In recent years, product portfolio management (PPM) has faced increasing pressure from various stakeholders due to the sustainability transformation. This resulted in a higher complexity of decision-making and necessitates the systematic integration of sustainability into PPM to achieve corporate sustainability targets. However, incorporating this new dimension introduces target conflicts as well as a significant data demand. This paper presents a concept for the sustainability-oriented variant management of technical products. The concept enables portfolio managers to define, streamline and operationalize the sustainability controlling in accordance with the company’s target system. Furthermore, it fosters the scalability of sustainability assessments of products with machine learning approaches. This addresses the industry’s need for faster and more practical assessment alternatives with sufficient preciseness to provide transparency for decision-making in multi-variant product portfolios. Finally, the concept supports users in linking the insights with measures of variant management, thus promotes the sustainable development of the product portfolio.

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Concept for the Sustainability-Oriented Variant Management of Technical Products Supported by Machine Learning

  • Nikolai Kelbel,
  • Alexander Keuper,
  • Günther Schuh

摘要

In recent years, product portfolio management (PPM) has faced increasing pressure from various stakeholders due to the sustainability transformation. This resulted in a higher complexity of decision-making and necessitates the systematic integration of sustainability into PPM to achieve corporate sustainability targets. However, incorporating this new dimension introduces target conflicts as well as a significant data demand. This paper presents a concept for the sustainability-oriented variant management of technical products. The concept enables portfolio managers to define, streamline and operationalize the sustainability controlling in accordance with the company’s target system. Furthermore, it fosters the scalability of sustainability assessments of products with machine learning approaches. This addresses the industry’s need for faster and more practical assessment alternatives with sufficient preciseness to provide transparency for decision-making in multi-variant product portfolios. Finally, the concept supports users in linking the insights with measures of variant management, thus promotes the sustainable development of the product portfolio.