The Effects of Output Growth on Preventive Investment Policy


We develop a multi-period dynamic model in which managers decide in each period how much to invest in improving process reliability. The optimal investment decision will minimize the firm’s total costs, which are comprised of its preventive costs and failure costs. We explicitly characterize the optimal investment scheme under different output growth projections and where the firm considers project obsolescence and investment salvageability. Our findings include: for sufficiently small output growth, investment will be made upfront; for sufficiently large output growth, investment will be made periodically until project termination; and for intermediate growth, investment will be staged until some period after which there will be no more investment. The general nature of the cost function in this model allows for its application in various cost reduction settings.

Share and Cite:

Y. Giat, "The Effects of Output Growth on Preventive Investment Policy," American Journal of Operations Research, Vol. 3 No. 6, 2013, pp. 474-486. doi: 10.4236/ajor.2013.36046.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] K. M. McDonald, “Recall the Recall,” Transportation Law Journal, Vol. 33, No. 3, 2006, pp. 253-292.
[2] K. M. McDonald, “Shifting out of Park: Moving Auto Safety from Recalls to Reason,” Lawyers & Judging Publishing, Tucson, 2006.
[3] A. Schiffauerova and V. Thomson, “A Review of Research on Cost of Quality Models and Best Practices,” International Journal of Quality & Reliability Management, Vol. 22, No. 6, 2006, pp. 647-669.
[4] S. T. Foster, “An Examination of the Relationship between Conformance and Quality-Related Costs,” International Journal of Quality & Reliability Management, Vol. 13, No. 4, 1996, pp. 50-63.
[5] A. V. Feigenbaum, “Total Quality Control,” 4th Edition, McGraw-Hill, New York, 2004.
[6] G. L. Gilardoni and E. A. Colosimo, “Optimal Maintenance Time for Repairable Systems,” Journal of Quality Technology, Vol. 39, No. 1, 2007, pp. 48-53.
[7] M. Freimer, D. Thomas and J. Tyworh, “The Value of Setup Cost Reduction and Process Improvement for the Economic Production Quantity Model with Defects,” European Journal of Operational Research, Vol. 173, No. 1, 2006, pp. 241-251.
[8] J. Freiesleben, “Costs and Benefits of Inspection Systems and Optimal inspection Allocation for Uniform Defect Propensity,” International Journal of Quality & Reliability Management, Vol. 23, No. 5, 2006, pp. 547-563.
[9] C. H. Fine and E. L. Porteus, “Dynamic Process Improvement,” Operations Research, Vol. 37, No. 4, 1989, pp. 580-591.
[10] J. Voros, “The Dynamics of Price, Quality and Productivity Improvement Decisions,” European Journal of Operational Research, Vol. 170, No. 3, 2006, pp. 809-823.
[11] G. Li and S. Rajagopalan, “The Impact of Quality on Learning,” Journal of Operations Management, Vol. 15, No. 3, 1997, pp. 181-191.
[12] G. Li and S. Rajagopalan, “Process Improvement, Quality, and Learning Effects,” Management Science, Vol. 44, No. 11, 1998, pp. 1517-1532.
[13] J. E. Carrillo and C. Gaimon, “Improving Manufacturing Performance through Process Change and Knowledge Creation,” Management Science, Vol. 46, No. 2, 2000, pp. 265-288.
[14] C. D. Ittner, “Exploratory Evidence on the Behavior of Quality Costs,” Operation Research, Vol. 44, No. 1, 1996, pp. 114-130.
[15] C. D. Ittner, V. Nagar and M. V. Rajan, “Empirical Examination of Dynamic Quality-Based Learning Models,” Management Science, Vol. 47, No. 4, 2001, pp. 563-578.
[16] C. H. Fine, “A Quality Control Model with Learning Effects,” Operations Research, Vol. 36, No. 3, 1988, pp. 437-444.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.