More Observations, More Variables or More Quality? - Data Acquisition Strategies to Enhance Uncertainty Analytics for Industrial Service Contracting

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 279)

Abstract

Service business models expose industrial service providers to an increasing amount of uncertainties. In order to design profitable offerings, providers need to understand how uncertainties affect contract profitability. Both, access to data and algorithms are key requirements for accurate analyses.

While current research focuses on developing algorithms to derive insights from data that already exist, the need for strategically acquiring relevant data sets has been neglected so far. In this article, we develop a method for defining data acquisition strategies to improve uncertainty analyses for industrial service contracting. We explain how lacking observations, variables and quality of data affect uncertainty analyses, propose data acquisition strategies as a systematic plan to acquire relevant data and develop an approach for ranking acquisition strategies by measuring their acquisition effort and business benefit.

The method is applied in an industrial use case to demonstrate its benefit for assessing cost uncertainties in full-service repair contracts.

Keywords

Strategic data acquisition Uncertainty analysis Service contracting Industrial services 

References

  1. 1.
    Herzog, M., Meuris, D., Bender, B., Sadek, T.: The nature of risk management in the early phase of IPS\(^2\) design. Procedia CIRP 16, 223–228 (2014)CrossRefGoogle Scholar
  2. 2.
    Erkoyuncu, J.A., Roy, R., Shehab, E., Kutsch, E.: An innovative uncertainty management framework to support contracting for product-service availability. J. Serv. Manag. 25(5), 603–638 (2014)CrossRefGoogle Scholar
  3. 3.
    Stremersch, S., Wuyts, S., Frambach, R.T.: The purchasing of full-service contracts: an exploratory study within the industrial maintenance market. Ind. Market. Manag. 30(1), 1–12 (2001)CrossRefGoogle Scholar
  4. 4.
    Hypko, P., Tilebein, M., Gleich, R.: Benefits and uncertainties of performance-based contracting in manufacturing industries: an agency theory perspective. J. Serv. Manag. 21(4), 460–489 (2010)CrossRefGoogle Scholar
  5. 5.
    Erkoyuncu, J.A., Durugbo, C., Shehab, E., Roy, R., Parker, R., Gath, A., Howell, D.: Uncertainty driven service cost estimation for decision support at the bidding stage. Int. J. Prod. Res. 51(19), 5771–5788 (2013)CrossRefGoogle Scholar
  6. 6.
    Huber, S., Spinler, S.: Pricing of full-service repair contracts. Eur. J. Operat. Res. 222(1), 113–121 (2012)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Baglee, D., Marttonen, S., Galar, D.: The need for Big Data collection and analyses to support the development of an advanced maintenance strategy. In: 11th International Conference on Data Mining, pp. 3–9. IEEE, Las Vegas (2015)Google Scholar
  8. 8.
    Kaisler, S., Armour, F., Espinosa, J.A., Money, W.: Big data: issues and challenges moving forward. In: 46th Hawaii International Conference on System Sciences. pp. 995–1004. IEEE, Wailea (2013)Google Scholar
  9. 9.
    Benedettini, O., Neely, A., Swink, M.: Why do servitized firms fail? A risk-based explanation. Int. J. Operat. Prod. Manag. 35(6), 946–979 (2015)CrossRefGoogle Scholar
  10. 10.
    Gebauer, H., Fleisch, E., Friedli, T.: Overcoming the service paradox in manufacturing companies. Eur. Manag. J. 23(1), 14–26 (2005)CrossRefGoogle Scholar
  11. 11.
    Erkoyuncu, J.A., Roy, R., Shehab, E., Wardle, P.: Uncertainty challenges in service cost estimation for product-service systems in the aerospace and defence industries. In: 1st CIRP Industrial Product-Service System (IPS\(^2\)) Conference, pp. 200–206. Cranfield University Press, Cranfield (2009)Google Scholar
  12. 12.
    Bolton, P., Dewatripont, M.: Contract Theory. MIT Press, Cambridge (2005)Google Scholar
  13. 13.
    Chesbrough, H., Rosenbloom, R.S.: The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies. Ind. Corp. Change 11(3), 529–555 (2002)CrossRefGoogle Scholar
  14. 14.
    Van Ostaeyen, J., Van Horenbeek, A., Pintelon, L., Duflou, J.R.: A refined typology of product-service systems based on functional hierarchy modeling. J. Clean. Prod. 51, 261–276 (2013)CrossRefGoogle Scholar
  15. 15.
    Bamberg, G., Coenenberg, A.G., Krapp, M.: Betriebswirtschaftliche Entscheidungslehre, 15th edn. Vahlens Kurzlehrbücher, Vahlen, München, Germany (2012)Google Scholar
  16. 16.
    Knight, F.H.: Risk, Uncertainty, and Profit. Houghton Mifflin, Boston (1921)Google Scholar
  17. 17.
    Erkoyuncu, J.A., Roy, R., Shehab, E., Cheruvu, K.: Understanding service uncertainties in industrial product-service system cost estimation. Int. J. Adv. Manuf. Technol. 52(9–12), 1223–1238 (2011)Google Scholar
  18. 18.
    Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26(2), xiii–xxiii (2002)Google Scholar
  19. 19.
    Baines, T.S., Lightfoot, H.W., Evans, S., Neely, A., et al.: State-of-the-art in product-service systems. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 221(10), 1543–1552 (2007)Google Scholar
  20. 20.
    Schulte, J.K., Steven, M.: Risk management of industrial product-service systems (IPS2) – how to consider risk and uncertainty over the IPS2 lifecycle? In: Dornfeld, D.A., Linke, B.S. (eds.) 19th CIRP Conference on Life Cycle Engineering, pp. 37–42. Springer, Berkeley (2012)Google Scholar
  21. 21.
    Erkoyuncu, J.A., Durugbo, C., Roy, R.: Identifying uncertainties for industrial service delivery: a systems approach. Int. J. Prod. Res. 51(21), 6295–6315 (2013)CrossRefGoogle Scholar
  22. 22.
    Sakao, T., Öhrwall Rönnbäck, A., Ölundh Sandström, G.: Uncovering benefits and risks of integrated product service offerings - using a case of technology encapsulation. J. Syst. Sci. Syst. Eng. 22(4), 421–439 (2013)CrossRefGoogle Scholar
  23. 23.
    Schwabe, O., Shehab, E., Erkoyuncu, J.: Uncertainty quantification metrics for whole product life cycle cost estimates in aerospace innovation. Prog. Aerosp. Sci. 77, 1–24 (2015)CrossRefGoogle Scholar
  24. 24.
    Bose, R.: Advanced analytics: opportunities and challenges. Ind. Manag. Data Syst 109(2), 155–172 (2009)CrossRefGoogle Scholar
  25. 25.
    Klöpper, B., Schlake, J.C.: Aufbrechen der Datensilos-Big Data Forschungsfragen aus dem Bereich Industrial Analytics. Lecture Notes in Informatics - INFORMATIK 2014, pp. 79–81. German Informatics Society, Stuttgart, Germany (2014)Google Scholar
  26. 26.
    Koronios, A., Redman, T., Gao, J.: Internal data markets: the opportunity and first steps. In: 4th International Conference on Cooperation and Promotion of Information Resources in Science and Technology, pp. 127–130. IEEE, Beijing (2009)Google Scholar
  27. 27.
    Jess, T., Woodal, P., McFarlane, D.: A framework for identifying suitable cases for using market-based approaches in industrial data acquisition. In: 1st International Data and Information Management Conference, pp. 113–124. Library and Information Statistics Unit, Loughborough University, Loughborough (2014)Google Scholar
  28. 28.
    Durugbo, C., Erkoyuncu, J.A., Tiwari, A., Alcock, J.R., Roy, R., Shehab, E.: Data uncertainty assessment and information flow analysis for product-service systems in a library case study. Int. J. Serv. Operat. Informat. 5(4), 330–350 (2010)Google Scholar
  29. 29.
    Schmitz, B., Düffort, F., Satzger, G.: Managing uncertainty in industrial full service contracts: digital support for design and delivery. In: 18th IEEE Conference on Business Informatics, pp. 123–132. IEEE, Paris (2016)Google Scholar
  30. 30.
    Gitzel, R., Turrin, S., Maczey, S., Shaomin, W., Schmitz, B.: A data quality metrics hierarchy for reliability data. In: 9th IMA International Conference on Modeling in Industrial Maintenance and Reliability, pp. 1–6. Kent Academic Repository, London (2016)Google Scholar
  31. 31.
    Gitzel, R.: Data quality in time series data - an experience report. In: Proceedings of the 18th IEEE Conference on Business Informatics - Industrial Track, pp. 41–49. CEUR, Paris (2016)Google Scholar
  32. 32.
    Fromm, H., Habryn, F., Satzger, G.: Service analytics leveraging data across enterprise boundaries for competitive advantage. In: Bäumer, U., Kreutter, P., Messner, W. (eds.) Globalization of Professional Services, Chap. 13, pp. 139–149. Springer, Heidelberg (2012)Google Scholar
  33. 33.
    Deutsche Kommission Elektrotechnik Elektronik Informationstechnik im DIN und VDE: International Standard IEC 62264-1:2013 Enterprise-control system integration - Part 1: Models and Terminology. Beuth, Berlin (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.ABB Corporate ResearchLadenburgGermany

Personalised recommendations