Prioritizing process improvement initiatives in manufacturing environments

Aqlan, Faisal; Al-Fandi, Lawrence

doi:10.1016/j.ijpe.2017.12.004

Cited by 60 publications

(46 citation statements)

References 15 publications

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“…Furthermore, an important decision for many managers is how much to invest in cost reducing process improvement (Veldman & Gaalman, 2015). Some framework for prioritizing process improvement initiatives in manufacturing environments is proposed by Aqlan & Al-Fandi (2018) aiming at considering process variables, resource constraints, and operator skills.…”

Section: Activity Input Datamentioning

confidence: 99%

“…are creating pressure on companies and their performances, so business process improvement projects have become a necessity for companies (Bakotic & Krnic, 2017). Companies need to focus on continuous improvement through selecting and prioritizing process improvement initiatives, that can be a challenging task, especially when multiple decision factors are involved (Aqlan & Al-Fandi, 2018). Continuous improvement encompasses a spectrum of activities, methods and approaches that seek to improve the effectiveness and efficiency of business processes over time and ensure the alignment of business processes with the competitive environment (Matthews et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

Glogovac

Filipović

Zivkovic

et al. 2019

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In this paper, the PAF (preventive-appraisal-failure) quality costs model is used on the process level, in order to identify those processes which are critical as a top-priority for improvement. The elements of quality costs are considered in relation to the outputs of the processes, where the principle that one process affects the quality of another process by its outputs is taken into account. That is why the failure costs of the affected processes should also be assigned to the quality costs of the observed process. Using the factors proposed in the Process cost model, costs are identified and calculated for each process for two moments: the current (before improvements) and desired (after improvements). Improvements are implemented on the basis of the causes of nonconformity analysis and according to the PAF model assumption that preventive and appraisal activities reduce failure costs and the total costs of quality. The difference between the total costs of quality in two observed moments is seen as the greatest possible quality cost reduction for the observed process. This value is used as a criterion for ranking the processes according to their priority for improvements, taking into account the assumption of the PAF model that greater costs mean lower quality and hence a higher priority for improvement.

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Section: Activity Input Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

Glogovac

Filipović

Zivkovic

et al. 2019

View full text Add to dashboard Cite

show abstract

“…It is very important to know which QTT to be used in improvement activities [9]. However, selection of the most appropriate tools has been always not easy due to a huge number of QTTs available [9], [10].…”

Section: A Selection Dilemmamentioning

confidence: 99%

Development of Intelligent Decision Support System for Selection of Quality Tools and Techniques

Amran¹,

Ikbar²,

Khairanum³

et al. 2019

IJMLC

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This research derives systematic analysis and synthesize previous research published with aim to highlight and develop a decision aid for Quality Tools and Techniques (QTT) selection which enable practitioner make a rational and structured decision though development of Intelligent Decision Support System (IDSS). Vast numbers of QTT for problemsolving and improvement activities give a significant challenge to determine the suitable QTT. The chosen QTT affect the effectiveness of improvement activity and misused of QTT can slow down the improvement process and flawed the conclusion. The IDSS namely eCIFOD was produced by translating the QTT mapping into the SQL Union and SQL Inner Join. PHP scripts were developed to run the system and uploaded to online after the Black Box Testing show good result. A single case with a single unit of the case study research strategy was used to conduct the user acceptance testing unveil the positive feedback from the user in term of feasibility, usability, and utility. Development of empirical IDSS for selection of QTT through providing structured and intelligent decision making provides holistic decision support for practitioner to select most optimize QTT to be used for improvement purpose.

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“…chronic heart diseases [CHD] and high and low chronic blood pressures [BP]). In general, the prioritisation process is a challenging task, especially when multiple decision factors are involved [13]. Thus, the prioritisation process for remote patients with MCDs is considered a challenging task, given that it requires decision making (DM) for each disease.…”

Section: Introductionmentioning

confidence: 99%

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

et al. 2020

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Telemedicine is increasingly used in the modern health care system because it provides health care services to patients amidst distant locations. However, the prioritisation process for patients with multiple chronic diseases (MCDs) over telemedicine is becoming increasingly complex due to diverse and big data generated from multiple disease conditions. To solve such a problem, massive datasets must be collected, and high velocity must be acquired, specifically in real-time processing. This process requires decision-making (DM) regarding the emergency degree of each chronic disease for every patient. Multi-criteria decisionmaking (MCDM) approaches (i.e. direct aggregation, distance measurement and compromise ranking) are the main solutions for dealing with this complex situation. However, each MCDM approach provides a unique rank from those of other methods. By far, the preferred DM approach that can provide an ideal rank better than other approaches has not been established. This study proposes an extension of the technique for reorganising opinion order to interval levels (TROOIL). Such an extension is called Hybrid DM and Voting Method (HDMVM) which is based on different DM approaches (i.e. direct aggregation, distance measurement and compromise ranking). HDMVM is used to prioritise big data of patients with MCDs in real-time through the remote health-monitoring procedure. In this paper, we propose a methodology that is based on three sequential stages. The first stage illustrates how the big data of patients with MCDs can be recognised in the telemedicine environment and identifies the target telemedicine tier in this study. The second stage describes the steps of the proposed HDMVM sequentially. The third stage applies the proposed method by prioritising the case study of big data of patients with MCDs based on the above DM approaches. Moreover, dataset of remote patients with MCDs (n = 500) is adopted, which contains three diseases, namely, chronic heart diseases and high and low blood pressures. The prioritisation results vary among direct aggregation, distance measurement and compromise approaches. The proposed HDMVM effectively provides a uniform and final ranking result for big data of patients with MCDs. A statistical method (i.e. mean) is performed to objectively validate the ranking results. Significant differences between the scores of the groups are identified in the objective validation, signifying identical ranking results. The evaluation of the proposed work with the benchmark study indicates that this study has tackled issues relevant to big data and diversity of MCDM approaches in the prioritisation of patients with MCDs.

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Prioritizing process improvement initiatives in manufacturing environments

Cited by 60 publications

References 15 publications

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

Development of Intelligent Decision Support System for Selection of Quality Tools and Techniques

A Uniform Intelligent Prioritisation for Solving Diverse and Big Data Generated From Multiple Chronic Diseases Patients Based on Hybrid Decision-Making and Voting Method

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