Optimizing the financial forecast: Evolving from stagnant methods to address complexity

Over time, accurate and granular financial forecasting evolved into an essential part of every financial planning and analysis (FP&A) organization. An activity that started as a static plan to review outcomes has become a central pillar of business attainment, and when done properly, unlocks value-add capabilities such as cause analysis, scenario planning, and real-time decision-making.

The old way

Forecasting as a process began when finance organizations realized they needed a tool to evaluate performance, compare budget versus actuals, identify areas where outcomes had deviated from expectations, and attempt to course-correct.

However, this was no easy feat. Quarterly checks of business performance provided limited transparency and a reactive state where outcomes could no longer be influenced at the time they were reported. This paired with limited access to data and the significant process burdens turned what should be a value-add activity into a chore. Finance organizations didn’t have the bandwidth to analyze results and due to constraints on resources, tools, flexibility, and collaboration between departments, couldn’t inject insights into reforecasting in any material way.

Fortunately, time-interval and stagnant forecasting is becoming a thing of the past. Global disruption and evolving stakeholder requirements are reshaping financial forecasts to be constantly refined and optimized on an ongoing basis. To address changing consumer behavior, an increasingly competitive and crowded marketplace, and deepening interests in social and environmental impacts, companies are required to be more agile and forecasting is no exception, especially as it relates to keeping pace with competitors.

Thanks to process improvements, data accessibility, resource adaptability, and modern technology, business leaders have more opportunities to maintain a higher-quality and more efficient financial forecast and can turn an age-old process into a strategic advantage for the organization.

The new way

Today, financial forecasting is completely evolved. Organizations are using continuous forecasting to account for rapid change, keeping planning “always-on” to be flexible enough to cater evolving market conditions. This is allowing leaders to evaluate multiple “what-if” scenarios using real-time data, actively engage cross-functional stakeholders, and proactively identify areas on which to capitalize before course correction is necessary.

Financial forecasting is becoming an activity all departments contribute to so the organization is unified in its direction. Scenario modeling unlocks more informed and impactful decisions, and always-on planning accounts for the real-time impact of changing data. With new technology providing visibility and insight, organizational leaders can plan for, react to, and weather challenges with rapid pivots.

That said, there is no one-size-fits-all approach to financial forecasting. An effective financial forecast can and should make use of modern techniques across processes. Of these techniques, a few are commonalities within financial forecasts of high performing organizations.

• Rolling forecasting: There is no better route to unlocking “always-on” planning than moving away from interval time-based forecasting. Rolling forecasts are enabled by modern tools that can harness real-time access to data and update results on an ongoing basis, rather than during periodic refreshes. For example, instead of doing a quarterly reforecast where an organization maintains two historic quarters and six forward looking quarters, they may move to monthly reforecast where the oldest historic month is dropped, and the newest forward-looking month is added, providing real-time views into trending, and rolling KPIs. This provides agility to maintain a more relevant and longer forward-looking horizon as well as react more efficiently to unforeseen events.
• Predictive forecasting: As organizations accrue unforeseen amounts of data, it is becoming necessary to augment analysis with more intelligent tools spanning beyond human capacity. Intelligent forecasting techniques harnessing machine learning and artificial intelligence, can recognize patterns in data blended from both internal and external sources, empowering robust insights. These predictive capabilities are especially useful when interpreting fluctuating consumer demand, variable supply chains, and global macroeconomic factors. Finance organizations need the ability to produce localized intelligence as well as consume intelligent operational forecasts from within the organization, to adequately participate in strategic decision-making.
• Exception-based forecasting: As evidenced by occurrences in the past couple years, a major factor in creating high performing financial forecasts is the ability to handle outlier events. With the pace at which the world is evolving, it can no longer be assumed that historic business cycles can be carried forward, and as unforeseen events happen, forecasting techniques need to be adaptable enough to predictably cater to these events to mitigate risk and take advantage of opportunities. If an international manufacturer is struggling with congested shipping ports, with the right levers and visibility in place, a leader can choose to shift material purchases to domestic partners and use alternative shipping methods like rail, with scenarios highlighting the implications from timing to final margin.
• Driver-based forecasting: Arguably the most foundational component of high efficacy financial forecasting is the ability to dynamically layer assumptions, or drivers, into planning. Leveraging driver-based forecasting uses decision assumptions that together build a comprehensive financial picture. This allows finance to arrive at a point of view that can be used to hold cross-functional counterparts accountable to, to create predictable financial outcomes. For example, as finance is building its 18-month forecast, scenario modeling may expose that a 5% reduction in a core product’s cost of goods sold (COGS) is the path to achieve the earnings target the organization set. Finance can use this knowledge to fuel analysis and conversations with their supply chain counterpart to assure the organization is on track to meet its goals.

Conclusion

No longer are organizations beholden to projecting forward with simple historic data methods. Modern financial forecasting is here today, but it’s only accessible through truly modern technology platforms providing access, intelligence, and agility. Advanced driver-based, sensitized financial forecasts help organizations maintain a real-time understanding of their organization’s financial trajectory and ultimately produce predictable results. While some organizations are still attempting to use spreadsheets and manual data manipulation, technology like Anaplan allows businesses to leverage the right data, involve the right people, and deliver the right information. Using Anaplan, organizational leaders can harness financial forecasting methods necessary create a high performance financial forecasting environment.