Future-Proofing Forecasting

Analyst Viewpoint

An important and durable lesson of recent massive disruptions is that organizations need to forecast and plan with agility so they can quickly adapt to evolving economic, market, legal, regulatory and behavioral conditions. Organizations that have invested in dedicated software to improve their forecasting and planning processes should enhance the accuracy, predictive potency and business value of these systems by explicitly incorporating a blend of internal and externally sourced predictions of the factors that drive outcomes. Considering internal plans in the context of external forecasts from reliable sources provides senior executives and the financial planning and analysis (FP&A) group a check to ensure that assumptions about demand, supply, costs and other key factors are not at odds with credible third-party forecasts. Moreover, organizations can use these sources of data and intelligence to detect early warning signals of diverging trends—positive or negative—in their environment that they otherwise would miss. Being able to detect reliable predictive signals sooner enables organizations to adapt and stay a step ahead of the competition while minimizing financial and operational risks.

The most effective planning incorporates external intelligence and data sources because organizations do not operate in a vacuum.

Over the past decade, technologies have emerged that make it feasible and practical for organizations to incorporate external intelligence and data in their planning, analysis and reporting processes. The most effective planning incorporates external sources because organizations do not operate in a vacuum. As FP&A groups increasingly use predictive analytics and artificial intelligence (AI), an external perspective will become essential to creating models and forecasts with strong predictive and explanatory power over a wide range of planning scenarios. For instance, if demand for a product is mainly a function of the sales of a complementary product offered by a different vendor, it would be a mistake to only use advertising expenditures or other internal drivers when modeling revenues since no amount of advertising would make up for an unexpected drop in the other vendor’s product sales. Organizations that exclusively utilize internal data often do so because past correlations can appear predictive in a steady-state environment. But this is a mirage that evaporates in times of crisis or simply as markets evolve.

Relying solely on internal assumptions and data when constructing analytical models or developing AI-enabled forecasting and planning diminishes their value even in steady state environments because they fail to consider external forecasts and data. This external intelligence can better inform an organization’s modeling and working assumptions because its forecasts are built on information than can have greater predictive power. Moreover, this same external data also augments core forecasting and planning efforts because it enables analysts to spot discontinuities in markets. In this sense, external data can serve as an early warning radar system, making it possible to continuously monitor the environment and detect signals that will have an impact on anything from the entire organization to specific business units or product lines.

FP&A groups that are using dedicated forecasting and planning software should make evaluating external sources of forecasts, intelligence and data a priority. The evaluation process should begin by focusing on the following:

• To what degree does the organization utilize predictive modeling? The purely numerical base/upside/downside approach has limited value in evaluating and explaining the causes of outcomes and impedes effective responses to them. Predictive planning enables organizations to have the operational agility necessary to anticipate or adapt quickly to market shifts, and external intelligence and data is a vital component to building robust models that can simultaneously span entire business units and have impacts down to individual product lines.
• To what extent are forecasting and planning models evaluated for their predictive quality? Over time, market conditions evolve and drivers that were once important can become less relevant. AI technologies now exist that can continuously monitor predictive model quality and provide alerts when future market conditions are shifting. This ultimately enables FP&A groups to future-proof their business by proactively adjusting forecasts and budgets based on automated market monitoring.
• How well do the dashboards and reports convey the causes of outcomes and variances? In some cases, purely internal factors are sufficient, but especially for product demand and factor costs, external intelligence and data plays an important role in explaining outcomes and their degree of importance in influencing results. Evaluating results from both an internal and external perspective drives better organizational performance when it replaces plausible but less-than-relevant explanations for outcomes.

The ultimate purpose of forecasting and planning is to enable executives and managers to make more informed decisions that result in consistently better outcomes. Predictive planning based on robust models that incorporate external intelligence and data provides those in operational roles with greater situational awareness. External information sources can be monitored continuously and can detect signals that alert those with operational responsibilities to shifts in demand, costs, supply chain conditions and economic trends—to name just a few factors that can provide a competitive advantage when acted on sooner. Providing executives and managers with a balanced internal/external perspective enables them to future-proof their forecasts by anticipating change and being able to react with agility, whether in a steady-state environment or when confronting crisis conditions.