Energy management in manufacturing: From literature review to a conceptual framework

May, Gökan; Stahl, Bojan; Taisch, Marco; Kiritsis, Dimitris

doi:10.1016/j.jclepro.2016.10.191

Cited by 214 publications

(115 citation statements)

References 61 publications

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“…Here, organizational drivers consist of three sub-categories: management (28%), competence (14%), and organizational structure (2%). This finding is supported by an emerging literature on energy management [46,68,69], and shows that both managers' personal engagement and management practices affect firms' energy efficiency. This includes managers' awareness and sensitivity to environmental issues [40,70], their ambitions, e.g., [24,71], and commitment, e.g., [32,72].…”

Section: Organizational Drivers For Energy Efficiencysupporting

confidence: 63%

Closing the Energy Efficiency Gap—A Systematic Review of Empirical Articles on Drivers to Energy Efficiency in Manufacturing Firms

Solnørdal

Foss

2018

Energies

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Abstract:Research has identified an extensive potential for energy efficiency within the manufacturing sector, which is responsible for a substantial share of global energy consumption and greenhouse gas emissions. The purpose of this study is to enhance the knowledge of vital drivers for energy efficiency in this sector by providing a critical and systematic review of the empirical literature on drivers to energy efficiency in manufacturing firms at the firm level. The systematic literature review (SLR) is based on peer-reviewed articles published between 1998 and 2016. The findings reveal that organizational and economic drivers are, from the firms' perspective, the most prominent stimulus for energy efficiency and that they consider policy instruments and market drivers to be less important. Secondly, firm size has a positive effect on the firms' energy efficiency, while the literature is inconclusive considering sectorial impact. Third, the studies are mainly conducted in the US and Western European countries, despite the fact that future increase in energy demand is expected outside these regions. These findings imply a potential mismatch between energy policy-makers' and firm mangers' understanding of which factors are most important for achieving increased energy efficiency in manufacturing firms. Energy policies should target the stimulation of management, competence, and organizational structure in addition to the provision of economic incentives. Further understanding about which and how internal resources, organizational capabilities, and management practices impact energy efficiency in manufacturing firms is needed. Future energy efficiency scholars should advance our theoretical understanding of the relationship between energy efficiency improvements in firms, the related change processes, and the drivers that affect these processes.

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Section: Organizational Drivers For Energy Efficiencysupporting

confidence: 63%

Closing the Energy Efficiency Gap—A Systematic Review of Empirical Articles on Drivers to Energy Efficiency in Manufacturing Firms

Solnørdal

Foss

2018

Energies

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“…The exergy based methodology that is presented may be readily expanded to take into account water flows in addition to energy and material, thus expanding the scope to resource management in factories (as suggested by recent literature [28]). Future work may be directed towards expanding the capability of such tools to generate Grassmann diagrams to visualize all of the resource flows on a common unit basis.…”

Section: Discussionmentioning

confidence: 99%

“…Other such examples of implementing industrial energy management and standards can readily be found in literature [36][37][38]. May et al [28] reviewed literature from 1995 to 2015, and identified four key aspects of energy management for manufacturing, one of which are the tools and methods that support energy management, see Figure 1. As Section 1.1 provided a review of such modelling and analysis methodologies for manufacturing, the following Section 1.3 will clearly outline the contribution of this paper.…”

Section: Energy Management Standardsmentioning

confidence: 99%

“…There are also limitations in the exergy concept with regards to the selection of the reference environment [27]. Nonetheless, owing to its advantages, it has potential in delivering improved energy and resource management in comparison to methods based on the first law of thermodynamics [28]. As such, it has been identified as a useful tool that can provide clearer information about energy efficiency opportunities, thus addressing a barrier to improved industrial energy management [29].…”

mentioning

confidence: 99%

“…Tools and methods pertaining to energy management can benefit from including non-energy based flows in the analyses [28].…”

mentioning

confidence: 99%

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Towards Improved Energy and Resource Management in Manufacturing

2018

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Exergy analysis has widely been used to assess resource consumption, and to identify opportunities for improvement within manufacturing. The main advantages of this method are its ability to account for energy quality and consumption. However, its application in industrial practice is limited, which may be due to the lack of its consistent application in practice. Current energy management standard, that facilitate consistent application of procedures do not consider the quality aspects of energy flows. An exergy based energy management standards is proposed in this paper that would take into account energy quality aspects, while facilitating the consistent application of exergy analysis in industrial practice. Building on ISO50001, this paper presents guidelines for implementing energy and resource management in factories, incorporating the concepts of exergy and holistic factory simulation, as illustrated through a manufacturing case study. From the factory level analysis, a chilling process was identified to have significant improvement potential. A dry fan cooler, using ambient air was proposed for the improved efficiency of the chillers. Energy based metrics portrayed a system that operated at high efficiency, however exergy analysis indicated much room for further improvement, therefore impacting decision making for technology selection. The contribution of this paper is in presenting a set of prescriptive guidelines that could possibly be further developed into a new energy management standard that would utilize the advantages of exergy analysis towards improved energy and resource management in manufacturing.

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Enabling Energy Efficiency in Manufacturing Environments Through Deep Learning Approaches: Lessons Learned

Nieto

Nabati

Bode

et al. 2019

IFIP Advances in Information and Communication Technology

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Currently, manufacturing industries are faced by ever-growing complexities. On the one hand, sustainability in economic and ecological domains should be considered in manufacturing. With respect to energy, many manufacturing companies still lack energy-efficient processes. On the other hand, Industry 4.0 provides large manufacturing datasets, which can potentially enhance energy efficiency. Here, traditional methods of data analytics reach their limits due to the increasing complexity, high dimensionality and variability in raw data of industrial processes. This paper outlines the potential of deep learning as an enabler for energy efficiency in manufacturing. We believe that enough consideration has not been given to make manufacturing efficient in terms of energy. In this paper, we present three manufacturing environments where available DL approaches are identified as opportunities for the realization of energy-efficient manufacturing.

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Energy management in manufacturing: From literature review to a conceptual framework

Cited by 214 publications

References 61 publications

Closing the Energy Efficiency Gap—A Systematic Review of Empirical Articles on Drivers to Energy Efficiency in Manufacturing Firms

Closing the Energy Efficiency Gap—A Systematic Review of Empirical Articles on Drivers to Energy Efficiency in Manufacturing Firms

Towards Improved Energy and Resource Management in Manufacturing

Enabling Energy Efficiency in Manufacturing Environments Through Deep Learning Approaches: Lessons Learned

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