AI-Powered Innovations are Transforming Supplier Management
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AI-Powered Innovations are Transforming Supplier Management

Blog by Paul Winterstein

AI In the Digital Supply Chain

Companies are endlessly seeking ways to improve the quality, reliability, and efficiency of their supply chain, and the savvy application of Artificial Intelligence (AI) represents opportunities for dramatic improvements. However, it’s hard to escape the impression that many supply chain professionals are at the very beginning of their understanding of AI, though it’s encouraging to see some industry thought leaders helping them on that journey. At the same time, AI has already been put to use in many ways. Demand planners have made use of data science and Machine Learning, a branch of AI, to optimize their forecasts since the early 2000s. In the warehouse, AI is being applied to material handling, which reduces operator errors and processing times, leading to increases in overall efficiency and productivity. And on the factory floor AI is used for the predictive, preemptive servicing of production machines based upon their time in service and length of product runs. These have been truly transformative developments. But what about the strategic sourcing, procurement, and supplier management functions? These areas have received far less attention.

AI In Procurement

The transformation to digitally informed decision making is challenging in business functions like these where the historical pace of innovation has been low, and firms largely rely on the experience and expertise of employees. Strategic sourcing and supplier management aren’t immune to this reality. According to Deloitte and their annual CPO survey, even though more Chief Procurement Officers are now pursuing analytics, AI is getting little attention.

What are CPOs focusing on and how can AI assist? According to Deloitte, it’s no surprise that controlling costs and spend reduction are consistent top priorities. One key 2019 result is that “External risks are gaining the attention of CPOs, even more so than continually pressing internal risks, like digital transformation.” This is driven by fear of global economic downturn and trade wars, matters outside their control, and many, according to Deloitte, feel unprepared to deal with these risks. So, when it comes to AI, CPOs need support for better decision making to drive savings and increase worker productivity so that they can spend more time on value-added activities.


“Most current supply chain risk management practices are reactive. The leaders will leverage digital technologies to be proactive and resolve problems before they become disruptive.”

James Wigfall VP, Business Support & Supplier Management Boeing (retired)

Machine Learning is a Powerful Ally for Procurement

Natural Language Processing (NLP), also known as “text analytics” and Machine Learning can crack open the insights locked away in internal documents and from public data on the web. Traditionally known as “unstructured” data, text has long been the frontier least tapped for analytics even though its high potential has long been recognized. In text there are massive amounts of insights available, but procurement professionals and supplier managers need the tools to sift through this vast trove and direct their attention to the matters of most importance.

Let’s look at two use cases in supplier management that highlight the business value of analyzing text using NLP and Machine Learning.

Case Study: Supplier Risk

The number of public web sites with information about suppliers is so vast that it’s very difficult for a supplier manager to stay atop of everything. But at the same time, this scale makes these web sites an ideal target for Machine Learning. To modern Machine Learning platforms, the internet looks like a collection of text documents, and NLP can process this text as easily as it can process internal documents like contracts, and extract from it events that are predictors of supplier risk. Machine Learning teaches the software all the ways, for example, that a civil lawsuit against a supplier could be written about and assures that “false positives” are filtered out. By continuously evaluating public news and data, this AI can quickly find the events that significantly change a supplier’s risk profile, including data breaches, negative news about executives, layoffs, consumer complaints, and 100’s of others, and recommend to the supplier manager follow-up actions for those events that require a response.

The supplier manager’s action can then be fed back to the system to help continuously improve its detection and recommendation accuracy. This intelligent automation is right in line with the CPO’s goals of managing external risks and for supplier managers to spend more time on strategic activities, while progressing on their digital transformation goals without the usual disruption.

Case Study: Procurement Contracts

Even when there is a centralized purchasing organization, the core language for procurement contracts is often written by the line of business where long-term relationships with suppliers can influence the negotiations, often resulting in non-conforming and overpriced terms. NLP and Machine Learning can evaluate the language of contracts and compare it with a company’s best practices to assure they meet requirements and use preferred language to meet legal and corporate Pg. 4 objectives. For example, master service agreement documents contain specific language for Intellectual Property (IP), confidentiality, and compliance with laws, whereas Service Level Agreement language and payment terms reside in a Statement of Work or service contract. An NLP application can be trained to detect critical text and information expected in these documents, recognize missing information and score sections based on their completeness. With a clear set of contract rules and objectives for supplier managers and suppliers, the process of completing a contract is greatly expedited by reducing the iterative cycles of making edits between the parties prior to signature, saving time and effort for both sides. Analytics produced by the application can demonstrate how well the system is meeting corporate objectives and the overall business value of the system.

Summary

Many supply chain management functions have been improving using AI and Machine Learning, but strategic sourcing, procurement, and supplier management have seen less innovation than these areas. CPOs are focused on reducing spend, and more are making analytics a priority, yet many still feel unprepared for AI and the disruption of digital transformation. At the same, the massive amount of information about suppliers available on the internet that could help them manage their external risks goes largely untapped. Now, applications of NLP and Machine Learning to unstructured data are delivering results that help manage supplier risk and procurement contracts and give the sourcing and procurement functions a taste of the AI-powered innovations that are transforming other parts of the supply chain.



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