One of the biggest challenges that business leaders face is planning for what’s coming. Unfortunately, that challenge is getting tougher by the day. Evolving technologies, changes in consumer preferences, new competition and disruptions, such as COVID-19, are all combining to accelerate the pace of change in business. Yet, with the right approach to strategic forecasting, companies can turn this challenge into an opportunity. This can be done by improving their ability to anticipate — and capitalize on — future changes. Quite simply, organizations need to become more efficient, accurate and agile in how they generate forecasting insights that drive key business decisions.
Most organizations recognize that they can improve. During a recent EY webcast on forecasting for recovery scenarios , only 9% of participants said they were “very confident” in their company’s ability to forecast demand for products or services. In fact, 35% said they were either “not at all confident” or “not very confident.” In the present environment of rapid-fire change, these numbers should be disconcerting.
Currently, the process at many organizations is decidedly inefficient. Each month, teams at individual business units are probably downloading data from one of several enterprise resource planning systems at the company and pouring it into a spreadsheet. They might be passing it along, running some analyses on that data to generate a forecast and then emailing that to other stakeholders. More progressive organizations might be applying driver-based insights and statistics on top of this process, but they are still struggling to move quickly enough to meet the demands of their business.
There are numerous challenges with this approach. The process is slow, meaning that critical information takes too long to generate meaningful insights. Forecasts might be accurate, or they might not be. And what’s worse is that, because each team runs its own forecasts (financial planning, sales forecasts, supply chain, and so on, all with their own set of assumptions), there’s little coherence to this approach. Teams are biased in favor of their own incentives and simply follow legacy processes.