Breaking down the impact of automation in manufacturing

Alison Fang*, Vickie Chen, and Matthew A. McDonald

Edited by Anuraag Singh

Review | Aug. 31 2023


DOI: 10.38105/spr.ja3pmglhj7/


  • Automation technologies are increasingly used in the manufacturing industry to boost productivity, achieve consistent quality, reduce costs, and allow human workers to pursue more creative and higher skilled work.
  • Low and medium skilled workers face the greatest threat for displacement by automation. The creation of new industries and economic growth are ultimately beneficial, but new policies are needed to address the potential inequities that may arise.
  • Automation can improve the safety of the manufacturing process for employees, but also introduces new safety risks that come with operating and collaborating with new equipment.
  • Consumers benefit from the increased efficiency of production through lower prices and shorter time-to-market. Supply chain resilience remains a developing issue that requires attention, as evidenced by the Covid-19 pandemic
  • Environmental impacts of automation technologies vary widely between industries and ecosystems, but they have potential to decrease pollution when national and local regulations are coordinated.

Article Summary

This review explores the phenomenon of ‘wishcycling’—a consequence of growing commitments to sustainable behaviors—and its impact on recycling contamination. While recycling rates have increased over the past six decades, high contamination rates have led to the closure of recycling programs and limited recycling of certain materials. Informational interventions as well as switching to a dual-stream recycling system would substantially reduce contamination. We discuss the role of individuals, corporations, and regulators in generating this problem. Further, we liken individual wishcycling to the recycling attitude of corporations and governments. We discuss policy options to address these issues.

Open Access


This MIT Science Policy Review article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit by/4.0/.

Alison Fang

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA

Vickie Chen

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Matthew A. McDonald

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA