In much the same way that UK airports tend not to make infrastructure decisions based on the threat of snow, during 2020 an enormous number of businesses worldwide found themselves caught out after having failed to anticipate the impact of a global pandemic on their employees’ need to work from home.
This is not really a criticism: COVID19 is without precedent, certainly in the 21st century. Businesses are often in the position of needing to make critical decisions with incomplete information, and with hindsight may look foolish for failing to anticipate events. Though both snow and pandemics are known risks, taking extreme measures to mitigate them may have looked similarly foolish given their very low frequency.
Beyond discovering an urgent need to switch to homeworking, business leaders have faced making a series of time-critical decisions with long-reaching implications during the COVID19 pandemic – and often with limited or obfuscated information. Companies with large customer-facing operations, such as banks and the insurance industry, have had to react rapidly to moving their organisation to cloud-based or otherwise distributed working situations. Businesses of all stripes have had to make decisions related to the enormously increased demand on digital infrastructure. It seems likely that the substantial shift towards digital-first customer experience provoked by COVID19 is here to stay – and there will be important decisions to make stemming from that. And these are just the decisions to be made within the relatively known and understood spheres of digital infrastructure and customer experience – add in innovative technologies like Blockchain and the branches of the decision tree for most companies multiply extensively.
So how can business leaders win?
At Imperial College Business School, Professor of Analytics and Operations, Wolfram Wiesemann, has been getting to grips with problems like this for some years. His group explores ways to apply quantitative methods to decision making even when information is absent or obscured. Because optimal decisions are hard to make when uncertainty is involved, the group’s work concentrates on designing ways to control and approximate information within strict margins. Wiesemann’s group is interdisciplinary and often works on problems inspired by real-world issues in operations management, energy systems and financial engineering.