Supplier Relationship Management & Machine Learning

supplier relationship management machine learning

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 and how suppliers will be chosen from the pool of candidates for each procurement category or need within an organization.

Once procurement organizations understand their strategic approach to supplier relationships, they can begin developing policies and procedures to support this procurement strategy. They also assess current. 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 procurement 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 processes get developed to ensure clarity with suppliers on roles & responsibilities, compliance requirements (such as anti-bribery/anti-corruption), and KPIs.
  • Supplier selection: Procurement organizations need to take a strategic approach when selecting their external partners that include focus areas such as understanding procurement strategy, supplier value proposition, procurement process maturity, and risk appetite.
  • Negotiation with suppliers: Procurement organizations (sourcing) need to negotiate with suppliers on rates & contracts as well as work collaboratively across procurement groups (such as engineering) and internal stakeholders such as sales teams.
  • Supplier performance management: The procurement SRM team should continually measure the performance of external partners against key deliverables identified in procurement contracts to ensure procurement activities are being executed as planned.
  • Collaboration with internal stakeholders: The procurement SRM team should collaborate across different teams to drive performance improvement initiatives and supplier relationship management processes. Supplier relationship management is a collaborative effort between procurement professionals, suppliers, business partners, and other key stakeholders within an organization.
  • Supplier risk management: The procurement supplier risk management team should have procurement risk management strategies in place to ensure supplier relationships are sustainable, compliant with procurement policies & procedures, and address changing business priorities.

How will AI/machine learning transform supplier relationship management?

Supplier relationship management is a procurement strategy that involves the procurement organization and the suppliers working together to best meet customer and supplier needs. Supplier selection is a crucial part of this procurement strategy because it’s important to select the right suppliers for your procurement needs. Machine learning can be used for supplier selection in procurement. 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. This procurement strategy can help procurement organizations source products at the best price with the lowest risk. It also helps procurement professionals become more efficient by using machine learning algorithms to determine 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.

What are some 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 goods & services quality, customer service (customer reviews), and price. This predictive analytics can provide procurement professionals with guidance for supplier selection based on data analytics 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 employees within the organization or 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.

What are some SRM KPIs and metrics which can be tracked by leveraging AI/machine learning?

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!

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
Posted in Artificial Intelligence, Data Science, Machine Learning, Procurement. Tagged with , , .

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