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Combining analyis and optimization - Datapred's buyometer

Posted by Datapred | Feb 1, 2022 1:42:32 PM

The buyometer is an innovative new widget on Datapred's dashboard, displaying the "buying temperature" of the day (and the recent past) for your raw material or energy: Strong Buy, Buy, Wait or Strong Wait.

 

Why the buyometer?

 

The big difference between industrial buyers of raw materials and energy on the one hand, and raw material and energy traders on the other hand, is that industrial buyers must take complex operational factors into account.

For them, understanding market dynamics is not enough. To buy well, they must also juggle with supply chain constraints, production requirements, hedging rules, sustainability targets...

Datapred's buyometer contributes to the situational awareness of buyers by showing how two basic decision parameters affect their "buying temperature", regardless of market developments.

 

The buyometer

 

It is thus a neat introduction to the power of "continuous intelligence" - the real-time combination of external data, internal data, predictive models and optimization models, to generate user-specific, directly actionable recommendations.

 

How does it work?

 

The buyometer mixes the market analyses and predictions that Datapred provides (price volatility analysis, price trend predictions, price corridors, price drivers) with the optimization of two adjustable decision parameters: the maximum price increase you can tolerate, and the time you can wait before making a decision.

Let's take the example of natural gas (EEX's PEG DA spot price).

If you can only tolerate a small price increase and wait one week at most before buying, your current buying temperature is Strong Buy (first screenshot on the left).

 

Buyometer example

 

But if your tolerance for a price increase is higher and you can wait three weeks before buying, then your buying temperature is Wait (second screenshot on the right).

Interesting 💰

 

Advanced optimization

 

The buyometer uses the optimization engine of Datapred's digital twin (described in more technical details in this datasheet):

 

Digital twin-1

 

For industrial buyers of raw materials and energy, that optimization engine can handle a great variety of decision parameters - not just the buyometer's two simple factors.

Examples from recent Datapred implementations include: inventory capacity, production schedule, raw material mix, delivery delays, hedging policy, demand predictions, sustainability index...

We find that optimized buying strategies regularly shave 3-5% off our clients' raw material and energy spend, year over year.

Don't hesitate to contact us to discuss how the Datapred digital twin could help with you buying and hedging challenges.

You can also visit this page for links and resources on digital procurement.

 

Bandeau guide

Written by Datapred

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