Efficiency and Productivity Analysis from a System Perspective: Historical Overview
Antonio Peyrache () and
Maria Silva
Additional contact information
Antonio Peyrache: University of Queensland
Chapter Chapter 4 in Advances in Economic Measurement, 2022, pp 173-230 from Springer
Abstract:
Abstract The last decade has witnessed an exponential proliferation of studies on Network Data Envelopment Analysis (NDEA) as a tool to measure efficiency and productivity for production systems. Those systems are composed of various layers of decision making (hierarchically organized) and potentially interconnected production processes. The decision makers face the problem of allocating resources to the various production processes in an efficient manner. This chapter provides a historical perspective to these developments by linking them to earlier works dating back to Kantorovich (Mathematical methods of organizing and planning production. Leningrad University, 1939) and Koopmans (Activity analysis of production and allocation, 1951). Both the allocation problem and the measures of efficiency used by these early authors are astonishingly relevant and similar to those in the recent NDEA literature. The modern researcher in NDEA should take stock of this early forgotten contributions.
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
Working Paper: Efficiency and Productivity Analysis from a System Perspective:Historical Overview (2021) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-2023-3_4
Ordering information: This item can be ordered from
http://www.springer.com/9789811920233
DOI: 10.1007/978-981-19-2023-3_4
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().