Detecting crises before they happen

From the June 2016 issue.

Resilience can be a key competitive advantage for businesses involved in complex international supply chains, but what if a disruption could be avoided before it even occurred?

Trucking companies routinely try to anticipate and avoid disruptions by, for example, monitoring changes in the weather and shifting traffic patterns. But as supply chains have become exposed to a wider array of risks, this ability to detect disruptive events has taken on much more importance for companies generally.
In fact, detectability lead time is one of the three key dimensions that define the nature of a supply chain disruption (the other two, likelihood and consequences are also described in my new book).

Some types of disruptions can be forecasted or detected well before they have an impact on the enterprise, while others hit without warning. Detectability adds a time dimension to the classification of disruptions, and is defined as the time between knowing that a disruptive event will take place and the first impact.
Note that the detectability of an event can be positive (detection before impact), zero (realisation at the instant of occurrence), or even negative (detection after the disruption has taken place).

Positive Detection Lead Time
Trends such as the aging of the world’s population in the Western world, China and Japan, come as no surprise and can be detected years – even decades – in advance. Growing demand for energy and natural resources in China, India and other emerging markets is also predictable. Long-term trends in urbanisation, mobile phone usage, economic growth in sub-Saharan Africa, robotics (including drones), rising food prices, and water scarcity will affect investments and supply chain patterns, too.

The difference between long-term and any other risk is that these trends offer an opportunity to incorporate them into a company’s strategy, thereby profiting from them. Nonetheless, like the proverbial frog in gradually heating water, some companies may not detect, prepare for, or take advantage of slowly shifting trends.

The end dates of labour contracts, for example, are known on the signing dates of the contracts. Yet, some companies miss that signal of higher risk of labour strikes at that time. Likewise, the phase-in dates of major regulatory changes (e.g. regarding toxic chemicals) can have months or years of lead time. Even most supplier bankruptcies should be of little surprise. Companies can detect which suppliers have precarious balance sheets, unfavourable patterns of profits, or negative cash flow months before the supplier reaches a point of insolvency or bankruptcy. Furthermore, shipping errors, quality problems and slow refunds can presage a troubled supplier even before this shows in financial data.

Other threats have shorter detection horizons but still offer some chance of a warning. As capricious as the weather seems, the physics of moist air flowing at different altitudes and at various temperatures is not mysterious. Hurricanes, many floods and winter storms now arrive with hours or days of warning, for instance. When a hurricane steams into the Gulf of Mexico, the oilrig workers know what to do. They are trained to carry out a series of “shut-in” procedures for closing key valves and securing equipment, thereby reducing the chances of an oil spill when the storms hits and enabling a rapid restart after the storm passes. Forewarning lets a company kick-off impact avoidance and recovery efforts.

Even earthquakes can be detected as they start, enabling early warnings to those more distant from the epicentre. Businesses and residents of Tokyo knew the 2011 quake was coming about 80 seconds before it struck and had up to 40 minutes warning of the tsunami’s arrival in Tokyo Bay. Data can flow faster than disaster.

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