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Energy-efficient supply chain design: Data aggregation and processing

In: Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 30

Author

Listed:
  • Schreiber, Lucas
  • Jarmer, Jan-Philipp
  • Kamphues, Josef

Abstract

Purpose: Due to changing customer requirements and political regulations more and more companies strive to optimize their energy efficiency in regards to products and processes. The optimization of processes within supply chain design (SCD) is one lever in this regard. Since required data is often not available, this paper elaborates how data can be generated on a suitable level of aggregation. Methodology: In order to highlight the research gap, established energy measurement procedures as well as existing energy databases for procurement, production and transportation are analyzed and compared with data requirements for SCD tasks. Based on these findings, necessary methods and procedures for data preparation are presented. Findings: Firstly, it is shown that addressing energy efficiency within SCD leads to new challenges in regards to data availability and preparation. Secondly, this paper elaborates the requirements for necessary data usable in the context of SCD. The findings are the basis for a comprehensive approach combining collection, aggregation and clustering of energy and product related data. Originality: This paper works out the gap between usually available energy related information and the requirements of SCD. Since key conditions for optimizing energy efficiency are defined in strategic planning, the findings create a necessary prerequisite for realizing energy-optimized supply chains on a large scale in the future.

Suggested Citation

  • Schreiber, Lucas & Jarmer, Jan-Philipp & Kamphues, Josef, 2020. "Energy-efficient supply chain design: Data aggregation and processing," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Data Science in Maritime and City Logistics: Data-driven Solutions for Logistics and Sustainability. Proceedings of the Hamburg International Conferen, volume 30, pages 129-155, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:228948
    DOI: 10.15480/882.3152
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    References listed on IDEAS

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