07 May 2020 by Paul Croft
COVID-19 confirms the ongoing presence of volatility, uncertainty and complexity in our economic and business environments. To survive, many business managers have learned how to solve increasingly complicated, complex and often, non-linear problems. In contrast, traditional approaches to understanding the causes of, and quantifying, economic loss have largely retained a strong technical bias, rather than adapt to current business dynamics.
Last week’s article discussed the role that timing plays in apportioning losses between two events. This article considers how traditional approaches can be adapted to today’s conditions to deliver more commercially-attuned insights into the underlying causes of loss.
The coronavirus crisis demonstrates how unplanned – yet foreseeable – events can expose inherent risks or weaknesses within a business’s operating model, leading to adverse financial outcomes. Take for example, a business entirely dependent on overseas suppliers for its manufacturing inputs. While the decision to use an overseas supplier was a commercial one (local products were more expensive), the decision exposed it to significant supply chain risk. However, although the business owner was happy to carry the risk to achieve a higher return on equity; the owner failed to implement an appropriate risk management strategy. The coronavirus disrupted its supply chain, causing manufacturing capacity to be reduced to 25%, reducing sales and leading to significant financial losses.
What caused the business’s losses: COVID-19 or the business’s risk profile?
The decline in manufacturing costs and sales can be reliably estimated by the decline in manufacturing output, confirming a linear (causal) relationship between COVID-19 and the losses. Attributing all, or part, of the losses to the risk profile implies the business owner contributed to the losses. A risk profile is an example of a non-linear (causal) relationship, which is typically graphed as a flattening curve, rather than as a straight line.
Traditional approaches to quantifying economic loss generally ignore non-linear relationships because, for example, they are not immediately evident from a set of financial statements, too difficult to identify, unpredictable or more complex because of the number of variables involved – despite their impact on company performance. A common non-linear relationship actively managed and measured in many businesses is employee satisfaction; for example, low employee satisfaction leads to employee turnover and the loss of corporate knowledge, increasing recruitment, training costs and operating inefficiencies.
To deliver more commercially-aligned outcomes that also capture the financial impacts of non-linear events, traditional approaches to quantifying economic loss should, where relevant, be adapted to incorporate management tools and practices used by many business managers to solve non-linear challenges. Before explaining how traditional approaches can be adapted, let’s return to the original example, but with the following adjustments to the assumed “facts”:
- On 1 December 2019, the defendant’s negligent / tortious act disrupts the plaintiff’s supply chain
- By 15 January 2020 manufacturing capacity reduces to 45%,and the scale of the Manufacturing and Sales functions/resources are reduced to mitigate further losses
- In February/March 2020, key members of the senior executive team resign
- From around 1 April 2020, COVID-19 (the intervening event) starts to shut down the manufacturing capacity, which slumps to 25%
- After identifying a local supplier, manufacturing recommences on 28 May 2020, ramping up gradually and returning to 100% capacity in July 2020
- The 2019/20 accounts show unusually high recruitment costs; and high unit manufacturing costs observed in June 2020.
Consistent with the earlier explanation, a traditional approach can explain the “as a result” losses using the causal relationships between the defendant’s actions, COVID-19 and changes in manufacturing capacity. Assuming the “as a result” losses are allocated consistently with the timeline of events, the defendant’s liability is assumed to cease on 1 April, recommencing sometime after 28 May, once manufacturing capacity has returned to the pre-COVID level of 45%. If the defendant’s liability is found to cease initially on 1 April, the traditional approach might attribute the high recruitment and unit manufacturing costs to COVID-19, as those costs were incurred from 1 April.
The plaintiff disagrees with the conclusion on the recruitment and unit manufacturing costs, insisting the losses were triggered by the defendant’s actions (that is, the defendant’s liability is held to extend beyond 1 April). So how we can adapt the “by the book” traditional approach to deliver more commercially-attuned insights into the underlying causes of loss? My approach incorporates “systems thinking” into the causal analysis to generate a commercial perspective through the eyes of the plaintiff. Systems thinking is a scenario planning tool used in many businesses to understand and plan for the potential outcomes of management decisions. I’ve adapted the tool to help explain the actual outcomes of decisions the plaintiff took to mitigate its losses, which were:
- The reduction in the Manufacturing and Sales functions had a negative impact on staff morale
- The senior executives, fearing for their futures, resigned, further reducing staff morale and driving an increase in manufacturing staff resignations through March
- The plaintiff filled the vacant manufacturing positions once a local supplier was confirmed; after which it took four weeks to train the new staff, increasing unit costs due to manufacturing inefficiencies.
This is a straight-forward explanation, likely supportable through readily available management information. More complex situations will, however, increase evidentiary challenges. We’re used to working with imperfect data and designing ways to overcome information obstacles, particularly where the ultimate goal is commercially reasonable and rational apportionment of the losses, rather than mathematical “by the book” precision.
The causal explanation above does not contemplate a potential claim by a plaintiff that the defendant’s actions reduced its ability to respond to the intervening event, amplifying the impact of coronavirus on the plaintiff’s business. That aspect of loss causation is addressed in the next article in our COVID-19 economic loss quantification series.