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With Procurement Analytics you can derive offensive
and defensive benefit from your procurement data.

Procurement Analytics

Your advantages



By ana­lyzing your procure­ment data and prices or price histories, new savings potential can be identi­fied. Like­wise, future nego­tia­tions can be better planned through a meaning­ful dove­tailing of nego­tia­tions with purcha­sing data. Learning effects from past negotiations are thus syste­ma­tized and made available.

In many cases, it makes sense for a model deve­loped on the basis of data to itself become the subject of a nego­tiation. If, instead of a single price, a price formula is nego­tiated that covers a wider range of possible servi­ces, this increases the effi­ciency of the nego­tiations. Such speci­fi­cations can be standardized across sites.

An inter­nal compa­ri­son of pro­cure­ment prices makes it pos­sible to exploit price diffe­ren­ces (price con­sis­tency ana­lysis). During due diligence, synergies can thus be identi­fied and quanti­fied. An ana­lysis of com­pe­ti­tive struc­tures can show whether there is price col­lusion or stra­te­gic supply reduction. This is done by evaluating supplier net­works in terms of their coherence and exclusivity. The insights gained in this way form the basis for targeted changes to supplier structures. These can be con­sidered in a com­petition enhancing way in contract, awarding, and negotiation design.

Cash flow optimization through Payment target negotiation


Projects aimed at impro­ving payment targets and thus the Days Payable Outstanding (DPO) ratio are unpo­pu­lar in purcha­sing: In addi­tion to the exis­ting savings targets, the pay­ment target is now also to be impro­ved. This is often per­ceived by buyers as weake­ning their barga­ining position.

Nego­tia­ting pay­ment targets is made much easier if a data­base is set up in advance that maps differences relevant to decision-making. This consider­ably reduces the effort and dur­ation of a DPO project.

Systematic use of data pays off. The better companies make deci­sions based on data, the higher their productivity. Our combined exper­tise in data science and game-theoretic negotiation opti­mi­za­tion enables us to develop the best possible solutions for our clients.

Identify cost drivers


Linear or non-linear perfor­mance pri­cing models can be used to iden­tify price drivers and deve­lop models for price fore­casting. 

On the one hand, this makes it possible to define nego­tiation targets. On the other hand, price formulae can be agreed in nego­tiations, thus covering a wide range of possible per­for­mances.

If a product is procured that has never been ordered before, the price can be calcu­lated accor­ding to the defined formula — this means a consi­derable reduc­tion in work­load. Once nego­tiating advan­tages have been achieved, they can thus be exten­ded to products that have not yet been procured. This applies in parti­cular to current contracts and offers oppor­tuni­ties to contain price increases in the event of changes.


Plan negotiations, track results


Linking purchasing data and negotiations can add significant value to future negotiations. Customers we have assisted in imple­men­ting a nego­tiation database have achieved lasting improve­ments in their results.

Interested? Contact us.

Get in touch.


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