Businesses are facing waves of disruptive events. The key to building resilience and continued growth is an understanding of those events through data.
What does corporate resilience look like post-Covid era? You could answer that question by arguing that well-managed companies have always sought to be resilient in the face of unexpected shocks. Thus, resilience in 2022 looks much the same as it did in 2020, 2015 or 2007.
But that would be failing to acknowledge that we are living through unprecedented times. Think back to the dark days of the great financial crisis. Although hugely disruptive and unprecedented, it was thought to be a once-in-a-lifetime event. Probably only a relatively small number of senior managers had the experience of steering their businesses through anything similar. And when the crisis faded, those that lived through could take comfort from history. It might be 10 or 15 years or more until something comparable happened again.
Now fast forward to the present day. The Covid crisis may not be finally over, but in terms of lockdowns and other restrictions, life has returned to normal. But instead of having the luxury of breathing a sigh of relief, leaders have been faced with a succession of related and unrelated problems. In the wake of the pandemic, supply chains came under pressure and fell over. Rising demand pushed energy prices to levels which are making some current business models unviable. Staff shortages have become acute in some sectors. But most unpredictably of all, 2022 was the year in which Vladimir Putin launched an invasion of Ukraine. In other words, we are faced with multiple economic and geopolitical uncertainty and, frankly, nobody can be quite sure of what is going to happen next. Then there is the longer-term problem of extreme weather.
One result of all this is that business leaders are having to make decisions based on facts and assumptions that are continually changing. Resilience is not about planning for one event, it is about dealing with multiple problems, all at once. And in many cases – for instance, if a supply chain breaks down – remedial decisions must be made in hours or days rather than weeks or months.
In this climate, building-in resilience has become crucially important but also a more complex undertaking than it perhaps used to be. But here’s the good news. the tools are available to make it easier to build resilience and, thus, grow in the face of adversity.
The Building Blocks
There are two fundamental building blocks for resilience. One is agility. The ability to respond to situations, events and problems quickly in order to minimise or prevent damage to the business. This kind of agility was apparent in abundance during the Covid lockdowns, as organisations pivoted their working practices while also changing how they engaged with customers and suppliers.
The second building block is preparation. Sense-testing business plans against worst and best-case scenarios. Putting plans in place to deal with potential catastrophes such as cyber attacks, a fire at a factory or the collapse of a key supplier.
That’s all good if we think in terms of infrequent one-off events. But how can agility and effective preparation come into play when waves of disruption are breaking on the shore ever more frequently.
The Data Solution
One of the enablers of good decision making is an abundance of actionable data. We may be living through chaotic times, but we also have increasingly sophisticated analytics tools at our disposal. The key to solving a problem lies in understanding it. That can potentially be difficult if the issue is complex and multi-faceted. Data provides a means to see all aspects of the problem in real time or something close. That in turn makes it much easier to make the right decisions.
But what does that mean in practice?
That was a question posed by McKinsey recently, in an article titled, Building Value Chain Resilience with AI. Based on a study of 50 tech-enabled companies, the article highlighted the diverse range of tools now available to decision-makers. These included forecasting tools, simulation models and digital optimisation.
As McKinsey pointed out, it is not only the complexity of supply chains that makes them difficult to manage, it is also their vulnerability to external factors. As things stand, the disruption caused by Covid in China has affected value chains as has – to some extent – the war in Ukraine. But even without those factors, a lot can go wrong on a day-to-day basis. The ability of suppliers to ship raw materials or components can be affected by adverse weather conditions (becoming more common), holdups at ports, equipment outages or industrial action.
Each of these events might be comparatively small, but in the context of a just-in-time world, even if a single stretch of the journey is disrupted the whole supply chain can grind to a halt.
Added to that is the fact that the decision making progress can be fragmented or siloed. For instance, a manager at a logistics centre may make a decision aimed at keeping traffic moving in the face of local problems without really thinking about the impact up or downstream.
The key to successfully optimising the value chain as a whole lies in giving key decision-makers a holistic view of the situation as it stands, plus tools to help them plan ahead for possible problems as they arise.
So, what do planners need to know? Well in addition to an understanding of how the value chain should work under optimal conditions and how to manage and re-route capacity when problems arise, it is increasingly important to have a clear view of the risks to supply. Some of those risks will be predictable (to a degree) but others may appear to fall from a clear blue sky, without warning.
That’s where AI comes in. By deploying technologies such as predictive analytics, it can draw on data from multiple sources to create best and worst-case scenarios and evaluate risks. This in turn provides a means for business leaders to make preparations. Equally, AI can help decision makers assess supply options in terms of cost, revenues, and any associated risks.
In other words, data enables both the optimisation of the supply chain and also provides the information necessary to mitigate against vulnerabilities. And one important aspect of optimisation, is the supply chain can be designed to be efficient while undermining any impact on the environment.
The bigger picture here is that data, modelling, and predictive analytics are increasingly the key to resilience across all functions within an organisation. When the time comes to change course in response to a problem, decision making is underpinned by the data.