A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation

Kamble, Sachin S.; Gunasekaran, Angappa; Ghadge, Abhijeet; Raut, Rakesh D.

doi:10.1016/j.ijpe.2020.107853

Cited by 220 publications

(120 citation statements)

References 119 publications

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“… Supply chain risk mitigation strategies Description References Proactive strategies Digital connectivity Digital technologies such as the Internet of things, blockchain technology, and digital twin are proving great potential to enhance supply chain resilience through high connectivity, accuracy, and transparency. Ralston and Blackhurst (2020) , Kamble et al. (2020a) , HYPERLINK \l "bib23" Hofmann et al., (2019); Ivanov et al., (2019) .…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

“… Supply chain collaboration Firms at every tier of the supply chain should work closely together to meet shared objectives of foreseeing, predicting, and preventing potential threats in the supply chain. Barrane et al., (2020) , Kamble et al., (2020 a), Kochan and Nowicki (2019) ; Tukamuhabwa et al., (2017) Social supply chain focus Focus on humanitarian and social performance all across the supply chain is of great need to mitigate the risk of disruption at all levels. Kamble et al., (2020b) , Kamble et al., (2020 a), HYPERLINK \l "bib80" Tukamuhabwa et al., (2017); Iakovou et al., (2014) Human capabilities Refers to the capabilities of the human concerning analysis of enormous information, monitoring, and controlling critical supply chain points.…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

“… Kamble and Gunasekaran (2020) , Belhadi et al., (2019) Virtual marketplaces Refers to the development of digital marketplace for delivering the products and services. Sharifi et al., (2006) , Kamble et al., (2020 a) Supply chain simulation Simulation is a proven way to support multi-criteria decision making while tackling uncertainty and complexity related to supply chains Ivanov and Dolgui (2020) , HYPERLINK \l "bib23" Hofmann et al., (2019); Kamble et al., (2018) Supply chain collaboration Firms at every tier of the supply chain should work closely together to meet shared objectives of recovery and help each other to mitigate disruption impact Barrane et al., (2020) , Villena and Gioia (2018) ; Zhu et al., (2017) Inventories and reserve capacity Supply chains use inventory and reserve capacity to mitigate the negative effects caused by the disruption. Lücker et al., (2019) , Simchi-Levi et al., (2015) .…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries

Belhadi

Kamble

Jabbour

et al. 2021

Technological Forecasting and Social Change

Self Cite

485

482

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Highlights Insights into the impact of COVID-19 outbreak on automobile and airline supply chain is provided. Integrated time-to-recovery and financial impact analysis, empirical survey and semi-structured interviews were used. Localized supply sources and industry 4.0 technologies identified as significant strategies by automobile industry. Business continuity by defining operations at the airport and flights perceived significant strategy by airline industry. Real-time information sharing and cooperation among supply chain stakeholders is critical.

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Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries

Belhadi

Kamble

Jabbour

et al. 2021

Technological Forecasting and Social Change

Self Cite

485

482

View full text Add to dashboard Cite

show abstract

“…Chang (2020) presented a new concept, Cloud Business Performance (CBP) and he demonstrated that the use of CBP calculation and prediction analysis is useful in SM. Kamble et al (2020) stated that SM systems are expanding as a strategy to improve organizations' performance. They studied about the planned investments in SMS and its performance dimensions.…”

Section: Literature Reviewmentioning

confidence: 99%

The investigation on infoecology in the field of smart manufacturing

Asemi

Kő

2020

LHT

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PurposeThe present study is aimed to determine the infoecology of scientific articles in the field of smart manufacturing (SM). The researchers designed a general framework for the investigation of infoecology.Design/methodology/approachThe qualitative and quantitative data collection methods are applied to collect data from the Scopus and experts. The bibliometric technique, clustering and graph mining are applied to analysis data by Scopus data analysis tools, VOSviewer and Excel software.FindingsIt is concluded that researchers paid attention to “Flow Control”, “Embedded Systems”, “IoT”, “Big Data” and “Cyber-Physical System” more than other infocenose. Finally, a thematic model presented based on the infoecology of SM in Scopus for future studies. Also, as future work, designing a “research-related” metamodel for SM would be beneficial for the researchers, to highlight the main future research directions.Practical implicationsThe results of the present study can be applied to the following issues: (1) To make decisions based on research and scientific evidence and conduct scientific research on real needs and issues in the field of SM, (2) Holding the workshops on infoecology to determine research priorities with the presence of experts in related industries, (3) Determining the most important areas of research in order to improve the index of applied research, (4) Assist in prioritizing research in the field of SM to select a set of research and technological activities and allocate resources effectively to these activities, (5) Helping to increase the relationship between research and technological activities with the economic and long-term goals of industry and society, (6) Helping to prioritize the issues of SM in research and technology in order to target the allocation of financial and human capital and solving the main challenges and take advantage of opportunities, (7) Helping to avoid fragmentation of work and providing educational infrastructure based on prioritized research needs and (8) Helping to hold start-ups and the activities of knowledge-based companies based on research priorities in the field of SM.Originality/valueThe analysis results demonstrated that the information ecosystem of SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of this field.

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“…There were 767 articles in 1992, reaching 9498 articles in 2019 [3] . More recently, studies have been applied in various areas such as industry 4.0 [ 4 , 5 , 6 ], the environment [ 7 , 8 , 9 , 10 ] and aviation [11] . Studies continue to be produced in traditional areas such as supply chain management [ 12 , 13 ], the hotel industry [14] , and civil construction [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

S-PI: A performance measurement system using an S-curve

2021

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This method is a tool for evaluating the performance of any production system. It is based on the work of (1), with an evaluation structure consisting of a three-level hierarchy. Evaluation groups are at the top level, below which groups of metrics or key performance indicators (KPIs) are located; performance indicators (PIs) are located below the KPIs. To consolidate several types of metrics, in a single evaluation, it is necessary to normalize the values. This study overcomes the deficiencies of the original method, which linearly normalizes the PIs between 0 and 1. For an indicator with a goal of 100%, a compliance of 70% would be evaluated as 70% of the desired value. In reality, the decision maker may consider 70% to be equivalent to 30% of the desired value. To more realistically simulate the evaluator's preference scale, an S -shaped curve based on the Weibull distribution is adopted. This curve must be calibrated to reflect the preferences of the evaluator. This study shows all the steps of structuring an evaluation system, with details for the calibration of the S -curve parameters. Evaluation scales are not linear. The S-curve better reflects stakeholder preferences.

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A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation

Cited by 220 publications

References 119 publications

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries

The investigation on infoecology in the field of smart manufacturing

S-PI: A performance measurement system using an S-curve

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