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This paper aims to presents a novel integrated fuzzy decision support system for analyzing the issues related to failure of a milk process plant unit.

Process failure mode effect analysis (PFMEA) approach was implemented to list failure causes under each subsystem/component and fuzzy ratings for three risk criteria, i.e. probability of failure occurrence (O_f), severity (S) and non-detection (O_d) are collected against the listed failure causes through experts feedback. A new doubly technique for order of preference by similarity to ideal solution (DTOPSIS) approach was implemented within fuzzy PFMEA tool for ranking of listed failure causes. The proposed decision support system overcomes the restrictions of classical PFMEA and IF-THEN rule base PFMEA approaches in an effective way.

Failure causes such as electrical winding failure (RM4), high pressure in plate region (C1), communication problem in supervisory control and data acquisition control (MS3), insulation problem (ST2), lever breakage (B2), gasket problem (D3), formation of holes (PHE5), cavitations (FP7), deposition of milk particle inside the pipeline because of improper cleaning (MHP2) were acknowledged as the most critical one with the application of proposed decision support system.

The analysis results are based on subjective judgments of the experts and therefore correctness of risk ranking results are totally dependent upon the quality of input data/information available from these experts. However, the analyst has taken proper care for considering the vagueness of the raw data by incorporating fuzzy set theory within the proposed decision support system.

The proposed fuzzy decision support system has been presented with its application on milk pasteurization plant of a milk process industry. The analysis based ranking results have been supplied to maintenance manager of the plant and a consent was shown by him with these results. Once the top management of the plant took decision for the implementation of these results, the detailed robustness of the proposed decision support system could be evaluated further.

The analysis result would be highly useful for minimizing sudden breakdowns and operational cost of the plant which directly contributes to plant's profitability. With the decrease in the chances of sudden breakdowns there would be high safety for the people working on/off the plant's site. Further, with increase in availability of the considered plant the societal daily demand related to dairy products could be easily fulfilled at reasonable prices.

The performance and proficiency of the proposed decision support system has been evaluated by comparing the ranking results with classical TOPSIS and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approaches based results.

The purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.

The analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.

It is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.

The proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.

The offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.

The application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.

The sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.

It was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.

The Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.

The proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.

This research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.

The purpose of this article is to report the application of failure mode and effect analysis (FMEA) to an automotive leaf spring manufacturing organization.

FMEA has been used as a decision‐making tool to prioritize the corrective actions so as to enhance product/system performance by reducing the failure rate. Both design and process FMEA documents have been developed by the systematic formation of team.

The study results indicated the actions that lead to improvement in design. There has been improvement in key decision factors apart from conventional factors. In addition, the quality of leaf springs produced also has been improved.

Conventional design and process FMEA approaches have been developed. In future, fuzzified FMEA can be used.

FMEA has been systematically deployed in a typical industrial scenario. The real improvements have been gained as a result of implementation.

The article presents the results of the case study conducted in an industrial scenario. The contributions of the study are original and valuable.

Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan‐do‐check‐act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma (6 σ) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

In 2016, the ISO/TS 16949 quality management standard for the automotive industry evolved to IATF 16949. The update brought new requirements that need to be analyzed before being implemented in organizations. Therefore, the purpose of this article is to propose guidelines to assist organizations in the automotive sector in the implementation of the elements added in the update to the IATF 16949 standard.

To fulfill this objective, the identification and analysis of the elements added in the evolution from ISO/TS 16949 to IATF 16949 was carried out, and four case studies were conducted in Brazilian automotive companies.

The main elements added to IATF 16949 with the update of the standard are the use of process failure mode effects analysis (PFMEA) for risk analysis; the development of a communication channel for employees to report cases of misconduct and non-conformities; procedures for controlling repaired/reworked products and temporary changes; and the inclusion of autonomous maintenance for the full implementation of total productive maintenance (TPM).

The main practical implication/contribution of the research is the proposed guidelines, which can support managers and automotive companies that want to implement, or will go through, the IATF certification process. The article's originality lies in the combination of a theoretical framework and case study analyses to develop the guidelines.

The purpose of this paper is to demonstrate the systematic application of Six Sigma tools for identification and reduction of cost of poor quality (COPQ). The study examines one of the chronic problems of failure of cooling fan assembly at repair division of a company dealing in helicopter components.

The case adopted Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology to achieve the goal of reduction in COPQ.

After completing the Define, Measure and Analyze phase, it was found that use of extreme tolerances and cross-fitment of bearings are the root cause of cooling fan assembly failure. The major recommendations made during the Improve phase were to design a bearing matching software for improving the cross-fitment of bearings and to procure a hydraulic jig with electronic jig instead of manual jig. The value of implementing these recommended solutions equate to a saving of INR 34 lacs per annum. Since it was a chronic problem, the company expects this to be a recurring saving.

This specific case exhibits the successful application of Six Sigma DMAIC methodology in repair and maintenance for driving down the cost of failure and improved processes.

Looks at problems experienced by manufacturers in matching their level of automation to local conditions. Particularly concentrates on the automotive manufacturing sector. Gives examples of different levels of automation. Presents a process developed by the author’s company (Assembly Technology and Test Ltd) called total manufacturing cell (TMC). It recognises that the manufacturing process is a key element in the success or failure of a product and sees development of the manufacturing solution as only one phase in the life of a product.

The aim of this research was to call attention to the implementation of failure mode and effects analysis (FMEA) in a collaborative environment, the issues occurred in the implementation process, and a tool that can be used by all parties in a collaborative environment for FMEA process.

The discussion includes the procedure of an integrated FMEA approach, how to implement the procedure in a supply chain, and the common problems occurred in its implementation in automotive industry under a collaborative environment.

The research provided an example of inconsistency in the ranking of severity, occurrence, and detection to show that the inconsistency may delay FMEA implementation in a supply chain.

This study offered guidelines for manufacturing industry in correcting the problems in FMEA applications, so companies can adopt their FMEA process into a collaborative supply chain environment. This paper also demonstrated a Microsoft EXCEL‐based tool that can ease the FMEA process in a collaborative environment for determining sampling size, reliability and confidence level for tests in design verification and control plan as a part of integrated FMEA process.