.webp)
Supplier Relationship Management (SRM) is at the heart of successfully managing interconnected, resilient global supply chains. Businesses face unprecedented disruptions, from geopolitical upheavals to natural disasters, making robust and flexible supply chains a necessity.
In our earlier Complete Guide to SRM, we walk through the fundamentals of what is SRM, how SRM can be leveraged to enhance performance, walk-through of getting started with SRM, as well as some common best practices.
We look at how acknowledging and dealing with internal resistance, and ensuring suppliers are fully on board with the initiative, are both integral for it to be a success. This is where the right communication strategy during roll-out is critical.
One vital subcomponent of SRM is Supplier Performance Management (SPM). Here, we focus more on operational performance, as opposed to strategic supplier management and partnership-focused collaboration.
Measuring day-to-day supplier performance has a number of challenges. The most obvious one is the ability to access good data consistently, which can then be used to drive accurate and measurable supplier performance KPIs.
Traditional SRM systems often fall short due to reliance on manual processes and siloed data. Enter AI-powered SRM, a transformative solution which enables more orchestration and automation across disparate systems, along with predictive analytics and real-time decision-making.
Supply chains operate in an increasingly unpredictable environment. Disruptions caused by the COVID-19 pandemic exposed the fragility of global networks, as .
“The overwhelming majority of respondents said that the crisis had revealed weaknesses in their supply chains that they’re now working to address. For example, 73 percent encountered problems in their supplier base, and 75 percent faced problems with production and distribution. A whopping 85 percent of respondents struggled with inefficient digital technologies in their supply chains.”
-McKinsey article
Meanwhile, geopolitical tensions, for example the effects of Brexit, and the threat of increased tariffs in international trade wars during the second Trump administration, add further layers of complexity.
The World Bank predicts global growth will slow to 2.7% in 2025 and 2026, compared to pre-pandemic levels of 3.1%, due to prolonged instability.
Additionally, climate change and the increasing frequency of extreme weather events pose growing risks to logistics networks. These all highlight the urgent need for resilient supply chains that can withstand and adapt to these shocks.
.webp)
Companies must pivot from reactive to proactive strategies to stay competitive. AI has the potential to be a game-changer here, offering tools that enhance supply chain agility and reduce vulnerabilities.
By integrating AI-powered SRM systems, businesses can streamline processes, optimize costs, and foster strong supplier relationships, even during economic downturns and crises.
As automation and analytics continue to fuel what’s possible with a more strategic and pro-active approach, the potential of AI in SRM is significant. So, are we on the cusp of being able to leverage this for supply chain resilience?
AI-powered tools address these supply chain challenges by delivering greater transparency and predictive capabilities. For example:
By embracing these technologies, organizations can navigate supply chain challenges more effectively, ensuring stability even in uncertain times.
AI technologies are redefining Supplier Relationship Management (SRM), enabling organizations to move from static processes to dynamic, data-driven systems. Here are just a few ways AI will revolutionize key aspects of SRM:
Enough with the theory. Let’s take a look at how this works in the real world.
Mercu AI is Mercanis’ built in AI Co-Pilot that brings together all of the data in all of the different modules of the Mercanis product suite. To get a more detailed overview, watch our recent webinar on this topic in conjunction with the BME or take a look at our website of MercuAI.
Data can be pulled from public sources, private external data sources e.g. financial data from credit rating agencies, as well as company internal data, for example from your ERP system, employees’ email inboxes and calendars, or other internal IT systems (subject to integration).
It utilizes several different AI platform services to bring all of this together, including Large Language Models (LLMs), Natural Language Processing (NLP) and Predictive Analytics.
These Agents have been developed initially to handle four specific use cases:
So, how would this translate into a real-life use case?
You’re a mid-market Tier 1 automotive supplier. Your customers are putting you under hard pressure for annual cost reductions because they are losing competitiveness and market share, and they have ongoing labour disputes with the trade union.
You run a material price analysis using the market intelligence agent.
You’re horrified to see that the price of cold rolled steel and aluminium are likely to rise next year, based on commodity price futures and metal price bulletins. Going to your customer and demanding a price increase may result in you losing some or all of the business.
Next, you turn to the business intelligence agent. You ask it to evaluate how many components would be affected by a 5% increase in the price of steel and a 10% increase in the price of aluminum.
But that’s not all. You then ask it to calculate how much of the final selling price is impacted by both of these materials. This enables you to get a shortlist of which SKUs will hit you the hardest.
Armed with this list, you’re able to then turn to the supplier discovery agent and ask it to look for potential vendors outside of Germany.
Even though they’re still going to be hit by commodity price rises, their lower labour and energy costs can offset this. Potentially this will enable you to offset the price increase, and avoid a difficult negotiation with the customer or worse, potentially losing the business.
Two possible sources catch your eye: one in Romania and one in Turkey. Great! No complex customs headaches or lengthy supply chains. You then turn to the sourcing event agent and ask it to create an initial RFQ document.
This is sent to the supplier with just a couple of clicks, and you eagerly await their response. Days worth of work is performed in just a couple of hours.
But perhaps the biggest advantage of this agentic AI Co-Pilot is its user-friendliness.
Naturally integrated into the Mercanis suite, it works with natural language commands rather than menus. The age-old issue of low user adoption in procurement tech is not a concern here.
AI is no longer an emerging technology in supply chain management—it’s becoming essential. The integration of AI tools into SRM software delivers far-reaching benefits, ranging from operational efficiency, supplier risk identification through to driving real competitive advantage.
AI excels in supplier risk identification, providing early warnings about potential issues. For example, machine learning algorithms can analyze supplier credit scores, delivery histories, and can scrape media sources to predict which suppliers should be on the critical list.
Such proactive insights allow businesses to adjust sourcing strategies before disruptions occur, or to plan and prepare for commercial negotiations without being caught off guard.
AI-driven automation transforms routine tasks, enabling procurement teams to triage and focus resources on more strategic initiatives. For example:
Procurement teams cannot manage 100% of the suppliers, especially when it comes to the long tail. AI will enable more spend to be brought under management, even when there is not a Category Manager in Procurement who is actively engaging with the supplier.
AI in manufacturing supply chains can help prevent a late delivery, an unpaid invoice, or a quality problem from a non-strategic supplier. These issues all impact the ability to manufacture or sell the end product. Any suppliers not actively managed by procurement could be a risk waiting to happen.
These efficiency gains will ultimately improve productivity and reduce operational costs.
AI tools consolidate vast amounts of data into actionable insights. For instance, they can analyze procurement trends across multiple regions, highlighting opportunities to negotiate better pricing or diversify suppliers.
Such data-driven approaches enhance decision-making and foster more effective supplier relationships.
Leveraging AI can enable a tailored approach to manage each supplier differently based on their individual performance, the product or service they supply, and how critical they are to the business.
AI enables proactive communication with suppliers. Tools such as AI chatbots provide instant responses to queries, while predictive analytics alert suppliers about potential order changes.
These features enhance collaboration and reduce friction, resulting in stronger partnerships.
Companies adopting these trends will be able to position themselves as leaders in innovation, ensuring they stay ahead of the competition. The investments into hardware and software for this will pay back multiple times in relation to the impact it can have on both revenue and profit margin.
AI-powered Supplier Relationship Management (SRM) is transforming supply chain operations.
By addressing inefficiencies, improving transparency, and enabling predictive capabilities, AI helps businesses build resilient supply chains that are able to withstand disruptions. Organizations leveraging AI tools gain a competitive edge through better decision-making, efficiency gains, and stronger supplier relationships.
The future of SRM lies in adopting AI technologies that anticipate challenges and unlock opportunities. Companies investing in these tools today will not only improve supply chain resilience but also drive long-term growth.
Ready to transform your supplier management processes?
Mercanis offers a state-of-the-art AI-powered SRM solution designed to enable proactive supplier management and tackle the challenge of poor data.
With easy-to-use dashboards to monitor supplier performance, mitigate risks, eliminate emails and manual updates, bringing all your key supplier data into one, easy-to-use single source of truth.
Schedule a demo today to future-proof your supply chain, take back control of your data, and mitigate any unforeseen risks.
AI-powered SRM refers to the integration of artificial intelligence technologies into supplier management processes. These technologies automate routine tasks, provide predictive insights, and enhance decision-making to improve supplier relationships and overall supply chain performance.
The benefits include: 1) Increased efficiency through automation; 2) Enhanced decision-making with data-driven insights; 3) Improved transparency across supply chain networks; 4) Stronger supplier relationships through more proactive engagement.
While some initial investment is required, many AI-powered SRM tools are designed for scalable implementation. They do not require complex integration with ERP systems, except for the ability to access data stored in them. Organizations can start small, and expand as they see ROI.
Industries with complex supply chains, such as automotive, machinery, precision manufacturing, consumer goods, and retail can gain significant advantages. However, any business looking to improve supply chain transparency and efficiency can benefit.
Fabian Heinrich is the CEO and co-founder of Mercanis. Previously he co-founded and grew the procurement company Scoutbee to become a global market leader in scouting with offices in Europe and the USA and serving clients like Siemens, Audi, Unilever. With a Bachelor's degree and a Master's in Accounting and Finance from the University of St. Gallen, his career spans roles at Deloitte and Rocket Internet SE.
.webp)
.webp)
.webp)
