Possibility theory: A foundation for theoretical and empirical explorations of uncertainty

Abstract

The field of operations and supply chain management (OSCM) has a long history of identifying and engaging with risk and uncertainty in operational practices.¹ We provide a brief review of uncertainty in the OSCM domain, alongside an overview of our special issue (SI) call and accepted manuscripts. This serves as a starting point for the introduction of a new theoretical framework that reframes uncertainty as unresolved states of possibility. In this framework, the term possibility can refer to a broad array of OSCM actions and solutions including the novel application of existing approaches or technology as well as completely novel practices that enhance organizational outcomes. We illustrate the path-dependent evolution in these possibilities, alongside the limitations and opportunities imposed on the set of available possibilities resulting from concurrent evolution in the broader socio-technical system. We present the benefits of deploying a broader array of methodologies in the empirical study of what is, and is not, possible at discrete points in time, as well as the dual process of constraint and expansion in possibilities over time. The resulting empirical efforts to understand possibilities in turn enable novel theory development, elaboration of existing OSCM theory, and opportunities for bridging to other disciplines.

Much of the OSCM literature views uncertainty through the lens of risk (real and probabilistic, or perceived). Other areas of management examine uncertainty from the perspective of available solutions and possible alternatives, as well as opportunity by design (e.g. in entrepreneurial ventures, strategic decision making, and policy), see Alvarez and Barney (2007), Dimov (2016), and McBride and Wuebker (2022). This latter framing shifts uncertainty away from a troublesome factor to be mitigated and towards a source of potential value creation and progress. We conceptualize economic activity and operations as part of a larger, nearly decomposable, evolving structure (Simon, 1962, 2002). This offers the opportunity to redirect OSCM uncertainty research towards a deeper reflection on what actions and solutions are possible or impossible in an evolving socio-technical system—a system in which OSCM is deeply embedded (Arthur, 2009; Simon, 2002). Artificial systems evolve not just through competition and selection but also through purposeful cultivation and design (Simon, 1996). Thus, with technology in the socio-economic context of a complex system, OSCM is part of this larger autopoietic³ system—continuously regenerating itself, from itself (Holland, 1995). This centres our attention on the temporal aspect of our work. Specifically, it requires a consideration of how the world is, how it can and cannot be in the future, alongside how it could have been in the past (Hintikka & Hintikka, 1989; von Wright, 1951).

As noted earlier, possibilities can entail a wide range of OSCM actions and solutions. They include novel technologies, practices, and procedures, as well as re-imagined applications of existing ways of doing things. Possibilities can involve the deployment of technologies (e.g. AI, drones, virtual reality), changes in management policies and practices (e.g. TQM), operational process infrastructure (e.g. digital supply chains), strategies that present a novel way of working (platforms, sharing economy), and the application of algorithms (forecasting mechanisms, replenishment policies, Vendor-Managed Inventory—VMI). If the construct appears broad, that is because it is. The opportunity rests not just in identifying solutions, but in the way in which the possibility sets expand or are restricted over time.

Perspectives such as the theory of constraints (Goldratt & Cox, 1984) provide the logic-driven thinking processes and problem-solving tools that can also support the discovery and charting of a wider set of possible operations scenarios (e.g. Groop et al., 2017; Öhman et al., 2021). However, it is challenging to formulate scenarios for a dynamically evolving socio-technical system. Even though we have a general understanding of the nature of technological evolution (Arthur, 2009), we lack methods for describing and investigating it. Industry 4.0 illustrates the methodological challenge in an OSCM setting. Despite the significant research efforts to understand the unfolding opportunity of technology in manufacturing industries, the literature lacks detailed studies of how possible operations and opportunity changes over time. While researchers do not have full visibility of the domain, we will suggest that one of the challenges is our preference for inferential statistics and theory testing. The backward-looking focus of these tools means they are not ideal for developing our empirical and theoretical understanding of emerging areas of practice.

Table 1 provides a simplified overview of how the research landscape changes with a shift in focus from (a) mitigation and response to knowable and unknowable events in an open system and towards (b) an understanding of the factors that constrain and enlarge our possibilities set for operations practices. This new perspective includes developing frameworks to understand how the possibilities set that firms can select from comes into being (evolves) and may be actively enriched or augmented in a more bounded (and knowable) socio-technical system. Incorporating the repertoire of possible and infeasible possibilities into our research endeavors, alongside the evolutionary constraints and opportunities on that repertoire imposed by the socio-technical system, provides important opportunities for developing novel theoretical lenses on understanding uncertainty. It also presents a valuable pathway to enrich the methodological approaches we draw upon.

Our proposal for how to approach uncertainty from the vantage point of possibilities is pictured in Figure 1. Within this framework of possibilities and unrealized possibilities, uncertainty manifests as unresolved states between actualized/unrealized and available/constrained possibilities. Viewed from left to right, the framework is akin to the technology funnel, with many more feasible possibilities than are ever actualized. Uncertainty pertains to what are feasible new combinations, as well as the emergence or removal of possibilities. This is a type of uncertainty of practical and theoretical interest: How do firms identify and enact desirable possibilities and avoid undesirable ones?

Flowing from right to left, the emergence of new technologies and practices expand the possibilities set. Further, time itself expands the possibility set available to organizations. For example, more possibilities for managing unanticipated demand emerge as one's time horizon lengthens into the future (e.g. via inventory buffers or stockpiles that require some time to establish). Equally, time may lead to the emergence of new and unforeseen possibilities, and it may lead to the emergence of enabling elements that allow possibilities to be actualized. For example, VMI was conceptualized as a means to provide suppliers with production and delivery flexibility by giving them responsibility for replenishment, decades before the practice was adopted by companies in the 1980s (Disney & Towill, 2003; Magee, 1958). The technological shifts that made it truly viable took time to materialize.

There are also restrictions that emerge going from right to left, resulting from path dependence. For example, VMI was superseded by forecast-based replenishment solutions. However, this step back from VMI means suppliers lose flexibility on supply delivery and real-time visibility on customer demand, leading to restrictions on the possibilities available to them. Both actualized and unrealized possibility can drive evolutionary change by adding to, and removing from, available possibilities and feasible possibilities. Our framework thus describes possibility as an evolutionary process and uncertainty as an unresolved possibility.

At any point in time, there are always many more feasible and available possibilities than are actually realized. Uncertainty manifests around the nature of possibilities (e.g. future feasible/infeasible, currently available/constrained, and prior actualized/unrealized). Uncertainty occurs when we cannot determine the next state of the possibility sequence from prior knowledge. The cause of uncertainty differs by type of possibly. For example, it could be the delayed impact of unrealized prior possibilities, or incomplete knowledge of the broad array of factors that might render a future possibility infeasible. Investigating and resolving the differences requires deploying appropriate methods and research designs. Our theoretical framework can also distinguish between the possible and the impossible. Note, the figure does not include what is certain. For example, the laws of physics dictate with certainty that the past cannot be changed, and that many things that can be said are impossible.⁵ Of course, managers and workers may operate with opposing perceptions of what is and is not possible, due to natural cognitive limitations, but that is a separate (albeit critical) discussion for OSCM researchers and practitioners.

Consider further our previous example of managing unanticipated demand and the possible solution of inventory buffers. Without upstream suppliers committing resources for manufacturing at an earlier point in time there is no buffer inventory to drawdown in an emergency, thus removing that inventory as a possible solution (Durach et al., 2024). Realized possibilities may likewise generate new possibilities. For example, Dube et al. (2024) show how operational practices and systems developed for one context may allow organizations the possibility to repurpose those systems in response to an unanticipated environmental context. Similarly, as external technological shifts occur, previously infeasible possibilities may become viable at a later date, expanding the realm of possible solutions. For example, although interest in VMI has waned, improvements in monitoring and diagnostics have allowed a vendor-managed solution for condition-based replacement of spare parts to become a possibility (Akkermans et al., 2024).

We explore research on possibilities in OSCM through the examination of two critical questions: How do we conduct empirical research on possibilities? How do we foster theoretical contributions based on empirical studies of possibilities? The current emphasis on confirmation and theory testing centers much of the empirical work in OSCM on actualized possibilities. Indeed, the statistical models at the core of these research endeavors assume ergodic stochastic processes where past expectations and probabilities predict future ones (Davidson, 1991). Not all possibilities are actualized, and future possibilities are subject to ongoing systemic change in the technological, institutional, and social environment. Expanding our methodological toolkit to examine the process of evolving possibility offers rich opportunities for both empirical exploration and theoretical innovation. In the next section, we illustrate how less common approaches to empirical research in OSCM can serve to enrich our understanding of feasible-available-actualized/infeasible-constrained-unrealized solutions. We follow this with a discussion of how empirical efforts can inform nascent theory building and theory elaboration, and provide important connections to, and influence on, other disciplines.

Establishing how uncertainty relates to possibility and unrealized opportunity, reveals new ways to conduct impactful research in operations management. Our possibilities framework empowers the OSCM community to recognize available operations that are not actualized and in turn prompts empirical investigations of the constraints that play a role for those possible operations. Likewise, identifying feasible operations that are not available, directs our attention to missing elements, such as developing technological solutions, introducing missing actors into the socio-technological system, or redesigning/reconfiguring current operations with future needs in mind. We can examine the value that is lost for different actors and for society when operations that are possible are not actualized, and in turn, inform relevant strategy and policy. Possibility thinking can likewise provide policy insight by shedding light on how we can enrich the set of possibilities to address important societal needs. This positions OSCM to contribute more effectively in efforts to solve grand challenges like geopolitical conflict, climate change, dwindling resources, and aging populations.

The possibility lens, and its focus on the temporal dynamics of what is and is not possible, requires that we broaden the methodological approaches we draw upon. We hope that in turn, the possibility perspective also spawns new theoretical ideas and frameworks for the OSCM field and other knowledge domains, alongside important insight for practice and policy.