Scenarios for Industrial Decision Making

Rudolf Koopmans

Koopmans Consulting, Switzerland

Bio

Prof. Dr. Rudy Koopmans is Director of the Plastics Innovation Competence Center, Director of the Institute of Applied Plastics research, and Professor at the University of Applied Sciences (HEIA-FR) at Fribourg (CH). He holds a visiting professorship at ETH Zürich (CH) where he also received the Staudinger-Dürer Medal for excellence in Materials Science.

With 40 years of research experience in the chemical industry he brings practical understanding to find solutions for the challenges associated with sustainability and circular economy models.  He is also expert for the EU commission and member of the Horizon Europe Mission on Clean Oceans assembly.

As owner of Koopmans Consulting GmbH, located in Zürich, Switzerland, he provides consultancy support for setting-up networks of experts, and supporting start-up companies in their endeavor towards a sustainable future.

He has published more than 70 papers in international journals, contributed several book chapters, wrote three books, presented multiple keynotes at international conferences, and is holder of 23 patents.

Abstract

Scenarios for Industrial Decision Making

The activity of science is mainly about generating understanding with the aim to predict observations.[1] The formulation of the acquired knowledge in mathematical constructs makes this possible. Over the last three decades digitalisation has enabled sorting out big data sets, their fast analysis, and the simulation of possible scenarios with a predictive outcome. In addition, the requirement of causality has become less important in favour of relations that predict a certain verified reality. Business on the other hand is an economic activity still and mostly based on and driven by 19th century theory including pseudo-science mathematics complemented with after the fact explanations of failures.[2], [3] Therefore it is timely that businesses benefit from and make use of the available scientific knowledge and digitalisation tools, without getting into the fallacy that much more data supports better decision making.

For many companies, achieving the objective of a sustainable operation in a carbon neutral economy by 2050 as outlined in the European Green Deal [4] requires business decision support systems that are capable of handling not only economic but equally environmental and societal aspects of doing business. This system thinking approach will be essential to progress for meaningful decision making, but equally implies being able to deal with complex adaptive systems. Several scenarios can be considered for modelling and implementing such support systems in which the complexity depends on the operation level desired. Companies typically produce products and are integrated in a value chain, which in turn is part of a multi-value-chain network that make up a socio-economic fabric that functions within the constraints of the environment and the ecological system. Therefore, depending on the scope and ambition of the company, systems can be developed to consider the life cycle thinking, i.e., a triple P [5]– people, profit, planet – support system, at the product level, product portfolio level, value chain level, and the value chain as part of the multi-value-chain network level. In essence a hierarchy of systems can be envisaged. The implied modelling includes multi-objective optimizations [6] in combination with big data analysis on potential customers or markets and all tailored to an important business model objective. As an example, at a product level this objective could be the ecological footprint minimization of a product with an economically viable cost. In addition, the technological feasibility for the product’s ecodesign options can be included.[7]

In brief, industrial business decision making can be upgraded by considering a system approach, which combines economic, environmental and societal knowledge. By taking advantage of the scientific modelling principles, the latest digital algorithms for data analysis, and digital infrastructure far more realistic actionable scenarios can be developed to set companies on track towards a sustainable future. Such business decision support systems then guide companies to become more effective in finding resolutions for coping with an ever more complex business environment.

REFERENCES

[1]        C. Ruhla, The physics of chance: from Blaise Pascal to Niels Bohr, Reprinted. Oxford: Oxford Univ. Press, 1995.
[2]        R. Nadeau, The environmental endgame: mainstream economics, ecological disaster, and human survival. New Brunswick, N.J: Rutgers University Press, 2006.
[3]        K. Raworth, Doughnut economics: seven ways to think like a 21st-century economist. London: Random House Business Books, 2017.
[4]        “A European Green Deal,” European Commission - European Commission. https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en (accessed Feb. 09, 2021).
[5]        J. Elkington, Cannibals with forks: the triple bottom line of 21st century business, Reprint. Oxford: Capstone, 2002.
[6]        D. M. Roijers and S. Whiteson, Multi-objective decision making. 2017.
[7]        R. J. Koopmans, K. Van Doorsselaer, and J. Diaz Luque, Ecodesign: a life cycle thinking approach, 1st ed. Cincinnati: Hanser Publications, 2020.

EMMC2021-S09-Koopmans-Abstract

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