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The Optimal Acquisition of Automation to Enhance the Productivity of Labor

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  • Cheryl Gaimon

    (Academic Faculty of Management Sciences, The Ohio State University, Columbus, Ohio 43210)

Abstract

Decisions concerning the mix of automation and labor employed by an organization are embedded in the long-term strategic plan since the composition of productive capacity impacts on an organization's ability to survive and compete. In this paper, the optimal mix of automation and labor is identified for automation which acts to enhance the productivity of an organization's workforce. The incentives considered for acquiring automation are increasing the level of output, reducing the high cost of labor, and compensating for a limited supply of labor. Factors explicitly examined by the model include the future long-term goal level of output, costs associated with maintaining the workforce (wages) and automation, and costs associated with changing the levels of workforce and automation. Since the formulation is dynamic so that all exogenous and decision variables may be expressed as functions of time, the effects of technological improvement, increasing wage rates, changing labor supply, and diminishing returns as additional automation is acquired are considered.

Suggested Citation

  • Cheryl Gaimon, 1985. "The Optimal Acquisition of Automation to Enhance the Productivity of Labor," Management Science, INFORMS, vol. 31(9), pages 1175-1190, September.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:9:p:1175-1190
    DOI: 10.1287/mnsc.31.9.1175
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    Citations

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    Cited by:

    1. Fine, Charles H., 1989. "Developments in manufacturing technology and economic evaluation models," Working papers 3012-89., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Sun, Wenyuan & Zhang, Zhonghui & Chen, Yang & Luan, Fushu, 2023. "Heterogeneous effects of robots on employment in agriculture, industry, and services sectors," Technology in Society, Elsevier, vol. 75(C).
    3. Kim, Bowon, 1998. "Optimal development of production technology when autonomous and induced learning are present," International Journal of Production Economics, Elsevier, vol. 55(1), pages 39-52, June.
    4. Ballestar, María Teresa & García-Lazaro, Aida & Sainz, Jorge & Sanz, Ismael, 2022. "Why is your company not robotic? The technology and human capital needed by firms to become robotic," Journal of Business Research, Elsevier, vol. 142(C), pages 328-343.
    5. Mustafa Dogan & Pinar Yildirim, 2022. "Managing automation in teams," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(1), pages 146-170, February.

    More about this item

    Keywords

    automation; labor productivity;

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