Remedies for Forecasting Illnesses




Yesterday I wrote about the seven common symptoms of forecasting illness and how they can lead to uninformed decisions making. In an increasingly volatile and uncertain world, the ability to anticipate the future, even if only a few months ahead, can mean the difference between survival and failure. Unfortunately, future uncertainty is much greater than most managers concede. Thus if managers fail to demonstrate an understanding of the dynamics of their businesses performance stakeholder confidence can seriously be undermined. As a result, managers must place an increasingly high priority on improving their forecasting processes.

How can managers use forecasting tools to plan effectively and build better strategies? Instead of seeking predictability, managers should channel their efforts into being prepared for different incidents and the reality of change. Take for example the Global Financial Crisis of 2008. Prior the financial meltdown, majority of global policy makers and business leaders were very upbeat about the performance of their economies despite signs of looming danger. Not many people anticipated the after effects of a global financial system collapse on their businesses and as a result some reputable organizations collapsed and filed for bankruptcy while others were bought over for at cheap market values.

Volatile markets make it enormously difficult to forecast effectively. However, although many things that occur in the business world may not be predictable, their randomness should at least be modelled. For example, there are bubbles, recessions, financial crises and natural disasters which are known not occur very often but do repeat at sporadic and irregular intervals. While leaders, managers and employees are reasonably aware that these rare events can occur, and may even be able to imagine several examples, they consistently underestimate the likelihood of at least one such event including the ones they didn’t imagine occurring. This is because human beings have a tendency to underestimate the size of rare events, which in most cases, leads to negative consequences.

It is not only business catastrophes that producers of forecasts fail to anticipate. They are also unable to predict business success. For example an unhealthy obsession with a particular forecast number as well as the failure to provide enough forward visibility and discern trends in performance makes the business to miss on emerging opportunities. When preparing forecasts; managers should accept that they are operating in an uncertain world, assess the level of uncertainty they face and augment the range of uncertainty.  It is critical to have the ability to perform “what if” scenarios and change analysis. Additionally, managers should be able to re-forecast as market conditions change. This will protect managers from having a single tunnel vision about the business which in turn helps them formulate appropriate strategies to deal with rare events when they occur.

To address their forecasting shortfalls, some organizations believe that investing in latest software tools will solve their problems. Unfortunately, the application of IT solution without understanding the real problem and its source will not fix a broken forecasting process. For example, some managers believe that investing in some sort of trendy software will help them collect, consolidate and analyze forecasting data quickly enough.  This is just a quick fix which often leads to lots of numbers, gaming behaviour and wastage. If you do not first solve the root cause of the problem, there will be no reprieve.

The other quick fix that other managers resort to involve using complex statistical methods hoping that these will help them better anticipate the future. Human judgement is worse at predicting the future than are statistical models. In fact, human beings are often extremely surprised by the extent of their forecasting errors. Thus instead of using simple statistical models to forecast, some managers are preoccupied with applying complex models. Simple statistical models such as moving averages are better at forecasting than complex ones. Complex models often attempt to find non-existent patterns in past data whereas simple models ignore patterns and just extrapolate trends.

The benefits of getting your forecasting right are considerable both in terms of improvements in efficiency and effectiveness. Better forecasting results in informed decision-making. Right things will be done at the right time – there will be no surprises as well as wastage of time and resources. Improved forecasting also leads to better situational awareness which helps the organization to spot discontinuities early, avoid unnecessary costs, formulate fitting contingency plans, become agile and exploit opportunities.

Good forecasting also enhances teamwork and collaboration. If sales, marketing, operations, finance etc. are all involved in the forecasting process this leads to one set of numbers being produced and agreed upon instead of having numerous competing forecasts. Lastly, by anticipating better and responding more quickly, the performance of your business will become more certain and less prone to surprises.

I welcome your thoughts and comments.

 

 

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