2. Fully integrated AX module
Demand Forecasting
Demand
forecasts
Sales orders
Transfer
orders
Purchase
Production
Master Planning
Total integration – enter information once!!

3. Key Benefits Lower stock levels Speed of implementation
Increased forecast accuracy reduces safety stocks without impacting customer service
Stock replenishments are better timed to meet demand
Speed of implementation
Familiar Dynamics AX look & feel
Lower operational costs
No interfacing of data

4. Why forecast?
Forecasting gives Master Planning visibility of what is likely to happen, which is better than waiting for it to happen
Would you drive a car without looking ahead?
Dynamics AX Master Planning can be run without future forecasts, but if so it will be running ‘blind’ and will plan stocks in the same way as an Inventory Re-order Point system would.
This may be adequate where replenishment lead times are very short or where a few small, low value items are being planned, but in most cases it will lead to over-stocking which ties up capital and can be very expensive.
This presentation shows how forecasts can be used to drive Master Planning and save money.

5. Stock Reordering Min. Stock with no forecasting
This is a traditional inventory reordering diagram that shows how reorders are placed to replenish stock levels when ‘available’ stock (stock on hand + on order – known demand) falls below a ‘safe’ level, avoiding stock-outs.
It applies to both Inventory Re-order Point replenishment and Materials Requirements Planning.
The difference is the level that triggers stock replenishments. If you are forecasting ahead, you are able to plan ahead of time and the level at which stock replenishment orders are generated is the Safety Stock (Minimum Stock in Master Planning)
If you aren’t forecasting, you aren’t looking ahead, so your trigger point has to include an allowance for the lead time it takes to receive any replenishment order you place. The Minimum Stock level in Master Planning must therefore be set to the Safety Stock + the estimated demand over the replenishment lead time.
Min. Stock with forecasting

6. Stock Reordering
The longer the lead time, the more important good forecasting becomes
Let’s look at the Safety Stock and Lead Time in more detail because they are critical to how efficiently you are managing stocks.
If you could forecast demand exactly, and assuming lead times don’t vary, you wouldn’t need safety stock at all because you could predict exactly when stocks need to be replenished to avoid stock-outs. Your safety stocks would be set to zero.
Obviously this is unrealistic, but it is important to understand that safety stocks only exist to manage variability in demand and in replenishment lead times. Recognising this is critical to managing safety stocks.
The length of the replenishment lead time is also important – a long lead time exposes you to risk if your forecasts are inaccurate, while a short lead time reduces the need for accurate forecasts.
Just in Time replenishment is a good example of a methodology that relies on short lead times to avoid exposure to demand variability
Safety Stock covers variability in demand

7. Let’s look at an example
This is a typical demand history pattern you could easily see for any product – quite a lot of variability, not much of a trend and a little bit of seasonality.

8. Let’s look at an example
This is a forecast calculated for this demand pattern with the forecasts recalculated periodically during the timespan of the history, but not within the replenishment lead time. To do so would produce unrealistically optimistic forecasts.

9. Now let’s compare the results
Forecasting
No Forecasting
Average Forecast Error %
35% -
Firstly, let’s look at how accurate the forecasts are – reasonable but not exceptionally good.

10. Now let’s compare the results
Forecasting
No Forecasting
Average Forecast Error %
35% -
Average Safety Stock
144
Average Stock Level
261
295
We can simulate the effect of running Master Planning with and without forecasts, and calculate the stock levels that result. These are the results.
For the sake of this exercise we have assumed a fixed 3 month lead time and exactly the same safety stocks in both situations.
Safety stocks have been dynamically calculated based on variability of demand using standard safety stock calculations to achieve a prescribed service level %. These are the same as those calculated using the Safety Stock Journal function in AX Master Planning.
These results compare the replenishment of stocks over an 18 month period for the item on the previous screen, assuming a 3 month replenishment lead time and a safety stock factor of 1.65 (95% service level)

11. Now let’s compare the results
Forecasting
No Forecasting
Average Forecast Error %
35% -
Average Safety Stock
144
Average Stock Level
261
295
Service Level %
100.0%
98.5%
We can simulate the effect of running Master Planning with and without forecasts, and calculate the stock levels that result. These are the results.
For the sake of this exercise we have assumed a fixed 3 month lead time and exactly the same safety stocks in both situations.
Safety stocks have been dynamically calculated based on variability of demand using standard safety stock calculations to achieve a prescribed service level %. These are the same as those calculated using the Safety Stock Journal function in AX Master Planning.

12. Now let’s compare the results
Forecasting
No Forecasting
Average Forecast Error %
35% -
Average Safety Stock
144
Average Stock Level
261
295
Service Level %
100.0%
98.5%
You can also lower safety stocks by using forecasting. Up until now we have calculated safety stocks based on the variation in demand history, which is how the AX Safety Stock Journal calculates them.
However, safety stocks can instead be calculated based on historical forecast error percentages. Here are the results of doing so.
You see a further reduction in stock levels, with no adverse effect on the 100% service level we have achieved.
If Safety Stocks are calculated based on forecast accuracy, not demand variability, even greater savings can be achieved. In this case a 17% reduction in average stock levels are produced, while still achieving a 100% service level

13. Major Points Safety stocks should be calculated, not guessed
It’s easy to inflate safety stocks to achieve service levels, but there’s a cost
Lead times are a major factor
Reducing lead times reduces risk of stock-outs (and reliance on forecast accuracy)
Forecasting can be simple
Your existing customers have the data already
It can be implemented quickly & easily

14. The Forecasting Cycle Generate demand history Generate base forecast
Sales orders or sales invoices
Allocations are used to distribute final forecasts over additional item dimensions (if required)
Generate demand history
Generate base forecast
Copy to final forecast
Generate forecast allocations
Review forecasts
Project item trans-
actions
Demand history is consolidated by SKU, customer groups & forecast periods. A SKU contains all item dimensions from demand transactions
Forecasts only contain the item dimensions specified in each item’s Input Dimension Group
Inventory BOM Jnl lines, Production BOM lines
Final forecasts may contain additional item dimensions
(depends on each item’s coverage
requirements)

15. Key features
Forecasts can be calculated automatically or entered manually
Forecasts can be changed by quantity and/or value and at multiple levels
Graphical view for comparison of forecasts and demand history
Adjust forecasts either online, or offline using Excel
Review forecasts within companies, or summarized over multiple companies
Built within Dynamics AX – no interfacing required!

16. Demand Forecasting R6.0
Flexible selection of transactions when generating demand history
Multi-level forecasting review
Forecasts can now be reviewed/changed by value
Regenerate forecasts for selected item groups only
Generate forecasts from any date in the past
Weekly periods can now start on the same weekday
User-defined number of periods for calculating average sales price
Improvements to Item Supersessions
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

17. Item Supersessions
Demand history for a new item can now be built before the Supersession End Date, and forecasts can be delayed to start at a later date
Start Date
End Date
Forecast Start
Old item demand
Demand copy
New item demand
New item forecast

18. Demand Forecasting R6.0
Forecast Model Copy (easy snapshotting of forecasts)
New Forecast Allocations replace use of Item Allocations Keys
Allocations corrected for different units of measure
Budget periods fixed (based on budget copy date)
Average sales price now calculated using only demand history records with the same item dimensions as the forecasts
Forecast Groups no longer attached directly to customers (removal of mods to standard AX tables)
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

19. Items are grouped for ease of maintenance
Customers are grouped to consolidate demand and improve forecast accuracy
Forecast at any dimension level

20. Forecast Item Groups
Forecasts can be calculated by item or any level of item grouping
User-defined item groups consolidate data
For forecasting
For viewing
Adjustments may be made at group level

21. Forecasting and Item Dimensions
The level at which forecasts are automatically calculated or manually entered
The Item Dimension ‘levels’ at which forecasts are
generated are configurable.
Here is an example:
Sales Invoices
Demand Forecast Calculation
Final Demand Forecast
Item 
Site
Warehouse
Configuration
Colour
Size
Style
The level at which forecasts are required by Master Planning
In this case, the final forecast is pro-rated from the calculated forecast based on demand history ratios

22. Collaborative Forecasting
Forecast Sales Groups specify who manages forecasts for each customer
Forecasts can be adjusted online, or offline using Excel

23. Abnormal Demand Ability to flag abnormal demand
For example an abnormally large order is received from a customer to stock stores for a sale
You want to adjust the demand history to prevent future forecasts being inflated
Manual adjustments are always held separately from actual demand history

24. Promotions
Ability to enter the likely impact of promotions for forward planning
A promotion may drive up sales in a period, but reduce sales in a subsequent period

25. Item Supersessions New product Old product
Automatically copy history from a similar or superseded item
Old product
Manually determine when the run-out should occur
Disable item from being forecast
Copy history to a new item

26. Forecasting Formulae Same as last year’s demand +/- n%
n-month moving average
n-month moving average with trend
Average of same months last 2 years
‘Best fit’ formula selection using advanced forecasting algorithms

27. Advanced Forecasting Formulae
Simple Moving Averages
Discrete Data Models
Croston’s Intermittent Demand Model
9 Exponential Smoothing Models
Univariate Box-Jenkins Model
Event Models
4 Curve Fitting Models
Dynamic Multiple Regression

28. Other Features Forecast periods Forecast accuracy
Forecasts are calculated by week, month or user-definable periods
Monthly forecasts can be automatically split into weekly forecasts (or even further if required) using period keys
Forecast accuracy
Multiple forecast models enable a forecast to be frozen and compared to actual demand over the forecast period
Fully multi-lingual capable
English language labels are shipped with the product
Other language labels to be developed by resellers

29. Demand Forecasting Roadmap
Additional item group
Multi-level forecast generation
Automatic detection of demand history ‘outliers’
Improved forecast exception tracking
Safety stock calculations
User-definable forecasting formulas
Extensions to notes to allow for changes at multiple levels
Audit recording of changes and who made them
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

30. Demand Forecasting R6.1
Ability to select individual Forecast Pro formulas
Automatic detection of demand history ‘outliers’
Improved forecast exception tracking
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

31. Demand Forecasting R4.3 for AX2009
New functionality
Generate forecasts for selected item groups only
Generate forecasts from any date in the past or future
Demand history and forecast clean up to optionally remove all forecasts (incl. those in Final Forecast model)
Other functionality (same as R5.1)
Increase the maximum number of periods of demand history used to generate forecasts to 5 years (monthly) and 104 weeks (weekly)
Auto-alignment of top & bottom grids on forecast screens
Performance improvements
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

32. Demand Forecasting R4.3 for AX2009
Major bugs fixed
Remove Demand History with Trend formula
Forecasts incorrectly calculated on last day of month
Demand history generation error when no warehouse on sales line
Budget starting period locked
Apply period key error when forecast periods not set up properly
Description of each point:
The module currently displays item forecasts by Forecast Customer Group and, optionally, Forecast Sales Group. We will be providing a summary enquiry screen showing item forecasts totalled over these groups for information purposes only initially.
Demand history adjustments must currently be entered manually – this will provide a detection of abnormal demand history, or ‘Outliers’, together with recommended adjustments that can be easily confirmed or rejected.
Currently only the latest forecast figures are displayed even if they have been altered multiple times by different people. With collaborative forecasting especially, this can be a problem where, for example, a figure altered by a sales rep is overridden by a manager. This will provided visibility of all adjustments made to forecasts and who made them.
We currently provide access to the advanced formulae used by the Forecast Pro engine only under the ‘Optimised’ formula option. The formulae options will be extended to enable selection of individual formulae.
There are industry-standard formulae for calculating safety stocks based on demand variability, forecast accuracy and lead time variability. We have been requested to include such calculations in the module. The AX Safety Stock Journal technically provides this but there are problems with it and it doesn’t include calculation based on forecast accuracy.
OLAP extensions are inevitable and will include MAPE calculations based on selected Forecast Models (currently only available for the current model).

33. Comparison with R3 Forecasting
Architecture: R3
Built on SSAS
Forecasts re-imported into AX
Single set of forecasts available for comparison with demand
Excel pivot table presentation (online only)
Adaptable
Developed within AX
SSAS used only for reporting
Multiple sets of forecasts available for comparison with demand
Excel used for offline maintenance only

34. Comparison with R3 Forecasting
Forecast Generation: R3
Forecasts generated by item or SKU & customer or group of customers (selected at run time)
Forecasts generated from raw transactions
Item Allocation Keys used to split forecasts (manually set up)
Adaptable
Forecasts generated at any level, controlled at system level, item group, or by item
Forecasts generated from consolidated demand history
Item Allocation Keys used to split forecasts (auto calculated or manually set up)

35. Comparison with R3 Forecasting
Forecasting Algorithms: R3
Two forecasting algorithms provided
Algorithms may be controlled by manually adjusting parameters globally or by group of items
Adaptable
Four simple algorithms plus Optimised option (system determines best algorithm & parameters)
Algorithms selectable globally, by item group or by item

36. Comparison with R3 Forecasting
Additional functionality: R3
MAPE Forecast Accuracy calculation (single forecast)
Additional demand sources (Kanban issues, transfers)
Adaptable
MAPE Forecast Accuracy calculation (multiple forecasts)
Item supersessions
Promotions
Budget generation