This blog is the second part of my blog series on Managing an SAP IBP implementation and Rollout. Please refer to the first part of the blog here.

Challenges pertaining to the goals of the SAP IBP Implementation and Rollout projects:

“Change is hard because people overestimate the value of what they have and underestimate the value of what they may gain by giving that up.” – James Belasco and Ralph Stayer

I would like to share my learnings on important factors to consider for a successful SAP IBP implementation and rollout that I have unearthed.

Change is a 2-way process in an organization. The classic top-down approach for change is the highest risk to success, as the impact of the changes require willingness and participation from the user-community in the organization doing the groundwork. These challenges could be addressed by a simple training gap or introducing the new tools/processes or bringing a change in the organization and process structure or a combination of all.

Dear Network,

We hope everyone is well?

None of us would have imagined 2020 to be the year of a worldwide pandemic with Covid-19 (Coronavirus). There is still uncertainty about what will happen next. These are indeed unprecedented times and a time that will not be forgotten in our lifetime or for that matter, in history. We extend our great respect and heartfelt thanks to Doctors, Nurses, Healthcare staff, Ambulance drivers, Supermarket workers, Cleaners, organisations and everyone involved in protecting and saving our lives whilst keeping society going.

Cloud based Advanced Planning technologies such as SAP IBP require data integration both into and out of ERP platforms, BI Systems and other 3^rd party systems. 

Data integration covers both Master Data and Transactional Data. Certain data sets such as BOM’s and Routings have more challenges due to the complexity of the underlying data.

Forecast Accuracy defines how accurate the forecast works against the actual sales and is usually defined in percentage terms as;

Forecast Accuracy = 1 – Forecast Error

Forecast Error determines the deviation between the forecast and the actual demand/sales.

As Evan Esar’s saying goes "An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today".

So, to be able to react quickly, it is very important to understand why this deviation has occurred. As there are various Error Measures available in software tools such as SAP APO or SAP IBP, it is vital to understand which error measure is applicable under what circumstances.

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.