Supplier relationship management (SRM) is the process of managing supplier relationships to develop and maintain a strategic procurement partnership. SRM includes focus areas such as supplier selection, procurement strategy development, procurement negotiation, and performance measurement and improvement. SRM has been around for over 20 years but we are now seeing new technologies such as machine learning come into play. What exactly does advanced analytics such as artificial intelligence (AI) / machine learning (ML) have to do with SRM? And how will AI/ML technologies transform procurement? What are some real-world machine learning use cases related to supplier relationships management? What are a few SRM KPIs/metrics which can be tracked by leveraging AI/ML? In this post, we will explore these questions in more detail.
What is Supplier Relationship Management (SRM)?
Supplier relationship management (SRM) is key to procurement and procurement strategy. The SRM process begins with the procurement organization taking a strategic approach to supplier relationships. Supplier relationship management team includes roles such as procurement operations managers, procurement professionals (buyers/negotiators), procurement consultants. The SRM team develops a procurement strategy that identifies supplier selection criteria, assesses supplier risks, how suppliers will be chosen from the pool of candidates for each procurement category or need within an organization, supplier innovation, supplier diversity, etc.
Once procurement organizations understand their strategic approach to supplier relationships, they can begin developing policies and procedures to support this procurement SRM strategy. Prior to laying down the SRM strategy, the SRM team must understand the goals of the procurement organization as well as how to create a supplier-relationship management plan that supports these goals. Strategic procurement involves developing formal SRM strategies, selecting suppliers, negotiating contracts/rates, and working collaboratively with the internal SRM team to drive performance improvement activities – such as reducing costs or increasing inventory turn. The key focus areas of supplier relationship management include some of the following:
- Supplier onboarding: Supplier onboarding is the process of onboarding new suppliers onto a company’s platform. It can be a challenge for companies to find and select the right supplier, and then to manage the Supplier relationship effectively. There are many Supplier onboarding challenges that companies face, such as: supplier selection, supplier qualification, supplier performance management, supplier payment terms and conditions, supplier communication and coordination, etc. The goal of supplier onboarding is to streamline the process of supplier integration into the company, and to improve supplier performance. By following a good supplier onboarding process, companies can improve their supplier relations and reduce the risk of supplier turnover. The following are some of the challenges in relation to supplier onboarding:
- How to ensure that supplier onboarding is a clear and concise process, and, suppliers are communicated appropriately about their onboarding?
- How to ensure that the new suppliers clearly understand company’s policies and procedures, as well as your expectations for quality and delivery?
- How to ensure that the suppliers are able to meet company’s legal and compliance requirements?
- How to ensure that the process is less time-consuming and resource-intensive as there is always the risk that Suppliers may not meet your expectations?
- How to ensure that the suppliers have access to appropriate documentation at all point in time during onboarding process?
- Suppliers negotiations: Negotiations with suppliers are a necessary part of doing business. Negotiating the best terms possible can be the difference between a profitable company and one that struggles to make ends meet. After all, the cost of goods and services is a major factor in determining a company’s profitability. As such, it’s important to approach negotiations with a clear strategy in mind. The goal is to come to an agreement that is mutually beneficial for both parties. However, negotiations can be challenging, and it is important to have a strategy in place. The following can the strategy one can follow while negotiating with the suppliers:
- First, you need to understand the needs of both parties. What does the supplier need in order to provide the best possible service? What does your company need in order to be successful?
- Research the market to get a sense of what similar businesses are paying for similar goods and services. This will give you a benchmark to work from.
- Identify your key leverage points. This could be anything from being a longstanding customer to having unique needs that can only be met by a specific supplier.
- Don’t be afraid to walk away from the negotiating table if you’re not getting the terms you want. Remember, there are other suppliers out there who may be more willing to meet your needs.
- Supplier performance management: Supplier performance management is the process of assessing supplier performance on an ongoing basis, setting objectives and expectations for improvement, and taking corrective action when necessary. The ultimate goal of supplier performance management is to ensure that suppliers are meeting or exceeding customer expectations. There are several key performance indicators (KPIs) that can be used to evaluate supplier performance. These KPIs can be grouped into four main categories: quality, delivery, cost, and service.
- Quality KPIs measure the percentage of defective products or the number of customer complaints.
- Delivery KPIs measure the timeliness and accuracy of shipments.
- Cost KPIs measure the price of goods or services relative to competitor prices.
- Service KPIs measure the responsiveness of customer service and the number of customer service issues resolved.
- Supplier collaboration: Supplier collaboration is a business strategy where organizations work together with their suppliers to improve the performance of the entire supply chain. This type of collaboration can take many different forms, but the overall goal is to create a more efficient and effective supply chain that meets the needs of all parties involved. Supplier collaboration can be a challenge for many organizations. There can be a number of reasons for this, such as different supplier objectives, a lack of trust, or incompatible technology systems. However, there are a number of ways to overcome these challenges. One way is to create a shared vision for the collaboration. This means that all parties need to be clear about what they want to achieve and why it is important. Another way is to establish trust between the parties. This can be done by setting up clear communication channels and agreeing on protocols for handling confidential information. Finally, it is important to choose the right technology platform for the collaboration. This platform should be user-friendly and compatible with the systems of all parties involved. There are many benefits of supplier collaboration such as some of the following:
- Greater access to innovation
- Additional revenue generation
- Improved risk management and resilience
- Improved sustainability
- Improved operational efficiencies
- Greater cost savings
- Supplier risk management: Supplier risk management is the process of identifying, assessing, and mitigating risks associated with supplier relationships. There are a variety of supplier risks that can adversely affect an organization, including financial instability, quality issues, and delivery delays. A comprehensive supplier risk management program should address all of these potential risks. An effective supplier risk management program begins with the identification of high-risk suppliers. Once high-risk suppliers have been identified, a methodology for assessing supplier risks should be implemented. This assessment should consider both the financial and operational risks associated with the supplier. Once supplier risks have been assessed, a plan for mitigating those risks should be put in place. This plan may include measures such as increased monitoring of supplier performance or the development of contingency plans in case of disruptions to the supply chain.
AI /Machine learning & Supplier Relationship Management
Supplier relationship management (SRM) is a key aspect of any procurement strategy that involves the procurement organization and the suppliers working together to best meet customers and suppliers needs. Supplier selection is a crucial part of SRM because it’s important to select the right suppliers for your procurement needs. Machine learning models can be used for supplier selection. One example of machine learning used for supplier selection in procurement is automatic pricing engine updating – machine learning algorithms are able to scan different data sources, provide feedback on pricing trends, analyze purchase history, recommend prices for new products, detect price alterations made by competitors, etc. The AI / machine learning based solutions can help procurement organizations source products at the best price with the lowest risk. It can also help procurement professionals become more efficient by determining which suppliers are performing well and worth keeping, as well as those who need improvement or should be replaced altogether.
AI/Machine learning will transform supplier relationship management by ensuring procurement teams are continuously measuring supplier performance, identifying trends in procurement spend & supplier value, and minimizing risk/compliance violations.
AI / Machine learning Use Cases for Supplier Relationship Management
Here are some machine learning use cases in relation to different aspects of supplier relationship management:
- Rank suppliers for effective suppliers’ selection: The supplier selection process is very important for procurement because it helps identify the most suitable partners that can substantially improve performance, reduce costs, or provide new capabilities through strategic sourcing. According to procurement experts, the supplier selection process is more of an art than a science because it requires subjective judgment by procurement professionals based on their knowledge and experience with various suppliers. Machine learning can help overcome this problem through predictive analytics that pinpoints potential best-in-class partners in terms of payment terms & conditions, goods & services quality, sustainability aspects, customer service (customer reviews), market competitiveness, price, etc. This predictive analytics can provide procurement professionals with guidance for supplier selection based on advanced analytics (AI / machine learning) models rather than their subjective judgment. For example, machine learning classification models can be trained to predict which suppliers deliver the best quality products at the lowest cost by tracking the performance of each supplier over time (e.g., delivery schedule adherence, product defects).
- Predictive effective supplier communication channel/mode: Improved communication between procurement professionals and suppliers is another machine learning use case for supplier relationship management. Machine learning models can be trained on communication patterns within various organizations in order to better predict which suppliers are more likely to respond quickly to requests or questions. For procurement professionals, this means they can communicate more effectively with suppliers and reduce the time-to-resolution for supplier issues or questions.
- Estimate supplier performance: Machine learning models can be trained to predict which suppliers are more likely to have poor or declining performance in the future based on factors such as financial health, market volatility, supply chain risk management capability of supplier partners, and other procurement metrics. This enables procurement professionals to take early action by engaging these suppliers in performance improvement plans or identifying potential replacement candidates before their poor supplier performance becomes a major problem for procurement operations.
- Estimate supplier risk score: Machine learning models can be trained on historical data relating to procurement incidents, such as fraud and theft events involving external suppliers. The procurement risk score can be used to identify potential high-risk supplier partners based on similar attributes of the past incident data, enabling procurement professionals to take action before a procurement incident takes place. Risk modeling techniques, such as decision trees and random forests, can be used to develop procurement risk scores.
- Reduce supplier contract disputes: Machine learning models can also help the procurement team resolve potential supplier contract issues before they escalate into costly litigation or arbitration cases. For example, machine learning classification algorithms can be trained on historical data sets of past supplier invoices disputes for various procurement departments. These classification models can then be used to predict which supplier invoices are more likely to contain disputes in the future, enabling procurement professionals to take action before a dispute takes place by engaging with their suppliers and negotiating better payment terms or discounts.
- Supplier contract compliance: Machine learning algorithms can also help procurement organizations identify potential supplier contracts that are non-compliant with procurement regulations (e.g., sourcing from conflict regions such as the Democratic Republic of Congo, Nigeria, and Colombia). These algorithms can be trained on procurement data sets to identify supplier contracts that may have a high risk of being in violation of compliance rules or laws, enabling procurement professionals to take action before they face financial penalties or operational disruptions. NLP techniques can be used to identify supplier contracts that are non-compliant with procurement regulations.
- Estimate supplier ESG score: Predictive analytics (classification models) can be used to estimate ESG score and classify suppliers appropriately based on factors such as environment, health & safety, and labor practices of a supplier. This machine learning application is important because it helps procurement professionals address their organization’s ethical procurement policies by identifying the most responsible suppliers from an ESG perspective.
SRM KPIs by leveraging AI / Machine learning Solutions
The following are a few KPIs/metrics which can be tracked by leveraging AI/ML:
- Supplier selection score: The supplier selection score can be used to track how strong your relationship with suppliers are. Strong relationships indicate that the procurement process is efficient and complemented by machine learning, which in turn indicates the high quality of service to customers. This metric will vary depending on business criticality, industry standards, etc.
- Supplier procurement time: Time to procure supplies is a key procurement KPI. Speed indicates the ability of the procurement team and machine learning helps in this metric by speeding up decision making, automating manual processes, etc., which results in a faster procurement process.
- Supplier risk score: A risk score is a measure of risk associated with procurement from a supplier. It can be defined as the probability that the procurement process will not meet its objectives due to the suppliers’ activities or lack thereof. In short, it measures how trustworthy and reliable a supplier is in terms of the quality and quantity of supplies required by the organization. This metric varies across procurement processes, business criticality, and industry standards.
- Supplier scorecard: A supplier scorecard is a procurement KPI that can be used to benchmark suppliers against your procurement process KPIs (e.g., spend per shipment). It provides insight into the strengths and weaknesses of suppliers in terms of quality, quantity, etc. for performing procurement activities. This procurement KPI is especially useful in supplier management because it helps procurement organizations compare suppliers against each other to identify the top performers.
Sourcing suppliers is a difficult process where procurement professionals need to make subjective judgments based on their knowledge and experience. Machine learning can help procurement professionals find the most suitable partners through data-driven predictive analytics that identify potential best-in-class partners in terms of goods & services quality, customer service (customer reviews), and price. If you’re interested in improving supplier relationship management or want more information about machine learning use cases for procurement, let us know!