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.Read Article
For many manufacturing organisations, Detailed Scheduling is mainly driven by the need to perform manufacturing by following a specific preferred sequence whilst generating an achievable finite plan. It is quite normal for companies to use Detailed Scheduling heuristics for this function, but there are scenarios where this does not quite fit the bill.Read Article
I have recently worked on an SAP Integrated Business Planning (IBP) project focusing on implementing an efficient Demand and Supply Planning process (with supporting S&OP layer), where there were many transactional interface requirements between SAP ECC and IBP. In this blog I will explain how powerful the SAP Cloud Platform Integration for Data Services (CPI-DS) tool is in integrating master data and transaction data from SAP ECC to SAP IBP and in passing the planning results back from SAP IBP to SAP ECC.Read Article
During my career I have worked as both a Demand and Supply planner, working across FMCG, Fresh Produce and Cosmetics industries. When it comes to creating baseline forecasts for New Product Introduction (NPI) or phasing out forecasts for End of Life (EoL), planners find it challenging to collect relevant information from various stake holders and spend more time crunching data sets than creating meaningful forecasts.
This blog will discuss the features of SAP's Integrated Business Planning (IBP) Product lifecycle application within the Demand module and how it will help planners to overcome these challenges and help create a more accurate base line forecast. Enjoy the read!Read Article
High-tech discrete manufacturers are starting to feel a very real and uncomfortable truth; the world has changed and the majority of supply chains simply have not innovated quickly enough to succeed effectively.
In an industry where the need for high levels of product customisation meets an ever-increasing demand for faster time to market, many discrete manufacturing supply chains are starting to burst at the seams.
- Consumers are expecting regular and rapid innovation at a pace never seen before. It is no coincidence that one of the most commonly used phrases in supply chain at the moment is ‘innovate or die’. Most supply chains have not developed at the same pace of innovation.
In any organisation, it is vital to maintain the demand and supply equilibrium. This objective drives the planning community to work on producing the right product mix and keeping optimal inventory levels whilst maintaining customer service levels - all of course within financial boundaries!
Demand Planners are expected to forecast more Product/Location combinations, but it would be unrealistic to expect equal focus and importance to be placed on every item. We therefore need a tool which helps focus their attention and prioritise their work load to add real value to the forecast. Standard Supply Chain practice centres around ABC-XYZ analysis, the ABC part of which is based on the Pareto principle, i.e. a limited number of tasks produce a significant overall effect (explained further later).
The ABC dimension is usually based on value (margin or revenue) or volume of the sales, whereas the XYZ dimension looks at the forecastability of the demand, i.e. how hard is it to predict future sales from the variability previous sales? The use of segmentation allows for different forecasting procedures to be applied to different segments of ABC-XYZ, moving away from the ‘one size fits all’ mentality to forecasting. This can drive better forecast accuracy once the optimal forecast profiles have been assigned based on the segmentation.
In this blog I am going to explain how ABC-XYZ functionality in the Demand Planning (DP) module of SAP's Advanced Planning and Optimisation (APO) application would help leverage this.Read Article
Forecast consumption is a core to most Planning systems, and is based on the assumption that the actual sales orders consume the forecasted quantities. This approach ensures that incoming sales orders are not treated as additional demand to the forecast during a planning period.
It has always been somewhat of a surprise that forecast consumption has previously not been part of SAP Integrated Business Planning (IBP) for supply as standard, and has only been introduced (time series based) as part of the recent 1805 release.
Prior to 1805, only a very simplistic forecast consumption method could be configured using key figure calculations within the week time bucket. In this blog I will explain the functionalities available within SAP IBP.Read Article
In today’s fast moving world, there is an ever increasing demand for customisation and personalisation, whether it be initials stitched on to your running shoes or a bespoke paint colour for your car. Everyone likes to have a personal touch in their day to day activities, so why should SAP Integrated Business Planning (IBP) be any different?
If you haven’t seen the SAP IBP Excel Add-In, or want to know more about the various functionalities offered, please take a read of this blog from Steve Rampton “SAP IBP Excel Views – A Planners dream?”. I will split this blog as to how Planners can create their own personalised views within SAP IBP in two;
- Basic formatting of the Excel Add-In
- The advanced Olivehorse way!
In our experience, most customers have planning data held on spreadsheets, with e.g. ProdLoc or ProdCust by row and a time dimension in each column, similar to the view below;
But! In order to send the above forecast data into SAP Integrated Business (IBP) via Cloud Platform Integration – Data Services (CPI-DS), there can only be a single row per time period, similar to the view below (note that the data is loaded in to a staging table prior to committing to the HANA database);
Obviously such manual reformatting of data every time a load is required for SAP IBP is a waste of planner time and effort.
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.Read Article