The future of Source-to-Pay: AI-driven process optimization

Source-to-Pay is a dynamic process that empowers businesses to effectively identify their needs, source solutions, negotiate terms, and execute payments with precision and speed. By mastering this cycle, organizations can enhance operational efficiency and drive significant value.

The source-to-pay process consists of six key steps:

1. Spend analysis: Companies have substantial data on the expenses related to the products and services acquired from suppliers. By analyzing these expenditure data and considering the requirements, procurement strategies are developed for cost-savings.
2. Vendor management: In this phase, existing vendors are assessed based on their financial stability, reputation, and previous performance. The goal is to make informed decisions by considering pricing, quality, and delivery time.
3. Contract management: The process involves sending Request for Proposals (RFPs), Requests for Information (RFIs), and Requests for Quotes (RFQs) to potential suppliers to evaluate them. Negotiations follow to get the best possible price after selecting a supplier. Companies also negotiate freight terms, allowances, and discounts during the procurement process. Once the negotiation and contracting is completed, a purchase order is generated specifying the item, quantity, price, and anticipated delivery date.
4. Goods/services delivery: On arrival the products are inspected to ensure that the quantity corresponds with what is stated in the purchase order. After the inspection, the company issues a Goods Received Note (GRN).
5. Record invoices: The accounts payable department is responsible for reconciling invoices from the supplier with the purchase orders and the goods received note.
6. Payment to the supplier: Once the goods are confirmed as satisfactory, the company makes payments to the supplier according to the payment terms outlined in the contract. For example, “one of the payment terms is "Net 10", which means that payment is due within 10 days.

Source-to-pay: How it works

• Assessment: A retail company determines that it requires 50,000 organic T-shirts to replenish 200 stores in its network. They specify the required dimensions, fabric, and color.
• Supplier discovery and qualification: The procurement team investigates and selects three certified vendors specializing in organic cotton apparel who have submitted a Request for Information (RFI).
• Sourcing and negotiation: After identifying the most suitable vendors, the retailer issues a Request for Quotation (RFQ) to their chosen suppliers. They negotiate with the top vendor and select the supplier that provides the best value.
• Contract management: The parties draft and execute a contract with the vendor company that outlines the terms for acquiring the organic T-shirts, including price, delivery date, and product quality.
• Purchase order: The retailer generates a purchase order for 50,000 T-shirts to confirm the quantity and pricing with the vendor via their purchasing system.
• Goods receipt and inspection: Upon receipt of the products, the company inspects them for quality and quantity. They compile a list of any discrepancies and grievances, and generate a Goods Receipt Note (GRN).
• Invoice processing: The accounts payable department receives an invoice from the vendor. They compare the invoice with the purchase order and the good receipt note to verify its accuracy.
• Payment processing: The retailer settles the purchase invoice on Net 30 (N30) terms, meaning that the vendor receives payment within 30 days.
• Supplier performance evaluation: The retailer evaluates the performance of its suppliers by assessing the quality of the products supplied, the allocated delivery time, and the quality of communication.

S2P processes: The current challenges

• Lack of data visibility: Businesses frequently encounter fragmented data sources, making it difficult to identify trends in supplier performance and expenditure.
• Errors in manual processes: Numerous organizations still rely on manual data input and paper invoicing. This labor-intensive approach is prone to errors and leads to delays in invoice processing and inaccurate payments.
• Compliance risks: The lack of centralized oversight mechanisms can make it difficult to monitor supplier compliance and ensure adherence to procurement policies.
• Resistance to change: Implementing new S2P processes is often met with opposition from employees who are accustomed to traditional workflows. This highlights the need for effective change management strategies.

S2P Process: The need for optimization

• An enterprise's top four priorities were driving operational efficiency (78%), cost reduction (76%), digital transformation (76%), and innovation (73%). (Source: Deloitte Global Chief Procurement Officer (CPO) Survey) [Source - Deloitte]
• Operational efficiency is cited by 40% of firms, highlighting the need for Chief Procurement Officers (CPOs) to optimize procurement processes and enhance productivity. This statistic is indicative of the widespread acknowledgement among organizations that optimizing operational processes is crucial for effectively managing costs and driving value.
• Additionally, 40% of organizations prioritize digital transformation, emphasizing the importance of implementing digital technologies and tools to enhance procurement capabilities and drive efficiency. Digital transformation is identified as the second most critical procurement strategy, following supplier collaboration. This suggests that CPOs clearly understand the necessity of utilizing technology to improve procurement processes.

The future of Source-to-Pay: AI opportunities to reduce manual operations

Use case #1 - Verification of compliance documents

Supplier registration requires submission of various documents, including licenses, certificates, and insurance policies, often in different formats. The manual processes involved are rigorous document evaluation, including preliminary checks for completeness and adherence to organizational requirements. Key steps include data extraction from compliance documents for system integration, verification of document authenticity through official sources, risk classification based on submitted information, and continuous monitoring of supplier compliance, certifications, and contracts to ensure sustained adherence to organizational standards.

Common challenges

• Human errors due to fatigue, distraction, or a lack of expertise in manual verification.
• Manual reviews require substantial resources, impacting focus on more critical tasks.
• Compliance validation is impeded by incomplete or inaccurate records.
• Document volume increases exponentially with organizational growth, which can make it difficult to maintain accuracy.

How AI can help

• AI technologies such as Optical Character Recognition (OCR) and machine learning can detect subtle discrepancies and patterns in documents that human evaluators may overlook.
• Using Intelligent Document Processing (IDP) can significantly enhance the efficacy of document processing by automating the extraction and validation of critical information from documents. Additionally, it can cross-check documents against external sources or regulatory databases to ensure compliance.

Use case #2 - Automated invoice matching

The manual invoice matching process is labor-intensive and susceptible to human error as it involves manual data entry, document retrieval, and comparison.

Common challenges

• Validation errors: Discrepancies between invoices, purchase orders (PO), and products receipts can lead to supplier dissatisfaction, compliance risks, and payment delays.
• Expensive: Manual invoice matching increases operational costs due to the time and resources used.
• Limited scalability: As businesses expand, manually managing increased invoice volumes becomes unsustainable, resulting in processing backlogs and inefficiencies.

How AI can help

• Natural Language Processing (NLP) models can read and comprehend structured and semi-structured text in invoices, purchase orders, and receipts to detect discrepancies. This process helps to validate invoices against purchase orders and merchandise receipts, thereby reducing manual labor and error rates.
• Rule-based and hybrid machine learning systems can compare the quantity on the purchase order with the invoices and identify any discrepancies.
• AI algorithms reduce human error, guaranteeing precise matching and detection of genuine discrepancies.

Use case #3 - Spend analytics

Consolidation of data from multiple sources is a common challenge in current spend analytics. This process requires substantial amount of manual labor for data cleansing, categorization, and analysis.

Common challenges

• This process is prone to errors and inconsistencies, which can result in inaccurate insights.
• Procurement teams encounter obstacles such as delayed reporting, limited visibility into expenditure patterns, and the inability to identify cost-saving opportunities in real time.

How AI can help

• AI can assist in outsourcing data extraction, cleansing, and categorization by optimizing these procedures.
• This not only reduces manual effort but also enables real-time, accurate decision-making, substantially enhancing the efficiency and impact of spend analytics.
• Machine learning algorithms can detect anomalies, predict spending trends, and provide actionable recommendations for cost optimization.

Generative AI use cases

Spend analysis

Generate detailed summaries of spending patterns across categories, regions, or suppliers to facilitate improved decision-making through dashboard narratives.

Vendor management

Personalize every vendor interaction for all vendor communication, from induction to compliance reminders, fostering stronger, more collaborative partnerships.

Contract management

Create preliminary contract drafts using predetermined templates to summarize contracts, emphasizing critical information.

Goods/services delivery

Gain visibility into every stage of the delivery process with prompt-based real-time status reports.

Invoicing

Streamline the payment process by eliminating invoice errors and accelerating payment cycles through automation.

Payments

Master cash flow management by optimizing payment schedules consistent with the cash flow forecast, supplier terms, and benchmark metrics.

End note

Integrating Artificial Intelligence (AI) and Machine Learning (ML) into the Source-to-Pay (S2P) process is not merely an operational enhancement; it is a strategic enabler promoting innovation, efficiency, and accuracy. AI empowers organizations to maximize the value of their procurement activities by automating repetitive tasks, uncovering actionable insights, and facilitating intelligent decision-making. As technology continues to advance, the potential for AI to transform the S2P landscape will only increase, providing businesses with unparalleled opportunities for growth and competitive advantage.