Introduction

Moving holidays like Ramadan, Easter & Chinese New Year can cause planners an increase in manual effort to get the demand right, in the right place, at the right time.

Traditional seasonal forecasting models handle the seasonal patterns for fixed holiday periods like Christmas & New Year, however no out-of-box forecasting models are available in any demand planning system for moving holidays.

This blog intends to deep dive into the functionality within SAP IBP for Demand module to determine what system features are available to handling this challenge.

For some time now, SAP have been clear with their intention to develop forecast automation via machine learning capability and functionality. With the 1811 release of SAP Integrated Business Planning (IBP), SAP are really starting on their machine learning journey by leveraging content within their Predictive Analytics Library (PAL) and have introduced Gradient Boosting of Decision Trees (GBDT) to the available set of statistical forecasting algorithms within the IBP for demand license.

It has been a while since I first published this Blog. In the 2+ years that have gone by, a lot of functionality has been delivered with each upgraded version of SAP Integrated Business Planning (IBP), especially with the Demand application. 

In the world of ‘Internet of Things’ and Digital Transformation, where companies want to respond faster to customer needs, we have all read about SAP’s vision to enable businesses to transform from what were previously Supply Chains into Demand Networks.

But where does SAP Integrated Business Planning (IBP) for Demand fit in to this?  And as a Planner, should you be Demanding IBP?

In order to remove some of the uncertainty and puzzlement as to what exactly IBP for Demand can offer, in this post I intend to explain some key differences between SAP Advanced Planning & Optimisation (APO) and SAP IBP for Demand.

In today’s fast moving world of growing customer expectations, shrinking lead times, reduced profit margins and inventories, Demand Planners have a big responsibility to act as the first gatekeeper to be able to foresee changes in Demand Signals and accurately make predictions for future. The margin for error is continuously decreasing, as any errors cause a major rippling effect on downstream processes. For example, a change of forecast bias by 5% may induce the following risks;

• Reduced customer service levels
• Increased lead times
• Improper mix of inventory levels leading to possibility of stock outs or over stocked DCs
• Added costs due to firefighting production builds and expediting fulfilment (air freight)

To cope up with this fast-paced supply chains, new metric must be adopted by demand planners all over the world. One of those is Forecast Value Add. I am going to take you through details of Forecast Value add and explain how we can use this to improve the forecasting process. I would also be explaining examples using SAP Integrated Business Planning.

There are many blogs on the topic of Agile, and there are many blog posts on the topic of SAP Integrated Business Planning (IBP). But not many blogs on the combined topic of Agile IBP Implementation! In this blog I intend to share my thoughts as to how a SAP IBP implementation is suited to an Agile approach. Really suited!

As SAP continue to enrich the functionality of Integrated Business Planning (IBP), in this blog I am going to describe the outlier detection and correction methods offered within IBP for demand (as at release 1608). I will not however be discussing which of these methods are the best, as from my experience I have found that the rules and methods vary depending on the organization’s perspectives and processes followed.

In this blog I will walk through the principles of ‘Attribute Transformations’ within SAP Integrated Business Planning (IBP) and discuss the basics of implementing such transformations.

We will also look at how some demand planning scenarios like displaying Year -1 data, Realignment (which is not currently offered as standard within SAP IBP), Distribution Centre closures and Product Substitutions can be achieved using Attribute Transformations.

For those of you exposed to the full force of SAP marketing for SAP Integrated Business Planning, I’m sure you will have heard the phrase ‘financialising the plan’. But have you ever stopped to think about why this is beneficial and what this means in practice when it comes to implementing it in SAP IBP?

It has become apparent to me during recent client conversations that the options available for implementing SAP Integrated Business Planning (IBP) for demand are unclear for many of our customers.  One of the underlying reasons for uncertainty in my opinion is however clear: SAP Advanced Planning and Optimisation (APO).  If you have already implemented APO, then your journey has probably been long and your system full of customisation, putting uncertainty as to what the timelines or benefits of actually implementing IBP for demand are.

In this blog I intend to highlight a number of different options on your journey to adopting IBP for demand.

In a previous blog (Should you be Demanding IBP?), I highlighted some of the high level differences between SAP APO Demand Planning and SAP Integrated Business Planning (IBP) for demand. 

With the impending release of IBP v6.3 due later this year, I intend to go under the bonnet of the IBP for demand statistical forecasting engine to see if all that glisters is really gold.