Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach

Abstract

External factors such as climate change and the current energy crisis due to global conflicts are leading to the increasing relevance of energy consumption and energy procurement in the manufacturing industry. In addition to the growing call for sustainability, companies are increasingly struggling with rising energy costs and the reliability of the power grid, which endangers the competitiveness of companies and regions affected by high energy prices. Appropriate measures for energy-efficient and, not least, energy-flexible production are necessary. In addition to innovations and optimizations of plants and processes, digital energy platforms for the visualization, analysis, optimization, and control of energy flows are becoming essential. Over time, several digital energy platforms emerged on the market. The number and the different functionalities of the platforms make it challenging for classic manufacturing companies to keep track and select the right digital energy platform. In literature, the characteristics and functionalities of digital energy platforms have already been identified and structured. However, a classification of existing platforms into archetypes makes it easier for companies to select the platforms providing the missing functionality. To tackle this issue, we conducted an explorative and data-driven cluster analysis based on 49 existing digital energy platforms to identify digital energy platform archetypes and derive implications for research and practice. The results show five different archetypes that differ primarily in terms of functionalities on energy market integration. The identified archetypes provide a well-founded overview of the similarities and differences of digital energy platforms. Decision makers in manufacturing companies will benefit from the archetypes in future analyses as decision support in procurement processes and modifications of digital energy platforms.