The SAS internal discussion boards are always full of fascinating topics, some of which are even decipherable to a non-Ph.D. in statistics like me. A recent topic involved how to calculate the benefits of good forecasting software, and my colleague Robin Way offered an interesting perspective that he allowed me to share:

Guest Blogger: Robin Way, Analytics Consulting Manager at SAS

Perhaps the primary benefit of good forecasting software is reduced uncertainty around the most likely forecast, as well as potential improvement in accuracy. Most of the time, I have found that lay business people who are not that familiar with forecasting and the business payoff of better forecasts immediately think that a better forecasting engine will lead to improved accuracy. However, most seasoned forecasters accept that nobody can really tell the future and every forecast is inherently wrong. The point of better forecasting processes is that you now have an enhanced ability to know how far wrong from the point forecast you are likely to end up, and what factors influence that degree of wrong-ness.

I know that all sounds pretty soft; so consider this business case made for a call center capacity planning group. The main driver of business performance in managing capacity for a call center is the cost of call handling to deliver a specified level of service to customers. (Note that call handling costs include personnel costs (salaries and benefits), scheduling, availability of current resources, likely attrition of existing staff, cost and timeframe to bring on new trained staff, and other factors.) Put too few call center agents on the phones, and your service level suffers; put too many agents on the phones, and cost per call suffers.

Improved accuracy around demand for inbound calls will certainly help the capacity planners target the most appropriate number of agents to schedule for any period of time, but everybody knows there’s no way to know for sure exactly how many calls will come in. This is the element of forecast error. You don’t want to staff for the most likely number—really you want to staff for the upper range of likely call volume, because it’s typically worse to deliver a low level of service than to have too many low-utilization agents on the phones. (You can always divert the slack resource to outbound calls or post-call cleanup for busy agents.)

So how to estimate this upper range? That’s where having a really good forecasting engine comes in. Tools like SAS Forecast Server help you reduce the confidence limits around the most likely forecast by reducing unexplained variance—that’s what you get by having a better model, via a better engine. You can identify what drives the uncertainty so that you can advise management whether the sources of uncertainty in the forecast are under their control (marketing promotions sponsored by the firm, price changes, new product introductions, contractual changes with existing customers), or outside their control (market and competitor forces, seasonal shifts, market events). And you can develop a more robust upper bound on the likely forecast in order to intelligently set your service levels for upcoming call handling periods.

If you then compare the upper bound on the forecast from a relatively naïve forecast model with one produced by a superior model, presumably generated with Forecast Server, you can show the difference in the upper bound (in this case, in call demand) as a reduced operational risk to the firm. All that on top of what is possibly a more accurate forecast to begin with, though I advise against making your case solely on the point of accuracy, because often times the process generating the actual results can vary relative to the forecast for reasons outside the control of the forecaster.

Robin delivers an important reminder. Your forecast should be more than just a point estimate representing your best guess at what is really going to happen. To drive better decisions and more profitable operations, you need to take into consideration the likely error in your point forecast. Forecasting for new products gives a prime illustration.

New products, especially those requiring new technology or manufacturing processes that require a lot of capital investment, can be a big gamble. If we fail to take into consideration the uncertainty (risk) in our point forecasts, we may take unnecessarily dangerous gambles. I’m not arguing against taking gambles with your business investments – every decision and action involves some risk. However, it is important to recognize the scale of the risk, and act accordingly.