BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Szozda Natalia (Wrocław University of Economics, Poland), Werbińska-Wojciechowska Sylwia (Wroclaw University of Technology)
Influence of the Demand Information Quality on Planning Process Accuracy in Supply Chain : Case Studies
Wpływ jakości informacji o popycie na dokładność procesu planowania w łańcuchu dostaw : studia przypadku
LogForum, 2013, vol. 9, nr 2, s. 73-90, rys., tab., bibliogr. 61 poz.
Przepływ informacji, Jakość informacji, Popyt, Łańcuch dostaw
Data flow, Information quality, Demand, Supply chain
summ., streszcz., Zfsg.
Wstęp: Identyfikacja i analiza czynników wpływających na dokładność procesu planowania popytu w łańcuchu dostaw jest jednym z ważniejszych problemów wpływających na efektywność przepływów materiałowych i informacyjnych.
Metody: W oparciu o badania procesu planowania popytu autorzy definiują główne elementy wpływające na poziom funkcjonowania łańcucha dostaw oraz badają możliwe zależności pomiędzy jakością informacji o popycie oraz dokładnością procesu planowania popytu. Następnie, przedstawiono przegląd literatury badanego obszaru naukowego.
Rezultaty: W oparciu o badania literatury, scharakteryzowano wpływ czynników na dokładność planu popytu w poszczególnych ogniwach łańcuchów dostaw analizowanych przedsiębiorstw produkcyjnych. Rozpatrzono trzy studia przypadków, w których rozpatrzono trzy przedsiębiorstwa produkcyjne z różnych branż. Skupiono się na procesie planowania popytu w analizowanych łańcuchach dostaw. Celem było określenie dokładności przyszłych planów sprzedaży w poszczególnych ogniwach łańcucha dostaw oraz czynników je zakłócających. W oparciu o analizę procesów planowania popytu przykładowych przedsiębiorstw produkcyjnych, zdefiniowano możliwe miary jakości, które mogą być wykorzystane podczas prognozowania popytu klienta.
Wnioski: Jednym z ważniejszych i trudniejszych obszarów planowania w przedsiębiorstwach jest planowanie popytu. Związane jest to z faktem, że błędy popełnione w procesie planowania przekładają się na funkcjonowanie całego łańcucha dostaw. Przedstawione studia przypadków pokazują, że wiele czynników ma wpływ na poprawność procesu planowania popytu w łańcuchu dostaw, jak np. technologie informacyjne, czas dostawy, czy liczba dostarczanych materiałów. Jednocześnie, można zauważyć iż model gromadzenia informacji rynkowej również jest istotnym zagadnieniem w procesie planowania popytu. (abstrakt oryginalny)

Background: Identification and analysis of factors that affect the accuracy of demand planning process across the supply chain is one of the most important problems which influence the effectiveness of its material and information flows.
Material and methods: On the basis of demand planning process investigation authors define the main elements affecting the right supply chain performance level and investigate the possible connections between demand information quality and demand planning process accuracy. Later, an overview of some recent developments in the analyzed research area is provided.
Results: Based on the literature review, there is described the defined factors impact on the accuracy of demand plan in each echelon for case companies. There are considered three cases. The examples illustrate supply chains of different manufacturing companies. The focus is placed on demand planning across the supply chains. The issue of determining the accuracy of future sales plans in each echelon of supply chains and factors affecting it are raised. Taking into account the case companies demand planning process analyses, there are defined possible quality measures, that are possible to be used when forecasting the customer demand.
Conclusions: One of the most important and difficult planning area in the companies is becoming planning demand. Errors in planning are reflected not just in the business resource planning but also in the entire supply chain. Presented cases show that many factors affect the proper demand planning process in the supply chain, like e.g. information technologies, lead-time, or number of supplied materials. As it can be seen from the case studies, the model of collecting information from the market plays an important role in the demand planning process. (original abstract)
Full text
  1. Aarset M. V., Ulvestad, E., 1993, Decision support for optimal logistics, In proc.: Annual Reliability and Maintainability Symposium.
  2. Ahmad B. N., Zailani, S., 2007, The effect of information quality on buyer-supplier relationships: a conceptual framework, Proceedings of 7th Global Conference on Business and Economics, Rome, Italy.
  3. Beamon B. M., 1998, Supply Chain Design and Analysis: Models and Methods. International Journal of Production Economic, Vol. 55, No. 3, pp. 281-294.
  4. Blanchard B. S., 2004, Logistics Engineering and Management (5th Ed). Upper Saddle River: Pearson Prentice Hall.
  5. Bolc L., Borodziewicz W., Wójcik M., 1991, Fundamentals of proceeding of uncertain and unfull information (in Polish), PWN, Warszawa.
  6. Carnero M. C., 2006, An evaluation system of the setting up of predictive maintenance programmes, Reliability Engineering and System Safety, 91, pp. 945-963.
  7. Chen P.Ch., Wolfe P.M., 2011, A data quality model of information-sharing in a two-level supply chain. International Journal of Electronic Business Management, Vol. 9, No. 1, pp. 70-77.
  8. Chen W.L., Huang C.Y., Lai Y.C., 2009, Multi-tier and multi-site collaborative production: illustrated by a case example of TFT-LCD manufacturing. Computers in Industry, Vol. 57, pp. 61-72.
  9. Christensen A., Voytek R. S.J., 1975, A data base management (DBMP) program for Integrated Logistics Support (ILS), Microelectronics and Reliability, Vol. 14, Issue 2, pp. 73-89.
  10. Cohen M.A., Lee H.L., 1988, Strategic analysis of integrated production-distribution systems: models and methods. Operational Research 36, 2, pp. 216-228.
  11. Coit D. W., 2004, System optimization with component reliability estimation uncertainty: a multi-criteria approach, IEEE Transactions on Reliability, Vol. 53, No. 3, pp. 369-380.
  12. Crum C., Palmatier G. E., 2003, Demand management best practices: process, principles, and collaboration. Integrated Business Management Series, J.ROSS Publishing, USA.
  13. De La Cruz, A.M.L., Veeke, H.P.M., Lodewijks G., 2006, Prognostics in the control of logistics system, Proceedings of SOLI'06 Conference: Service Operations and Logistics, and Informatics.
  14. Dunbar N. E., Jensen M. L., Burgoon J. K., Bessarabova E., Bernard D. R., Roberston K. J., Kelley K. M., Adame B., Eckstein J. M., 2010, The Influence of Power, Deception and Dominance on Credibility and Decision-Making Outcomes, Proceedings of the Credibility Assessment and Information Quality in Government and Business Symposium 2010, Hawaii.
  15. Durango-Cohen P. L., Sarutipand P., 2009, Maintenance optimization for transportation systems with demand responsiveness, Transportation Research Part C, 17, pp. 337-348.
  16. Evaluation of measurement uncertainty. Guidebook, 1999, Główny Urząd Miar, Warszawa.
  17. Gunasekaran A., Nagai E.W.T., 2004, Information systems in supply chain integration and management, European Journal of Operational Research, Vol. 159, pp. 269-295.
  18. Gustavsson M., Jonsson P., 2008, Perceived quality deficiencies of demand information and their consequences. International Journal of Logistics: Research and Applications, Vol. 11, No 4, pp. 295-312.
  19. Huang Ch-Y., Lo J-H., 2006, Optimal resource allocation for cost and reliability of modular software systems in the testing phase, The Journal of Systems and Software, Vol. 79, Issue 5, pp. 653-664.
  20. Kaipia R., Holmstrom J., 2007, Selecting the right planning approach for a product. Supply Chain Management: An International Journal, Vol. 12, No. 1, pp. 3-13.
  21. Kehoe D.F., Little D., Lyons A.C., 1992, Measuring a Company IQ, Factory 2000 - 3rd International Journal of Flexible Manufacturing Systems, Vol. 2, pp. 173-178.
  22. Kian Ng W., Piplani R., 2003, Simulation workbench for analyzing multi-echelon supply chains, Integrated Manufacturing Systems, Vol. 14 Iss: 5, pp.449 - 457.
  23. Kilger C., Reuter B., 2002, Collaborative planning in Stadtler, H. and Kilger, C. (Eds), Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, 2nd ed., Springer, Berlin, pp. 223-37.
  24. Klir G. J., 2004, General information theory: aims, results and open problems, Reliability Engineering and System Safety, 85, pp. 21-38.
  25. Kristianto Y., Ajmal M.M., Helo P., 2011, Advanced planning and scheduling with collaboration processes in agile supply and demand networks. Business Process Management Journal, Vol. 17 Iss: 1, pp.107-126.
  26. Landeghem H. V., Vanmaele H., 2002, Robust planning: A New Paradigm for Demand Chain Planning. Journal of Operations Management. Vol. 20, No. 6: 769-783.
  27. Li L., Schulze L., 2011, Uncertainty in Logistics Network Design: A Review. Proceedings of the International MultiConference of Engineers and Computer Scientists 2011, Hong Kong, Vol. II.
  28. Lin J., Brombacher A. C., Wong Y. S., Chai, K. H., 2004, Analyzing quality information flows in cross-company distributed product development processes, IEEE International Engineering Management Conference 2004.
  29. Lin Y-H., Lee P-Ch., Chang T-P., Ting H-I., 2008, Multi-attribute group decision making model under the condition of uncertain information, Automation n Construction, 17, pp. 792-797.
  30. Lin W-T., Wu F-T., Lee Y-H., 2003, An extended reliability model for global logistics systems under uncertain environment. International Journal of Electronic Business Management. Vol. 1, No. 1, pp. 46-53.
  31. Lopez I., Sarigul-Klijn N., 2010, A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities, Progress in Aerospace Sciences, 46, pp. 247-273.
  32. Madanat S., 1993, Optimal infrastructure management decisions under uncertainty. Transportation Reserach, Part C, Vol. 1, No. 1, pp. 77-88.
  33. Makridakis S., Wheelwright S.C., 1997, Forecasting: issues & challenges for marketing management. Journal of Marketing, Vol. 41 Issue 4, pp. 24-38.
  34. Marsaguerra M., Zio E., Podofillini L., 2005, Optimal design of reliable network systems in presence of uncertainty, IEEE Transactions on Reliability, Vol. 54, No. 2, pp. 243-253.
  35. Meyr H., Rohde J., Schneeweiss L., Wagner M., 2002, Structure of advanced planning system, in Stadtler, H. and Kilger, C. (Eds), Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, 2nd ed., Springer, Berlin, pp. 99-104.
  36. Molenaar P. A., Huijben A. J. M., Bouwhuis D., Brombacher, A. C., 2002, Why do quality and reliability feedback loops not always work in practice: a case study, Reliability Engineering and System Safety, 75, pp. 295-302.
  37. Nasser S.M., 1986, Software reliability, Proceedings of VIII Annual Conference on Computers and Industrial Engineering, Orlando Florida.
  38. Nowakowski T., 2006, Analysis of possibilities of logistic system reliability assessment. In proc. symp.: ESREL 2006 Conference. Estoril, 18-22 September 2006. Estoril: Balkema.
  39. Nowakowski T. Werbińska S., 2006, Problem of logistic system availability assessment. In proc. symp.: X Total Logistic Management Conference. Zakopane, 7-9 December 2006.
  40. Nowakowski T., 1999, Modele niepewności informacji eksploatacyjnej [Models of exploitation information uncertainty], In proc. symp.: KONBiN'99, Szczyrk, pp. 373-380.
  41. Nowakowski T., 1995, Problemy wykorzystania nadmiaru informacyjnego do podwyższania niezawodności maszyn [Problems of information redundancy use for enhancement of machine reliability level], Proceedings of XXIII Winter School of Reliability Szczyrk.
  42. Nowakowski T., 2011, Dependability of logistics systems. Wroclaw Technical University Publ. House, Wroclaw.
  43. Nowakowski T., 2010, Problems with analyzing operational data uncertainty, Archives of Civil and Mechanical Engineering, Vol. X, No 3, pp. 95-109.
  44. Palladino A.P., 2010, Zara and Benetton: Comparison of two business models, available at:, 10.01.2012.
  45. Petkova V. T., Yuan L., Ion R. A., Sander P. C., 2005, Designing reliability information flows, Reliability Engineering and System Safety, 88, pp. 147-155.
  46. Pierskalla W. P., Voelker J. A., 1976, A survey of maintenance models: the control and surveillance of deteriorating systems, Naval Research Logistics Quarterly Vo. 23, Issue 3, pp. 353-388.
  47. Przystupa F.W., 2005a, Monitorowanie diagnostyczne zakłóceń przepływów informacji w systemach logistycznych [Diagnostic monitoring of information flow disruptions occurred in logistic systems], SYSTEMS Journal of Transdisciplinary Systems Science, Vol. 10, No. 1, pp. 142-159.
  48. Przystupa F.W., 2005b, Monitoring of Information Disturbances in Logistic Systems, SYSTEMS Journal of Transdisciplinary Systems Science, Vol. 10, No. 2, pp. 32-43.
  49. Rakowski U. K., 2005, Some notes on probabilities and non-probabilities reliability measures, Proc. of ESREL 2005 Conference, A. A. Balkema Publishers, London, pp. 1645-1654.
  50. Rocco C. M., Miller A. J., Moreno J. A., Carrasqueo N., Medina M., 2000, Sensitivity and uncertainty analysis in optimization programs using an evolutionary approach: a maintenance application, Reliability Engineering and System Safety, 67, pp. 249-256.
  51. Rohde J., 2002, Coordination and integration, in Stadtler, H. and Kilger, C. (Eds), Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, 2nd ed., Springer, Berlin, pp. 211-222.
  52. Sanchez A., Carlos S., Martorell S., Villanueva J. F., 2009, Addressing imperfect maintenance modeling uncertainty in unavailability and cost based optimization, Reliability Engineering and System Safety, 94, pp. 22-32.
  53. Sander P.C., Brombacker A.C., 2000, Analysis of quality information flows in the product creation process of high-volume consumer products, International Journal of Production Economics, 67, pp. 37-52.
  54. Sarang D.N., Laxmidhar, M., 2006, master'sthesis, Exploratory Investigation of Sales Forecasting Process and Sales Forecasting System. Case Study of Three Companies. Jönköping International Business School, Jönköping University in Sweden.
  55. Suhong L., Binshan, L., 2006, Accessing information sharing and information quality in supply chain management, Decision Support Systems, 42, pp. 1641-1656.
  56. Vanpoucke E., Boyer K., Vereecke A., 2010, Supply Chain Information Flow Strategies: an empirical taxonomy. Working Paper, Universiteit Gent, Gent, Belgium.
  57. Vlajic J.V., van der Vorst J.G.A.J., Hendrix E.M.T., 2008, Food supply chain network robustness - A literature review and research agenda. Proceedings of the International Conference on Management in Agrifood Chains and Networks. 2008, Wageningen, the Netherlands, pp. 1 - 17.
  58. Wang, N., Lu J., Kram P., 2004, Multi-scale spatial modelling for logistic system reliability. (11.12.2006r.)
  59. Werbińska S., 2008, Model of logistic support for exploitation system of means of transport. Ph.D. dissertation, Wroclaw University of Technology.
  60. Wu T., Blackhurst J., O'Grady P., 2007, Methodology for supply chain disruption analysis. International Journal of Production Research Vol. 45, No. 7, pp. 1665-1682.
  61. Zhou H., Benton W.C., 2007, Supply chain practice and information sharing, Journal of Operational Management, 25, pp. 1348-1365.
Cited by
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu