Tremendous cost-saving efficiencies can result from optimizing inventory stocking levels using the best predictions of future demand. Familiarity with forecasting basics is an important part of being effective with the software tools designed to exploit this efficiency. This concise introduction (the first in a short series of blog posts) offers the busy professional a primer in the basic ideas you need to bring to bear on forecasting. How do you evaluate your forecasting efforts, and how reliable are the results?

A good forecast is “unbiased.” It correctly captures predictable structure in the demand history, including: trend (a regular increase or decrease in demand); seasonality (cyclical variation); special events (e.g. sales promotions) that could impact demand or have a cannibalization effect on other items; and other, macroeconomic events.

By “unbiased,” we mean that the estimated forecast is not projecting too high or too low; the actual demand is equally likely to be above or below predicted demand. Think of the forecast as your best guess of what could happen in the future. If that forecast is “unbiased,” the overall picture will show that measures of actual future demand will “bracket” the forecasts—distributed in balance above and below predictions by the equal odds.

You can think of this as if you are an artillery officer and your job is to destroy a target with your cannon. You aim your cannon (“the forecast”) and then shoot and watch the shells fall. If you aimed the cannon correctly (producing an “unbiased” forecast), those shells will “bracket” the target; some shells will fall in front and some shells fall behind, but some shells will hit the target. The falling shells can be thought of as the “actual demand” that will occur in the future. If you forecasted well (aimed your cannon well), then those actuals will bracket the forecasts, falling equally above and below the forecast.

Once you have obtained an “unbiased” forecast (in other words, you aimed your cannon correctly), the question is: how accurate was your forecast? Using the artillery example, how wide is the range around the target in which your shells are falling? You want to have as narrow a range as possible. A good forecast will be one with the minimal possible “spread” around the target.

However, just because the actuals are falling widely around the forecast does not mean you have a bad forecast. It may merely indicate that you have very “volatile” demand history. Again, using the artillery example, if you are starting to shoot in a hurricane, you should expect the shells to fall around the target with a wide error.

Your goal is to obtain as accurate a forecast as is possible with the data you have. If that data is very volatile (you’re shooting in a hurricane), then you should expect a large error. If your data is stable, then you should expect a small error and your actuals will fall close to the forecast—you’re shooting on a clear day!

So that you can understand both the usefulness of your forecasts and the degree of caution appropriate when applying them, you need to be able to review and measure how well your forecast is doing. How well is it estimating what actually occurs? SmartForecasts does this automatically by running its “sliding simulation” through the history. It simulates “forecasts” that could have occurred in the past. An older part of the history, without the most recent numbers, is isolated and used to build forecasts. Because these forecasts then “predict” what might happen in the more recent past—a period for which you already have actual demand data—the forecasts can be compared to the real recent history.

In this manner, SmartForecasts can empirically compute the actual forecast error—and those errors are needed to properly estimate safety stock. Safety stock is the amount of extra stock you need to carry in order to account for the anticipated error in your forecasts. In a subsequent essay, I’ll discuss how we use our estimated forecasts error (via the SmartForecasts sliding simulation) to correctly estimate safety stocks.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

Contributed to The Smart Forecaster by Dr. Greg Parlier (Colonel, U.S. Army, retired). Details on Dr. Parlier’s background conclude the post.

For over two decades, the General Accounting Office (GAO) has indicated that the Defense Department’s logistics management has been ineffective and wasteful, and that the Services lack strategic plans to improve overall inventory management and supply chain performance.

For the US Army, this problem is directly related to a persistent inability to link inventory investment levels and policies with supply chain effectiveness to achieve combat equipment readiness objectives required for globally deployed forces. This shortcoming has been attributed to numerous complexities associated with managing geographically dispersed, independently operating organizations, further compounded by a lack of visibility, authority and accountability across this vast global enterprise.

Unlike the corporate world, where powerful forces encourage innovation to drive competitiveness and efficiency, the Army is not a revenue generating organization focused on “quarterly earnings” and profitability. Certainly, the Army wants to be an efficient consumer of resources—but unlike the private sector’s focus on profit as a bottom line, the surrogate motivator for the Army is ‘force readiness’. This includes equipment availability and weapon system readiness for current operations in Afghanistan, as well as future capability requirements directed by the National Command Authority.

To sustain that equipment availability, the Army must synchronize disparate organizational components using myriad processes with disconnected legacy management information systems across numerous supply support activities which frequently relocate to support deploying forces.

Today, while still engaged in Afghanistan, the Army is also committed to a comprehensive and ongoing transformation. Central to this effort is recognition that dramatic improvements must be achieved in logistics operations and supply chain management. Owning one of the world’s largest and most complex supply chains, the Army is now investing in historically unprecedented efforts to fully capitalize on the promises offered by new information-based technologies. For example, the “Single Army Logistics Enterprise” is believed to be the most ambitious and expensive Enterprise Resource Planning (ERP) implementation project ever undertaken.

These ERP implementation projects have met with very mixed results. While the evidence suggests that dramatic performance improvements for competitive advantage can be achieved in the commercial sector, this has occurred only where so called “IT solutions” are applied to an underlying foundation of mature, efficient and appropriate business processes.

The reality of most cases in recent years, however, has not been this success. Rather, attempts have been made to “bolt on” a solution (like an ERP system, for example) to existing business processes, in misguided efforts to replicate legacy management practices. Such efforts to automate existing processes have, all too often, simply created chaos. In fact, these attempts have not only failed to achieve anticipated improvements, but have actually resulted in reduced performance.

The general pattern has been: the greater the IT investment and organizational scope, the more likely “failure” occurs, in the form of cost overruns, missed schedules, and even project failure—where the effort has finally been abandoned.

We believe the way to enable a coordinated, comprehensive approach for logistics transformation is by creating an “engine for innovation” to accelerate and sustain continuous performance improvement for Army logistics and supply chain management. We are developing a ‘Center for Innovation in Logistics Systems’ to systematically evaluate major organizational components, conduct root cause analyses, diagnose structural disorders and prescribe integrated solutions. We have now identified several ‘catalysts for innovation’ to reduce supply side variability and demand uncertainty—the proximate causes of the notorious ‘bull whip effect’. These include what we refer to as the ‘readiness equation’, ‘mission-based forecasting’, ‘readiness-based sparing’ and ‘readiness responsive retrograde’.

Our goal is to develop a comprehensive modeling capacity to generate and test these innovation catalysts along with several other initiatives in order to estimate cost effective approaches before they are adopted as policy and implemented in practice. We are looking at performance analysis, organizational design, management information and decision support concepts, enterprise systems engineering and workforce considerations including human capital investment needs.

Examining the ‘catalysts’ in isolation, we have seen significant potential for improvement which could yield hundreds of millions of dollars in savings. When combined into new, integrated management practices, however, the potential magnitude for improvement is truly dramatic—billions of dollars in further savings are likely. More importantly, it becomes possible to relate investment levels to current readiness and future capabilities.

The center is capable of developing ‘management innovation as a strategic technology’ by integrating advanced analytics with transformational strategic planning. By harnessing, focusing and applying the power of analysis, we are promoting both qualitative and quantitative common sense—the compelling analytical arguments for necessary change to pursue a common vision. With this power, we are beginning to educate the Army’s leadership, motivate logistics managers to action and provide a source for innovation the culture can embrace. During our journey, we have certainly adapted and applied much from both academic domains and the corporate sector. They, in turn, might now benefit from what we have been able to learn and achieve as well.

Prior to his retirement, Colonel Parlier was the Army’s senior, most experienced operations research analyst and served as Army Aviation and Missile Command’s (AMCOM) Deputy Commander for Transformation. He is the author of Transforming U.S. Army Supply Chains: Strategies for Management Innovation, describing the analytical framework of a multi-year Army Materiel Command (AMC) research and development project providing operations research insights for use by the Army and Department of Defense.