Data Analysis Applications in the Optimal Integration of Energy Supply Chain

Sai Tejaswini Thumpala ()
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Sai Tejaswini Thumpala: University of North Texas

A chapter in Handbook of Smart Energy Systems, 2023, pp 1377-1391 from Springer

Abstract: Abstract The demand for fuel sources grows as human civilizations evolve, which leads to a faster depletion of energy resources. Since households and businesses began utilizing greater amounts of energy, particularly in electricity, the primary fuel for most daily activities and processes, energy conservation has become a major problem. Energy companies are increasingly rethinking their supply chains to maximize opportunities, reduce costs, and achieve competitive advantage. The optimal energy production simulation selection uses a set of the most important aspects and criteria that can accurately describe the energy supply chains. Based on the current energy production technology and environmental factors, it is possible to select the optimal energy production alternative from a set of different energy production chains. Enterprises can smartly save energy by developing an energy supply chain using Data analytics technologies. Using these kinds of strategies can help increase the visibility and control of the moving parts in the company and have a positive influence on their supply chain. Data analytics helps improve the way how companies utilize energy to achieve energy efficiency and progress towards the sustainability.

Keywords: Supply chain; Data analytics; Big data; Energy usage; Power generation; Environment; Organizations; Prediction; Innovative (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_45

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DOI: 10.1007/978-3-030-97940-9_45

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