Identifying high resource consumption areas of assembly-centric manufacturing in the United States

Douglas Thomas () and Anand Kandaswamy ()
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Douglas Thomas: National Institute of Standards and Technology
Anand Kandaswamy: National Institute of Standards and Technology

The Journal of Technology Transfer, 2019, vol. 44, issue 1, No 13, 264-311

Abstract: Abstract This paper examines supply chain value added in the US for producing assembly-centric products, which includes machinery, computers, electronics, and transportation equipment, and determines whether costs are disproportionally distributed. The implication being that reductions in resource consumption in some cost areas can disproportionally reduce total resource consumption. Efforts to develop and disseminate innovative solutions to improve efficiency can, therefore, be targeted to these high cost areas, resulting in larger efficiency improvements than might otherwise be achieved. An input–output model is used for this examination and is combined with labor data and data on assets. The top 20 industries, occupations, and industry occupation combinations contributing to production are identified. A sensitivity analysis is conducted on the model using Monte Carlo simulation. The results confirm that costs are disproportionally distributed, having a Gini coefficient of 0.75 for value added and for compensation it is 0.86. Wholesale trade, aircraft manufacturing, and the management of companies and enterprises were the industries with the largest contribution to assembly-centric manufacturing, even when including imports. Energy in the form of electricity and natural gas were discussed separately, but would rank 8th if compared to the industry rankings. In terms of occupation activities, team assemblers, general and operations managers, and sales representatives were the largest occupations. Public entities might use this model and results to identify efficiency improvement efforts that will have the largest impact on industry per dollar of expenditure.

Keywords: Supply chain; Input output analysis; Manufacturing; Efficiency; Public investment (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10961-017-9577-9

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