Measuring the accuracy of forecasts is an undeniably important part of the demand planning process. This forecasting scorecard could be built based on one of two contrasting viewpoints for computing metrics. The error viewpoint asks, “how far was the forecast from the actual?” The accuracy viewpoint asks, “how close was the forecast to the actual?” Both are valid, but error metrics provide more information.

Accuracy is represented as a percentage between zero and 100, while error percentages start at zero but have no upper limit. Reports of MAPE (mean absolute percent error) or other error metrics can be titled “forecast accuracy” reports, which blurs the distinction.  So, you may want to know how to convert from the error viewpoint to the accuracy viewpoint that your company espouses.  This blog describes how with some examples.

Accuracy metrics are computed such that when the actual equals the forecast then the accuracy is 100% and when the forecast is either double or half of the actual, then accuracy is 0%. Reports that compare the forecast to the actual often include the following:

• The Actual
• The Forecast
• Unit Error = Forecast – Actual
• Absolute Error = Absolute Value of Unit Error
• Absolute % Error = Abs Error / Actual, as a %
• Accuracy % = 100% – Absolute % Error

Look at a couple examples that illustrate the difference in the approaches. Say the Actual = 8 and the forecast is 10.

Unit Error is 10 – 8 = 2

Absolute % Error = 2 / 8, as a % = 0.25 * 100 = 25%

Accuracy = 100% – 25% = 75%.

Now let’s say the actual is 8 and the forecast is 24.

Unit Error is 24– 8 = 16

Absolute % Error = 16 / 8 as a % = 2 * 100 = 200%

Accuracy = 100% – 200% = negative is set to 0%.

In the first example, accuracy measurements provide the same information as error measurements since the forecast and actual are already relatively close. But when the error is more than double the actual, accuracy measurements bottom out at zero. It does correctly indicate the forecast was not at all accurate. But the second example is more accurate than a third, where the actual is 8 and the forecast is 200. That’s a distinction a 0 to 100% range of accuracy doesn’t register. In this final example:

Unit Error is 200 – 8 = 192

Absolute % Error = 192 / 8, as a % = 24 * 100 = 2,400%

Accuracy = 100% – 2,400% = negative is set to 0%.

Error metrics continue to provide information on how far the forecast is from the actual and arguably better represent forecast accuracy.

We encourage adopting the error viewpoint. You simply hope for a small error percentage to indicate the forecast was not far from the actual, instead of hoping for a large accuracy percentage to indicate the forecast was close to the actual.  This shift in mindset offers the same insights while eliminating distortions.

Automatic forecasting is the most popular and most used feature of SmartForecasts and Smart Demand Planner. Creating Automatic forecasts is easy. But, the simplicity of Automatic Forecasting masks a powerful interaction of a number of highly effective methods of forecasting. In this blog, we discuss some of the theory behind this core feature. We focus on Automatic forecasting, in part because of its popularity and in part because many other forecasting methods produce similar outputs. Knowledge of Automatic forecasting immediately carries over to Simple Moving Average, Linear Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Winters’ Exponential Smoothing, and Promo forecasting.

Forecasting tournament

Automatic forecasting works by conducting a tournament among a set of competing methods. Because personal computers and cloud computing are fast, and because we have coded very efficient algorithms into the SmartForecasts’ Automatic forecasting engine, it is practical to take a purely empirical approach to deciding which extrapolative forecasting method to use. This means that you can afford to try out a number of approaches and then retain the one that does best at forecasting the particular data series at hand. SmartForecasts fully automates this process for you by trying the different forecasting methods in a simulated forecasting tournament. The winner of the tournament is the method that comes closest to  predicting new data values from old. Accuracy is measured by average absolute error (that is, the average error, ignoring any minus signs). The average is computed over a set of forecasts, each using a portion of the data, in a process known as sliding simulation.

Sliding simulation

The sliding simulation sweeps repeatedly through ever-longer portions of the historical data, in each case forecasting ahead the desired number of periods in your forecast horizon. Suppose there are 36 historical data values and you need to forecast six periods ahead. Imagine that you want to assess the forecast accuracy of some particular method, say a moving average of four observations, on the data series at hand.

At one point in the sliding simulation, the first 24 points (only) are used to forecast the 25th through 30th historical data values, which we temporarily regard as unknown. We say that points 25-30 are “held out” of the analysis. Computing the absolute values of the differences between the six forecasts and the corresponding actual historical values provides one instance each of a 1-step, 2-step, 3-step, 4-step, 5-step, and 6-step ahead absolute forecast error. Repeating this process using the first 25 points provides more instances of 1-step, 2-step, 3-step ahead errors, and so on. The average over all of the absolute error estimates obtained this way provides a single-number summary of accuracy.

Methods used in Automatic forecasting

Normally, there are six extrapolative forecasting methods competing in the Automatic forecasting tournament:

• Simple moving average
• Linear moving average
• Single exponential smoothing
• Double exponential smoothing
• Additive version of Winters’ exponential smoothing
• Multiplicative version of Winters’ exponential smoothing

The latter two methods are appropriate for seasonal series; however, they are automatically excluded from the tournament if there are fewer than two full seasonal cycles of data (for example, fewer than 24 periods of monthly data or eight periods of quarterly data).

These six classical, smoothing-based methods have proven themselves to be easy to understand, easy to compute and accurate. You can exclude any of these methods from the tournament if you have a preference for some of the competitors and not others.

1. Forecasting is an art that requires a mix of professional judgment and objective statistical analysis. Successful demand forecasts require a baseline prediction leveraging statistical forecasting methods. Once established, the process can focus on how best to adjust statistical forecasts based on your own insights and business knowledge.

2. The forecasting process is usually iterative. You may need to make several refinements of your initial forecast before you are satisfied. It is important to be able to generate and compare alternative forecasts quickly and easily. Tracking accuracy of these forecasts over time, including alternatives that were not used, helps inform and improve the process.

3. The credibility of forecasts depends heavily on graphical comparisons with historical data.  A picture is worth a thousand words, so always display forecasts via instantly available graphical displays with supporting numerical reports.

4. One of the major technical tasks in forecasting is to match the choice of forecasting technique to the nature of the data. Effective demand forecasting processes employ capabilities that identify the right method to use.  Features of a data series like trend, seasonality or abrupt shifts in level suggest certain techniques instead of others. An automatic selection, which selects and uses the appropriate forecasting method automatically, saves time and ensures your baseline forecast is as accurate as possible.

5. Successful demand forecasting processes work in tandem with other business processes.   For example, forecasting can be an essential first step in financial analysis.  In addition, accurate sales and product demand forecasts are fundamental inputs to a manufacturing company’s production planning and inventory control processes.

6. A good planning process recognizes that forecasts are never exactly correct. Because some error creeps into even the best forecasting process, one of the most useful supplements to a forecast are honest estimates of its margin of error and forecast bias.

Many companies looking to improve their forecasting process don’t know where to start. It can be confusing to contend with learning new statistical methods, making sure data is properly structured and updated, agreeing on who “owns” the forecast, defining what ownership means, and measuring accuracy. Having seen this over forty-plus years of practice, we wrote this blog to outline the core focus and to encourage you to keep it simple early on.

1. Objectivity. First, understand and communicate that the Demand Planning and Forecasting process is an exercise in objectivity. The focus is on getting inputs from various sources (stakeholders, customers, functional managers, databases, suppliers, etc.) and deciding whether those inputs add value. For example, if you override a statistical forecast and add 20% to the projection, you should not just assume that you automatically got it right. Instead, be objective and check whether that override increased or decreased forecast accuracy. If you find that your overrides made things worse, you’ve gained something: This informs the process and you know to better scrutinize override decisions in the future.

2.  Teamwork. Recognize that forecasting and demand planning are team sports. Agree on who will captain the team. The captain is responsible for creating the baseline statistical forecasts and supervising the demand planning process. But results depend on everyone on the team making positive contributions, providing data, suggesting alternative methodologies, questioning assumptions, and executing recommended actions. The final results are owned by the company and every single stakeholder.

3. Measurement. Don’t fixate on industry forecast accuracy benchmarks. Every SKU has its own level of “forecastability”, and you may be managing any number of difficult items. Instead, create your own benchmarks based on a sequence of increasingly advanced forecasting methods. Advanced statistical forecasts may seem dauntingly complex at first, so start simple with a basic method, such as forecasting the historical average demand. Then measure how close that simple forecast comes to the actual observed demand. Work up from there to techniques that deal with complications like trend and seasonality. Measure progress using accuracy metrics calculated by your software, such as the mean absolute percentage error (MAPE). This will allow your company to get a little bit better each forecast cycle.

4. Tempo. Then focus efforts on making forecasting a standalone process that isn’t combined with the complex process of inventory optimization. Inventory management is built on a foundation of sound demand forecasting, but it is focused on other topics: what to purchase, when to purchase, minimum order quantities, safety stocks, inventory levels, supplier lead times, etc. Let inventory management go to later. First build up “forecasting muscle” by creating, reviewing, and evolving the forecasting process to have a regular cadence. When your process is sufficiently matured, catch up with the increasing speed of business by increasing the tempo of your forecasting process to at least a monthly cadence.

Remarks

Revising a company’s forecasting process can be a major step. Sometimes it happens when there is executive turnover, sometimes when there is a new ERP system, sometimes when there is new forecasting software. Whatever the precipitating event, this change is an opportunity to rethink and refine whatever process you had before. But trying to eat the whole elephant in one go is a mistake. In this blog, we’ve outlined some discrete steps you can take to make for a successful evolution to a better forecasting process.

Here are examples of forecasting problems that SmartForecasts can solve, along with the kinds of business data representative of each.

Forecasting an item based on its pattern

Given the following six quarterly sales figures, what sales can you expect for the third and fourth quarters of 2023?

Sales by Quarter

SmartForecasts gives you many ways to approach this problem. You can make your own statistical forecasts using any of six different exponential smoothing and moving average methods. Or, like most nontechnical forecasters, you can use the time-saving Automatic command, which has been programmed to automatically select and use the most accurate method for your data. Finally, to incorporate your business judgment into the forecasting process, you can graphically adjust any statistical forecast result using SmartForecasts’ “eyeball” adjustment capabilities.

Forecasting an item based on its relationship to other variables.

Given the following historical relationship between unit sales and the number of sales representatives, what sales levels can you expect when the planned increase in sales staff takes place over the final two quarters of 2023?

Sales and Sales Representatives by Quarter

You can answer a question like this using SmartForecasts’ powerful Regression command, designed specifically to facilitate forecasting applications that require regression analysis solutions. Regression models with an essentially unlimited number of independent/predictor variables are possible, although most useful regression models use only a handful of predictors.

Simultaneously forecasting a number of product items and their total

Given the following total sales for all dress shirts and the distribution of sales by color, what will individual and total sales be over the next six months?

Monthly Dress Shirt Sales by Color

SmartForecasts’ unique Group Forecasting features automatically and simultaneously forecasts closely related time series, such as these items in the same product group. This saves considerable time and provides forecast results not only for the individual items but also for their total. “Eyeball” adjustments at both the item and group levels are easy to make. You can quickly create forecasts for product groups with hundreds or even thousands of items.

Forecasting thousands of items automatically

Given the following record of product demand at the SKU level, what can you expect demand to be over the next six months for each of the 5,000 SKUs?

Monthly Product Demand by SKU (Stock Keeping Unit)

In just a few minutes, SmartForecasts’ powerful Automatic Selection can take a forecasting job of this size, read the product demand data, automatically create statistical forecasts for each SKU, and saves the result. The results are then ready for export to your ERP system leveraging any one of our API-based connectors or via file export.  Once set up, forecasts will automatically be produced each planning cycle without intervention by the user.

Forecasting demand that is most often zero

A distinct and especially challenging type of data to forecast is intermittent demand, which is most often zero but jumps up to random nonzero values at random times. This pattern is typical of demand for slow moving items, such as service parts or big ticket capital goods.

For example, consider the following sample of demand for aircraft service parts. Note the preponderance of zero values with nonzero values mixed in, often in bursts.

SmartForecasts has a unique method designed especially for this type of data: the Intermittent Demand forecasting feature. Since intermittent demand arises most often in the context of inventory control, this feature focuses on forecasting the range of likely values for the total demand over a lead time, e.g., cumulative demand over the period Jun-23 to Aug-23 in the example above.

Forecasting inventory requirements

Forecasting inventory requirements is a specialized variant of forecasting that focuses on the high end of the range of possible future values.

For simplicity, consider the problem of forecasting inventory requirements for just one period ahead, say one day ahead. Usually, the forecasting job is to estimate the most likely or average level of product demand. However, if available inventory equals the average demand, there is about a 50% chance that demand will exceed inventory, resulting in lost sales and/or lost good will. Setting the inventory level at, say, ten times the average demand will probably eliminate the problem of stockouts, but will just as surely result in bloated inventory costs.

The trick of inventory optimization is to find a satisfactory balance between having enough inventory to meet most demand without tying up too many resources in the process. Usually, the solution is a blend of business judgment and statistics. The judgmental part is to define an acceptable inventory service level, such as meeting 95% of demand immediately from stock. The statistical part is to estimate the 95th percentile of demand.

When not dealing with intermittent demand, SmartForecasts estimates the required inventory level by assuming a bell-shaped (Normal) curve of demand, estimating both the middle and the width of the bell curve, then using a standard statistical formula to estimate the desired percentile. The difference between the desired inventory level and the average level of demand is called the safety stock because it protects against the possibility of stockouts.

When dealing with intermittent demand, the bell-shaped curve is a poor approximation to the statistical distribution of demand. In this special case, SmartForecasts uses patented intermittent demand forecasting technology to estimate the required inventory service level.