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Sourcing in the New Normal: How ‘should-cost’ modeling and AI can help unlock value

06/30/2022 By

Enterprises need smart and resilient sourcing strategies, which requires a strategic approach to procurement. To add to the challenge, they must meet these demands while continuing to manage costs.

This development is nothing new. Procurement has always faced immense pressure to balance cost and drive innovation. The ongoing demand for better sourcing was only heightened by the Covid-19 pandemic, the economic uncertainty of the past few years, and other disruptions.

The path forward won’t be easy or straightforward. Not only are there trade-offs to be made between some of these objectives, but procurement teams will also need to reimagine sourcing processes.

How can procurement leaders add value? They must embrace the tools that can help them manage the modern sourcing agenda. We’ll look at two of the more dynamic sourcing technologies — ‘should-cost’ modeling and AI — that are essential to navigating the new normal.

Why should-cost modeling matters

You don’t want a supplier walking into a meeting with more data than you. Automated should-cost modeling puts enterprises in the best possible position to perform analyses in real-time and automatically update should-cost buildups as the underlying data changes.

Should-cost modeling offers an element of accuracy that instills greater confidence when negotiating with a supplier. “You’re not having to guess the impact of market developments on their prices,” says Samir Patel, VP for consulting and head of the chemical industry practice at GEP. “You know the impact of those developments. To me, that’s strategic.”

To get even more value from should-cost modeling, enterprises are leveraging artificial intelligence and machine learning technologies to include qualitative attributes such as geopolitical issues or supply chain risks in the model.

Should-cost modeling is a powerful technique in its own right, Patel argues. “Automating it, then adding artificial intelligence and machine-learning capabilities turns it into something that is genuinely strategically transformative.”

Businesses can carry out should-cost modeling at speed and scale, Patel notes. “They can factor in many more costs from many more real-time data sources.”

The Power of AI for better sourcing

When coupled with should-cost modeling, AI changes the sourcing paradigm, says Saratendu Sethi, VP for AI and data science strategy at procurement software and consulting firm GEP. AI and machine learning have powerful applicability at almost every stage of the procurement lifecycle — including spend analysis, cost modeling, sourcing, contract management, supplier management, demand management and inventory optimization.

“AI can help with many aspects of strategic sourcing, from forecasting through to natural language process, but it’s with the perennial challenges of spend classification and supplier normalization that it really does the heavy lifting,” he says.

The holy grail of procurement analytics is to leverage AI and machine learning, combined with decision support systems, to automate the complete cycle, Sethi says. “GEP’s vision is to make procurement cognitive, where self-learning AI techniques help buyers and procurement teams to identify new opportunities and automate activities so as to make cost savings a continuous operation for our customers.”


Headquartered in Clark, New Jersey, GEP has offices and operations centers across Europe, Asia, Africa and the Americas. To learn more, visit