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Micro, Small, Medium Enterprises (MSMEs) are witnessing an accelerated transformation by the advent of Industry-4.0 (I4.0) in the post-pandemic period. It is offering promising customer responsiveness, competitiveness, business growth and sustainability and thereby, compelling its integration to MSMEs. Therefore, it is imperative for researchers to explore Industry 4.0 challenges and their specific implementation requirements and also provide useful insights to the stakeholders.

This research paper has identified, explained and analysed various determinants of the I4.0 implementation, in MSME context. Focus group approach has been applied for taking inputs from experts for developing the House of Quality (HOQ) tool of the Quality Function Deployment (QFD) methodology from the Total Quality Management (TQM) tool-box.

Based on the responses and after applying QFD, a conceptual model suggesting relevant strategies to execute I4.0 by Indian MSMEs has been developed. The model highlights three key challenges being faced by the Indian MSMEs –Top management support, Incompatible resources and Transition cost. The model also reveals vital few designer's descriptors – Cultural reorientation, IT enabled digitization, Process automation and knowledge and skill in I4.0 implementation (Knowhow) for a structured implementation of I4.0.

The evolved HOQ framework has provided some useful insights - priority areas in the MSME challenges and the designer's descriptors for I4.0 implementation in MSME. The research has also provided the understanding of the dynamics between the I4.0 components through the 10 × 10 interrelationship matrix of the HOQ. Farsighted MSME leaders, practising consultants, sourcing managers and policy makers can use the developed framework as a reference in formulating tactics to mitigate the I4.0 implementation barriers.

The non-conventional application of HOQ in the QFD approach from the TQM tool-box is a useful value addition to the TQM practitioners. The useful insights to the MSME leaders, policy makers, sourcing managers of OEM, consultants engaged in I4.0 transformation and academic researchers are the other contribution.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

This study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.

Based on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.

The results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.

This study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.

This paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

The purpose of this paper is to present an approach to start industry–university (I-U) collaboration through a stepped process aimed at building a portfolio of research and development (R&D) projects.

It devises from an 18-month action-research program held between a multinational automotive manufacturer and the a top-ranked Brazilian university.

The three-stage R&D shared portfolio methodology results from a combined application of quality function deployment-like correlation matrices and roadmapping. A first matrix tackles industry interests and correlates product performance dimensions and components to reveal broad research areas of interest. A second matrix correlates research areas and engineering competences, highlighting the types of the required know-how from the university standpoint. Thirdly, academic experts help to fill a roadmap-like layer with possible collaborative R&D deliverables over time.

Since the study lies on a single experience, extensions to other contexts should be made with care. However, the proposal offers robust rationale and a set of supporting tools to nurture new applications.

Theoretical and methodological reflections help managers tackling the long-standing problem of setting a shared R&D agenda.

Literature on I-U collaboration tends or to over-emphasize the role of technology transfer offices in promoting the partnerships or to seek implications for public policy. This research offers a valuable approach to build shared R&D project portfolio from a managerial viewpoint, filling an academic gap and offering guidance for managers in both sides.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

This paper aims to systematically review the literature on quality management in agri-food supply chains (SCs) and propose an integrated conceptual framework.

A systematic literature review that analyses 93 papers in peer-reviewed academic journals published from 1996 to November 2021 is conducted. A conceptual model is advanced.

Based on a hierarchy of capabilities perspective, the authors develop an integrated conceptual framework in which SC quality (SCQ) management practices promote three levels of SC dynamic capabilities, which in turn lead to agri-food SCQ performance.

The authors propose a hierarchy of capabilities perspective of quality management in agri-food SCs and develop a conceptual framework. Furthermore, a number of propositions based on dynamic capabilities and the review findings are provided. Four future research directions are presented.

Adopting lean principles can unleash several opportunities for firms seeking to increase the efficiency and effectiveness of their product development (PD) process. This study aims to investigate the implementation paths of lean tools in the innovation process of small and medium-sized enterprises (SMEs).

A set of 47 lean tools are identified from the literature and ascribed to the five lean thinking principles, i.e. Value, Map, Flow, Pull and Perfection. Their practical adoption – in terms of “when” and “how” – is then explored in a multiple case study of three SMEs in the manufacturing industry.

SMEs adopt multiple lean tools in different phases of their innovation process. They are still at the beginning of the holistic adoption of lean PD, but some core lean tools, such as A3 reports and visual management, are adopted systematically. Results reveal that specific sets of lean tools and supporting principles are more valuable in certain phases of SMEs innovation process. Specifically, the lean tools concerning the principle of Value and Map can enable the phases of Innovation inputs, Concept development and Solution implementation; the ones ascribed to Flow and Pull the phases of Concept development, Testing and experimentation, and Solution implementation; the Perfection tools to the final phases of Testing and experimentation, Solution implementation and Market introduction.

Results provide a reference for SMEs already adopting lean tools in their production process to be extended to the PD process, especially when the delivery of new products is pivotal. Innovative SMEs could evaluate the introduction of specific lean tools in one or more definite phases of their PD process.

The study contributes to the literature on the complementarity between lean and innovation by studying the context of SMEs with a process perspective, thus unveiling the potential paths of a widespread application of lean innovation in SMEs.

The present paper proposes a framework for zero-defect manufacturing in Indian industries. Due to the current competitive market, there is a strong need to achieve zero defects from the customer's perspective. A survey questionnaire is analyzed based on the responses and a structured framework is drafted to implement zero defect manufacturing in the Indian industry.

To analyze zero-defect in Indian industries, a literature review and a survey questionnaire constituted a framework. This framework is independent of the type of process and product.

The findings of this study are based on a total of 925 responses received through survey questionnaires by different mediums. The framework has been tested in different manufacturing organizations to achieve zero-defect through the continuous improvement approach.

The study results aim to achieve zero-defect, help to improve customer satisfaction, reduce waste and rework in the manufacturing process. This framework is also used as a problem-solving approach to implement Six Sigma in the Indian industries.

Zero defect manufacturing is growing in India and globally. This framework helps to implement zero defect manufacturing in Indian industries. It is an essential tool to capture the voice of the customer.