In a recent LinkedIn post, I detailed four questions that, when answered, will reveal how forecasts are being used in your business.  In this article, we’ve listed questions you can ask that will reveal how forecasts are created.

1. When we ask users how they create forecasts, their answer will often be “we use history.” This obviously isn’t enough information, as there are different types of demand history that require different forecasting methods. If you are using historical data, then make sure to find out if you are using an averaging model, a trending model, a seasonal model, or something else to forecast.

2. Once you know the model used, ask about the parameter values of those models. The forecast output of an “average” will differ, sometimes significantly, depending on the number of periods you are averaging.  So, find out whether you are using an average of the last 3 months, 6 months, 12 months, etc.

3. If you are using trending models, ask how the model weights are set. For example, in a trending model, such as double exponential smoothing, the forecasts will differ significantly depending on how the calculations weight recent data compared to older data (higher weights put more emphasis on the recent data).

4. If you are using seasonal models, the forecast results are going to be impacted by the “level” and “trending weights” used. You should also determine whether seasonal periods are forecasted with multiplicative or additive seasonality.  (Additive seasonality says, e.g., “Add 100 units for July”, whereas multiplicative seasonality says “Multiply by 1.25 for July.”) Finally, you may not be using these types of methods at all.  Some practitioners will use a forecast method that simply averages prior periods (i.e., next June will be forecasted based on the average of the prior three Junes).

5. How do you go about choosing one model over another? Does the choice of technique depend on the type of demand data or when new demand data are available? Is this process automated? Or if a planner chooses a trend model subjectively, will that item continue to be forecasted with that model until the planner changes it again?

6. Are your forecasts “fully automatic,” so that trend and/or seasonality are detected automatically? Or are your forecasts dependent on item classifications that must be maintained by users? The latter requires more time and attention from planners to define what behavior constitutes trend, seasonality, etc.

7. What are the item classification rules used? For example, an item may be considered a trending item if demand increases by more than 5% period-over-period. An item may be considered seasonal if 70% or more of the annual demand occurs in four or fewer periods. Such rules are user-defined and often require overly broad assumptions. Sometimes they are configured when a system was originally implemented but never revised even as conditions change. It’s important to make sure any classification rules are understood and, if necessary, updated.

8. Does the forecast regenerate automatically when new data are available, or do you have to manually regenerate the forecasts?

9. Do you check for any change in forecast from one period to the next before deciding whether to use the new forecast? Or do you default to the new forecast?

10. How are forecast overrides that were made in prior planning cycles treated when a new forecast is created? Are they reused or replaced?

11. How do you incorporate forecasts made by your sales team or by your customers? Do these forecasts replace the baseline forecast, or do you use these inputs to make planner overrides to the baseline forecast?

12. Under what circumstances would you ignore the baseline forecast and use exactly what sales or customers are telling you?

13. If you rely on customer forecasts, what do you do about customers who don’t provide forecasts?

14. How do you document the effectiveness of your forecasting approach?  Most companies only measure the accuracy of the final forecast that is submitted to the ERP system, if they measure anything. But they don’t assess alternative predictions that might have been used. It is important to compare what you are doing to benchmarks. For example, do the methods you are using outperform a naïve forecast (i.e., “tomorrow equals today,” which requires no thought), or what you saw last year, or the average of the last 12 months.  Benchmarking your baseline forecast insures you are squeezing as much accuracy as possible out of the data.

15. Do you measure whether overrides from sales, customers, and planners are making the forecast better or worse? This is just as important as measuring whether your statistical approaches are outperforming the naïve method.  If you don’t know whether overrides are helping or hurting, the business can’t get better at forecasting – you need to know which steps are adding value so that you can do more of those and get even better. If you aren’t documenting forecast accuracy and conducting “forecast value add” analysis, then you aren’t able to properly assess whether the forecasts being produced are the best you could make.  You’ll miss opportunities to improve the process, increase accuracy, and educate the business on what type of forecast error is to be expected.

Smart IP&O is powered by the SmartForecasts® forecasting engine that automatically selects the most appropriate method for each item.  Smart Forecast methods are listed below:

• Simple Moving Average and Single Exponential Smoothing for flat, noisy data
• Linear Moving Average and Double Exponential Smoothing for trending data
• Winters Additive and Winters Multiplicative for seasonal and seasonal & trending data.

This blog explains how each model works using time plots of historical and forecast data.  It outlines how to go about choosing which model to use.   The examples below show the same history, in red, forecasted with each method, in dark green, compared to the Smart-chosen winning method, in light green.

Seasonality
If you want to force (or prevent) seasonality to show in the forecast, then choose Winters models.  Both methods require 2 full years of history.

`Winter’s multiplicative will determine the size of the peaks or valleys of seasonal effects based on a percentage difference from a trending average volume.  It is not a good fit for very low volume items due to division by zero when determining that percentage. Note in the image below that the large percentage drop in seasonal demand in the history is being projected to continue over the forecast horizon making it look like there isn’t any seasonal demand despite using a seasonal method.

Statistical forecast produced with Winter’s multiplicative method. 

Winter’s additive will determine the size of the peaks or valleys of seasonal effects based on a unit difference from the average volume.  It is not a good fit if there’s significant trend to the data.  Note in the image below that seasonality is now being forecasted based on the average unit change in seasonality. So, the forecast still clearly reflects the seasonal pattern despite the down trend in both the level and seasonal peaks/valleys.

Statistical forecast produced with Winter’s additive method.

Trend

If you want to force (or prevent) trend up or down to show in the forecast, then restrict the chosen methods to (or remove the methods of) Linear Moving Average and Double Exponential Smoothing.

 Double exponential smoothing will pick up on a long-term trend.  It is not a good fit if there are few historical data points.

Statistical forecast produced with Double Exponential Smoothing

Linear moving average will pick up on nearer term trends.  It is not a good fit for highly volatile data

Non-Trending and Non-Seasonal Data
If you want to force (or prevent) an average from showing in the forecast, then restrict the chosen methods to (or remove the methods of) Simple Moving Average and Single Exponential Smoothing.

Single exponential smoothing will weigh the most recent data more heavily and produce a flat-line forecast.  It is not a good fit for trending or seasonal data.

Statistical forecast using Single Exponential Smoothing

Simple moving average will find an average for each period, sometimes appearing to wiggle, and better for longer-term averaging.  It is not a good fit for trending or seasonal data.

Statistical forecast using Simple Moving Average

Sometimes a statistical forecast just doesn’t make sense.  Every forecaster has been there.  They may double-check that the data was input correctly or review the model settings but are still left scratching their head over why the forecast looks very unlike the demand history.   When the occasional forecast doesn’t make sense, it can erode confidence in the entire statistical forecasting process.

This blog will help a layman understand what the Smart statistical models are and how they are chosen automatically.  It will address how that choice sometimes fails, how you can know if it did, and what you can do to ensure that the forecasts can always be justified.  It’s important to know to expect, and how to catch the exceptions so you can rely on your forecasting system.

How methods are chosen automatically

The criteria to automatically choose one statistical method out of a set is based on which method came closest to correctly predicting held-out history.  Earlier history is passed to each method and the result is compared to actuals to find the one that came closest overall.  That automatically chosen method is then fed all the history to produce the forecast. Check out this blog to learn more about the model selection https://smartcorp.com/uncategorized/statistical-forecasting-how-automatic-method-selection-works/

For most time series, this process can capture trends, seasonality, and average volume accurately. But sometimes a chosen method comes mathematically closest to predicting the held-out history but doesn’t project it forward in a way that makes sense.  That means the system selected method isn’t best and for some “hard to forecast”

Hard to forecast items

Hard to forecast items may have large, unpredictable spikes in demand, or typically no demand but random irregular blips, or unusual recent activity.  Noise in the data sometimes randomly wanders up or down, and the automated best-pick method might forecast a runaway trend or a grind into zero.  It will do worse than common sense and in a small percentage of any reasonably varied group of items.  So, you will need to identify these cases and respond by overriding the forecast or changing the forecast inputs.

How to find the exceptions

Best practice is to filter or sort the forecasted items to identify those where the sum of the forecast over the next year is significantly different than the corresponding history last year.  The forecast sum may be much lower than the history or vice versa.  Use supplied metrics to identify these items; then you can choose to apply overrides to the forecast or modify the forecast settings.

How to fix the exceptions

Often when the forecast seems odd, an averaging method, like Single Exponential Smoothing or even a simple average using Freestyle, will produce a more reasonable forecast.  If trend is possibly valid, you can remove only seasonal methods to avoid a falsely seasonal result.  Or do the opposite and use only seasonal methods if seasonality is expected but wasn’t projected in the default forecast.  You can use the what-if features to create any number of forecasts, evaluate & compare, and continue to fine tune the settings until you are comfortable with the forecast.

Cleaning the history, with or without changing the automatic method selection, is also effective at producing reasonable forecasts. You can embed forecast parameters to reduce the amount of history used to forecast those items or the number of periods passed into the algorithm so earlier, outdated history is no longer considered.  You can edit spikes or drops in the demand history that are known anomalies so they don’t influence the outcome.  You can also work with the Smart team to implement automatic outlier detection and removal so that data prior to being forecasted is already cleansed of these anomalies.

If the demand is truly intermittent, it is going to be nearly impossible to forecast “accurately” per period. If a level-loading average is not acceptable, handling the item by setting inventory policy with a lead time forecast can be effective.  Alternatively, you may choose to use “same as last year” models which while not prone to accuracy will be generally accepted by the business given the alternatives forecasts.

Finally, if the item was introduced so recently that the algorithms do not have enough input to accurately forecast, a simple average or manual forecast may be best.  You can identify new items by filtering on the number of historical periods.

Manual selection of methods

Once you have identified rows where the forecast doesn’t make sense to the human eye, you can choose a smaller subset of all methods to allow into the forecast run and compare to history.  Smart will allow you to use a restricted set of methods just for one forecast run or embed the restricted set to use for all forecast runs going forward. Different methods will project the history into the future in different ways.  Having a sense of how each works will help you choose which to allow.

Rely on your forecasting tool

The more you use Smart period over period to embed your decisions about how to forecast and what historical data to consider, the less often you will face exceptions as described in this blog.  Entering forecast parameters is a manageable task when starting with critical or high impact items.  Even if you don’t embed any manual decisions on forecast methods, the forecast re-runs every period with new data. So, an item with an odd result today can become easily forecastable in time.

Service-Level-Driven Service Parts Planning for Microsoft Dynamics BC or F&SC is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support.

The math to determine this level of planning simply does not exist in D365 functionality.  It requires math and AI that passes thousands of times through calculations for each part and part center (locations).  Math and AI like this are unique to Smart.  To understand more, please read on. 

Step 1. Ensure that all stakeholders agree on the metrics that matter. 

All participants in the service parts inventory planning process must agree on the definitions and what metrics matter most to the organization. Service Levels detail the percentage of time you can completely satisfy required usage without stocking out. Fill Rates detail the percentage of the requested usage that is immediately filled from stock. (To learn more about the differences between service levels and fill rate, watch this 4-minute lesson here.) Availability details the percentage of active spare parts with an on-hand inventory of at least one unit. Holding costs are the annualized costs of holding stock accounting for obsolescence, taxes, interest, warehousing, and other expenses. Shortage costs are the cost of running out of stock, including vehicle/equipment downtime, expedites, lost sales, and more. Ordering costs are the costs associated with placing and receiving replenishment orders.

Step 2. Benchmark historical and predicted current service level performance.

All participants in the service parts inventory planning process must hold a common understanding of predicted future service levels, fill rates, and costs and their implications for your service parts operations. It is critical to measure both historical Key Performance Indicators (KPIs) and their predictive equivalents, Key Performance Predictions (KPPs).  Leveraging modern software, you can benchmark past performance and leverage probabilistic forecasting methods to simulate future performance.  Virtually every Demand Planning solution stops here.  Smart goes further by stress-testing your current inventory stocking policies against all plausible future demand scenarios.  It is these thousands of calculations that build our KPPs.  The accuracy of this improves D365’s ability to balance the costs of holding too much with the costs of not having enough. You will know ahead of time how current and proposed stocking policies are likely to perform.

Step 3. Agree on targeted service levels for each spare part and take proactive corrective action when targets are predicted to miss. 

Parts planners, supply chain leadership, and the mechanical/maintenance teams should agree on the desired service level targets with a full understanding of the tradeoffs between stockout risk and inventory cost.  A call out here is that our D365 customers are almost always stunned by the stocking levels difference between 100% and 99.5% availability.   With the logic for nearly 10,000 scenarios that half a percent outage is almost never hit.   You achieve full stocking policy with much lower costs.   You find the parts that are understocked and correct those.  The balancing point is often a 7-12% reduction in inventory costs. 

This leveraging of what-if scenarios in our parts planning software gives management and buyers the ability to easily compare alternative stocking policies and identify those that best meet business objectives.  For some parts, a small stock out is okay.  For others, we need that 99.5% parts availability.  Once these limits are agreed upon, we use the Power of D365 to optimize inventory using D365 core ERP as it should be.   The planning is automatically uploaded to engage Dynamics with modified reorder points, safety stock levels, and/or Min/Max parameters.  This supports a single Enterprise center point, and people are not using multiple systems for their daily parts management and purchasing.

Step 4. Make it so and keep it so. 

Empower the planning team with the knowledge and tools it needs to ensure that you strike agreed-upon balance between service levels and costs.  This is critical and important.  Using Dynamics F&SC or BC to execute your ERP transactions is also important.  These two Dynamics ERPs have the highest level of new ERP growth on the planet.  Using them as they are intended to be used makes sense.   Filling the white space for the math and AI calculations for Maintenance and Parts management also makes sense.  This requires a more complex and targeted solution to help.  Smart Software Inventory Optimization for EAM and Dynamics ERPs holds the answer.    

Remember: Recalibration of your service parts inventory policy is preventive maintenance against both stockouts and excess stock.  It helps costs, frees capital for other uses, and supports best practices for your team. 

Extend Microsoft 365 F&SC and AX with Smart IP&O

To see a recording of the Microsoft Dynamics Communities Webinar showcasing Smart IP&O, register here:

https://smartcorp.com/inventory-planning-with-microsoft-365-fsc-and-ax/

Smart IP&O offers automated statistical forecasting that selects the right forecast method that best forecasts the data.  It does this for each time-series in the data set.  This blog will help a laymen understand how the forecast methods are chosen automatically.

Smart makes many methods available including single and double exponential smoothing, linear and simple moving average, and Winters models.  Each model is designed to capture a different sort of pattern.  The criteria to automatically choose one statistical method out of a set of choices is based on which method came closest to correctly predicting held-out history.

Earlier demand history is passed to each method and the result is compared to actuals to find the one that came closest overall.  That “winning” automatically chosen method is then fed all the history for that item to produce the forecast.

The overall nature of the demand pattern for the item is captured by holding out different portions of the history so that an occasional outlier does not unduly influence the choice of method.  You can visualize it using the below diagram where each row represents a 3-period forecast in held out history, based on different amounts of the red earlier history.  The variances of each pass are averaged together to determine the method’s overall ranking against all other methods.

For most time series, this process can accurately capture trends, seasonality, and average volume accurately. But sometimes a chosen method comes mathematically closest to predicting the held-out history but doesn’t project it forward in a way that makes sense.

Users can correct this by using the system’s exception reports and filtering features to identify items that merit review.  They can then configure the automatic forecast methods that they wish to be considered for that item.