In today’s tumultuous environment, many companies are wondering how to make their businesses more resilient to internal and external disruptors.  Anything could greatly upset your short and long term business strategies and operations from power grid shortfalls, to disruptions in your supply chain or technological innovations from your competitors.  Analytical methods from the fields of data science and systems engineering can be used to help model aspects of your business as a system, measure its resiliency and figure out changes that would increase its stability.  In addition, if unknown influences start forcing your company towards a collapse, data science and causal analysis can be used to figure out the driving factors leading to the collapse in order to help you find a course correction.

A traditional way to add resiliency to a business is to include a margin baked into the logistics and financial design to account for unknown expenses, delays, etc.  In engineering, that term is called a factor of safety.  Usually, this approach over-estimates the solution for a risk adverse design eating into profits or it underestimates for a risk tolerant design decreasing resiliency.  Many areas of engineering have moved to complex modeling, analysis and simulations that require increased computer processing power to help optimize the balance between safety and opportunity.  These methods create more insight into the variables affecting the solution and their sensitivities to changes.

In order to build resiliency into business models, an analysis needs to calculate which actions lead to higher chance of collapse vs leading to a higher chance for a stable system.  An emerging field combining operations research, data science and engineering has been developing unique ways of how to solve this stability problem.  After modeling a system, such as an energy grid or supply chain operations, utilizing network theory modelling techniques, the resilience of the system is measured by calculating the network’s tipping point and the energy level leading towards the tipping point.  Afterwards, the analyst can add variations representing different business decisions and disturbances representing external effects to the inputs of the model and calculate the change to the system’s energy and tipping point location.  This analysis sheds light on what forces can lead the system to become more or less stable.

What if your business is already on the verge of collapse?  Figuring out the root cause of the collapse is the first step when applying a course correction.  In engineering, a fishbone diagram and analysis are common tools used in anomaly investigations when something goes wrong in a design to shed light into the causation of the problem.  A similar technique can be used to help track down what is causing a business to collapse by systematically eliminating possible causes as contributing factors to the collapse.  Another analytical tool that can be used to find the root cause is factor analysis that uses big data principles to discover possible contributing factors and eliminate those that don’t have a high enough relation to the cause of the business collapse.