Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics

Bumblauskas, Daniel; Gemmill, Douglas D.; Igou, Amy; Anzengruber, Johanna

doi:10.1016/j.eswa.2017.08.025

Cited by 92 publications

(41 citation statements)

References 64 publications

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“…Such competency makes digital twin Yet, in today's fast-changing market dynamics, maintaining such multi-dimensional trade-in practices triggers persistent functional and financial burdens caused by the unpredictability surrounding the actual quality status of returned products. This ambiguity leads to a number of inspection and disassembly steps in determining the condition of EOLPs in addition to prolonged bargaining processes between customers and OEMs, increasing the overall complexity and product-recovery cost of operations [9,10]. The appropriate utilization of newly available information technologies, on the other hand, allows manufacturers to reduce the ambiguities in the returned product conditions significantly, therefore promptly responding to the customer needs while reducing the complexity across operational layers.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

2020

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“…This has supported a change in focus from equipment-centric to asset-operation centered. The concept of smart maintenance decision support systems is being trialed in the US where the data analytics is used to extend the linkage between the analysis of condition monitoring data and statistical trending with prediction and simulation-based scenarios [175]. The availability of large volumes of data to build accurate simulations of complex systems can extend the predictive maintenance concept within large infrastructure organizations.…”

Section: E Big Data Analytics Tool Selection and Challengesmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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Nowadays, huge amounts of data have been captured along with the day-today operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented. INDEX TERMS Big data analytics, asset management, railway, data management, blockchain.

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“…As mentioned earlier, within the recent literature (beyond the time horizons of the Bousdekis et al 2015), the same trend on selecting various combinations of statistics and machine learning methods for predictive maintenance is apparent. As an example, one may refer to the following selected studies: for developing a periodic preventive maintenance model (Franciosi, Lambiase, and Miranda 2017), enhancing preventive maintenance through integrating probabilistic and predictive models (Ruschel, Santos, and Loures 2017), establishing a generic simulation-based predictive maintenance (Zarte, Wunder, and Pechmann 2017), developing cloud-based predictive maintenance framework (Schmidt, Wang, and Galar 2017), and introducing a smart maintenance decision support using corporate big data analytics (Bumblauskas et al 2017) as well as applying various combinations of statistical datamining and supervised machine learning for condition-based maintenance examined in (Accorsi et al 2017). Specifically, dynamic-based prognostic models are used for predicting dependability in (Aizpurua et al 2017), Bayesian modelling is employed for optimisation of maintenance strategies in (Belyi et al 2017), and application of various machine learning methods for self-parameterising process monitoring and selfadjusting process strategies for series production has been investigated in (Denkena et al 2017).…”

Section: Review Of Related Kbm Approachesmentioning

confidence: 99%

PriMa: a prescriptive maintenance model for cyber-physical production systems

Ansari

Grepl

Nemeth

2019

International Journal of Computer Integrated Manufacturing

134

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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined. ARTICLE HISTORY

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Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics

Cited by 92 publications

References 64 publications

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

Requirements for Big Data Adoption for Railway Asset Management

PriMa: a prescriptive maintenance model for cyber-physical production systems

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