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Annals of Computer Science and Information Systems, Volume 17

Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems

B2B Price Management using Price Waterfall Model and Business Intelligence solution


DOI: http://dx.doi.org/10.15439/2018F209

Citation: Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 17, pages 179185 ()

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Abstract. The price setting and negotiation process in the B2B field is a complex process that requires a solid methodology and usually also advanced IT tools to make the process as efficient as possible. The Price Waterfall model is a flexible tool that allows for making the final price determination and revenue creation task much more manageable. In this paper, we introduce a software solution which integrates functionalities of a standard Business Intelligence system with a methodology given by the idea of the Price Waterfall model. The tool is designed as a dedicated decision-making support tool, with a complex internal workflow that should be applied within the price and revenue management process, to induce profitability of the whole business through informed decisions.


  1. E. Maltby, “Raising Prices Pays Off for Some,” The Wall Street Journal, 2010.
  2. S. Petter, W. H. DeLone, E. R. McLean, “Measuring information systems success: models, dimensions, measures, and interrelationships,” European Journal of Information Systems, vol. 17, 2008, pp. 236-263.
  3. R. Němec, F. Zapletal, “The Perception of User Satisfaction in Context of Business Intelligence Systems’ Success Assessment,” Proceedings of the IDIMT-2012: ICT Support for Complex Systems: 20th Interdisciplinary Information Management Talks, 2012, pp. 203-211.
  4. M. Łobaziewicz, “The Role of ICT Solutions In the Intelligent Enterprise Business Activity,” Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, vol. 8, 2016, pp. 1335–1340. http://dx.doi.org/10.15439/2016F534.
  5. R. Němec, “Assessment of query execution performance using selected Business Intelligence tools and experimental agile oriented data modeling approach,” Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, vol. 5, 2015, pp. 1327–1333. http://dx.doi.org/10.15439/2015F267.
  6. CRM Magazine, “Death of a (B2B) Salesman?,” digital version, 2015, url: http://www.destinationcrm.com/Articles/Columns- Departments/Insight/Death-of-a-(B2B)-Salesman-104687.aspx.
  7. Gartner Research, “Magic Quadrant for Digital Commerce 2016 edition”, 2016.
  8. M. Marn, E. Roegner, C. Zawada, The Price Advantage, New Jersey, Wiley & Sons, 2004.
  9. T. Poleto, V. D. H. de Carvalho, A. P. C. S. Costa, “The Roles of Big Data in the Decision-Support Process: An Empirical Investigation,” Proceedings of International Conference on Decision Support System Technology 2015 (Lecture Notes in Business Information Processing 216), 2015, pp. 10-21. http://dx.doi.org/10.1007/978-3-319-18533-02.
  10. N. Kowalczyk, P. Buxmann, “An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study,” Decision Support Systems, vol. 80, 2015, pp. 1–13. http://dx.doi.org/10.1016/j.dss.2015.08.010.
  11. K. Kambatlaa, G. Kollias, V. Kumarc, A. Gramaa, “Trends in big data analytics,” J. Parallel Distrib. Comput, vol. 74, 2014, pp. 2561–2573. http://dx.doi.org/10.1016/j.jpdc.2014.01.003.
  12. S. Liu, A. H. B. Duffy, R. I. Whitfield, I. M. Boyle, “Integration of decision support systems to improve decision support performance,” Knowl Inf Syst, vol. 22, 2010, pp. 261–286. http://dx.doi.org/10.1007/s10115-009-0192-4.
  13. R. Phillips, Why Are Prices Set The Way They Are?, Chapter 2, The Oxford Handbook of Pricing Management, New York: Oxford University Press, 2014.
  14. A. Hinterhuber, S. M. Liozu, Innovation in Pricing: Contemporary Theories and Best Practices, 2nd ed., Abingdon-on-Thames, Routledge, 2017.
  15. M. Olszak, E. Ziemba, Systemy Inteligencji biznesowej, jako przedmiot badań ekonomicznych, ZN nr 113, Uniwersytet Ekonomiczny Katowice, 2012., pp. 13.
  16. B. Devlin, Business UnIntelligence, LLC, New Jersey, 2013.
  17. A. Elragal, “ERP and Big Data: The Inept Couple,” Procedia Technology, vol. 16, 2014, pp. 242–249. http://dx.doi.org/10.1016/j.protcy.2014.10.089.
  18. H. Chen, R. H. L. Chiang, V. C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” MIS Quarterly, vol. 36, no. 4, 2012, pp. 1165-1188.
  19. D. E. O’Leary, “Supporting decisions in real-time enterprises: autonomic supply chain systems,” Inf Syst E-Bus Manage, vol. 6, 2008, pp. 239–255. http://dx.doi.org/10.1007/s10257-008-0086-0.
  20. C.L. P. Chen, C.-Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data”, Information Sciences, vol. 275, 2014, pp. 314–347. http://dx.doi.org/10.1016/j.ins.2014.01.015.
  21. H. Al-Aqrabi, L. Liu, R. Hill, N. Antonopoulos, “Cloud BI: Future of business intelligence in the Cloud,” Journal of Computer and System Sciences, vol. 81, 2015, pp. 85–96. http://dx.doi.org/10.1016/j.jcss.2014.06.013.
  22. E. Gummesson, F. Polese, "B2B is not an island!", Journal of Business & Industrial Marketing, vol. 24, no. 5/6, 2009, pp. 337-350. https://doi.org/10.1108/08858620910966228.
  23. A. Smirnov, N. Shilov, A. Oroszi, M. Sinko, T. Krebs, “Product Knowledge Management Support for Customer-Oriented System Configuration,” Business Information System Workshops BIS 2017, Lecture Notes in Business Information Processing 303, 2017, pp. 49-58. https://doi.org/10.1007/978-3-319-69023-0_5.
  24. A. Patel, B. Jaumard, “Design and Implementation of a Smart Quotation System,” Advances in Artificial Intelligence: Proceedings of 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Edmonton, AB, Canada, Lecture Notes in Artificial Intelligence 10233, 2017, pp. 191-202. https://doi.org/10.1007/978-3-319-57351-9_24.
  25. K. Senczyna, “The Use of Price Waterfall Model in Logistics”, Zeszyty Naukowe Politechniki Częstochowskiej Zarządzanie, vol. 21, 2016, pp. 179–188.
  26. P. Kopalle, D. Biswas, P. K. Chintagunta, J. Fan, K. Pauwels, B. T. Ratchford, J. A. Sills, “Retailer Pricing and Competitive Effects”, Journal of Retailing, vol. 85, no. 1, 2009, pp. 56-70. https://doi.org/10.1016/j.jretai.2008.11.005.
  27. K. Sharma, I. Karlin, J. Keasler, J. R. McGraw, V. Sarkar, “Data Layout Optimization for Portable Performance”, Proceedings of Euro-Par 2015: Parallel Processing, LNCS 9233, 2015, pp. 250–262, https://doi.org/10.1007/978-3-662-48096-0 20
  28. S. van der Walt, S. C. Colbert, G. Varoquaux, “The NumPy Array: A Structure for Efficient Numerical Computation”, Computing in Science & Engineering, vol. 13, no. 2, 2011, pp. 22-30. https://doi.org/10.1109/MCSE.2011.37.
  29. W. Lieberman, “From yield management to price optimization: Lessons learned”, Journal of Revenue and Pricing Management, vol. 11, no. 1, 2011, pp. 40-43. https://doi.org/10.1057/rpm.2010.44.
  30. O. Roll, “Pricing trends from a management perspective”, Journal of Revenue and Pricing Management, vol. 8, no. 4, 2009, pp. 396-398. https://doi.org/10.1057/rpm.2009.22.
  31. C. Cizaire, “Pricing: The third business skill: Principles of price management”, Journal of Revenue and Pricing Management, vol. 13, no. 4, 2014, pp. 339-340. https://doi.org/10.1057/rpm.2014.4.
  32. T. L Jacobs, R. Ratliff, B. C Smith, “Understanding the relationship between price, revenue management controls and scheduled capacity – A price balance statistic for optimizing pricing strategies”, Journal of Revenue and Pricing Management, vol. 9, no. 4, 2010, pp. 356-373. https://doi.org/10.1057/rpm.2010.18.
  33. B. M Noone, L. Canina, C. A Enz, “Strategic price positioning for revenue management: The effects of relative price position and fluctuation on performance”, Journal of Revenue and Pricing Management, vol. 12, no. 3, 2013, pp. 207-220. https://doi.org/10.1057/rpm.2012.48.
  34. A. E.-M. Bayoumi, M. Saleh, A. F. Atiya, H. A. Aziz, “Dynamic pricing for hotel revenue management using price multipliers“, Journal of Revenue and Pricing Management, vol. 12, no. 3, pp. 271-285. https://doi.org/10.1057/rpm.2012.44.
  35. A. A. Levis, L. G. Papageorgiou, “Active demand management for substitute products through price optimisation”, In: Supply Chain Planning: Quantitative Decision Support and Advanced Planning Solutions, Berlin: Springer, 2009. https://doi.org/10.1007/978-3-540-93775-3_4.
  36. D. Zhang, R. Kallesen, “Incorporating competitive price information into revenue management”, Journal of Revenue and Pricing Management, vol. 7, no. 1, 2008, pp. 17-26. https://doi.org/10.1057/palgrave.rpm.5160120.
  37. B.-N. Hwang, J. Tsai, H.-Ch. Yu, S.-Ch. Chang, “An effective pricing framework in a competitive industry: Management processes and implementation guidelines”, Journal of Revenue and Pricing Management, vol. 10, no. 3, 2011, pp. 231-243. https://doi.org/10.1057/rpm.2009.47.