JO
Josef Oehmen
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Uncertainty is the third major perspective in understanding and designing engineering systems, along with complexity and human behaviour. Risk, a corollary of uncertainty, is understood as the effect of uncertainty on objectives. When designing engineering systems, you cannot not manage risk - even ignoring risk equates to a decision to accept it. Engineering systems are characterised by long life cycles, changing operational environments, and evolving stakeholder values, leading to a wide range of uncertainties in their design and operation. Productively engaging with this uncertainty is critical for successfully operating and especially (re-)designing engineering systems. This chapter provides an overview of managerial practices to address the three levels of increasing uncertainty in engineering systems design: from (1) managing risk, to (2) managing uncertainty, to (3) managing ignorance. We differentiate for each level of uncertainty between two levels of value diversity: (1) primarily commensurate values (i.e. agreement on core values by critical stakeholders) and (2) primarily incommensurate values (i.e. no agreement on core values). The managerial practices we discuss are “classic” risk management, public engagement, scenario planning, robust decision-making, resilience, and applying the precautionary principle. In addition, we briefly illuminate the actuality of management practices dealing with the different levels of uncertainty beyond explicit, formal processes, the understanding of managing uncertainty as both modelling and decision support practices and personal and organisational biases in the context of addressing uncertainty.
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Uncertainty is the third major perspective in understanding and designing engineering systems, along with complexity and human behaviour. Risk, a corollary of uncertainty, is understood as the effect of uncertainty on objectives. When designing engineering systems, you cannot not manage risk - even ignoring risk equates to a decision to accept it. Engineering systems are characterised by long life cycles, changing operational environments, and evolving stakeholder values, leading to a wide range of uncertainties in their design and operation. Productively engaging with this uncertainty is critical for successfully operating and especially (re-)designing engineering systems. This chapter provides an overview of managerial practices to address the three levels of increasing uncertainty in engineering systems design: from (1) managing risk, to (2) managing uncertainty, to (3) managing ignorance. We differentiate for each level of uncertainty between two levels of value diversity: (1) primarily commensurate values (i.e. agreement on core values by critical stakeholders) and (2) primarily incommensurate values (i.e. no agreement on core values). The managerial practices we discuss are “classic” risk management, public engagement, scenario planning, robust decision-making, resilience, and applying the precautionary principle. In addition, we briefly illuminate the actuality of management practices dealing with the different levels of uncertainty beyond explicit, formal processes, the understanding of managing uncertainty as both modelling and decision support practices and personal and organisational biases in the context of addressing uncertainty.
Engineering Systems Design is an emerging perspective with a growing community. The preceding chapters in the Handbook of Engineering Systems Design presented the engineering systems perspective, models for describing and methods for designing interventions in engineering systems, as well as reflections on the use of those methods and upcoming practice, educational and policy challenges. In this chapter, we are taking a look at the future of Engineering Systems Design. We start by highlighting productivity, sustainability and resilience as three societal objectives, and proceed to discuss critical paradoxes we must address through engineering systems interventions: providing a high standard of living for everyone, without paying the environmental price; a fast minimisation and mitigation of climate change without taking risks; and the challenge of global transformations respecting local needs. We continue to discuss what we consider three critical engineering systems design capabilities we must develop to resolve these paradoxes: the ability to manage systems requirements at societal scale; the development of scale-covariant engineering systems; and mastering connectability. We conclude the chapter with a call to action for researchers, practitioners and policy makers to advance theory, design methods and tools, and stakeholder outreach development to strengthen our engineering systems design capabilities.
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Engineering Systems Design is an emerging perspective with a growing community. The preceding chapters in the Handbook of Engineering Systems Design presented the engineering systems perspective, models for describing and methods for designing interventions in engineering systems, as well as reflections on the use of those methods and upcoming practice, educational and policy challenges. In this chapter, we are taking a look at the future of Engineering Systems Design. We start by highlighting productivity, sustainability and resilience as three societal objectives, and proceed to discuss critical paradoxes we must address through engineering systems interventions: providing a high standard of living for everyone, without paying the environmental price; a fast minimisation and mitigation of climate change without taking risks; and the challenge of global transformations respecting local needs. We continue to discuss what we consider three critical engineering systems design capabilities we must develop to resolve these paradoxes: the ability to manage systems requirements at societal scale; the development of scale-covariant engineering systems; and mastering connectability. We conclude the chapter with a call to action for researchers, practitioners and policy makers to advance theory, design methods and tools, and stakeholder outreach development to strengthen our engineering systems design capabilities.
Uncertainty assessment and management, as well as the associated decision making are increasingly important in a variety of scientific fields. While uncertainty analysis has a long tradition, meeting sustainable development goals through long-term Life Cycle Engineering (LCE) decision making demands addressing Deep Uncertainty (DU). DU characterizes situations where there is no agreement on exact causal structures, let alone probabilities. In this case traditional, probability based approaches cannot produce reliable results, as there is a lack of information and experts are unlikely to agree upon probabilities. Due to the nature of LCE, this paper argues that methods to better cope with DU can make a significant contribution to the management of LCE. We introduce a set of methods that use computational experiments to analyze DU and have been successfully applied in other fields. We describe Robust Decision Making (RDM) as the most promising approach for addressing DU challenges in LCE. We then illustrate the difference between applying traditional risk management approaches and RDM through an example, complemented with the interview findings from a company using RDM. We conclude with a discussion on future research directions.
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Uncertainty assessment and management, as well as the associated decision making are increasingly important in a variety of scientific fields. While uncertainty analysis has a long tradition, meeting sustainable development goals through long-term Life Cycle Engineering (LCE) decision making demands addressing Deep Uncertainty (DU). DU characterizes situations where there is no agreement on exact causal structures, let alone probabilities. In this case traditional, probability based approaches cannot produce reliable results, as there is a lack of information and experts are unlikely to agree upon probabilities. Due to the nature of LCE, this paper argues that methods to better cope with DU can make a significant contribution to the management of LCE. We introduce a set of methods that use computational experiments to analyze DU and have been successfully applied in other fields. We describe Robust Decision Making (RDM) as the most promising approach for addressing DU challenges in LCE. We then illustrate the difference between applying traditional risk management approaches and RDM through an example, complemented with the interview findings from a company using RDM. We conclude with a discussion on future research directions.