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"This book features an inclusive overview of various management practices that contribute to the sustainability efforts of an organization, highlighting successful techniques being implemented and utilized by different companies"--

The ability of manufacturing companies to adapt to their changing environment is frequently a key to long‐term success. As a consequence the strategic flexibility of manufacturing operations has become an increasingly important issue for organisations. There have been much theoretical work and some case studies in this domain. This paper reports part of a major study in the area. A key stage in this work has been an empirical study of UK manufacturing to investigate a broad range of issues surrounding manufacturing operations and strategic flexibility. In part this has been carried out through a questionnaire survey. This paper summarises some of the principal findings. These include respondents' descriptions of their business strategies, the part played by manufacturing, the interfaces with customers, and the role of the information system and its contribution to manufacturing. This is complemented by a summary of 32 interviews/case histories which allow these issues to be explored further and which provide the input to the subsequent stages of the overall project.

A detailed analysis of Material Requirements Planning (MRP), Kanban, optimised Production Technology (OPT) and Flexible Manufacturing Systems (FMS), including the applied assumptions behind these and their limitations and weaknesses, shows that each system is sound in its own way and can accomplish low cost, high quality, on‐time production. People problems, however, can destroy the effectiveness of any system and in this respect Kanban and OPT systems solve the majority of people problems, while FMS installations, by design, eliminate most problems of this type. The experience gained during the coming decade may lead factory managers to use two or more of these systems side by side.

Shows how to implement a preventive maintenance (PM) system in a
high volume manufacturing operation with a just‐in‐time environment.
Determines the tasks that are performance‐coupled in a specific machine
by using a simple flowchart. Computes for every component suitable for
PM, earliest economic replacement time and earliest expected replacement
time. Using a time graph, the PM is scheduled for components that are
performance coupled in a specific machine. This procedure is
straightforward and can be easily followed by plant floor personnel.

PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

PurposeThe study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts' opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.Design/methodology/approachThe study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts' opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.FindingsThe study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.Practical implicationsThis study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.Originality/valueThe novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.