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Big Data Analytics in Oil and Gas Industry
Published in Anirbid Sircar, Gautami Tripathi, Namrata Bist, Kashish Ara Shakil, Mithileysh Sathiyanarayanan, Emerging Technologies for Sustainable and Smart Energy, 2022
Vrutang Shah, Jaimin Shah, Kaushalkumar Dudhat, Payal Mehta, Manan Shah
The two models utilised for refinery energy analysis are DEA and Principal Component Analysis (PCA). The DEA approach is used to ascertain the efficiency of a particular dataset of decision-making units (DMUs). There are two sorts of DEA models: input-oriented and output-oriented. Input-oriented DEA models are used in the refinery application because the efficiency of refinery operations is evaluated using a fixed structure. The input-oriented DEA model enables the evaluation of refineries’ performance in terms of producing refinery products with the least amount of power and fuel. The DEA’s numerous models include the CCR model, the Andersen model, and the Petersen model. Generally, the Andersen and Petersen model is employed in the refining business. CCR is not widely utilised in the refining business. Because it uses the same index for all decision-making units, it is unable to give rank-efficient units to all decision-making units. If the value of the decision-making unit’s efficiency score is equal to or greater than one, the Andersen and Petersen model is significantly more efficient. PCA is mostly used in multivariate statistics to reduce the number of variables in all decision-making units. PCA is utilised in a variety of settings, including medical facilities, companies, and communities. PCA generates the outputs by combining the inputs from numerous sources. PCA analysis is used to determine the concentrations of air contaminants in the Athabasca oil sands (Patel et al., 2020).
Data Analysis
Published in Shyama Prasad Mukherjee, A Guide to Research Methodology, 2019
Data envelopment analysis (DEA) is a relatively simple non-parametric method to compare the efficiencies of multi-input, multi-output decision-making units (DMUs) based on observed data. Such units are engaged in producing similar outputs using similar inputs, most often working within the same corporate organization but each enjoying some amount of authority to decide on the deployment of the resource inputs. Examples could be branches of a bank, schools working under the same board or council, hospitals under the same health department, service units of a municipal corporation, and the like. Sometimes, the DMUs could be different organizations in the same business. Efficiency (sometimes referred to as technical efficiency) is defined as the ratio between the weighted total of outputs produced and the weighted total of inputs used, where weights are to be endogenously generated from the data and not exogenously assigned by experts. In fact, these weights are determined by repeated application of linear programming in DEA. DEA is preferred to other comparable forms in econometric analysis which assume some parametric model linking outputs as functions of inputs.
Introduction to Multi-Attribute Decision-Making in Business Analytics
Published in William P. Fox, Mathematical Modeling for Business Analytics, 2017
DEA is a data input–output-driven approach for evaluating the performance of entities called decision-making units (DMUs) that convert multiple inputs into multiple outputs (Cooper et al., 2000). The definition of a DMU is generic and very flexible so that any entity to be ranked might be a DMU. DEA has been used to evaluate the performance or efficiencies of hospitals, schools, departments, U.S. Air Force wings, U.S. Armed Forces recruiting agencies, universities, cities, courts, businesses, banking facilities, countries, regions, Special Operations Forces (SOF) airbases, key nodes in networks, and the list goes on. According to Cooper et al. (2000), DEA has been used to gain insights into activities that were not obtained by other quantitative or qualitative methods.
Investigating the energy efficiency determinants in EU countries by using multi-criteria decision analysis and the Tobit regression model
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2023
Salih Çam, Muhammed Ali Kağızman
DEA is a non-parametric, linear programming-based method for calculating the efficiency of decision-making units (DMUs) in cases where there are complex relationships between inputs and outputs. DEA constructs an efficient frontier over the data and computes the efficiency of each DMU relative to the frontier. DEA efficiency indicates how far a DMU is from the frontier compared to its peers and represents its ability to convert multiple inputs into multiple outputs (Charnes, Cooper, and Rhodes 1978; Farrell 1957; Mehra et al. 2014). The initial mathematical form of DEA was proposed by Farrell (1957) with one input and one output. Charnes, Cooper, and Rhodes (1978) improved a multivariable model known as CCR based on Farrell’s model, while Banker, Charnes, and Cooper (1984) introduced an alternative model known as BCC (Banker, Charnes, and Cooper) to measure overall efficiency under the variable returns to scale (VRS) assumption. The CCR and BCC models can be set up as input or output-oriented in two different mathematical formulations (Ji and Lee 2010). In the following, we present a general input-oriented CCR model that assumes constant returns to scale:
Assessing the sustainability of cloud computing service providers for Industry 4.0: a state-of-the-art analytical approach
Published in International Journal of Production Research, 2023
Majid Azadi, Zohreh Moghaddas, T.C.E. Cheng, Reza Farzipoor Saen
Data envelopment analysis (DEA) is a potent technique for evaluating the performance of decision-making units (DMUs). DEA is a nonparametric approach to measure the performance of a set of peer entities that take into consideration multiple inputs and multiple outputs. Termed DMUs, the peer entities might be companies operating in a particular market sector or a group of individuals involved in a business process (Emrouznejad and Yang 2018). DEA is a popular tool for performance measurement and benchmarking in many areas such as education, healthcare, and transportation. DEA can deal with multiple variables, including qualitative and quantitative measures. Furthermore, DEA does not need to specify the relationships among the performance measures (Shafiee, Lotfi, and Saleh 2014). Nevertheless, despite the wide application of DEA, its application to evaluating CSPs is scarce. Furthermore, our literature survey shows that there is no reference to evaluate the sustainability of CSPs. Moreover, the existing DEA models cannot deal with different types of data such as integer, ratio, undesirable outputs, and quasi-fixed.
Assessing the operational efficiency of wastewater services whilst accounting for data uncertainty and service quality: a semi-parametric approach
Published in Water International, 2020
DEA models can be specified as input-oriented (input-minimization focus) or output-oriented (output-maximization focus). A DEA model with an input orientation minimizes input quantities proportionately whilst holding its outputs constant. Conversely, an output-oriented DEA model maximizes output quantities proportionately whilst holding input quantities constant (Coelli et al., 1998). The selection of orientation is typically an empirical matter. From an empirical perspective, wastewater utilities have little control over their outputs and typically attempt to minimize inputs to achieve a given level of output. A wastewater utility is not efficient if it is possible to decrease any input without augmenting any other input and without decreasing any output. Previous wastewater management studies have used input orientation, as most wastewater utilities aim to comply with environmental standards and other obligations at the lowest cost (Gómez et al., 2017). DEA model specification also involves variable returns to scale assumption of technology used. The Charnes, Cooper and Rhodes model is input oriented and assumes constant returns to scale (CRS) (Charnes et al., 1978). CRS technology assumes that all wastewater utilities operate at an optimum scale. Conversely, variable returns to scale (VRS) technology allows for imperfect competition, constraints on finance, etc., and is not confounded by scale efficiency effects. Interested readers are directed to Cooper et al. (2007) and Bogetoft and Otto (2011) for more details on the DEA methodology.