Smart Maintenance and Analytics for Indian SMEs

Krishnaraj, S.; Prabha, M. Gomathi; Mani, Yuvaraja

doi:10.1007/978-981-15-9809-8_50

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…[51]- [53], [39], [54], [58] 23 RQ3 RQ3.2 Tools [25], [26], [28], [30], [48], [31], [38], [55]- [57] 10…”

Section: Inclusion-exclusion Criteriaunclassified

“…[30] recommended that the best practices for existing old systems like Manufacturing Execution System (MES), SCADA and DCS would not be replaced with new systems and it can be integrated with new technologies of control. [31] recommended predictive maintenance and presented case study of self-aware machines for condition monitoring in polymer industry that increased overall equipment effectiveness (OEE). [57] presented PdM implementation approach to ensure adaptability and costeffectiveness of the legacy data so as SMEs align and adopt smart factory architecture and principles.…”

Section: Best Practices (Rq4)mentioning

confidence: 99%

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A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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“…[51]- [53], [39], [54], [58] 23 RQ3 RQ3.2 Tools [25], [26], [28], [30], [48], [31], [38], [55]- [57] 10…”

Section: Inclusion-exclusion Criteriaunclassified

Section: Best Practices (Rq4)mentioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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“…The results show that AR-based instructions were safe to use by workers. Krishnaraj et al 27 have presented a case study where IoT-enabled application leads to uninterrupted flow in production by achieving better OEE of the machines in the rubber encapsulation industry from 55% to 76% through continuous improvement. Zhong et al 28 proposed a method in which real-time machine status was captured using IoT technologies like RFID and wireless communications.…”

Section: Lean Six Sigmamentioning

confidence: 99%

Development of an IoT integrated DMAIC model for process improvement in Indian SMEs: An empirical study

Prabha

Mani

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Lean Six Sigma (LSS) and the Internet of Things (IoT) facilitate an organisation to compete in the global market. The benefits of implementing the above-mentioned approaches were not realised by many Indian SMEs. The Indian SMEs contribute nearly about 30% of the country's GDP. Hence, this work focused on how Lean Six Sigma and the Internet of Things were deployed to improve the process performance in an Indian SME. IoT integrated LSS model was implemented to minimise lead time and enhance the quality of the product. Value Stream Mapping and line balancing techniques were used to minimise the lead time. To improve quality and service, an IoT-enabled in-house testing procedure was adopted and based on customer feedback, an android application was also developed to monitor the critical parameters at the customer end to enable preventive maintenance. An Augmented Reality based application was developed to simulate the sequence of the assembly process to facilitate training for the workers. To assess the benefits of implementation, a survey was conducted among people at various levels in the organization. This research resulted in a reduction of assembly lead time from 63.8 min to 24.4 min and an improvement in sigma level from 3.9 to 4.7. In the responses received from the survey, key issues faced during the implementation of the proposed model were identified. Key issues like resistance to change, poor interpersonal, lack of training, poor autonomy, timely decision making, and demand fluctuations during implementation are to be addressed for further effective implementation. The proposed model enhances the overall performance of the process in the manufacturing industry. Furthermore, it creates a driveway for Indian SMEs towards Industry 4.0.

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Smart Maintenance and Analytics for Indian SMEs

Cited by 2 publications

References 11 publications

A Systematic Mapping Study of Predictive Maintenance in SMEs

A Systematic Mapping Study of Predictive Maintenance in SMEs

Development of an IoT integrated DMAIC model for process improvement in Indian SMEs: An empirical study

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