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Computer aided process planning (CAPP) is generally acknowledged as a significant activity to achieve computer‐integrated manufacturing (CIM). In coping with the dynamic changes in the modern manufacturing environment, the awareness of developing intelligent CAPP systems has to be raised, in an attempt to generate more successful implementations of intelligent manufacturing systems. In this paper, the architecture of a hybrid intelligent inference model for implementing the intelligent CAPP system is developed. The detailed structure for such a model is also constructed. The establishment of the hybrid intelligent inference model will enable the CAPP system to adapt automatically to the dynamic manufacturing environment, with a view to the ultimate realization of full implementation of intelligent manufacturing systems in enterprises.

The purpose of this paper is to introduce a step‐by‐step implementation framework for lean product development (LPD), from the marketing research on product development process, product design to the launch of final production.

The research approach taken in this paper is built around the primary industry cases, practical approaches and partial solutions available within the existing literature.

The most recent improvement of LPD, from the authors' perspective, focuses on tools and implementation for LPD. In this paper, a detailed step‐by‐step implementation is given after the framework is introduced. Led by value and waste analysis in product development, different tools and techniques which can be used to eliminate wastes were discussed briefly, and then the implementation from Doing the Right Thing to Doing the Right Thing for company transition to lean were proposed elaborately.

Due to time and economic environment limitations, the authors have not covered and implemented this approach in all existing different environments to ensure that it is robust.

The approach described here seeks to overcome other frameworks' weaknesses in terms of the realistic aspect and feasibility, and combines more existing best practice from industry, consultancy and academia into a step‐by‐step framework for the achievement of effective LPD.

The application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.

This paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.

The feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.

The research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.

A novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.

The purpose of this paper is to study the barriers to institutionalize the product lifecycle management (PLM) in large manufacturing organizations. The paper explores the hurdles and identifies the causal barriers to support the organizations’ transformation into digitized firms.

The paper utilized the multi-criteria decision making technique, i.e., DEMATEL (DEcision MAking Trial and Evaluation Laboratory) method to find the causal barriers, and adopted maximum mean de-entropy (MMDE) algorithm to determine the threshold value based on the information entropy of the relations among the barriers to PLM institutionalization.

This study explored nine barriers to PLM institutionalization and empirically identified the four critical barriers among the nine.

The present work is exploratory case-based research which is limited to a case of an Indian aircraft manufacturing firm with a limited number of respondents. More sophisticated statistical tools can be utilized to consider the subjectivity of the respondents. However, this research explores the various hurdles to PLM success and serves as a relevant outcome to identify the critical barriers to institutionalize the PLM concept.

The findings of the paper provide guidelines to the case company and similar firms for obtaining maximum benefits of PLM. The methodology shown in this paper will be useful to various large scale industries in identifying the critical barriers to PLM institutionalization among all existing barriers so that they can take appropriate measures before they proceed to adopt PLM.

The present work discusses the different reasons for which the companies are not able to derive the maximum benefits of PLM even after the implementation of PLM systems. This work uniquely applied the DEMATEL and MMDE methods to investigate the critical barriers to PLM institutionalization in an aircraft manufacturing firm.

In the supply chain disruption risk, the issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

Considering the influence of irrational emotions of decision makers, an evaluation model is designed by the regret theory and VIKOR method, which makes the decision-making process closer to reality.

The paper has some innovations in the evaluation index system and evaluation model construction. The method has good stability under the risk of supply chain interruption.

The mixed evaluation information is used to describe the attributes, and the evaluation index system is constructed by the combined method of the social network analysis method and the literature research method to ensure the accuracy and accuracy of the extracted attributes. The issue of supplier evaluation and selection is solved by an extended VIKOR method based on regret theory.

The purpose of this paper is to contribute to the lean manufacturing literature by providing a roadmap for pull production control system (PCS) implementation.

Axiomatic Design (AD) methodology is used to develop the proposed pull PCS transformation roadmap.

The proposed design methodology is validated in a real-life manufacturing system. The results show that the proposed methodology significantly reduces the design efforts. The methodology effectively helps to choose the most appropriate pull PCS and determine its operational settings with respect to the manufacturing system characteristics.

This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different manufacturing sectors by real-life implementations.

In many real-life lean production projects, companies do not know where to start or how to proceed, which leads to repetitive design efforts and inefficient designs. The developed roadmap of this study minimizes incorrect or imperfect design trials and increases the success of pull production transformation projects.

The implementation of pull PCS requires extensive design knowledge and expertise. Therefore, many real-life applications fail due to costly and time-consuming trial-and-error-based design efforts. In the literature, there is no comprehensive guideline or roadmap for pull PCS implementation. To address this issue, this study provides a novel holistic roadmap to transform an existing push PCS to pull. The proposed methodology uses AD principles and combines fragmentary studies of the pull production literature.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Industrial Internet Platform (IIP) integrates various new information technologies and forms an ecosystem around the platform. It promotes the optimization of resource elements and the collaboration of industrial chains, driving traditional enterprises towards comprehensive Digital Transformation (DT). This research explores the mechanisms through which the Industrial Internet Platform enables the digital development of enterprises.

This study constructs an Industrial Internet Platform Ecosystem (IIPE) from an ecosystem perspective. Later, a systematic literature review was used to design a specific path for IIPE to enable enterprises' DT from the perspective of basic activities and organizational structure.

The results indicate that in IIPE there is a hierarchical structure in the enabling mechanism of IIP. Firstly, the IIPE enhances the digital capabilities of enterprises through the foundational activities of DT. Secondly, the IIPE promotes the adjustment in enterprise structure and strategic orientation for adapting to the DT.

More and more enterprises enter the IIPE and grow together in the ecosystem. As a result, the overall level of digitalization of the industry can be enhanced and all enterprises realize the expected benefits of DT.

Existing research recognized the role of IIP in enterprise management or production processes, but the DT of enterprises is not a single aspect. This research elaborates the mechanism of comprehensive DT of enterprises from the perspective of ecosystems and discovers specific paths for DT.

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 current industrial revolution is powered by data, which is also referred as Industry 4.0. The Industry 4.0 has attracted significant attention from academia and the industry professionals. The supply chain integration (SCI) has played a significant role in enhancing supply chain performance and organizational performance. This study explores the relationship between Industry 4.0 and SCI via an extensive literature review to understand the various levels of integration with the supply chain processes and to identify missing links, through a framework, and suggest further research directions.

In this paper, we have used systematic literature review approach to identify the building blocks of the conceptual framework, which is the main contribution of the present study. We have used Scopus database to search literature using keywords.

The study offers some interesting insights that may help scholars to advance theoretical debates. Moreover, the study also provides interesting direction to the practitioners engaged in supply chain management in data-driven environment. In this study, we have proposed a conceptual framework for the adoption of Industry 4.0 and SCI.

In this study we have proposed a conceptual framework. However, the framework is yet to be empirically tested. Hence, we caution readers to evaluate the findings of the present study in context to its limitations. This is an attempt to develop a conceptual framework which may be tested using longitudinal data.

The present work helps in integrating two independent subjects', i.e. Industry 4.0 and SCI. The theoretical framework presented here integrates Industry 4.0 and SCI which can be useful to the practitioners and policymakers engaged in implementing Industry 4.0.