses some of the current utilization of these tools and proposes different optimizing techniques and variations to make robust quality implementation tools.
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Introduction

Background

One of the weak links in quality is sustaining good quality. It is easy to assume that once a quality system is implemented, it will operate at the desired and expected quality for an extended period. However, this is hardly realistic. A quality improvement project needs to be continuously monitored and tweaked to produce and maintain the desired level of quality. One way to achieve this is a continuous improvement model. Therefore, this paper seeks to discuss some of the ways to optimize the tools and techniques used to improve and maintain good quality in projects. Some tools for a continuous improvement model include:

  • Define, Measure, Analyse, Improve and Control (DMAIC)

  • Plan, Do, Check, and Act (PDCA)

  • Identify, Measure, Problem Analysis, Remedy, Operationalize, Validate, and Evaluate (IMPROVE)

There are special events known in the industry as the “Kaizen Event.” This is when the above-stated tools get utilized by employing an optimizing technique. This maintains and further improves the quality of a process.

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Research Objective And Problem Statement

The problem with continuous improvement process optimization begins when the DMAIC process is repeated continuously with the expectations of achieving the same level of success gained prior. This may lead to failure of the process. This paper discusses some implementation techniques of continuous improvement models to enhance the tools’ success rate since there is a false expectation that these tools will produce the same successful results.

Research Gap

The existing literature exemplifies what gap exists in the current research. Although much literature demonstrates the pivotal role optimization has concerning continuous improvement projects, there is a gap relating to how this optimization enables a smooth progression in continuous improvement projects. As a result, this study focuses on the gaps existing in related literation regarding optimization and continuous improvement projects. The study evaluates the elements and applicability of modern optimization tools as well as concepts in regard to continuous improvement projects. Thus, this study seeks to fill in the gaps in existing literature.

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