What is “A Good Forecast”

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

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.

Supply Chain Math: Don’t Bring a Knife to a Gunfight

Math and the supply chain go hand and hand. As supply chains grow, increasing complexity will drive companies to look for ways to manage large-scale decision-making. Math is a fact of life for anyone in inventory management and demand forecasting who is hoping to remain competitive in the modern world. Read our article to learn more.

Scenario-based Forecasting vs. Equations

Traditionally, software has served as a delivery vehicle for equations. This is fine, as far as it goes. But we at Smart Software think you would do better by trading in your equations for scenarios. Learn why Scenario-based planning helps planners better manage risk and create better outcomes.

Top Five Tips for New Demand Planners and Forecasters

Good forecasting can make a big difference to your company’s performance, whether you are forecasting to support sales, marketing, production, inventory, or finance. This blog is aimed primarily at those fortunate individuals who are about to start this adventure. Welcome to the field!

Inventory Optimization for Manufacturers, Distributors, and MRO

Managing Spare Parts Inventory: Best Practices
In this blog, we’ll explore several effective strategies for managing spare parts inventory, emphasizing the importance of optimizing stock levels, maintaining service levels, and using smart tools to aid in decision-making. Managing spare parts inventory is a critical component for businesses that depend on equipment uptime and service reliability. Unlike regular inventory items, spare parts often have unpredictable demand patterns, making them more challenging to manage effectively. An efficient spare parts inventory management system helps prevent stockouts that can lead to operational downtime and costly delays while also avoiding overstocking that unnecessarily ties up capital and increases holding costs. […]
Innovating the OEM Aftermarket with AI-Driven Inventory Optimization
The aftermarket sector provides OEMs with a decisive advantage by offering a steady revenue stream and fostering customer loyalty through the reliable and timely delivery of service parts. However, managing inventory and forecasting demand in the aftermarket is fraught with challenges, including unpredictable demand patterns, vast product ranges, and the necessity for quick turnarounds. Traditional methods often fall short due to the complexity and variability of demand in the aftermarket. The latest technologies can analyze large datasets to predict future demand more accurately and optimize inventory levels, leading to better service and lower costs. […]
Future-Proofing Utilities: Advanced Analytics for Supply Chain Optimization
Utilities in the electrical, natural gas, urban water, and telecommunications fields are all asset-intensive and reliant on physical infrastructure that must be properly maintained, updated, and upgraded over time. Maximizing asset uptime and the reliability of physical infrastructure demands effective inventory management, spare parts forecasting, and supplier management. A utility that executes these processes effectively will outperform its peers, provide better returns for its investors and higher service levels for its customers, while reducing its environmental impact. […]
Centering Act: Spare Parts Timing, Pricing, and Reliability
In this article, we'll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We'll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we'll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we'll explore ways to enhance your service-level-driven inventory plan consistently. […]

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Smart Software Awarded National Science Foundation Innovation Research Grant

New research to improve service and spare parts planning for the multi-billion dollar aerospace, automotive, high tech, and utilities markets Belmont, Mass., November 28, 2012 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that it has been awarded a Phase I Small Business Innovation Research (SBIR) grant from the National Science Foundation (NSF). Smart Software will investigate new statistical methods to forecast intermittent demand, with the ultimate objective of helping enterprises worldwide reduce inventories by tens of billions of dollars. The new research will build upon Smart Software’s patented solution for forecasting slow-moving or intermittent demand, developed with the support of a previous NSF grant. The current method, commercialized as part of the company’s flagship product, SmartForecasts^®, evaluates historical demand for each item and establishes the optimum level of inventory that will be required to achieve service level objectives. The new research seeks to extend demand forecasting beyond individual products and parts, identifying and interpreting interactions across clusters of items whose demands fluctuate together. The new forecasting capabilities will benefit customers in several significant ways:

A more dynamic statistical model of parts will enable forecasts to better reflect a variety of external factors that include part usage by itself or in combination with other products, as well as the impact of macroeconomic and environmental factors.
Research results will provide planners with a dynamic model of item usage, enabling planners to develop functional maps of the interrelationships of large numbers of parts. Knowing which parts have demands that co-vary can be useful in at least two ways. First, item managers can be assigned to work with coherent clusters rather than arbitrary collections of miscellaneous parts, and second, parts can be co-located in warehouses for more efficient storage and retrieval.
Another benefit from this new approach will be improved forecasts of “aggregates” where intermittent demand is present, such as all items in a product line, or all items at a particular warehouse. Better forecasts of aggregate demand across groups of parts will also be useful for raw materials purchasing, as well as for financial planning when parts are a source of revenue.

According to Nelson Hartunian, president of Smart Software, “Any organization that builds or supports capital equipment experiences intermittent demand for some portion of its inventory. This grant is a terrific opportunity to impact one of the biggest forecasting challenges facing these organizations – accurately forecasting parts and optimizing inventories. Ultimately, the goal is to have the right part at the right place at the right time. The research we are undertaking will make this goal more achievable.” The Small Business Innovation Research grant program from the National Science Foundation is extremely competitive. More than a thousand companies compete in a two-stage screening: one for intellectual merit, and the other for commercial potential. This Phase 1 grant is the third Smart Software has received. About Smart Software, Inc. Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning and inventory optimization solutions. Smart Software’s flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Mitsubishi, Siemens, Disney, Nestle, GE and The Coca-Cola Company. SmartForecasts gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items. It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels. Smart Software is headquartered in Belmont, Massachusetts and can be found on the World Wide Web at www.smartsoftware.wpengine.com. SmartForecasts is a registered trademark of Smart Software, Inc. All other trademarks are the property of their respective owners.

For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478. Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com

Smart Software Executive to Speak on Optimizing Military Spare Parts Inventories

Tom Willemain to lead session and tutorial at 2012 INFORMS Conference to help military logistics personnel manage $70 Billion worth of parts & supplies

Belmont, Mass., October 9, 2012 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that Tom Willemain, vice president for research, will have two roles at this year’s INFORMS 2012 Annual Meeting, in Phoenix, Arizona, October 14-17. Dr. Willemain, who is also a professor of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, will present a tutorial on managing spare parts on October 16, 8:00 – 9:30 am. He will also chair a session on “Methods Supporting Military Logistics and Testing” where he will also discuss “Accurate Forecasts of Spare Part Demand,” October 17, 8:00 – 9:30 am.

Operations and support of military hardware, which includes maintaining, refurbishing and overhauling, can be 60 to 70 percent of the cost of owning a weapon system over its entire lifetime, which can be decades. Improving the management of parts involved in those operations poses a significant challenge for the U.S. military.

According to a study by Deloitte Consulting LLP, the Department of Defense spends $70 billion a year on parts and supplies. Accurately forecasting spare parts is a major problem for any parts organizations because as much as 70% of spare parts have what’s known as “intermittent demand” which is very difficult to accurately forecast. This typically results in unbalanced inventories with many items overstocked and others under-stocked.

In a book titled, Transforming U.S. Army Supply Chains: Strategies for Management Innovation, retired Army Col. Greg H. Parlier, who is now a defense logistics consultant, has proposed “mission based forecasting” software tools that will help the military to stop buying things they do not need. Dr. Willemain’s experience has helped numerous companies with similar inventory challenges do just that.

Dr. Willemain has been at the forefront of research on better ways to forecast intermittent demand. With other colleagues at Smart Software, he holds a patent that provides accurate service level forecasts and estimates of safety stock and inventory stocking level requirements. Commercialized in Smart’s flagship product, SmartForecasts^®, the patented technology has helped numerous manufacturing, distribution, and service/spare parts organizations optimize their inventories, save millions of dollars, improve cash flows, and meet corporate cost reduction objectives.

INFORMS stands for The Institute for Operations Research and the Management Sciences. It is an international scientific society, with 10,000 members, dedicated to applying scientific methods to help improve decision-making, management, and operations. To learn more about INFORMS or its annual research conference, see www.informs.org.

About Smart Software, Inc.
Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning and inventory optimization solutions. Smart Software’s flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Mitsubishi, Siemens, Disney, Nestle, GE and The Coca-Cola Company. SmartForecasts gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items. It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels. Smart Software is headquartered in Belmont, Massachusetts and can be found on the World Wide Web at www.smartsoftware.wpengine.com.

SmartForecasts is a registered trademark of Smart Software, Inc. All other trademarks are the property of their respective owners.

For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com