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

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. When uncertainty is foreseeable or predictable it can be modeled, under various states of nature, as a set of choices and their outcomes. This lends itself to the application of statistical approaches and analytical modeling tools (e.g. maximax, minimax regret, Hurwitz criterion, and expected opportunity loss). Considerable research effort has explored the events and conditions that may impact these probabilistic models and underlying probabilities, as well as developing extensions to optimize the response to uncertainty in operational outcomes (e.g. utilization, yield, throughput) and efficacious ways to respond to external influences like customer demand and preferences (e.g., Boute et al., 2022; Charpin et al., 2021; Gao & Chen, 2015; Leung & Sun, 2021). A parallel stream of research explores the extent to which uncertainty is knowable. One example of such uncertainty arises from managerial ignorance—events that are knowable in principle, but not taken into consideration—with the resulting epistemic uncertainty labeled knowable unknowns (Packard & Clark, 2020; Ramasesh & Browning, 2014). In the face of this form of uncertainty, firms benefit from revisiting their decisions around complexity and complicatedness, and the heuristics they draw upon when responding to, or mitigating, operational consequences of unanticipated events (e.g. Feduzi et al., 2022; Ramasesh & Browning, 2014). Another type of uncertainty is unforeseeable, or aleatory, uncertainty (so-called unknown unknowns). Here, the probabilistic approaches and assumptions that predominate OSCM research fall short as this form of uncertainty is the result of “…some inherent, a priori causal indeterminism that cannot be mitigated…” (Packard & Clark, 2020, p.770). In practice, tools like insurance and forward contracts can provide straightforward and fruitful pathways for managerial action in the presence of unknown unknowns (Holmes & Westgren, 2020). At the same time, mitigation of known or knowable forms of uncertainty may not be viable due to the cost/benefit ratio or bounded rationality. Nevertheless, distinguishing epistemic uncertainty from aleatory uncertainty guides the use of predictive and non-predictive strategies. This distinction may also be helpful in rationalizing perceptions of uncertainty that can emerge at the intersection of high complexity processes and the cognitive limitations of managers and workers; where one can effectively consider the unfortunate emergence of unknown (or incomprehensible) knowns' (Bendoly et al., 2006; Sterman et al., 2015). In developing the proposal for the SI, we envisioned that the operational disruptions and uncertainty caused by recent events such as the pandemic, regional conflicts, and natural disasters, presented a unique opportunity for advancing the theoretical frameworks and practical relevance of the OSCM field (Disney et al., 2021). We sought contributions providing novel theoretical and empirical insights for coping with uncertainty and environmental turbulence, as well as case and interventionist research to revisit and augment existing theory and generate novel insights for practice. We also solicited manuscripts that embraced uncertainty as a pathway to generate new goals and purposes, and studies introducing the role of creativity, experimentation, and innovation in relation to uncertainty; these aspects of our call received little response. The majority of the 64 manuscripts submitted to the SI align with OSCM's established theoretical and empirical lenses on uncertainty: events, responses to events, and mitigation of undesired outcomes. These submissions adopted a combination of qualitative and probabilistic approaches to managing uncertainty within the limits of managerial decision making. The final editorial process yielded five accepted papers. Two manuscripts followed our call for work on OSCM planning and managerial decision-making under uncertainty. Thirumalai et al. (2024) undertake a conventional statistical exploration of how variability in demand affects performance. In examining the impact of patient mix and volume variability in 731 hospitals in the Midwest U.S., they find both mix and volume uncertainty impact the number of procedures performed and patients' length of stay. Lu et al. (2024) examine managerial decision-making, leveraging scenario-based experiments to assess managerial sensemaking during supply chain disruptions. The authors find that disruption breadth and depth influence managerial perceptions of the anticipated impact of the disruption. As the scope of countries and suppliers involved increases, so does the managerial propensity to explore ideas of supply chain resilience. A common theme across most of the submissions we received was the OSCM repercussions of difficult to forecast, high-impact events. One of our accepted manuscripts provides an excellent example of how existing OSCM practice choices influence possibilities for novel reconfiguration and repurposing of operational systems in response to environmental demands. Dube et al. (2024) illustrate the path dependent nature of system-level strategic agility. Their longitudinal case study shows how the UK government's OSCM strategies developed to manage medicine shortages in anticipation of Brexit subsequently enabled a pivot to address the needs of the COVID-pandemic. High impact events also provide opportunities to develop new insight on the use and efficacy of existing OSCM approaches. Here, Durach et al. (2024) illustrate how the location of inventory buffers across the supply chain influenced financial performance in the wake of a major earthquake. Ignatius et al. (2024) investigate the impact of a typhoon on the deliveries of home appliances. Based on detailed monitoring of individual deliveries before, during, and after the storm, the authors make inferences on how supply-chain complexity affects performance. We thank the reviewers and Associate Editors who supported the SI. We are deeply grateful for their time and insight. As we reflected on the editorial process, we were also struck by the often-insurmountable expectations the OM community placed on manuscripts following a normative theory testing pathway. Of the almost 150 reviewer and Associate Editor reports that we handled, all but a handful recommended rejection. We applaud rigor but also recognize the various challenges that accompany empirical work in real world contexts. These contexts are inherently complex and empirical work will come with limitations and shortcomings. We encourage our colleagues undertaking the critically important and often thankless work of reviewing, to seek the value and unique insights manuscripts may offer.2 In broadening our theoretical and empirical lenses to incorporate a possibilities framework, and the temporal dependance and evolution in the OSCM practices and solutions that are possible over time, there is an opportunity for a more encompassing perspective on uncertainty. Realization of this potential requires openness to a broader set of methodologies and an interest in outliers and unique exemplars. It also requires that we, as the OSCM community, embrace a broader array of methodological tools and celebrate efforts at nascent theory building and theory elaboration. 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. 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