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Article
Publication date: 13 November 2019

Diamantino Torres, Carina Pimentel and Susana Duarte

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital

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Abstract

Purpose

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features.

Design/methodology/approach

To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey.

Findings

Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement.

Research limitations/implications

The study was applied in one multinational company from the automotive sector.

Originality/value

To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.

Details

International Journal of Lean Six Sigma, vol. 11 no. 5
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 15 July 2022

Minakshi Kumari and Makarand S. Kulkarni

The reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time…

Abstract

Purpose

The reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment.

Design/methodology/approach

The schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work.

Findings

The study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism.

Originality/value

The proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.

Details

Industrial Management & Data Systems, vol. 122 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 10 June 2020

Flávio Gaspar and Fabiano Leal

The purpose of this paper is to define a method for sustain lean tools and philosophies on a manufacturing environment.

Abstract

Purpose

The purpose of this paper is to define a method for sustain lean tools and philosophies on a manufacturing environment.

Design/methodology/approach

An in-depth action research (AR) methodology procedures applied in two cycles were conducted in an automobile company located in southeastern Brazil. The objective is to test the applicability of the shop floor management (SFM) implementation model as presented by Hanenkamp (2013).

Findings

The SFM model presented by Hanenkamp (2013) has been outlined, detailed and applied in practice. Opportunities for improvement during the application process of this model were verified by changes in its steps. After developing the AR, the authors have shown that the SFM model, as proposed in this paper, can indeed assist managers in applying and maintaining lean manufacturing practices on the shop floor.

Originality/value

This paper contributes by aiding in filling the gap between practical applications and the sustainability of lean manufacturing concepts and solutions. Furthermore, the guidelines introduced serve as a benchmark for other companies interested in the topic.

Details

International Journal of Lean Six Sigma, vol. 11 no. 6
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 8 February 2024

Bassel Kassem, Maira Callupe, Monica Rossi, Matteo Rossini and Alberto Portioli-Staudacher

Prior to managing a company’s processes in the presence of a combination of paradigms, there is a need to understand their underlying interaction. This paper systematically…

Abstract

Purpose

Prior to managing a company’s processes in the presence of a combination of paradigms, there is a need to understand their underlying interaction. This paper systematically reviews the existing literature that discusses the interaction between lean production (LP) and the fourth industrial revolution (i.e. Industry 4.0). The study aims to understand how the interaction unfolds and whether it is synergistic.

Design/methodology/approach

The research relies on a systematic literature review of peer-reviewed articles from Scopus and Web of Science that discuss the interaction between the two paradigms. The final set of articles pertaining to the topic was analysed.

Findings

The article presents that the interaction between the two paradigms occurs through a representation of the pillars of the House of Lean (HoL) interacting with the nine technological pillars of Industry 4.0. There is a consensus on the synergistic nexus among the pillars and their positive impact on operational performance. We also demonstrate the weights of the interactions between the two paradigms and the areas of operations management where this interaction takes place through Sankey charts. Our research indicates that the largest synergistic interaction occurs between just-in-time and industrial Internet of Things (IIoT) and that companies should invest in IoT and cyber-physical systems as they have the greatest weight of interactions with the pillars of the HoL.

Research limitations/implications

This research facilitates a deeper insight into the interaction between LP and Industry 4.0 by organising and discussing existing research on the subject matter. It serves as a starting point for future researchers to formulate hypotheses about the interaction among the various pillars of LP and Industry 4.0, apply these interactions and test them through empirical research.

Practical implications

It could serve as a guide for managers to understand with which interactions they should start the digitalisation process.

Originality/value

With the rise in discussions on the interaction between the two paradigms, there is still an opportunity to understand the specificity of this interaction. Compared to the initial seminal works on the subject, such as Buer et al. (2018b), which investigated the direction of interaction between the two paradigms, this research contributes to further investigating this specificity and gaining a better understanding of the relationship governing the interaction between LP and Industry 4.0 by delineating the interaction state among the pillars of the two paradigms and its relevant importance.

Details

Journal of Manufacturing Technology Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-038X

Keywords

Article
Publication date: 12 March 2024

Daryl John Powell, Désirée A. Laubengaier, Guilherme Luz Tortorella, Henrik Saabye, Jiju Antony and Raffaella Cagliano

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications…

Abstract

Purpose

The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory.

Design/methodology/approach

Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits.

Findings

The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion.

Originality/value

The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning.

Details

International Journal of Operations & Production Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 22 October 2021

Mohd Javaid, Abid Haleem, Ravi Pratap Singh, Shanay Rab, Rajiv Suman and Shahbaz Khan

Over the past few decades, lean manufacturing has focussed on being customer-centred and now Lean 4.0 technologies have made it possible for manufacturers to have a deeper view of…

Abstract

Purpose

Over the past few decades, lean manufacturing has focussed on being customer-centred and now Lean 4.0 technologies have made it possible for manufacturers to have a deeper view of waste reduction. Technologies such as the internet of things, artificial intelligence, three-dimensional printing, robotics, real-time data, cloud computing, predictive analytics and augmented reality, are helpful to achieve Lean 4.0. This study aims to develop the conceptual understanding of Lean 4.0, related tools and linkage with Industry 4.0. Further, it provides the strategies for implementing Lean 4.0, developing lean culture and highlights the Lean 4.0 application in the manufacturing context.

Design/methodology/approach

This study relates to Lean 4.0 and its technologies. Prominent research is identified through Scopus, Web of Science, ScienceDirect and Google Scholar and studied as per the objective of this study. This lean revolution provides customers desire for personalisation, connectedness, high-quality and valuable products. Lean 4.0 provides valuable information on the value chain and production process. This revolution has significantly impacted refining production processes for a greater level of adaptability and cost reduction.

Findings

This paper is brief about Lean 4.0 and its capabilities for the reduction of waste. The authors discussed different tools used in Lean 4.0 and its relationship with Industry 4.0. The classical strategies and progressive features of Lean 4.0 for overall enhancing the manufacturing sphere are discussed diagrammatically. Finally, it identified and discussed 14 significant applications of Lean 4.0 for manufacturing industries.

Originality/value

This study provides a comprehensive understanding of Lean 4.0 and related tools and strategies that help the upcoming manufacturing industries.

Details

Industrial Robot: the international journal of robotics research and application, vol. 49 no. 3
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 7 March 2023

Marte D.-Q. Holmemo and Eirik Bådsvik Hamre Korsen

This paper aims to gain empirical insights into the relationship between lean production and digitalization within Industry 4.0 from a process-theoretical perspective. Following…

Abstract

Purpose

This paper aims to gain empirical insights into the relationship between lean production and digitalization within Industry 4.0 from a process-theoretical perspective. Following an initial report at the European Lean Educators Conference 2021 conference, the authors searched for explanations as to why digital lean tools stagnate, whereas production improves continuously.

Design/methodology/approach

This paper is based on a qualitative case study in a Norwegian processing industry company over a period of 18 months from 2020 to 2022.

Findings

Process theory offers explanations of why digitalization and lean can change over time. Despite agile development, digitalization is still characterized by centralization and programmatic planning. Lean production is decentralized, with long-term and continuous change processes. This creates challenges for coordination between digitalization and lean.

Practical implications

Organizations should strive for coordination and collaboration between central and local decision makers and between digital and business process competence. Digital systems should have built-in flexibility for local setup, and local managers need sufficient competence to set up systems that are aligned with continuous improved production.

Originality/value

This study contributes empirical insights into real-life industry challenges to a literature that has until now been theoretical and focused on potential synergies.

Details

International Journal of Lean Six Sigma, vol. 14 no. 6
Type: Research Article
ISSN: 2040-4166

Keywords

Article
Publication date: 26 March 2021

Anilkumar Malaga and S. Vinodh

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the…

Abstract

Purpose

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Design/methodology/approach

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

Findings

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

Research limitations/implications

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

Practical implications

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

Originality/value

The analysis of drivers of smart manufacturing is the original contribution of the authors.

Article
Publication date: 1 March 2001

K.G.B. Bakewell

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18;…

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Abstract

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Structural Survey, vol. 19 no. 3
Type: Research Article
ISSN: 0263-080X

Article
Publication date: 1 September 2001

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management

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Abstract

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Details

Facilities, vol. 19 no. 9
Type: Research Article
ISSN: 0263-2772

1 – 10 of over 2000