PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Analysis of the after-sales service process using data mining – results of empirical proceedings

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The main purpose of the article was to present the results of the analysis of the after-sales service process using data mining on the example of data gathered in an authorized car service station. As a result of the completed literature review and identification of cognitive gaps, two research questions were formulated (RQ). RQ1: Does the after-sales service meet the parameters of business process category? RQ2: Is the after-sales service characterized by trends or is it seasonal in nature? Design/methodology/approach: The following research methods were used in the study: quantitative bibliographic analysis, systematic literature review, participant observation and statistical methods. Theoretical and empirical study used R programming language and Gretl software. Findings: Basing on relational database designed for the purpose of carrying out the research procedure, the presented results were of: the analysis of the service sales structure, sales dynamics, as well as trend and seasonality analyses. As a result of research procedure, the effects of after-sales service process were presented in terms of quantity and value (amount). In addition, it has been shown that after-sales service should be identified in the business process category. Originality/value: The article uses data mining and R programming language to analyze the effects generated in after-sales service on the example of a complete sample of 13,418 completed repairs carried out in 2013-2018. On the basis of empirical proceedings carried out, the structure of a customer-supplier relationship was recreated in external and internal terms on the example of examined organization. In addition, the possibilities of using data generated from the domain system were characterized and further research directions, as well as application recommendations in the area of after-sales services was presented.
Rocznik
Tom
Strony
277--295
Opis fizyczny
Bibliogr. 46 poz.
Twórcy
autor
  • University of Gdańsk. Faculty of Management. Department of Organization and Management, Sopot
Bibliografia
  • 1. Akturk, M.S., Ketzenberg, M., and Heim, G.R. (2018). Assessing impacts of introducing ship-to-store service on sales and returns in omnichannel retailing: A data analytics study. Journal of Operations Management, 61, 15-45.
  • 2. Alexander, W.L., Dayal, S., Dempsey, J.J., Vander Ark, J.D. (2002). The secret life of factory service centres. The McKinsey Quarterly, 3, 106-115.
  • 3. Asugman, G., Johnson, J.L., and McCullough, J. (1997). The role of after-sales service in international marketing. Journal of International Marketing, 5(4), 11-28.
  • 4. Aulkemeier, F., Daukuls, R., Iacob, M.E., Boter, J., Van Hillegersberg, J., and De Leeuw, S. (2016). Sales Forecasting as a Service – A Cloud based Pluggable E-Commerce Data Analytics Service.
  • 5. Austin, D., Noble, K.E., Lotero, R.J., and Chadalavada, S. (2003). U.S. Patent No. 6.615.220. Washington, DC: U.S. Patent and Trademark Office.
  • 6. Berry, M.J.A., Linoff, G.S. (2000). Mastering Data Mining. Wiley, NY.
  • 7. Brewer P., Speh, T. (2000). Using the Balanced Scorecard to measure supply chain performance. Journal of Business Logistics, 21(1), 75-93.
  • 8. Bundschuh, R.G., Dezvane, T.M. (2003). How to make after sale services pay off. The McKinsey Quarterly, 4, 116-127.
  • 9. Burns, E.M., MacDonald, O., and Champaneri, A. (2000). Data quality assessment methodology: A framework. In: Joint Statistical Meetings-Section on Government Statistics (pp. 334-337).
  • 10. Cleveland, W.S., Devlin, S.J., and Grosse, E. (1988). Regression by local fitting: methods, properties, and computational algorithms. Journal of Econometrics, 37(1).
  • 11. Cleveland, W.S., Devlin, S.J. (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403).
  • 12. Cleveland, W.S., LOWESS (1981). A program for smoothing scatterplots by robust locally weighted regression. American Statistician, 181, 35(1).
  • 13. Cleveland, W.S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American statistical association, 74(368).
  • 14. Clifton, C., and Thuraisingham, B. (2001). Emerging standards for data mining. Computer Standards & Interfaces, 23(3), 187-193.
  • 15. Cohen, M.A., Lee, H.L. (1990). Out of touch with customer needs? Spare parts and after sales service. Sloan Management Review, 31(2), 55-66.
  • 16. Dombrowski, U., and Malorny, C. (2017). Service planning as support process for a Lean After Sales Service. Procedia CIRP, 64, 324-329.
  • 17. Ehinlanwo, O.O., Zairi, M. (1996). Best practice in the car aftersales service: An empirical study of Ford, Toyota, Nissan and Fiat in Germany — Part 1. Business Process Re-engineering and Management Journal, 2(2), 39-56.
  • 18. Farahani, R., Rezapour, S., Karda, L. (2011). Logistics Operations and Management: Concepts and Models. Elsevier, United States.
  • 19. Feldmann, A., and Olhager, J. (2008). Internal and external suppliers in manufacturing networks — An empirical analysis. Operations Management Research, 1(2), 141-149.
  • 20. Fisher, D.M. (2004). The business process maturity model: a practical approach for identifying opportunities for optimization. Business Process Trends, 9(4), 11-15.
  • 21. Gaiardelli, P., Saccani, N., and Songini, L. (2007). Performance measurement of the after-sales service network — Evidence from the automotive industry. Computers in Industry, 58(7), 698-708.
  • 22. Grajewski, P., Rybicki, J. (2016). Paradoks radykalizmu zmiany na przykładzie organizacji procesowej. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, Iss. 422, pp. 275-286.
  • 23. http://www.crisp-dm.org.
  • 24. Johansson, P., and Olhager, J. (2004). Industrial service profiling: Matching service offerings and processes. International Journal of Production Economics, 89(3), 309-320.
  • 25. Kaplan, R., and Anderson, S. (2003). Time-driven activity-based costing.
  • 26. Liu, H. Q., Liu Y. (2018). Computer Sales and After-sales Service System – Front Office Management Subsystem, 2018 5th International Conference On Electrical & Electronics Engineering And Computer Science (ICEEECS 2018), Wu, A. (eds.), Beijing, China, pp. 116-119.
  • 27. Lockamy III, A., and McCormack, K. (2004). The development of a supply chain management process maturity model using the concepts of business process orientation. Supply Chain Management: An International Journal, 9(4), 272-278.
  • 28. Loomba, A. (1996). Linkages between product distribution and service support functions. International Journal of Physical Distribution and Logistics Management, 26(4), 4-22.
  • 29. Milind, M.L. (1997). After‐sales service ‐ necessary evil or strategic opportunity? Managing Service Quality: An International Journal, Vol. 7, Iss. 3, pp. 141-145, https://doi.org/10.1108/09604529710166914.
  • 30. Patelli, L., Pistoni, A., and Songini, L. (2004). The appraisal of after-sales service contribution to value creation. A conceptual framework. 27th Annual Congress of the European Accounting Association, pp. 1-3.
  • 31. Rószkiewicz, M. (2002). Narzędzia statystyczne w analizach marketingowych. CH Beck.
  • 32. Saccani, N., Johansson, P., and Perona, M. (2007). Configuring the after-sales service supply chain: A multiple case study. International Journal of Production Economics, 110(1-2), 52-69.
  • 33. Sánchez González, L., García Rubio, F., Ruiz González, F., and Piattini Velthuis, M. (2010). Measurement in business processes: a systematic review. Business Process Management Journal, 16(1), 114-134.,
  • 34. Sanchez, R., and Mahoney, J.T. (1996). Modularity, flexibility, and knowledge management in product and organization design. Strategic Management Journal, 17(S2), 63-76.
  • 35. Shearer, C. (2000). The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5(4), 13-22.
  • 36. Sliż, P. (2018). Concept of the organization process maturity assessment. Journal of Economics & Management, 33, 80-95.
  • 37. Sliż, P. (2018). The concept of a strategy of transformation of a functional organization into process organization on the example of car dealerships in Poland. Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzanie, 130.
  • 38. Sliż, P., and Wojnicka-Sycz, E. (2019). The analysis of the occurrence of faults in passenger cars as an element of improving the management of the production process. In Advances in Manufacturing, II (pp. 277-289). Cham: Springer.
  • 39. Sung, C., Zhang, B., Higgins, C. Y., and Choe, Y. (2016, October). Data-driven Sales Leads Prediction for Everything-as-a-Service in the Cloud. 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 557-563). IEEE.
  • 40. Tornberg, K., Jämsen, M., and Paranko, J. (2002). Activity-based costing and process modeling for cost-conscious product design: A case study in a manufacturing company. International Journal of Production Economics, 79(1), 75-82.
  • 41. Urbaniak, A.J. (2001). After the Sale – What Really Happens to Customer Service. American Salesman, 46(2), 14-17.
  • 42. Valerio, D.O., Santos, E.A.P., Loures, E.F.R., and Cestari, J.M.A.P. (2017). Application of process mining in after-sales on an automotive industry. DEStech Transactions on Engineering and Technology Research, (icpr).
  • 43. Verstrepen, S., Deschoolmeester, D., and Van den Berg, R.J. (1999). Servitization in the automotive sector: creating value and competitive advantage through service after sales. In Global production management. Boston, MA: Springer, pp. 538-545.
  • 44. Wang, H., Wang, S., A knowledge management approach to data mining process for business intelligence.
  • 45. Werrmann, J. (2013). Workshop process optimization based on the collective intelligence of workshop employees involved in after-sales intelligence of Mercedes-Benz cars. International Journal of Cooperative Information Systems, 22(03), 1340005.
  • 46. Wise, R., Baumgartner, P. (1999). Go downstream — The new profit imperative in manufacturing. Harvard Business Review, 77(5), 133-141.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aa6985bd-fe1c-4206-aaf1-22bceadc5419
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.