Three ways to improve your forecasting and scenario planning

New forecasting and scenario planning methods based on sound data analysis help businesses meet changing customer demands due to pandemic. 

Companies are moving to the next phase of pandemic planning, with one eye on the recovery period and the other on what their business and industry will look like as they emerge from and go beyond the crisis. But the future is a blind spot for many companies. Prior business forecasting methods, based on past customer demand, no longer suffice. Prior assumptions must be challenged and, in some cases, deemed invalid.

Change how you look at your customer

Customer behavior and spending potential are changing dynamically across many industries. The key question is which of those changes will become permanent and how long the temporary changes will last. When will consumers be willing to travel by air, enjoy a sit-down restaurant meal or gather in a movie theater? Will businesses be able to pay rent, and will companies cut down on office space long-term? Will patients be willing to go to a hospital for elective surgery?

In the EY webcast poll, 77% said that changes in customer behavior were the key risk for their company when it comes to forecasting, significantly more than those who identified liquidity and capital restraints (the next most frequent answer).

Knowing their customers have become even more important as their circumstances and behavior have massively changed, how should companies adapt?

Change your data analysis techniques

The information used in the past may have become too static, too imprecise or no longer predictive. For example, low gas prices would normally correlate with increased restaurant traffic, but that relationship has obviously broken down.

In some businesses, social media analysis can be used to improve data forecasting. Outside data on pandemic hot spots, weather data, government regulations, mobility data, consumer sentiment and other measures can be run through regression or more advanced artificial intelligence (AI) neural network models to see what can best be used to augment and inform company forecasts.

One consumer company we worked with brainstormed to decide which data they needed to augment their typical syndicated sales data and other inputs to develop a more accurate, timely forecast. Among the inputs they chose were weather data, cell phone tower data, social media mentions of their products and ZIP-code level unemployment data. Their finance team then performed a regression analysis to see which metrics would show causation. The result was more accurate forecasts that are now updated in hours, rather than weeks.

Identify where the data sits and “free” it

During our webcast, 44% of participants said access to meaningful and quality data was one of their biggest impediments to timely and accurate forecasting. When those that answered “all of the above” are included, more than three-quarters of participants saw access to meaningful and quality data as a significant issue.

In some cases, as mentioned above, the reason is likely that the data they have relied on in the past is not sufficient for the future. But we also know that in some cases, data is difficult to access because it is created and stored in business units, geographies and organizational functions and often outside of the corporation’s systems.

Companies need to establish a culture of quickly sharing data across the organization and put the systems such as centralized dashboards and alerting systems in place. These systems can regularly update the data for the finance and strategic forecasting teams, identify when foundational assumptions may have become questionable, and enable key decision makers to make informed choices more quickly.

Always be forecasting with your data

Companies have typically spent 80% of their time on financial planning and 20% on analysis. That needs to flip in the current environment.

Once a company has defined the necessary data inputs and ways to free that data from organizational silos, they can:

1. Establish a team solely focused on forecasting.
   A cross-functional team can establish a process for continuous updating of data and ongoing evaluation of demand and supply shocks. Using a collaborative team approach helps deliver robust, scenario-enabled modeling that is fact-based and supports real-time decision-making.
2. Evolve data systems so that data can be refreshed automatically.
   Initially, pulling in all the necessary data will require a daily or weekly refresh. As companies free up capital to upgrade to different technology solutions, they can move to rapid automation and enhanced insight.
3. Don’t forget the long term.
   Scenario planning methods need to be aligned to capital allocation strategy. Forecasting should look at the balance sheet as well as the P&L, in an integrated fashion. In addition to monitoring liquidity, there are capital allocation decisions that will help companies thrive after a crisis. Will capital need to be reallocated amongst business units under various recovery scenarios? Will the company have the capital to invest in new technologies or bolt-on acquisitions, while also maintaining or increasing a dividend or share repurchases?

As an example, Ernst & Young LLP is working with a global life sciences company to reimagine the industry. Regardless of the current trends and forecasts, the expectation is that the post-COVID-19 market will be on an entirely new growth path in terms of consumer behavior, spending levels, segments, and product and service innovation.

Forecasted scenarios using a future-back approach (looking at future expectations for the state of the business and then working backward) are now being used to identify and challenge assumptions where post-COVID-19 revenue models are likely to differ from the past. These scenarios are used to make revised capital allocation and investment decisions today.

Blending the deeper forecasts for today with a future-back approach provides a more complete view of scenarios, helping a company to better set expectations with investors and business operations for planning and risk management.

A robust forecasting program should help you answer these stakeholder and board questions:

• How much of your demand do you expect to come back and when?
• How will customer behavior change?
  
• When can you bring back furloughed workers?
  
• Are there immediate needs for short-term liquidity?
  
• How many weeks of cash should you have on hand?
  
• Are any of your suppliers at risk of bankruptcy?
  
• What are you doing to make sure you have stability in your supply chain?
  
• Should you maintain more inventory?
  
• Will you need to raise longer term capital?
  

The time to act is now

Fortunately, our webcast poll also shows companies are planning a variety of steps to enhance their forecasting process, including enabling more cross-functional involvement, utilizing new internal and external data sources, and enhancing stress testing and boundary scenarios.