How digitization enables savings using a negotiation bot

Digitization is a huge lever for reducing costs!

Many companies have problems presenting real digitization as a cost-saving measure. What do we mean by true digitization? We don't understand the mapping of manual processes in a software here, but the redesign of processes based on data.

But why is it so difficult to present real digitization as a cost-cutting tool? There are usually several reasons for this:

Companies must first improve data quality, since machine learning is not possible based on the data available today
Data analysts are not available or not available to a sufficient extent to be able to draw conclusions about the necessary data from possible or necessary recommendations of the software
The digitization of know-how is not possible because companies do not manage to convert existing know-how into an algorithm that, based on the data, enables digitization on the one hand and machine learning on the other
In addition, the costs of the initial investment for personnel, data analysis and the development of a digital infrastructure are not only high, but often also not calculable, which is why a more detailed ROI calculation in advance is not so easy

When redesigning processes based on data, the assumption is that the data provides additional insights and that better decisions can be made based on the data.

A classic example where this methodology was successfully implemented a few years ago is Google Maps. Who can still imagine using a navigation device that does not use real-time data and uses this data for precise route recommendations?

And now you have to imagine that you can have a negotiation bot that shows you the way for the most efficient negotiation and if you accept the proposal, then implement it immediately.

This not only saves you a lot of energy, you also achieve your goal with the best possible result.

The previously mentioned reasons why digitization is so difficult are thus overridden. Since the algorithm has already been developed, there is no need to laboriously extract your own know-how and set up a database. On the contrary: in some cases, the algorithm is based on game theory principles and is therefore not only tried and tested in practice, but also scientifically verifiable. The existing data quality is also not important here, since the existing database from many thousands of negotiations is used.

Since there is no need to start a large IT project, you can start quickly and easily and gain initial experience immediately.

This results in several levels at which savings can be achieved:

Process change: In the area of the so-called tail-end spend, many companies follow the "3 bids and a buy" strategy, i.e. 3 offers are obtained and the cheapest one is commissioned without negotiation, as it is assumed that the (process) costs of the negotiation are higher than the additional savings. If there is now software that covers exactly this process without additional effort, every percentage point of savings is an additional hard saving.
Quality: Even if you negotiate, it is still not certain that the buyer will choose the best negotiation strategy. If you could ensure that every buyer, in every region, and no matter what level of experience he had, would always select the most efficient negotiation method, you could certainly achieve additional savings.
Adaptability: Now there are companies that operate in specific markets and it is therefore not so easy to select the most efficient strategy. This is where the "self-learning" algorithm comes into play, which adapts precisely to these special framework conditions and thus also changes its recommendations.
Reduction of process costs: Of course, such a tool can not only be used for better negotiations, i.e. an increase in the quality of negotiations, but simply to reduce process costs. Where strategies used to be laboriously worked out and implemented semi-automatically with suppliers, this is now possible fully automatically. It should be noted that the sourcing tools available on the market today only support and partially automate the solicitation of offers, but cannot support the negotiation itself in the form of recommendations or implement it independently in any way.
Usability: This brings us to the last aspect of successful digitization: the optimization of the user interface to such an extent that end users can easily handle it without training. After all, what good is digitization if it is simply not implemented or processes are not successfully adapted?

The advantages described here can be achieved, for example, with software like mysupply.com or keelvar.com.