A Data-based Method for Industrial Big Data Project Prioritization

ABSTRACT

The application of Big Data Techniques (BDT) in discrete manufacturing appears to be very promising, considering lighthouse projects in this area. In general, the goal is to collect all data from manufacturing systems comprehensively, in order to enable new findings and decision support by means of appropriate Industrial Big Data (IBD) analysis procedures. However, due to limited human and economic resources, potential IBD projects need to get prioritized -- in the best case according to their cost-benefit ratio. Available methods for this purpose are insufficient, due to their limited ability to be operationalized, error-proneness, and lack of scientific evidence. In this paper, we discuss how cost-benefit-analysis frameworks can be applied to the preliminary selection of production use cases for the implementation of BDT in larger production systems. It supports the use case selection process from information about production needs, available BDT, and given condition(s) per use case. This concept paper attempts to consolidate the hitherto fragmented discourse on how to prioritize IBD projects, evaluates the challenges of prioritization in this field, and presents a prioritization concept to overcome these challenges.

References

1. Goh, T. N., Xie, M. and Xie, W. 1998. Prioritizing processes in initial implementation of statistical process control. IEEE Transactions on Engineering Management, 45(1). 66--72.
2. Mourtzis, D., Vlachou, E. and Milas, N. 2016. Industrial Big Data as a result of IoT adoption in manufacturing. Procedia CIRP, 55. 290--295.
3. Gardner, D. 2013. Ford scours for more big data to bolster quality, improve manufacturing, streamline processes. 29 January 2013. On zdnet: http://www.zdnet.com {Accessed 2 April 2017}.
4. General Electric. 2012. The Rise of Industrial Big Data. Whitepaper.
5. McKinsey. 2015. Industry 4.0 How to navigate digitization of the manufacturing sector. Whitepaper.
6. Chen, C. P. and Zhang, C. Y. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275. 314--347.
7. Gluchowski, P., Gabriel, R. and Dittmar, C. 2008. Management support systeme und business intelligence. Berlin.
8. Brownlee, K. A. 1951. Correlation Methods Applied to Production Process Data. Industrial & Engineering Chemistry, 43(9). 2068--2071.
9. O'Donovan, P et al. 2015. Big data in manufacturing: a systematic mapping study. Journal of Big Data, 2(1). 20.
10. Langer, M., & Vogel, B. 2002. Synthesis of plantwide quality prediction system for a sintering plant. IFAC Proceedings Volumes, 35(1). 71--76.
11. Downey, J. et al. 2016. Real time monitoring of the CNC process in a production environment-the data collection & analysis phase. Procedia CIRP, 41. 920--926.
12. Lee, J., Kao, H. A. and Yang, S. 2014. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16. 3--8.
13. Archer, N. P., & Ghasemzadeh, F. 1999. An integrated framework for project portfolio selection. International Journal of Project Management, 17(4). 207--216.
14. Mousannif, H. et al. 2014. From Big Data to big projects: A step-by-step roadmap. In Proceedings of IEEE International Conference on Future Internet of Things and Cloud (FiCloud). 373--378.
15. Pappis, C. et al. 2011. Look ahead to the power of predictive service solutions, Nanochip.
16. Kobielus, J. 2014. The delicate art of data science project prioritization and triage. On IBM Big Dara & Analytics Hub: http://www.ibmbigdatahub.com {Accessed 13 May 2017}.
17. McDonald, D. 2015. Developing a Basic Model for Data Analytics Project Selection. Website: http://webcache. googleusercontent.com {Accessed 14 May 2017}.
18. Fayet, L. 2015. How to prioritise your Big Data analytics use cases. On bicorner: https://bicorner.com {Accessed 20 May 2017}.
19. Chapman, P. et al. 1999. The CRISP-DM user guide. In 4th CRISP-DM SIG Workshop
20. Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P. 1996. From data mining to knowledge discovery in databases. AI magazine, 17(3). 37.
21. Sardana, D. et al. 2015. Data driven predictive analytics for a spindle's health. In Proceedings of IEEE International Conference on Big Data (Big Data). 1378--1387.
22. Khan, A. R. et al. 2015. Statistical data mining for efficient quality control in manufacturing. In Proceedings of IEEE 20th International Conference on Emerging Technologies & Factory Automation (ETFA). 1--4.
23. Lieber, D. et al. 2013. Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. In Procedia CIRP, 7. 193--198.
24. Sanghera, P. P. D. 2014. CAPM in depth: Certified Associate in Project Management study guide for the CAPM exam. Cengage Learning.
25. Anandarajan, A. and Wen, H. J. 1999. Evaluation of information technology investment. Management decision, 37(4). 329--339.
26. Asosheh, A., Nalchigar, S. and Jamporazmey, M. 2010. Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37(8). 5931--5938.
27. Milis, K., & Mercken, R. (2004). The use of the balanced scorecard for the evaluation of information and communication technology projects. International Journal of Project Management, 22(2). 87--97.
28. Forbes Insights and Teradata. 2015. Betting on Big Data: How the right culture, strategy and investments. Forbes: https://www.forbes.com {Accessed 25 March 2017}.
29. Bange, C. 2016. Big Data And Digitalization Use Cases: How To Identify And Prioritize. SAP: http://blog-sap.com {Accessed 11 February 2017}.
30. Sharma, S. and Chetiya, A. R. 2010. Six Sigma project selection: an analysis of responsible factors. International Journal of Lean Six Sigma, 1(4). 280--292.
31. Büyüközkan, G., & Öztürkcan, D. 2010. An integrated analytic approach for Six Sigma project selection. Expert Systems with Applications, 37(8). 5835--5847.
32. Hauser, J. R., & Clausing, D. 1988. The house of quality.
33. Schmarzo, B. 2013. Prioritization Matrix: Aligning Business and IT On The Big Data Journey," Infocus: https://infocus.emc.com {Accessed 23 March 2017}.
34. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137--144.

Index Terms

• Applied computing

  • Operations research

    • Decision analysis

      • Multi-criterion optimization and decision-making

• Social and professional topics

  • Professional topics

    • Management of computing and information systems

      • Project and people management

        • Project management techniques