Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research
- How many publications have been published in different maintenance contexts and how does this number change over time?
- Which analytical methods were used?
- How has the use of analytical methods evolved?
- Which technologies were used?
- Which attributes of big data could be found?
- What are the applications of data analytics in a maintenance context?
- Which types of analytics are used in maintenance planning?
2. Relevant Terms
2.1. Big Data
- Volume describes the amount of data. This includes the size of a single record as well as the quantity of records .
- Velocity includes the rate at which data is recorded and rate at which it must be processed .
- Variety describes the differences in the data, especially in context of data structure .
2.2. Data Analytics
- Descriptive analytics indicates applications of data analytics in describing and understanding situations based on past and present data .
- Diagnostic analytics is an application of data analytics to investigate the causes and effects of situations .
- Predictive analytics is an application of data analytics that use data and mathematical concepts to show the relationship between data, in order to predict future outcomes based on changes in the dataset .
- Prescriptive analytics describes the application of data analytics using mathematical models to create a set of complex alternatives from the available data. This is then used to prescribe the best possible solution .
- Machine learning: the design and study of algorithms which infer the function they compute from the sample data. In other words, machine learning can learn from the data and adapt to the changes and progress made, without the need for explicit programming .
- Statistics: this unifies methods to condense, describe and evaluate data, thus helping to create a summary of (large) volumes of data. Thus, statistics provide more comprehensible information about data and support the drawing of conclusions .
- Simulation: this means imitating complex real-world systems by constructing a mathematical model, which can then be evaluated numerically. Thus simulation affords the opportunity to estimate the behaviour and characteristics of a system in certain scenarios .
- Optimisation: this process comprises independent variables and a measure of “goodness” (objective function), depending on the variables. At the end of an optimisation, the combination of certain variable values leads to an “optimal” objective function value .
3. Principles and Methods
3.1. Search Strategy
3.2. Search Results, Filtering and Classification
4.1. Publications Per Year
4.2. Applied Methods and Techniques
5. Discussion of the Research Questions
Conflicts of Interest
- Ahuja, I.P.S.; Khamba, J.S. An evaluation of TPM implementation initiatives in an Indian manufacturing enterprise. J. Qual. Maint. Eng. 2007, 13, 338–352. [Google Scholar] [CrossRef]
- Cooke, F.L. Plant maintenance strategy: Evidence from four British manufacturing firms. J. Qual. Maint. Eng. 2003, 9, 239–249. [Google Scholar] [CrossRef]
- Fraser, K.; Hvolby, H.-H.; Tseng, B. Maintenance management models: A study of the published literature to identify empirical evidence. A greater practical focus is needed. Int. J. Qual. Reliab. Manag. 2015, 32, 635–664. [Google Scholar] [CrossRef]
- Cooke, F.L. Implementing TPM in plant maintenance: Some organizational barriers’. Int. J. Qual. Reliab. Manag. 2000, 17, 1003–1016. [Google Scholar] [CrossRef]
- Bokrantz, J.; Skoogh, A.; Berlin, C.; Stahre, J. Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. Int. J. Prod. Econ. 2017, 191, 154–169. [Google Scholar] [CrossRef]
- Fraser, K.; Hvolby, H.-H.; Watanabe, C. A review of the three most popular maintenance systems: How well is the energy sector represented? Int. J. Glob. Energy Issues 2011, 35, 287–309. [Google Scholar] [CrossRef]
- Ahuja, I.P.S.; Khamba, J.S. An evaluation of TPM initiatives in Indian industry for enhanced manufacturing performance. Int. J. Qual. Reliab. Manag. 2008, 25, 147–172. [Google Scholar] [CrossRef]
- Subramaniyan, M.; Skoogh, A.; Salomonsson, H.; Bangalore, P.; Gopalakrishnan, M.; Sheikh Muhammad, A. Data-driven algorithm for throughput bottleneck analysis of production systems. Prod. Manuf. Res. 2018, 6, 225–246. [Google Scholar] [CrossRef]
- Shao, G.; Shin, S.J.; Jain, S. Data analytics using simulation for smart manufacturing. In Proceedings of the Winter Simulation Conference, Savanah, GA, USA, 7–10 December 2014; pp. 2192–2203. [Google Scholar] [CrossRef]
- Wuest, T.; Weimer, D.; Irgens, C.; Thoben, K.; Wuest, T.; Weimer, D.; Thoben, K. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016, 4, 23–45. [Google Scholar] [CrossRef]
- Lee, J.; Kao, H.-A.; Ardakani, H.D.; Siegel, D. Intelligent Factory Agents with Predictive Analytics for Asset Management; Elsevier: Amsterdam, The Netherlands, 2015; pp. 341–360. [Google Scholar]
- Lee, J.; Ardakani, H.D.; Yang, S.; Bagheri, B. Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. Procedia CIRP 2015, 38, 3–7. [Google Scholar] [CrossRef]
- Kejela, G.; Esteves, R.M.; Rong, C. Predictive analytics of sensor data using distributed machine learning techniques. In Proceedings of the International Conference on Cloud Computing Technology and Science, Singapore, 15–18 December 2014; pp. 626–631. [Google Scholar] [CrossRef]
- Russom, P. Big Data Analytics. In TDWI Best Practices Report 2011, Fourth Quarter; The Data Warehousing Institute: Renton, WA, USA, 2011; pp. 6–9. [Google Scholar]
- Runkler, T.A. Data Analytics: Models and Algorithms for Intelligent Data Analysis, 2nd ed.; Springer Vieweg: Wiesbaden, Germany, 2016; p. 1. ISBN 9783658140755. [Google Scholar]
- Delen, D.; Demirkan, H. Data, information and analytics as services. Decis. Support Syst. 2013, 55, 359–363. [Google Scholar] [CrossRef]
- Fleckenstein, M.; Fellows, L. Modern Data Strategy, 1st ed.; Springer: Cham, Switzerland, 2018; p. 133. ISBN 9783319689937. [Google Scholar]
- Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M. Machine Learning—An Artificial Intelligence Approach (Volume I), 1st ed.; Elsevier: Amsterdam, The Netherlands, 1983; pp. 5–6. ISBN 9780934613095. [Google Scholar]
- Taylor, J.K.; Cihon, C. Statistical Techniques for Data Analysis, 2nd ed.; Chapman & Hall/CRC: Boca Raton, FL, USA, 2004; pp. 19–22. ISBN 9781584883852. [Google Scholar]
- Law, A.M. Simulation Modeling and Analysis, 5th ed.; McGraw-Hill Education: New York, NY, USA, 2014; pp. 1–6. ISBN 9780073401324. [Google Scholar]
- Gill, P.E.; Murray, W.; Wright, M.H. Practical Optimization, 1st ed.; Academic Press Limited: San Diego, CA, USA, 1981; pp. 1–3. ISBN 9780122839504. [Google Scholar]
- David, B.; Brereton, P. Performing systematic literature reviews in software engineering. In Proceedings of the 28th International Conference on Software Engineering, Shanghai, China, 20–28 May 2006. [Google Scholar]
|Title||Used as an identifier||-|
|Author||Used as an identifier||-|
|Year||Used to identify trends on the popularity of the subject over time||1|
|Taxonomy||Used to identify the used data analytics methods||2, 3, 6, 7|
|Technology||Used to identify the used data analytics technologies||4, 6|
|Big data attributes (Velocity, Variety, Volume)||Used to further elaborate on the possible attributes||5|
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Baum, J.; Laroque, C.; Oeser, B.; Skoogh, A.; Subramaniyan, M. Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research. Machines 2018, 6, 54. https://doi.org/10.3390/machines6040054
Baum J, Laroque C, Oeser B, Skoogh A, Subramaniyan M. Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research. Machines. 2018; 6(4):54. https://doi.org/10.3390/machines6040054Chicago/Turabian Style
Baum, Jens, Christoph Laroque, Benjamin Oeser, Anders Skoogh, and Mukund Subramaniyan. 2018. "Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research" Machines 6, no. 4: 54. https://doi.org/10.3390/machines6040054