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Hedging supply chain through an automated ecosystem

Hedging supply chain through an automated ecosystem


Companies that produce and sell physical products have to deal with extensive and complex sourcing and procurement cases, in order to reduce their production costs. Our partner envisioned a digital ecosystem, which blended consumer patterns and procurement prices, while feeding processed data to a smart data layer that refined sourcing processes, improved procurement, marketing, sales and R&D. 

The chain of manufacturing

Supply chain - a term used initially by the newspaper The Independent in 1905, a concept that has been around since the beginnings of industrialism. 

Innovation and automatisation are two words that this segment of the market always welcomes but in the end the process never changes - a company buys raws materials from someone, places someone or some machine converting these raw materials into a product, and in the end sells the product at a fixed price or the highest bidder. Even 300 years after, the process remains unchanged -> just more digitally transformed. Our partner, by having the same concept for his business, partnered up with us to create an ecosystem that makes the process more…hedged.The challenge was not only looking at all the data and figuring out a correlation between the different faucets, but to look at the all process and predict sourcing criteria changes and customer buying patterns.

Data faucets at the beginning of the pipeline - Raw materials

Vendors change the prices of raw materials quite often. Raw materials are subject to inflation prices and availability, and the all-global market exchange of commodities exchange makes the price vary quite often. And this is all data…stored in our partner Datawarehouse. Together, we created a smart data layer that looks at this data and produces a forecast with price fluctuations according to supplier habits and pricing patterns. 

Looking at production and lead-times

Converting raw materials into a product is an in-house process - but it does not mean it cannot be optimised. Lead production times, materials transformation processes, and employee workflow measuring data created data lakes that when combined and filtered, allowed to produce SOPs of workflows keeping the production times very similar and then reducing lead times.  

The end of the pipeline

“It’s no longer about interrupting, pitching, and closing. But it is about listening, diagnosing and prescribing.”- Mark Roberge

Customers are customers. B2C and B2B have huge differences and yet both are done by humans. Humans decide, whether to follow the corporative lines or to break them and do something outside the standards. B2C clients, on the other hand, decide by listening to their gut or desire. From the data perspective side - it’s all about evaluating the human gut. Collecting data from Customer acquisition patterns is already a step to have better overview of most the sold products and control of stock inventory or restocking, however analyzing human doubt and the need for information, gives insights into where to develop the next product. Nortb Consultants helped our partner to move forward in product development due to this fact. We set up a data harvester that would look for the doubts and concerns of prospects, and reshape the products and products selection according to those doubts - as well setting up stronger marketing campaigns based on that. In the end, our customers stop manufacturing products based on their gut or market studies around the product but produce products according to the customers' needs.

The cyclic ecosystem 

At this point, our partner had BI insights from the all supply chain - departing from the criteria of choosing suppliers and raw materials according to their pricing, quality, and characteristics, to the point of analyzing the end user, his shopping behaviors, preferences, and patterns and his concerns and doubts when searching for information regarding the product. There was one final step to take > to enclose the circle and create a learning ecosystem. 

The final question was - how to make the smart data layer / AI understand the End customer preferences so that these affect the definition of criteria to choose suppliers and raw materials. The answer was R&D. Understanding the customer view of the current products, the flaws, and what could be improved, created a sub-Data-layer that would organise new sourcing criteria set. This would then be a major influencer over the main layer and thus complete the circle.

The marketing perspective 

There was another way coming out of this Data layer. - Marketing

Marketing is the sweet art of modeling value to become a vision. Not a product, not a service, not a perspective, but a vision! And we had already the worries of the customers.

By taking the scraped data of the customers' concerns and critics, and redirecting it into marketing campaigns, our partner did not break the ecosystem but made it thicker instead. 

It’s more complex than it seems

Ecosystems, data layers, data faucets, data warehouses, and Business Intelligence - terms that are used a lot on marketing campaigns and lose their value every single day. Although the processes described before seem trivial, our partner spend three months with our consultants specialized in engineering and chemistry to set up the whole ecosystem. 

The project had a tremendous outcome thanks to the hard effort of our partner and our team. 



Our partner envisioned an ecosystem where a smart data layer can extract customer buying preferences and use them to optimise sourcing and procurement processes in the organisation. 


The ecosystem made the data chain produce not only results to procurement and sourcing, but to the marketing and R&D department as well. This because the smart Data-layer was made of an AI that learned the customers preferences and created insights about the products development and possible campaigns marketing was not exploring.


· 21.42% more sales. 

· 1.8% Faster product development.

. 42% cost reduction with supply chain and procurement.

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