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The ever-happening disruptive events interrupt the operationalisation of manufacturing organisations resulting in stalling the production flow and depleting societies with products. Advancements in cutting-edge technologies, viz. blockchain, artificial intelligence, virtual reality, digital twin, etc. have attracted the practitioners' attention to overcome such saddled conditions. This study attempts to explore the role of artificial intelligence (AI) in building the resilience of production function at manufacturing organisations during a COVID-19 pandemic. In this regard, a decision support system comprising an integrated voting analytical hierarchy process (VAHP) and Bayesian network (BN) method is developed. Initially, through a comprehensive literature review, the critical success factors (CSFs) for implementing AI are determined. Further, using a multi-criteria decision-making (MCDM) based VAHP, CSFs are prioritised to determine the prominent ones. Finally, the machine learning based BN method is adopted to predict and understand the influential CSFs that help achieve the highest production resilience. The present research is one of the early attempts to know the essence of AI and bridge the interplay between AI and production resilience during COVID-19. This study can support academicians, practitioners, and decision-makers in assessing the AI adoption in manufacturing organisations and evaluate the impact of different CSFs of AI on production resilience. [ABSTRACT FROM AUTHOR]

A well-determined manufacturing system helps organisations achieve a desired competitive advantage. So, the manufacturing system selection concerns the critical decision of manufacturing strategy deployment. In the present era of industry 4.0, there exist four traditional manufacturing systems (TMS) (i.e. job-shop, batch-shop, mass, and continuous) and additive manufacturing system (AMS). Different process choice criteria (PCC) are responsible for selecting the best-suited system from five or a hybrid (AMS + TMS) configuration. This research formulates a two-stage framework comprising Delphi and Voting analytical hierarchy process (VAHP) techniques for quantifying the congruence between PCC and manufacturing systems. Initially, extant literature is reviewed to identify the critical PCC and further validated by industry experts through Delphi. This results in retaining thirty-six PCC. The relative importance of an individual criterion concerning a particular manufacturing system is computed using VAHP in the second stage to understand the alignment of PCC in different manufacturing systems for exhibiting the congruence between PCC and manufacturing system (TPS and AMS). The findings offer critical insights about the different PCC and their level-of-fit in TMS and AMS, which can assist researchers and practitioners in evaluating a suitable manufacturing system for an organisation using identified PCC. [ABSTRACT FROM AUTHOR]

The deployment of smart manufacturing technologies like communication, computing, sensors, cyber-physical systems, simulation, and data sciences has enabled the operational transformations of the factory ecosystem. Therefore, it is essential to monitor the alterations commenced by the business due to smart manufacturing implementation. Smart Manufacturing Performance Measures (SMPMs) are used to evaluate operational transformations realized through digitalization of systems. However, there is no reported work focusing on the quantification of SMPMs. This study addresses the research gap by defining potential indicators to quantify SMPMs referred to as smart manufacturing performance indicators (SMPIs) identified through literature review methodology. The SMPIs are discussed in detail besides their mathematical expression advocated through in-depth literature study. Further, a conceptual framework for decision-making in smart manufacturing environment based on SMPIs is proposed. The conceptual framework provides guidelines to plan and select the preferred focused manufacturing output and the relevant set of SMPIs contributing to the outputs for expediting effective smart manufacturing implementation. The research findings are beneficial for the managers and consultants to gauge the potential structure of measurement that can be used to evaluate smart manufacturing systems' performance. [ABSTRACT FROM AUTHOR]

Purpose: The role of industry 4.0 (I4.0) technologies for organizations to achieve a competitive advantage and mitigate disruptive emergency situations are well exhibited in literature. However, more light needs to be thrown into implementing I4.0 technologies to digitally transform organizations. This paper introduces a novel framework for formulating manufacturing strategy 4.0 (MS 4.0) that guides organizations to implement I4.0 successfully. Design/methodology/approach: The experts working in I4.0 and technology management domains were interviewed to determine the definition, role and process for formulating MS 4.0. Text mining using VOSViewer© is performed on the experts' opinions to determine the key terms from the opinions through keyword analysis. The identified key terms are mapped together using the existing traditional manufacturing strategy formulation framework to develop the MS 4.0 framework. Finally, the proposed MS 4.0 framework is validated through a triangulation approach. Findings: This study captured the role, definition and process to formulate MS 4.0 and proposed a framework to help practitioners implement I4.0 at manufacturing organizations to achieve competitiveness during normal and emergency situations. Research limitations/implications: The proposed MS 4.0 framework can assist industry practitioners in formulating the strategy for implementing the I4.0 technology/gies to digitally transform their manufacturing firm to retain the maximum manufacturing output and become market competent in normal and emergency situations. Originality/value: This study is the first of its kind in the body of knowledge to formulate a digital transformation strategy, i.e. MS 4.0, to implement I4.0 technologies through a manufacturing strategic lens. [ABSTRACT FROM AUTHOR]