Using ABC-XYZ analysis to optimise your forecast in SAP APO

Posted by Ali Malik on 22-Aug-2018 15:07:19

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.

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Topics: SAP APO, Demand Planning, Forecast Optimisation, ABC/XYZ

Are you adding value to your forecast?

Posted by Jacky Jain on 06-Nov-2017 15:59:54

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 thru 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.

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Topics: SAP IBP, Integrated Business Planning, Demand Planning, IBP for demand, Forecast Value Add, FVA

Statistical Forecast Errors

Posted by Suresh Sellaiah on 14-Sep-2017 08:07:28

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.

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Topics: SAP APO, Statistical Forecasing, Demand Planning, Forecast Error