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Modeling Information Manufacturing Systems to Determine Information Product Quality

Author

Listed:
  • Donald Ballou

    (Management Science and Information Systems, State University of New York at Albany, Albany, New York 12222)

  • Richard Wang

    (Total Data Quality Management (TDQM) Research Program, Room E53-320, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Harold Pazer

    (Management Science and Information Systems, State University of New York at Albany, Albany, New York 12222)

  • Giri Kumar Tayi

    (Management Science and Information Systems, State University of New York at Albany, Albany, New York 12222)

Abstract

Many of the concepts and procedures of product quality control can be applied to the problem of producing better quality information outputs. From this perspective, information outputs can be viewed as information products, and many information systems can be modeled as information manufacturing systems. The use of information products is becoming increasingly prevalent both within and across organizational boundaries. This paper presents a set of ideas, concepts, models, and procedures appropriate to information manufacturing systems that can be used to determine the quality of information products delivered, or transferred, to information customers. These systems produce information products on a regular or as-requested basis. The model systematically tracks relevant attributes of the information product such as timeliness, accuracy and cost. This is facilitated through an information manufacturing analysis matrix that relates data units and various system components. Measures of these attributes can then be used to analyze potential improvements to the information manufacturing system under consideration. An illustrative example is given to demonstrate the various features of the information manufacturing system and show how it can be used to analyze and improve the system. Following that is an actual application, which, although not as involved as the illustrative example, does demonstrate the applicability of the model and its associated concepts and procedures.

Suggested Citation

  • Donald Ballou & Richard Wang & Harold Pazer & Giri Kumar Tayi, 1998. "Modeling Information Manufacturing Systems to Determine Information Product Quality," Management Science, INFORMS, vol. 44(4), pages 462-484, April.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:4:p:462-484
    DOI: 10.1287/mnsc.44.4.462
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    References listed on IDEAS

    as
    1. Donald P. Ballou & Harold L. Pazer, 1985. "Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems," Management Science, INFORMS, vol. 31(2), pages 150-162, February.
    2. Donald P. Ballou & Harold L. Pazer, 1982. "The Impact of Inspector Fallibility on the Inspection Policy in Serial Production Systems," Management Science, INFORMS, vol. 28(4), pages 387-399, April.
    3. Donald P. Ballou & Harold L. Pazer, 1995. "Designing Information Systems to Optimize the Accuracy-Timeliness Tradeoff," Information Systems Research, INFORMS, vol. 6(1), pages 51-72, March.
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    Cited by:

    1. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    2. Juha-Miikka Nurmilaakso, 2014. "Coordination costs and ICT investments: an economic analysis," Netnomics, Springer, vol. 15(2), pages 57-67, September.
    3. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    4. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2009. "Impact of the Union and Difference Operations on the Quality of Information Products," Information Systems Research, INFORMS, vol. 20(1), pages 99-120, March.
    5. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    6. Xue Bai, 2012. "A Mathematical Framework for Data Quality Management in Enterprise Systems," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 648-664, November.
    7. Melchor Medina José & Lavín Verástegui Jesús & Pedraza Melo Norma Angélica, 2012. "Seguridad en la administración y calidad de los datos de un sistema de información contable en el desempeño organizacional," Contaduría y Administración, Accounting and Management, vol. 57(4), pages 11-34, octubre-d.
    8. Dominikus Kleindienst, 2017. "The data quality improvement plan: deciding on choice and sequence of data quality improvements," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 387-398, November.
    9. André Marie Mbakop & Joseph Voufo & Florent Biyeme & Louise Angèle Ngozag & Lucien Meva’a, 2021. "Analysis of Information Flow Characteristics in Shop Floor: State-of-the-Art and Future Research Directions for Developing Countries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(1), pages 43-53, March.
    10. Davidson, Ian & Tayi, Giri, 2009. "Data preparation using data quality matrices for classification mining," European Journal of Operational Research, Elsevier, vol. 197(2), pages 764-772, September.
    11. Even, Adir & Shankaranarayanan, G. & Berger, Paul D., 2010. "Managing the Quality of Marketing Data: Cost/benefit Tradeoffs and Optimal Configuration," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 209-221.
    12. Xitong Li & Hongwei Zhu & Luo Zuo, 2021. "Reporting Technologies and Textual Readability: Evidence from the XBRL Mandate," Information Systems Research, INFORMS, vol. 32(3), pages 1025-1042, September.
    13. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2004. "Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product," Management Science, INFORMS, vol. 50(7), pages 967-982, July.
    14. Bonney, Maurice & Jaber, Mohamad Y., 2013. "Developing an input–output activity matrix (IOAM) for environmental and economic analysis of manufacturing systems and logistics chains," International Journal of Production Economics, Elsevier, vol. 143(2), pages 589-597.
    15. Paul Glowalla & Ali Sunyaev, 2013. "Process-Driven Data Quality Management Through Integration of Data Quality into Existing Process Models," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(6), pages 433-448, December.
    16. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
    17. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
    18. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.
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    20. Park, JungKun & Chung, HoEun & Yoo, Weon Sang, 2009. "Is the Internet a primary source for consumer information search?: Group comparison for channel choices," Journal of Retailing and Consumer Services, Elsevier, vol. 16(2), pages 92-99.

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