Shop floor management system in the context of smart manufacturing: a case study

Torres, Diamantino; Pimentel, Carina; Duarte, Susana

doi:10.1108/ijlss-12-2017-0151

Cited by 23 publications

(29 citation statements)

References 36 publications

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“…This technique has been able to accurately solve problems with a single action, thus improving the performance of the manufacturing firm (Malaga & Vinodh, 2021 ; Schmidhuber, 2015 ). Other researchers have also noted that the application of deep learning technology improves the overall performance of manufacturing firms (Curtis & Scheinberg, 2017 ; Torres et al, 2020 ). These inputs suggest the following hypotheses:…”

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 97%

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

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Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 97%

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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“…Lean management means [8,9] "creating value without waste". The goal is to coordinate optimally all activities that are necessary for value creation and to avoid superfluous activities (waste, Japanese: Muda).…”

Section: The Core Ideamentioning

confidence: 99%

Shopfloor Management – A Tool of Lean Management

Wester

Hitka

2022

Management Systems in Production Engineering

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Lean management is an approach to continuous process optimisation. The methodology involves the entire value chain. The individual links are made more efficient and thus leaner. The main goal of lean management is to use various lean methods, procedures and thinking principles to coordinate all processes and activities in such a way that any kind of waste along the value chain is avoided in a holistic production system. This would result in the following main difficulties: Transporting information quickly and purposefully to the right places in the company. This is where the lean tool of shopfloor Management (SFM) comes into its own. The shopfloor board is the communication platform of SFM. Shopfloor takes place very close to the employees in a direct interaction between employees and managers. Staff are directly involved in the process and should and can contribute their own approaches to solutions. The article provides an overview of the structure of shopfloor management (SFM) and proves that the goals set by the introduction itself – namely the optimised flow of information in the company with the involvement of all employees – are achieved through shopfloor Management. The method of a structured interview with 63 respondents is used to obtain data.

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“…This study demonstrates that the transition to the smart manufacturing set-up has enhanced the effectiveness of the manufacturing. However, the company being studied has not fused the flows of data/information, materials and digitalised equipment into CPS systems as suggested by Tao and Zhang [10], Qi et al [11], eliminated information walls proposed by Dai et al [13] and thereby enabled promptly data-driven decision making [2]. Actually, shop floor board meetings draw on analogue information written or printed on papers or whiteboards.…”

Section: Y Pmentioning

confidence: 99%

“…Companies witness an ongoing transition towards smart manufacturing [1], which makes Shop Floor Management (SFM) decision-making topical. SFM decision making is pivotal for an efficient execution of manufacturing [2], but to what extent if any does decision making in a smart manufacturing influence the amount of superfluous work? Superfluous is defined in Cambridge English Dictionary as "more than is needed; extra and not necessary".…”

Section: Introductionmentioning

confidence: 99%

In the Era of Digitalising Shop Floor Management: In Blissful Ignorance of Superfluous Work

Mathiasen

Haas

2020

Advances in Transdisciplinary Engineering

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This study aims to understand the extent of superfluous work at shop floors and suggests some managerial opportunities for reducing superfluous work. Drawing on the abductive reasoning, the research systematically combines a theoretical conceptualisation of decision-making processes in a digitalised manufacturing with an empirical enquiry of a smart manufacturing. The paper reveals superfluous work if decision-making processes cross disciplinary and/or organisational boundaries. Superfluous work occurs because of lacking data and information to guide reflective thinking and knowledge sharing. In relation to high complex decision making the ongoing implementation of workarounds does also cause superfluous work. Prerequisites for reducing superfluous work are accessibility of applicable data to guide reflective thinking and knowledge sharing.

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Shop floor management system in the context of smart manufacturing: a case study

Cited by 23 publications

References 36 publications

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Shopfloor Management – A Tool of Lean Management

In the Era of Digitalising Shop Floor Management: In Blissful Ignorance of Superfluous Work

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