Abstract
Energy efficiency analyses and life cycle assessments are two examples of data-driven applications focused on environmental sustainability in the industrial manufacturing context. Both use methodologies based on aggregating operational data, extracted from multiple data sources along a value chain. However, the possibility to source, utilize, and share data is often obstructed by heterogeneous or non-transparent data operations across organizations, as well as poor availability of digital interfaces for data collection. In practice, the underlying application processes lack fidelity and granularity, as a trade-of to simplify the modeling and data collection tasks. Therefore, new opportunities arise for automated workflows on data collection (data pipelines) as a relevant component of every digital transformation strategy. This study reflects on the challenges experienced by two industrial actors collecting operational data for an energy efficiency analysis and a Life-cycle Assessment (LCA), each with a different manufacturing context and architectural approach. Finally, it presents the opportunities for new technologies at affordable costs that potentially ease the development and operation of data pipelines to solve such challenges.
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