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Quality Engineering Techniques from Past to Future

A Discussion on Comparison of Previous Research with the Proposed Model

Of the previous research done on statistical techniques (ST) and non-statistical techniques (N-ST), the research that focused on singular applications in several industries or scientific fields are valuable. Table 5.1, for instance, sheds light on the real path of the research conducted so far.

Not one of the previous research studies has a comprehensive model for implementing QET, in particular, for industrial factories or manufacturing industrial products. Moreover, the process approach for describing the proposed model is the key point of the book.

Figure 5.1 presents the two main approaches for applying statistical and non- statistical techniques from the past to the present.


Path of Research on (ST) and (N-ST) Applications



Research title


(Sima el al., 2019)

Feasibility of Using Simulation Technique for Line Balancing in Apparel Industry


(Tulcidas et al.. 2019)

Statistical methodology for scale-up of an anti-solvent crystallization in the pharmaceutical industry


(Ваша et al., 2018)

Chapter 10 - Statistical Techniques in Pharmaceutical Product Development


(Andrade et al., 2018)

Application of waves trapping statistical technique to estimate an extreme value in train aerodynamics


(Cristovao et al., 2018)

Fish canning industry wastewater variability assessment using multivariate statistical methods


(Memon and Shaikh, 2016)

Confidence bounds for energy conservation in electric motors: An economical solution using ST


(Lim and Antony, 2016)

Statistical process control readiness in the food industry: Development of a self-assessment tool


(Lin et al., 2015)

Identifying water recycling strategy using multivariate statistical analysis for high-tech industries

Trend of two main approaches in the development of QET

FIGURE 5.1 Trend of two main approaches in the development of QET.

A mixture of both ST and N-ST approaches in the form of an integrated model for industrial organizations has not been studied or at least has not been reported. For the first time, such an approach has been considered and applied in this research. Furthermore, several aspects of the proposed model in this book offer new approaches for carrying out systematic studies in industrial organizations.

Final Results of the Research in Manufacturing Industries

Figure 5.2 provides a summary of the main points of the proposed model in this research.

The most creative aspect of this book is the introduction of a process map within the organization, besides exploiting several quality engineering techniques including statistical and non-statistical tools applied to different levels of organization. The most innovative recommendation of this book is to execute the proposed model in an

Proposed model of QET for industrial factories

FIGURE 5.2 Proposed model of QET for industrial factories.

effective format for the defense sectors of the country and to create or increase added values for manufacturing industrial products. The proposed model in this book utilizes several features as follows:

  • • Z-MR control charts for describing the impacts of units on productivity and sustainability
  • • Calculations of the total score of productivity and sustainability before and after implementing the model
  • • Demonstration of the growth in added values for manufacturing industrial products
  • • Generalization of the assessment results of the model to other organizations
  • • Assessment of risks and opportunities with using the model in manufacturing industries
  • • Impacts of implementing the model in industrial economics

Suggestions for Future Research

One of the best suggestions that can be made is to design and implement the proposed model in fuzzy environments in industrial organizations. The fuzzy environments require advanced statistical and non-statistical techniques. Moreover, analytical mathematics must be considered. Furthermore, the uncertainty issue related to different results in statistical calculations is one of the most important points to consider and it seems to be the greatest challenge of the 21st century. The generalization of the proposed model to uncertain situations can be regarded as an attractive project which could change the future of industrial organizations. In the end, it is hoped that the proposed approach can lead to productivity and sustainability through the application of applying QE. The authors hope that the model proposed in this book could open a new door toward achieving superior goals in the industrial world of today.


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