Procurement
Analytics
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.