Today companies are faced with the reality of supply chain disruptions caused by rapidly changing geopolitical realities. This has placed greater pressure to improve supplier relationships as a way of ensuring continuity of supply and protecting a company’s operations.
But this is easier said than done.
Ensuring suppliers are trusted partners for your supply chain requires robust supplier relationship management. Today, supplier relationship management should go beyond list keeping to continuously monitor supplier relationships looking for risk, for performance issues, for compliance issues, and for procurement efficiency. To accomplish these goals a key foundation is advanced data analytics.
In this article, we’ll delve into the significance of advanced data analytics for making informed strategic decisions in supplier relationship management. We’ll discuss how to collect supplier data to use in data analytics from three sources: Internal scorecards, ERP performance metrics, and supplier surveys. This information is harmonized to create a current view of each supplier and can be compared to historical data to understand supplier trends. We’ll also look at how this information can help manage sourcing events, lessen dependency on Tier 2 suppliers, and meet diversity objectives. Finally, we’ll underscore the importance of user-friendly SRM tools in enhancing adoption rates among customers and suppliers.
The Role of Data in Supplier Relationship Management
A key approach to gathering data on suppliers is to use supplier scorecards. These are internal scorecards distributed to company stakeholders who have direct interaction with suppliers. The scorecards collect data from these stakeholders on aspects of supplier performance that is important to the company. This can include how reliable suppliers have been, how responsive they are to customer requests, their product or service quality, etc.
A second approach to collecting data is to tap into ERP systems. From these ERP systems, customers can obtain highly granular and quantitative data on supplier performance. This can include quantities such as defect rates, performance against delivery dates, pricing performance, etc.
A third approach is to use supplier surveys. This approach can collect qualitative information as well as detail on supplier initiatives that may be of interest to the company. This can include information on initiatives or other areas of importance such as roadmaps.
Leveraging Data for Supplier Performance Management
Once data has been collected, an SRM system should be such that it can analyze and draw insights from this data. Examples of this insight can include:
Year over year performance of each supplier and performance compared to their cohort Absolute measures on performance such as delivery performance, quality, quantities ordered vs received performance, etc.
Data from the different data sources can be combined to get a holistic view of each supplier. For example, a supplier may score high in customer service from an internal performance scorecard, but their actual delivery data from the ERP can reveal recurring delays that impact the company’s operations.
Harmonizing with Historical Data
When it comes to managing supplier relationships, historical data plays a crucial role. SRM tools allow procurement teams to access past data, including quotes, delivery times, product quality, and even supplier-specific challenges. This insight helps organizations decide whether to invite certain suppliers to continue their relationship with suppliers or consider adding new vendors. For example, if a high-value supplier has consistently increased their prices for specific commodities, the data can help procurement teams determine whether the price hike is justified due to market conditions or whether the supplier is sourcing the commodity from a Tier 2 supplier with lower efficiency. Armed with this data, procurement teams can take a more proactive approach to:
- Negotiate More Effectively: With detailed insights into past quotes and performance, teams can negotiate pricing and terms based on a supplier’s historical trends.
- Reduce Supplier Dependency: If a supplier’s performance is slipping or their pricing is increasing, procurement teams can consider second sourcing, reducing dependency on a single vendor.
- Improve Collaboration: By understanding a supplier’s previous performance, procurement teams can engage in more meaningful discussions and set realistic expectations for future partnerships.
The role of AI and predictive analytics
Leveraging AI technologies such as predictive analytics and Large Language Models (LLM), allows SRM systems to scan through unstructured data to offer predictive insights based on supplier behavior, past performance, and external factors. This can lead to benefits such as:
- Forecasting Supplier Performance: AI can analyze historical performance data and external risk factors to predict how a supplier might perform in the future. This helps procurement teams make proactive decisions, like adding new suppliers or renegotiating contracts.
- Detecting Supplier Risks: AI and LLM technology can detect early warning signs of supplier risk, such as news about financial instability, regulatory violations, or environmental compliance issues. This is especially important for high-spend suppliers who play a critical role in the supply chain.
Benefits to Data Analytics in Supplier Relationship Management
Some of the key benefits to using a data driven analytical approach to supplier relationship management include:
- Make better decisions regarding supplier selection, sourcing events, and performance management.
- Drive operational efficiency and cost savings.
- Improve supplier collaboration, transparency, and accountability.
- Full Visibility: Analytics can provide real-time visibility into supplier performance metrics, including on-time delivery, product returns, and contract compliance.
- Risk Mitigation: By analyzing SRM data organizations can forecast issues like stock outs or production delays caused by supplier underperformance.
- Informed Decision-Making. SRM analytics allows procurement teams to invite only the most reliable suppliers to new sourcing events, based on past performance and delivery accuracy.
Enhanced Supplier Collaboration. By engaging with suppliers based on data-driven insights, organizations can foster stronger relationships, leading to better performance and long-term partnerships
Conclusion
Incorporating data-driven insights into your SRM strategy is critical for improving supplier performance, managing risks, and achieving procurement goals. By leveraging advanced analytics, real-time performance tracking, and ERP integration, businesses can gain a comprehensive view of supplier performance, costs, and risks. Additionally, with AI-powered predictive analytics and automated workflows, organizations can stay ahead of potential challenges, optimize supplier relationships, and drive better outcomes.
By choosing an SRM solution like Gainfront, organizations can ensure they have the tools needed to collect, analyze, and act on critical supplier data—driving strategic decisions, improving efficiency, and ensuring compliance with key business objectives. With a seamless user experience for both suppliers and customers, Gainfront provides the insights and tools needed to thrive in today’s competitive procurement landscape.
Start your journey towards smarter, data-driven supplier management today!
Rahul Asthana has a PhD in Operations Management from the Anderson School at UCLA. He has 25 years of experience in supply chain management, starting his career in IBM working in supply chain operations. He then moved into product management and product marketing of supply chain software while at SAP and Oracle. He manages product strategy and product management at Gainfront. In terms of hobbies outside of work, he really enjoys tennis. Follow Rahul Asthana on Linkedin!