The lifecycle planning in APO demand planning can be used to model the various phases of product lifecycle. The like modeling helps to forecast new characteristic value combinations (CVCs) which do not have historical data. The phase-in/ phase-out profiles help to model lifecycle stages like launch, growth, maturity and discontinuation of a product. The lifecycle planning is fully integrated with product interchangeability functionality and can be leveraged in univariate, multiple linear regression and composite forecasting.


Functional Overview

To work with lifecycle planning, you need to specify the characteristics which are relevant for lifecycle planning. These characteristics are then used for selection purposes during planning. You can select up to six characteristics from your planning area. You can not only use lifecycle planning for new products launches but also can use the same for introduction of new locations or customers. Therefore, the product characteristic does not have to be one of the chosen characteristics.

The following restrictions apply for the selected characteristics:

  • A maximum of 6 characteristics can be used
  • Compound characteristics cannot be selected
  • CBF characteristics are not supported

Like Modeling is used to carry out forecasting for new CVCs which have no historical data. Like profiles are set up to define how the history of the existing characteristics is to be used for new combinations. Using realignment function also the history of existing CVCs can be used to forecast for new CVCs. However, this will copy the historical data to the new CVCs leading to increase in redundant data. On the other hand, with like modeling the historical data is only referenced not copied.

Phase-In/ Phase-Out Modeling help to model the various phases in a product lifecycle. For example, the demand for a product is different during various lifecycle phases; the demand generally has an upward trend during launch and growth phase while it has a declining trend from maturity to end of lifecycle. Using phase-in/phase-out profiles, you can define this demand trend during these phases over time.

Aggregated Lifecycle Planning helps you to forecast at aggregate level while the like profile and phase-in/ phase-out profiles are defined at the detail level. For example, you can forecast at the product family level while the like modeling and phase-in/ phase-out profiles are defined at the product level.


Process Flow Diagram

The diagram below shows the like modeling and phase-in/ phase-out profiles in Product Lifecycle Planning:

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Figure 1: Product Lifecycle Management



Design Consideration

To set up lifecycle planning in APO DP, we need to first define the basic settings.

Menu Path: Advanced Planning and Optimization > Demand Planning > Environment > Lifecycle Planning

Basic Settings:

Here you define the characteristics which are relevant for lifecycle planning in your planning area:


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Figure 2: Basic Settings- Lifecycle Planning


In the basic settings here, we have selected 3 characteristics (Item, Demand Channel and Sold-to) from our planning area for lifecycle planning.


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Figure 3: Define Basic Settings for Life

If you want to carry out the aggregated lifecycle planning using like modeling and phase-in/ phase-out profiles, the 2 checkboxes highlighted above must be selected.

Like Profile:

In this profile, you first define the characteristic of your planning area for which the profile is to be defined.


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Figure 4: Like Profile- Lifecycle Planning

In the profile, you can reference single or multiple products based on which the historical data for the new product will be derived. The figure below shows the like profile uses the 100% history values of product 610214637031.


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Figure 5: Define Like Profile

Action in the profile has 2 options Sum (S) and Average (A).

Sum (S): In this case, the historical value of the new product is derived by multiplying the weighting factors with history data for individual old products.

For example: If a new product 1 is to use the historical data of product 2, 3 and 4 as per the below like profile, then

The historical data for product 1 = (0.30 x product 2) + (0.20 x product 3) + (0.50 x product 4)


             Ref. Values

Action

a/b

Product2

S

30

Product3

S

20

Product4

S

50


Note: The sum of the weighting factors does not have to be 100%.

Average (A): This option means the average of historical data of all listed product with action A in like profile.

Phase-in/ Phase-out Profiles:


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Figure 6: Phase-in/ Phase-out Profiles- Lifecycle Planning

The phase-in profile below shows the period-wise factors to be applied to forecast of the product being introduced.

As per the profile the product should reach to its maturity by week 6/ 2016.


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Figure 7: Phase-in Profile

The phase-out profile below shows that the forecast of the existing product will reduce to zero by the week 6/2016.


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Figure 8: Phase-in Profile

Profile Assignment for Lifecycle:

Here you specify lifecycle profiles defined above to the characteristic values:


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Figure 9: Profile Assignments

In the above figure the existing product 610214637031 is assigned phase-out profile while the new product PROD2 has been assigned like profile and phase-in profile.

Setting in Forecast Profile: For being able to consider lifecycle planning, the lifecycle planning active indicator must be selected in the forecast profile.


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Figure 10: Lifecycle Setting in Forecast Profile


Demonstration


Scenario 1: Lifecycle Planning at Detail Level

Below are the CVCs for existing product 610214637031 and new product PROD2. Both the products have sold to customers as 6845 and 14994 for demand channel NATRTL.


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Figure 11: CVCs in the Planning object Structure (MPOS)

Historical data for the existing product 610214637031 in planning book:


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Figure 12: Historical data in Planning Book

The new product PROD2 does not have any history. The forecast was executed:

Forecast Results for new product PROD2:

Since in the profile assignment setting the like profile is assigned to sold to 6845 only, therefore the history for this sold to was referenced from existing product and forecast was executed at sold to 6845.

Based on the phase-in profile the forecast of this new product PROD2 started from week1/2016 and reached to 100% by week 6/2016.


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Figure 13: Forecast data in Planning Book for PROD2

Since the sold to 14994 was not maintained in the profile assignment, no history data could be referenced and used for forecasting for this sold to.

Forecasting logs:


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Figure 14: Forecast Logs

Forecast Results for existing product 610214637031:


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Figure 15: Forecast Data for Existing Product 610214637031

The product 610214637031 got phased-out as per the phase-out profile; by week 6/2016 it’s forecast reduced to zero. While for the other sold to 14994 the forecast remains constant as this sold to is not maintained in the profile assignments.

Now the sold to 14994 is also added to profile assignment as below:


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Figure 16: New Entry in Profile Assignment


Added “*” in sold to, this will take care of all sold to for product-channel combinations. Now, for sold to 14944 also the history can be referenced based on like profile and product can be phased-in.

Forecast executed again. Now the forecast also executed for sold to 14994 using the like and phase-in profiles as expected.



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Figure 17: Forecast Log for Product 610214637031, Sold to 14994



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Figure 18: Forecast Data for Product 610214637031, Sold to 14994

Scenario 2: Aggregated Lifecycle Planning

In aggregated lifecycle planning, the planning is performed at aggregated level while the like profile and phase-in/phase-out profiles are maintained at detail level.

In this scenario, we will forecast at Product family- Demand Channel- Sold to level.

The family LGFM03 consists of 2 products 610214635211 and PROD2. The product 610214635211 is an established product having historical data, while the PROD2 is a new product being introduced and has no history.


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Figure 19: CVCs in the MPOS- Family LGFM03

Historical data for product within family LGFM03:


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Figure 20: Historical Data in Planning Book

Forecast was executed at aggregated level:

Family level forecast processed the like profile and phase-in profiles maintained at product level for PROD2.


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Figure 21: Forecast Log- Aggregated Lifecycle Planning

Forecast Data in planning book at family and product level:


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Figure 22: Forecast Results in Planning Book- Family and Product Level

The planning at aggregated level (family level) here considered the like profile and phase-in profiles maintained at product level (PROD2 in this scenario).



Conclusion

The lifecycle planning in APO demand planning can model various phases of product lifecycle using phase-in/ phase-out functionality. The new characteristics can also be forecasted using like modeling which do not have any historical data on which the forecast can be based. This how to do guide will help readers to understand lifecycle planning functionality and will help them to configure and implement the same in APO.

Abbreviations / Acronyms

SAP

Systems, Applications, and Products in Data Processing

APO

Advanced Planning & Optimization

DP

Demand Planning

MLR

Multiple Linear Regression

CVC

Characteristic Value Combinations

MPOS

Master Planning Object Structure



References

https://help.sap.com/saphelp_scm70/helpdata/en/30/3bc95360267614e10000000a174cb4/content.htm?current_toc=/en/48/3ec95360267614e10000000a174cb4/plain.htm&show_children=true

SCM220- Demand Planning: mySAP Supply Chain Management




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