The Rise of AI in Supplier Management
Over the past two decades, supplier selection and risk management in retail and consumer packaged goods sectors have significantly matured. The focus has shifted from finding the lowest-cost supplier to prioritizing those that are not only cost-effective but also excel in customer satisfaction, scalability, and Environmental, Social and Governance (ESG) scorecards.
In addition, external factors such as the pandemic, geopolitical conflict and climate change have profoundly elevated the level of scrutiny that companies place on their suppliers and global supply chains. With billions of dollars on the line, better management of supplier risks is critical. However, comprehensive supplier risk management has remained out of reach for most organizations – an excellent idea but either too expensive or incomplete.
Enter Artificial Intelligence (AI): New technology is revolutionizing supplier selection and risk management, offering new, proactive ways to tackle the challenges of a 21st century supply chain.
A survey by Deloitte1 revealed that 70 percent of CPOs indicated an increase in procurement-related risks and supply chain disruptions. The same study identified AI-led supplier management as the foremost procurement strategy to unlock value. Moreover, a recent McKinsey survey2 on Generative AI (Gen AI) reported that respondents experienced revenue increases exceeding 5 percent in supply chain and inventory management.
AI across the Supplier Selection Lifecycle
AI is revolutionizing supplier selection and risk management across the lifecycle, from initial profiling to ongoing performance evaluation. Let's explore its practical applications and the realities organizations face today .
Market Analysis and Supplier Identification
According to McKinsey3, new technologies like AI can reduce the time needed to identify the right suppliers by 90 percent or more. AI algorithms can analyze heaps of data from various sources, including supplier performance data, market trends and risk indicators. By leveraging large language models, Machine Learning (ML) and predictive analytics, AI provides insights and recommendations for supplier selection and risk assessment much faster than traditional methods. This speed to value is critical in making timely and informed decisions.
Unilever4 utilizes AI to rapidly identify alternative supply sources. The technology scans websites to collect data on suppliers' financial stability, customer reviews, sustainability ratings, diversity metrics, intellectual property details like patents and design awards and customs records from the U.S. This comprehensive data gathering allows Unilever to generate a list of potential new suppliers swiftly, ensuring they can maintain supply chain continuity under various circumstances.
Supplier Evaluation and Qualification
AI can automate large parts of supplier evaluation and qualification by analyzing supplier profiles, compliance data and certifications, financial data and performance metrics. AI-based analytics platforms can integrate various information about suppliers and, crucially, keep track of changing circumstances to ensure risk evaluations remain continuous rather than happening at a single point in time. This helps companies identify reliable and high-quality suppliers more efficiently and respond nimbly to any changes in supplier risk profiles.
Koch Industries5 uses an AI-powered tool to optimize its supplier base by analyzing granular data at the Stock-keeping Unit (SKU) level, unlike traditional procurement methods focusing on high-level purchasing categories and aggregate spending. This tool ingests comprehensive datasets, including supplier information, purchase orders, invoices and previous unsuccessful quotes, to offer detailed insights into qualified suppliers and identify backup options within the existing supplier network, thereby minimizing the need for lengthy Request for Quotes (RFQs).
Supplier Evaluation and Qualification
Risk Assessment and Monitoring
One of AI's most significant impacts on supplier risk management is its ability to conduct continuous risk assessment and monitoring. AI can track supplier performance, market conditions and any external factors impacting the supply chain in real-time. Next-gen, AI-led risk monitoring platforms surpass traditional methods, incorporating recent incidents, operational intricacies, financial stability and cybersecurity considerations into a comprehensive evaluation of suppliers’ risk profiles.
By utilizing predictive analytics, AI identifies potential risks, such as geopolitical issues or natural disasters, and provides early warnings to companies. For example, a supplier's exposure to conflict zones like Ukraine or Russia can disrupt supply chains, as seen with commodity shortages. AI’s real-time monitoring and early warning systems help mitigate such risks effectively.
In another example, WNS collaborated with a leader in frozen foods, leveraging an AI-driven 360-degree and continuous risk intelligence platform to enhance supply chain management. This advanced technology streamlined the supplier network, identified potential risks and allowed the company to proactively adapt to market demands. The platform's comprehensive approach ensured a robust and responsive supply chain, capable of addressing various challenges while maintaining optimal performance.
Supply Chain Optimization
AI algorithms can optimize supply chain networks by considering factors like supplier capabilities, lead times, transportation costs and demand fluctuations. By simulating different scenarios and analyzing historical data, AI helps companies make better-informed decisions about supplier selection and inventory management. For instance, in 2021, Home Depot leased its container ship to navigate pandemic-induced shipping disruptions, ensuring timely product delivery for the holiday season. This type of strategic decision-making, supported by AI-driven scenario analysis, can significantly enhance supply chain resilience.
Home Depot7 is now leveraging cloud-based Google AI technologies, such as ML, computer vision and Gen AI, to enhance its inventory management operations and improve overall supply chain efficiency.
Making It Happen
While it seems that everyone in retail and CPG is talking about AI, implementing it effectively in procurement and the supply chain can be tricky. Successful AI-driven supplier selection and management relies on several key elements, which must be considered to harness the technology’s full potential :
01. Establish a Solid Data Foundation for AI
AI algorithms rely heavily on high-quality data to make precise predictions and decisions. Outdated or inconsistent data can lead to incorrect conclusions, undermining the effectiveness of AI. Integrating data, creating scalable data lakes and establishing strong data governance are essential to ensure that AI has the accurate and comprehensive data it needs. Mature organizations with dedicated Centers of Excellence and a focus on procurement innovation often have solid data in place. However, many small and medium-sized companies struggle due to the rapid pace of change and siloed operations.
02. Continually Invest in AI Training
Since AI systems rely solely on their training data and lack a true understanding of the tasks they perform, they are not infallible. The reliability of their outcomes can be compromised if the input data is biased, incomplete or outdated. So, investing in training AI models to improve their predictive capabilities is essential for enhancing the quality of insights, ensuring that the insights generated remain relevant and actionable. Additionally, training the workforce to effectively use AI tools is key.
03. Find the Right Solutions
Any retail or CPG company needs to decide how best to acquire next-generation functionality – and the answer will be a complex variant of the classic ‘make or buy’ question. The first port of call is usually the existing estate – and most leading supplier management platforms are bringing at least some AI into their platforms. At the same time, there is a massive and growing range of ProcureTech and related point solutions, for example, that provide AI-powered vendor scanning, risk management, contract analysis and so on. Finally, there is scope to use custom-developed functionality, drawing on emerging team specialisms such as ML, Gen AI and sentiment analysis. The right sources for AI functionality will depend on the organization’s starting position in terms of legacy and recent investments, as well as its goals and means for tapping into newer technologies.
04. Integrate with Human Expertise
With the hype around the ever-increasing capabilities of AI and ML, we shouldn’t forget that AI processes cannot operate independently without human oversight. Specialists must validate and provide actionable insights derived from AI analyses. For example, human sign-off is crucial for risk assessments to ensure all factors are considered and accurately interpreted.
Looking Ahead
AI is enabling retail and CPG companies to make better data-driven decisions, select the right suppliers, mitigate risks, maintain efficient supplier relationships and enhance the resilience of their supply chains. As AI evolves, its integration with human expertise will be pivotal in driving the future of supplier risk management in these industries.
The nature of work within supplier management will undergo rapid transformation alongside technological advancements. Manual work will be dramatically reduced; rich, real-time insights into supplier capabilities and risk will become more accessible. These insights will need to be analyzed and acted upon. AI will elevate what supplier management can achieve, transforming the expectations and responsibilities of supplier management professionals.
About the Author
Michael Tracy is a Senior Vice President in the WNS Manufacturing, Retail and Consumer Goods Practice. He has over 30 years of experience in sales and business transformation, helping Fortune 500 companies achieve cost optimization, productivity benefits and stakeholder delight through innovative and customized BPM solutions. As a subject matter expert in finance and accounting, source-to-pay and analytics, Mike focuses on the consumer, retail, technology and manufacturing sectors.
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References
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2023 Global Chief Procurement Officer Survey | Deloitte
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The state of AI in early 2024 | McKinsey
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With artificial intelligence, find new suppliers in days, not months | McKinsey
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How Global Companies Use AI to Prevent Supply Chain Disruptions | Harvard Business Review
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Smarter Supply Chains | Koch News
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How Walmart Automated Supplier Negotiations | Harvard Business Review
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Home Depot optimizes customer experience, operations | CSA