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Reimagine Forecasting: High Stakes Decision Making for Cfos Unlock data possibilities www.pwc.com/analytics Make better and faster decisions Expand your perspective with trusted and actionable data-driven insights for extraordinary results Reimagine forecasting: High stakes decision making for CFOs May 2016 How can CFOs deliver more value to their organizations? Reimagining forecasting Unlocking the power of may seem like a gratuitous data and analytics to think exercise in crystal ball about what will happen, design. But it is becoming a assessing its likelihood, critical step that CFOs should and contemplating the take to make sure their implications mean the companies are using leading difference between accurate indicators to maintain their or inaccurate forecasts, positions as market-leading with multi-million or billion organizations, strive to dollar impacts. innovate, and protect the brand – instead of lagging the pack with sub-optimal indicators. “The future ain’t what it used to be.” —Yogi Berra It’s time to reimagine forecasting: Get started We are always forecasting - thinking The signals in the noise increasing cost or ceding revenue to about what will happen, assessing It can be very difficult to find the signal competitors with close substitutes. its likelihood, and contemplating the in the noise, and it is becoming more In services industries, the inability to implications. But for CFOs, the stakes are important than ever. Short-termism accurately forecast demand for specific much higher – the difference between and fast news cycles exacerbate the offerings requiring specific skill sets to accurate versus inaccurate forecasts can effects of missing a forecast on company deliver may result in having too much be worth billions of dollars, and a job. value. Activist investors may see it as a talent with the wrong skills – rippling A CFO’s worst nightmare: the forecast signal that management doesn’t have a into brand-crippling layoffs – or being is made, as time unravels, expected firm hand on the business. The cost of short in areas where a war for talent is and actual revenue and profitability being off the mark can also be huge, not being waged. In each of these situations, fail to converge. “Why” questions are just in perception but also in dollars. A the cost of being wrong today can asked – with no answers. Is there a company’s cost of capital (debt or equity) become extremely costly in the future. fundamental problem with the appeal of can be impacted by their assumptions Now, there is the potential to reimagine our products/services? Did a competitor and forecasts related to GDP growth, forecasting in a way that allows CFOs make a move we didn’t expect? Is it currency exchange rates and interest and their counterparts in the C-Suite to due to macro factors that are out of our rates. Failure to accurately forecast tease out signals that matter and deliver control? And, finally, how much does demand fluctuations can result in too more value to their organizations in the it matter to the next forecast about the much, or not enough, inventory – either short and long term. future value of our business? Companies need to make better use of the (sometimes massive) data sources available to them and st apply different types of analytical techniques in their forecasting approach. Our Five Factor Forecasting 1 Model (page 2) takes a ‘top down’ and ‘bottom up’ approach to data. Look within and across strategic, managerial and operational domains through better integrating nd forecasts of leading macro-economic and industry trends, with micro ‘feet on the street’ indicators 2 regarding what customers are thinking, feeling and doing – all of which impact financial results. Bringing the ‘reimagined forecast’ to life, and capturing the value, requires effectively changing rd the organization’s mindset using novel visual and analytical approaches to embed it into macro 3 and micro decision making. PwC Reimagine forecasting: High stakes decision making for CFOs I 1 A guide to Five Factor Forecasting Model: Use data and analytics to broaden aperture, create leading indicators and sharpen response Many forecasts are built on relatively Five Factor Forecasting Model: Data you can use for better forecasting thin, high-level historical time-series data using inefficient or, in some cases, questionable modeling techniques. Reimagining forecasting starts with 1 designing a “Five Factor Forecasting Model” that uses a more comprehensive 5 cross-section and timeseries of “top Macro economic down” and “bottom up” data. It Industry includes knowledge of macro-economic drivers, potential policy and regulatory changes, industry supply and demand, Reimagine environmental impact on resources and forecasting: consumer behavior – the ultimate drivers strategic, of business performance. Combining managerial, Policy/ these data and accumulating it over 2 time makes it possible to apply an array Environment operational Regulatory of modeling techniques to broaden the forecast aperture, establish leading indicators that inform macro and micro 4 changes and sharpen management’s Consumer response. The figure to the right behavior illustrates the types of data that can be applied, and value that can be generated, with this more comprehensive approach. 3 2 I PwC Reimagine forecasting: High stakes decision making for CFOs Example application: Robust Regression technique to explain Macro-economic data FP&A outliers With the proliferation of ‘big data’, Rather than wait for lagging, macro-economic indicators, it is becoming possible, many entertainment and media using new data collection approaches, to establish near real-time or leading CFOs’ FP&A process is subject indicators that are proxies for measures of economic activity including GDP, to unexplainable outliers, such labor supply, liquidity, and emerging market risks. For example, “nowcasting” is that simply determining if they an approach using internet search data combined with other sources of industry should keep the outlier, normalize and consumer data to provide alternative and more timely GDP indicators that it, or disregard it is becoming companies can use to make adjustments to their forecasts. Companies are using increasingly difficult, and a topic real-time crowd-sourcing techniques to capture more granular, real-time data of debate. In some cases, this about price volatility of similar goods in markets around the world, which can leads to a disbelief in a financial be used to inform hedging strategies involving currencies and commodities. organization’s ability to set the Companies are using satellite imagery to evaluate the expansion rate of appropriate KPIs to achieve a populations and residences in emerging markets and create proxies for electricity company’s growth strategy. Robust demand or bandwidth usage. Lastly, more granular data of regional and local Regression is one technique that traffic patterns can be analyzed over time to improve supply chain configuration helps deal with outliers while and logistics services, as well as retail expansion strategies. Using these new building forecasting models, thus techniques, companies can do a better job of anticipating economic headwinds tightening the confidence intervals and insulating the business from what previously seemed like random or from which planning decisions unpredictable events. can be made. This technique assigns a weight to each data point automatically and iteratively throughout the modeling process, assigning lower weights to those data points with the least amount of contribution to an accurate prediction. Techniques to try • Real-time data crowdsourcing • Robust Regression • Time-Series PwC Reimagine forecasting: High stakes decision making for CFOs I 3 Example application: Rare Event Modeling to predict maintenance Industry data delays Utilizing Rare Event Modeling Aggregating industry level data about supply capacity and demand potential in the Airline industry to better for products and services can help companies better manage short-, medium- predict the likelihood of a delay and long-term demand/supply imbalances, thereby helping them to time or cancellation not only helps investments and cut-backs more effectively. To do this requires CFOs to collect operations but also improves and integrate new types of data to provide a more complete picture of the value customer satisfaction. By chain, including raw material availability, labor supply, production/delivery collecting structured sensor data capacity, and productivity and demand estimates at each stage of the supply from the airframe and combining chain. For example, companies in asset intensive industries such as Energy, that data with unstructured data Travel and Transportation, and Semi-Conductors make billion dollar supply bets coming from maintenance logs, years in advance. Companies in these industries are starting to use sensor and analytical models can be built to unstructured work log data to build models that inform timing of investments help pinpoint which aircraft parts to replace equipment, anticipate major maintenance needs, and identify safety are likely to fail, with sufficient and operational disruption risks – all of which help create better supply forecasts. lead time to allow the airline to These models can then be further improved by including proxies for total re-route the aircraft accordingly, industry capacity and cost of production, to help guide improvements in capacity and minimize the likelihood of an utilization, pricing and profitability. Maintaining time-series data about aggregate
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