arbiter
JournalsPricingAccount

New inverse DEA models for budgeting and planning

Tahere Sayar et al.

RAIRO - Operations Research2021https://doi.org/10.1051/ro/2021069article
AJG 1
Weight
0.59

What the paper says

Data envelopment analysis (DEA) measures the efficiency score of a set of homogeneous decision-making units (DMUs) based on observed input and output. Considering input-oriented, the inverse DEA models find the required input level for producing a given amount of production in the current efficiency level. This article proposes a new form of the inverse DEA model considering income (for planning) and budget (for finance and budgeting) constraints. In contrast with the classical inverse model, both input and output levels are variable in proposed models to meet income (or budget) constraints. Proposed models help decision-makers (DMs) to find the required value of each input and each output’s income share to meet the income or budget constraint. We apply the proposed model in the efficiency analysis of 58 supermarkets belonging to the same chain. However, these methods are general and can be used in the budgeting and planning process of any production system, including business sectors and firms that provide services.

26 citations

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1051/ro/2021069

Or copy a formatted citation

@article{tahere2021,
  title        = {{New inverse DEA models for budgeting and planning}},
  author       = {Tahere Sayar et al.},
  journal      = {RAIRO - Operations Research},
  year         = {2021},
  doi          = {https://doi.org/https://doi.org/10.1051/ro/2021069},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

New inverse DEA models for budgeting and planning

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.59

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.60 × 0.4 = 0.24
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.