An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

Azadeh, A.; Amalnick, Mohsen Sadegh; Ghaderi, S.F.; Asadzadeh, Seyed Mohammad

doi:10.1016/j.enpol.2007.01.018

Cited by 151 publications

(54 citation statements)

References 28 publications

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“…This approach calculates an Energy Efficiency Index (EEI) for each plant by determining the difference between actual energy intensity of each process unit and that of a hypothetical stateof-the-art unit and then aggregating units within an entire plant. Their approach is comparable to the Solomon's Energy Intensity Index ® (as discussed in Phylipsen, Blok et al) Another energy benchmarking approach which draws from concepts in the process stage methods but employs data envelopment analysis (DEA) is Azadeh et al (2007). They use DEA to account for the Bstructural factors^of energy use, such as input and product mix to create energy efficiency rankings but apply it to countrylevel OECD data rather than plant-level data.…”

Section: Relationship To Other Benchmarking Approachesmentioning

confidence: 98%

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Boyd

2016

Energy Efficiency

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There is growing interest among policy makers and others regarding the role that industrial energy efficiency can play in mitigation of climate change. For over 10 years, the US Environmental Protection Agency (EPA) has supported the development of sector-specific industrial energy efficiency case studies using statistical analysis of plant level on energy use, controlling for a variety of plant production activities and characteristics. These case studies are the basis for the ENERGY STAR ® Energy Performance Indicators (EPIs). These case studies fill an important gap by estimating the distribution of efficiency using detailed, sector-specific, plant-level data. These estimated distributions allow Energy Star to create Benergy-efficient plant benchmarks^in a variety of industries using the upper quartile of the estimated efficiency distribution. Case studies have been conducted for 14 broad industries, 2 dozen sectors, and many more detailed product types. This paper is a meta-analysis of the approach that has been used in this research and the general findings regarding the estimated distribution of performance within and across industries. We find that there are few sectors that are well represented by a simple Benergy per widget^benchmark that less energy-intensive sectors tend to exhibit a wider range of within-industry efficiency than energy-intensive sectors, but changes over time in the level and range of energy efficiency do not reveal any single pattern.

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Section: Relationship To Other Benchmarking Approachesmentioning

confidence: 98%

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Boyd

2016

Energy Efficiency

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“…Many researchers have used PCA for data analysis purposes. Researchers claim that the main idea for the development of the PCA goes back to the work of Pearson in 1901, expanded by Hotelling in 1933 [2,42]. To study a specific issue in society, one needs to take p variables (indices) into consideration.…”

Section: Proposed Models For Assessment Of Suppliersmentioning

confidence: 99%

“…Some of these methods are: (1) Algebraic addition of criteria [30]; (2) Analytical Hierarchy Process [3,19]; (3) Mathematical model of facility location [8]; (4) Stochastic models [25]; (5) Simulation models [28]; and (6) Data Envelopment Model [2,42]. In this research, to assess and rank the suppliers, we will use the Principal Component Analysis (PCA) model.…”

Section: Proposed Models For Assessment Of Suppliersmentioning

confidence: 99%

Measuring the Leanness of Suppliers Using Principal Component Analysis Technique

Mehrjerdi

2011

The South African Journal of Industrial Engineering

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A technique that helps management to reduce costs and improve quality is 'lean supply chain management', which focuses on the elimination of all wastes in every stage of the supply chain and is derived from 'agile production'. This research aims to assess and rank the suppliers in an auto industry, based upon the concept of 'production leanness'. The focus of this research is on the suppliers of a company called Touse-Omron Naein. We have examined the literature about leanness, and classified its criteria into ten dimensions and 76 factors. A questionnaire was used to collect the data, and the suppliers were ranked using the principal component analysis (PCA) technique.

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“…We applied a distance matrix instead of a covariance matrix in Step 2, which was modified according to the multi-dimensional scaling algorithm. Rossi and Tomas [25] and Azadeh et al [26] also used the distance matrix to rank the DMUs in their studies.…”

Section: Mds Algorithmmentioning

confidence: 99%

“…Premacandra [24] extended this approach by incorporating other important features of ranking that Zhu has not considered. Besides, Rossi and Tomas [25] and Azadeh et al [26] have shown that the distance matrix also could be used to rank the DMUs.…”

Section: Introductionmentioning

confidence: 99%

Ranking Decision Making Units with the Integration of the Multi-Dimensional Scaling Algorithm into PCA-DEA

Ünsal¹,

Örkçü²

2016

HJMS

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Data envelopment analysis (DEA) has being used commonly in a variety of fields since it was developed, and its development continues through interacting with other techniques. Since the method can be applied to multiple inputs and outputs, it interacts with multivariate statistical methods. Principle component analysis (PCA) is a multivariate analysis method used to destroy the independence structure between variables or to reduce the number of dimensions. In literature, PCA and DEA are compared for ranking decision making units. Then, PCA-DEA procedure was modified. In this study, the multi-dimensional scaling (MDS) algorithm, which is one of the commonly used methods in multivariate statistics, is integrated to the PCA-DEA method to rank the decision making units (DMUs). According to Spearman rank correlation, the proposed method gives a higher correlation with super efficiency compared to other methods.

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An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors

Cited by 151 publications

References 28 publications

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)

Measuring the Leanness of Suppliers Using Principal Component Analysis Technique

Ranking Decision Making Units with the Integration of the Multi-Dimensional Scaling Algorithm into PCA-DEA

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