Recently a customer who presented a case study at the SAP Insider, made some specific observations about their experience about Classification process. Here are my 2 cents on what those bullet point means for others –
- You will learn significantly more about areas of opportunity in your data as you analyze and review DSE classified spend
- So that’s the output of the classified transaction. Once the data is loaded – one needs to continuously review, revise and align with the business process.
- As you continue looking at various reports – analyze it and draw conclusions – you may come up with more and more areas of potential savings opportunities – be it vendor optimization, material optimization or just your internal process optimization.
- Be ready with a good taxonomy established by your Procurement organization for managing all commodities
- A good taxonomy – a hierarchical representation of all your classification codes and descriptions will certainly have direct impact on quality of classification, the results that you want to get out of your reports.
- If you make your taxonomy too granular at nuts & bolts details, it will look good, but may not be useful when you are analyzing your saving opportunity.
- If you make it too high level – it wont help because you can’t actually find many saving opportunities – and would be simply amazed by big numbers.
- If you get into a completely new taxonomy structure – probably your business users will not like the change and it will confuse the plan.
- A industry standard taxonomy like UNSPSC might be a good idea.
- In my view a blended approach where a existing taxonomy structure combined with industry standard taxonomy – is a good one. A sap partner subject matter expert should be able to help you to get to this route.
- Classified spend reviewed by commodity and regional breakdowns
- How you view the data – there are multiple ways to do that. In this case a commodity structure and regional breaks downs were important criteria. Since these systems are global – multi-platform and multilingual – geography is a good criteria.
- Be ready to check and adjust your initial data enrichment plan at each checkpoint/pit stop
- Very Important step indeed. Checkpoints. Many time customer would interpret data classification as a machine where you put the data from one side and get it classified at other. THAT’S NOT THE CASE.
- You need to have “multiple” checkpoints. These are mainly meetings between the spend analysts and business stakeholders. We call it pit stop – as your process still continues but you have a checkpoint to make sure you are on track.
- You discuss out data issues, understand rules applicable for classifying it and whether it aligns with customer’s business process. This is important as you don’t want to get to Go Live and say – “that might be correct for industry – but not for me”. So check upfront and many times.
Let me know what you think. Welcome to post comments here or tweet @pmendki
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