‘should-cost’ modeling and AI to unlock value

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Businesses need smart and resilient procurement strategies, which requires a strategic approach to procurement. To make the challenge even greater, they must meet these requirements while continuing to control costs.

This development is nothing new. Procurement has always been under tremendous pressure to balance costs and drive innovation. Continued demand for better supplies has only been exacerbated by the Covid-19 pandemic, the economic uncertainty of recent years and other disruptions.

The way forward will not be easy or straight forward. Not only do trade-offs need to be made between some of these objectives, but procurement teams will also have to reinvent procurement processes.

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

Why cost modeling is important

You don’t want a vendor walking into a meeting with more data than you. Automated cost modeling puts companies in the best possible position to perform analytics in real time and automatically update the expected cost buildup as the underlying data changes.

Cost-based modeling provides an element of accuracy that instills more confidence when negotiating with a supplier. “You don’t have to guess the impact of market developments on their prices,” said Samir Patel, VP of consulting and head of chemical industry practice at GEP† “You know the impact of those developments. That is strategic for me.”

To extract even more value from cost-based modeling, enterprises are leveraging artificial intelligence and machine learning technologies to model qualitative characteristics such as geopolitical concerns or supply chain risks.

Should-cost modeling is a powerful technique in its own right, Patel argues. “By automating it and then adding artificial intelligence and machine learning capabilities, it becomes something that is really strategically transformative.”

Companies can perform cost modeling at speed and scale, notes Patel. “They can charge a lot more from a lot more real-time data sources.”

The power of AI for better sourcing

When combined with cost modeling, AI is changing the procurement paradigm, said Saratendu Sethi, VP for AI and data science strategy at procurement software and consultancy GEP. AI and machine learning are powerfully applicable 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 procurement, from forecasting to natural language processes, but it’s with the perennial challenges of spend classification and supplier standardization that it really does the heavy lifting,” he says.

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

Headquartered in Clark, New Jersey, GEP has offices and operations centers in Europe, Asia, Africa, and the Americas. For more information, visit www.gep.com

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