Bayesian detection of clusters in efficiency score maps: An application to Brazilian energy regulation

Costa, Marcelo Azevedo; Mineti, Leandro Brioschi; Mayrink, Vinícius D.; Lopes, Ana Lúcia Miranda

doi:10.1016/j.apm.2018.11.009

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Alternatively, the cluster analysis precedes the DEA, which is then performed only within the cluster to calculate for each DMU a score of efficiency that is only relative to the cluster it belongs to. The first approach is used to divide the investigated set of DMUs into multiple reference subsets of homogeneous DMUs whose efficiency is calculated on the entire group of DMUs (Sharma and Yu, 2009; Li et al ., 2016; Costa et al ., 2019, only to name a few). The second is also called the meta-frontier approach, which assumes that the DMUs operate in contexts with different characteristics that can be, among others, of environmental, social, cultural or technological nature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…IJPPM73,11 to divide the investigated set of DMUs into multiple reference subsets of homogeneous DMUs whose efficiency is calculated on the entire group of DMUs(Sharma and Yu, 2009;Li et al, 2016;Costa et al, 2019, only to name a few). The second is also called the meta-frontier approach, which assumes that the DMUs operate in contexts with different characteristics that can be, among others, of environmental, social, cultural or technological nature.…”

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Using Big Data to enhance data envelopment analysis of retail store productivity

Castellano,

Del Gobbo,

Leto

2023

IJPPM

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PurposeThe concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.Design/methodology/approachThe methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.FindingsThe proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.Practical implicationsThe use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.Originality/valueThis article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

Using Big Data to enhance data envelopment analysis of retail store productivity

Castellano,

Del Gobbo,

Leto

2023

IJPPM

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“…Despite the liberalization and the introduction of market rules, especially in the generation segments, the electricity transmission and distribution continued to function as natural monopolies and, for this reason, price regulation became very important as it restrains any power monopoly abuse. In this context, one of the most relevant aspects of the recent reforms introduced in the electricity power sector is the search for a price model that preserves the interests of consumers, guarantees the profitability of investors and stimulates sector efficiency (Pereira et al 2004;Arango et al 2008;Pereira et al 2013Pereira et al , 2015Cortez et al 2018a, b;Cullmann and Nieswand 2016;Scalzer et al 2019;Costa et al 2019;Arango et al 2017Arango et al , 2011.…”

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Aggregated Economic Analysis of the Brazilian Electricity Distribution Companies Using a Regulated Market Economic Model

Cortez

Bonatto

Arango

et al. 2020

J Control Autom Electr Syst

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While most of the research work in the field of electrical engineering was aroused by the scientific challenges to be overcome, it is fair to recognize that the practical importance of engineering hinges on its economic impact on the market, the whole society and the environment. Thus, nowadays economic modeling, analysis and optimization of the electricity market is of the utmost significance. This work presents an aggregated economic analysis of the Brazilian electricity distribution companies using an economic model of a regulated market. The fundamental agents (consumer, power utility, government, society) are represented in the TAROT-Optimized Tariff Model, which is applied to the majority of the distribution companies in Brazil, based on public real data available from ANEEL-the Brazilian electrical energy regulatory agency. This reveals the economic flows in the regulated market of each company and also presents their financial status in terms of over-investments or under-investments compared to optimal investments and positive, negative or zero economic value added of each company. Moreover, the model analysis shows the socioeconomic welfare added to society in face of different optimization objectives.

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Data clustering using hybrid water cycle algorithm and a local pattern search method

Taib

Bahreininejad

2021

Advances in Engineering Software

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Bayesian detection of clusters in efficiency score maps: An application to Brazilian energy regulation

Cited by 6 publications

References 23 publications

Using Big Data to enhance data envelopment analysis of retail store productivity

Using Big Data to enhance data envelopment analysis of retail store productivity

Aggregated Economic Analysis of the Brazilian Electricity Distribution Companies Using a Regulated Market Economic Model

Data clustering using hybrid water cycle algorithm and a local pattern search method

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