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Using an Expert System to Monitor an Automatic Stock Control System

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Abstract

The theory of the control of stock is well understood, and a large number of firms use automatic stock control and ordering techniques as a major part of their inventory control systems. However, the effectiveness of such automatic stock control systems depends on the correct selection of system parameters for each item in the inventory and on the accurate reporting of information. It is not unusual for errors to be made in both the selection of parameters and the entry of data, and regular failures of either can cause severe problems in stock control. It is generally difficult for a human stock controller to discover errors quickly because of the large number of items that is usually found in a company inventory. This paper describes the use of an expert system to examine stock records and diagnose anomalies in the data provided or parameters applied to individual items in a stock control system. The expert system ‘reasons’ from symptoms such as reports from the forecasting system or high stock levels, to defects in the stock model used or data-collection processes, and from there to possible remedial action. Results are presented for runs of the expert system on a simulated stock control system.

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References

  1. C. D. Lewis (1981) Scientific Inventory Control. Butterworth, London.

    Google Scholar 

  2. A. C. Hax and D. Candea (1984) Production and Inventory Management. Prentice-Hall, New York.

    Google Scholar 

  3. R. W. Blanning (1984) Management applications of expert systems. Information and Management7, 311–316.

    Article  Google Scholar 

  4. R. Marcus (1984) An application of artificial intelligence to operational research. Comms of ACM 27, 1044–1047.

    Article  Google Scholar 

  5. R. H. Hokans (1984) An artificial intelligence application to timber harvesting scheduling. Interfaces 14, 77–84.

    Article  Google Scholar 

  6. T. J. Grant (1986) Lessons for OR from AI: a scheduling case study. J. Opl Res. Soc. 37, 41–57.

    Article  Google Scholar 

  7. E. H. Shortliffe (1976) Computer-based Medical Consultations: MYCIN. Elsevier, New York.

    Google Scholar 

  8. R. Davis (1976) Applications of meta-level knowledge to the construction, maintenance and use of large knowledge bases. In Knowledge-based Systems in Artificial Intelligence (R. Davis and D. B. Lenant, Eds), pp. 229–490. McGrawHill, New York.

    Google Scholar 

  9. H. E. Pople, J. D. Myers and R. A. Miller (1975) DIALOG: a model of diagnostic logic for internal medicine. Proceedings of the Fourth International Joint Conference on Al, pp. 848–855.

    Google Scholar 

  10. H. E. Pople (1977) The formation of composite hypotheses in diagnostic problem solving: an exercise in synthetic reasoning. Proceedings of the Fifth International Joint Conference on AI, pp. 147–152.

    Google Scholar 

  11. C. D. Myers, J. Fox, S. M. Pegram and M. F. Greavfs (1983) Knowledge acquisition for expert systems: experience using EMYCIN for leukemia diagnosis. Proceedings Expert Systems 83, pp. 277–283.

    Google Scholar 

  12. J. Aitkins (1983) Prototypical knowledge for expert systems. Artificial Intelligence 20, 163–210.

    Article  Google Scholar 

  13. J. R. Quinlan (1979) Induction over large databases. Standard University report HPP-79–14.

    Google Scholar 

  14. J. R. Quinlan (1986) Learning from Noisy Data. Machine Learning, Vol. 2. (R. S. Michalski, J. G. Carbonell, T. M. Mitchell, and J. R. Anderson, Eds). Morgan Kaufman, Los Altos, California.

    Google Scholar 

  15. J. Mingers (1987) Expert systems—rule induction with statistical data. J. Opl Res. Soc. 38, 39–48.

    Google Scholar 

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© 1992 Operational Research Society Ltd

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Thorpe, J.C., Marr, A., Slack, R.S. (1992). Using an Expert System to Monitor an Automatic Stock Control System. In: Doukidis, G.I., Paul, R.J. (eds) Artificial Intelligence in Operational Research. Palgrave, London. https://doi.org/10.1007/978-1-349-12362-9_10

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  • DOI: https://doi.org/10.1007/978-1-349-12362-9_10

  • Publisher Name: Palgrave, London

  • Print ISBN: 978-1-349-12364-3

  • Online ISBN: 978-1-349-12362-9

  • eBook Packages: EngineeringEngineering (R0)

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