Predictive intelligence helps CFOs forecast demand: A new form of software can help finance executives get a window on the actions and decisions that can improve demand--and the bottom line. (Forecasting).

Combine the state of the economy and the ever-increasing operational role the CFO plays in today's corporations, and it is crystal clear that accurately predicting demand and profitably creating demand are crucial to success. CFOs stick their necks out each quarter to give investors and boards of directors not just the facts about the company's past or its current performance, but projections on where the company is headed.

The planning process is inherently collaborative. To date, the CFO has had to predict demand by relying on individual spreadsheets -- highly summarized projections and plans from various managers representing individual business units, geo graphical regions and lines of business, and based on disparate information resources within the firm. The aggregated spreadsheets become the annual operating plan and are updated regularly.

Because of the highly aggregated nature of the information, however, most of the details get lost in a sea of averages -- resulting in tremendous volatility and uncertainty as the plans are executed on a granular level -- ultimately causing gaps in the planning process. One example is that hundreds of millions of dollars of lost opportunity occur because out-ofstocks at mass merchandisers run at 14 percent or more on promoted items. In today's climate, an organization cannot afford to lose revenue, and potentially customers, by running out of promotional products at the point of purchase.

In an attempt to better control the demand planning process, most organizations develop in parallel a bottom-up plan based on weekly sales forecasts, taking inputs from sales and field representatives. As a result, companies spend valuable time and resources combining projected revenue from merchandisers and actual sales data to update the operational plan. Yet, they lack the ability to provide real-time insight on all of the demand information by product, customer region and store -- insight that facilitates strategic planning and reveals day-to-day adjustments of plans not captured in pure statistical models.

In addition, the desktop spreadsheets currently used in finance and planning departments are usually not even using the best data available -- the data in the data warehouse -- to make projections. The data warehouse holds the standards for the product, geography, time hierarchy, rules and workflow, as well as definitions for measures and calculations like activity-based costing, economic order quantity, gross margin and profit.

Simply put, in a dynamic environment, the lack of data and process can have a significant and negative impact on a company's bottom line. Symptoms manifested by inaccurate demand prediction and ultimately poor forecasting include out-of-stocks, markdowns, spoilage, returns, distribution and assortment gaps and new product failures. The added complexity is that the symptoms differ from one company to the next. But the universal outcome of unexpected changes in demand has remained unchanged until now.

Prescription for Demand Creation

Every CFO knows that an accurate company revenue forecast is essential for strategic planning. Accuracy depends on taking control and leveraging the factors that create demand -- on an account-by-account, store-by-store level, while also tying in the demand drivers such as promotions, inventory, etc. The prescription for demand creation is a new software solution, "predictive intelligence," that enables managers and the CFO to conduct "what if" analyses to predict demand.

Predictive intelligence uses data from a company's unique demand drivers to best reveal where the company is headed. The predictive intelligence methodology is based on four premises: analyze the overall picture of demand on an ongoing basis; effectively assess the volume, revenue, variable cost and margin impact of changing a course of action; enable collaborative planning by all parties involved in the forecast; and continuously track actual performance against targets to quickly respond to changes.

Demand creation requires "demand visibility" to discover where demand is coming from at any point in time. Demand visibility tells CFOs where the future of the company lies, yet they typically lack the ability to access the best available data on demand.

By pulling that data from the data warehouse and using predictive intelligence solutions to forecast future demand, the CFO can improve pricing and promotions, improve profit, grow margins, improve new item introductions, reduce inventory, predict product cannibalization, rescue closeouts and improve distribution assortment and replenishment. By utilizing the data warehouse, predictive intelligence enables CFOs to tap not only the resource with a common language standard for products and revenue streams, but also to get hard numbers to take into consideration when forecasting future performance.

The forecasts can then be added to the data warehouse as a tool for refining future analyses and measuring performance versus forecast results. These types of analyses can be looked at, measured and re-calibrated more frequently than ever before because they rely on real-time information residing in the data warehouse.

The demand creation proposition through demand visibility is that CFOs and managers now have the ability to more quickly move through the cycle of reacting to changes in demand for the company's products and services. By utilizing predictive intelligence, margins can be increased to improve bottom line revenue.

Demand Creation at Work

Slight improvements in demand visibility and being able to more accurately predict demand creation can provide significant value. Consider the example of a large, well-respected technology company with approximately $20 billion in annual revenues, which had the following experiences over a six-month time frame.

* Month 1: The company's quarterly earnings report reveals 60 percent year-to-year revenue growth and 14 percent sequential revenue growth. The company provided guidance for 50-60 percent revenue growth for its current fiscal year.

* Month 2: Although some companies were becoming nervous about an economic slowdown, management reiterated its prospects for 50-60 percent growth.

* Month 3: The company's CEO reported that the current quarter is more challenging than anticipated.

* Month 4: The company issues its quarterly earnings report that says sequential revenue growth dropped to 3.5 percent, while inventory increased by 30 percent. The company slightly missed analysts' earnings per share projection.

* Month 5: The company announces a large workforce reduction.

* Month 6: The company pre-announces quarterly results. Sequential revenues are expected to drop by 30 percent, with YTY revenues flat to down 10 percent. The workforce reductions announced in the fifth month are expected to result in $1 billion annual pretax payroll and indirect expense savings. In addition, the company announces a $2.5 billion inventory charge.

Obviously, demand visibility was not adequate to predict demand creation during the first three months. What would the value proposition associated with improved demand visibility have been during this period? In this example, one action was a major workforce reduction that resulted in annual savings of $1 billion. Speeding up this process by as little as 30 days would have resulted in permanent savings of $83 million. A second action was the $2.5 billion inventory charge. Depending upon a company's funding cost, carrying $2.5 billion less of inventory would conservatively result in savings in the range of $150 million to $250 million per year

Inventory obsolescence carries a much more substantial cost than that associated with carrying inventory In some cases, the inventory can be sold only with substantial discounts in order to lure customers away from newer products. In other cases, it becomes totally worthless. Determining precisely what the savings would be with better demand visibility is less relevant than the simple fact that whatever the true savings, the amount would be relevant, significant and material.

For the CFO, better demand visibility can also add value in a reverse scenario of increasing rather than diminishing demand. Not meeting an unforeseen surge in demand, particularly for commoditized or undifferentiated products, would not only result in a lost opportunity and lost revenues--it would mean a loss in market share to more nimble competitors.

Managing Demand Creation

Even if a CFO were lucky enough to operate in an industry with a fairly consistent and predictable revenue stream, better demand visibility through predictive intelligence would still be highly valuable. Although total revenue may be consistent, there is, typically, significant' deviation among individual products. Seasonal elements that may change from year to year are just one of several factors contributing to deviations among product forecasts. Value can be realized by better managing inventory and seasonal headcount.

Many enterprises have understood this and implemented supply chain management solutions to optimize these potential efficiencies. While these systems are useful, their value is obviously a function of the ability of management to predict demand. In the absence of good demand visibility, these systems cannot realize their full potential.

Essentially, an effective predictive intelligence solution on the front end that enables demand visibility of all the demand drivers for these supply chain management systems can help the CFO optimize financial performance.

Similarly, sales and marketing managers often institute special price promotions on short notice when changing market conditions call for action--for instance, when sales are slowing unexpectedly, resulting in increasing inventory levels. Sales promotions can also be a very effective tool when suppliers offer price incentives over a limited time period in order to alleviate their own revenue problems, providing a window of opportunity to enhance sales and profitability.

With demand visibility, managers can be alerted on a real-time basis when important changes are occurring and can evaluate the ramifications of different promotion strategies in making their recommendations. They can model the outcomes of various promotional approaches by combining historical data on previous price promotions with the ability to perform "what if" analyses. This helps answer questions about the projected bottom line impact of various pricing and promotion alternatives, allowing managers to quickly make the most informed decision about the proper pricing strategy to recommend to the CFO.

Demand creation cures the headaches brought on by not having a solution to embrace inherent market changes and make new decisions that preserve and or/add to the bottom line. In our current ROT-focused economy, the additional good news is the payback period for investing in these capabilities can be measured in months instead of years.

Dale Mahaffy is Vice President and CEO of e.Intelligence Inc., a Minneapolis-based company that provides software for predictive intelligence. He can be reached at 952.920.0478.