Source to Pay automation makes it easier to track vendor data such as terms and conditions, current and potential discounts and overall performance. Vendors promising opportunity for long term benefits can then be prioritized over underperforming vendors.

Automation reduces human error, invisible spend, fraud and sub-standard supply chain management. It also allows enterprises to generate accurate financial reporting documents with a detailed audit trail for each transaction. Automation eliminates situations of likely frauds by identifying patterns and proactively highlighting anomalies to the right stakeholders.

According to a McKinsey report, almost 60 percent of source to pay processes have the potential to be fully or largely and significant automation potential not only in transactional activities, such as order and invoice processing, but also in sourcing’s strategic elements, such as vendor selection and management. Now, several emerging technologies, including robotic process automation (RPA), machine learning, and artificial-intelligence have the potential to significantly transform the Source-to-Pay function.

RPA speeds up process cycle times by automating repetitive tasks and freeing up source-to-pay professionals to engage in other value-added tasks. RPA has the capability to help enterprises integrate their existing systems, eliminating manual interventions at the interfaces between systems and process steps. Automation interventions include Purchase requisitions creation and approvals, Purchase Order creation, supplier statement reconciliation, invoice processing and payment cycles.

Unlike RPA, Machine Learning can automate tasks that involve complex rules and require some form of pattern recognition. Machine learning can be used for tasks that traditionally require some level of human judgment, such as the assignment of transactions to formal spend categories. With machine learning, enterprises can match vendor capabilities to meet demand, recommend the most favourable terms, track efficiencies and highlight aspects of non-compliance. Machine learning can also classify vendors by performance and throw out the most suited vendor based on specific requirements.

NLP technologies process textual data and provide a convenient way for purchasers to document requirements without resorting to structured lists or drop-downs. In procurement, NLP may assist in organizing many types of unstructured information. As the source-to-pay function moves from a transactional function to more tactical and strategic in nature, it becomes essential to forego the existing siloed approach filled with redundancies. Source to Pay automation involves efficient management of processes from analysing spend to strategic sourcing and contract management.

This is perhaps one of the most critical area to automate in any enterprises. Understanding where the monies are going in the form of detailed spend data and providing insights to reduce spends will be a huge area of focus for enterprises. The full benefits of an effective spend analysis could however be hampered by fragmented legacy systems that provide incomplete or inaccurate data. With an adept spend analytics solution enterprises can analyse patterns of spend and compliance. An in-depth spend-analysis can effectively monitor the supplier base, reduce the maverick spend (purchases outside agreed contracts) and match pricing across buying centres.

Pharmaceutical Giant slashes expenditure and enhances visibility with Spend Analytics

A major pharmaceutical company needed to organize its fragmented source-to-pay processes for a clear view of spend across different categories. With the utilization of a comprehensive spend analytics solution the enterprise was able to:

• Improve refresh frequency from quarterly to monthly
• Cut reporting cycle from 30 to 8 days
• Reduce spend by 50% by identifying non-compliant suppliers

Strategic sourcing is a procurement process that connects data collection, spend analysis, market research, negotiation, and contracting and is among the top priorities for procurement and supply chain. It has the capability to deliver the needed cost reductions that can substantially improve the bottom line. However, there are some challenges in adopting and realizing value from strategic sourcing.

• Inability to derive analysis of current spend and selecting sourcing projects accordingly
• Inefficient management of expectations between executive and sourcing team for performance on a specific sourcing project
• Absence of mechanism to document and share best practices

Enterprises should combine sourcing automation technology, process and change-management in order to successfully implement strategic sourcing. Strategic sourcing automation when done right can provide valuable recommendations on improving sourcing and contracting processes and methodologies. Automation can help in supplier identification, effective evaluation of RFx, supplier selection and contract management.

A renowned manufacturer of industrial equipment with business operations spread across the globe was facing issues in visibility into their spending and wanted to develop a robust strategic sourcing analysis capability across categories.

With a strategic sourcing automation solution, the enterprise was able to achieve a single view of all its procurement activities and a significant reduction in sourcing cycle time.

A vendor management scorecard can help enterprises measure the performance and effectiveness of their supplier base. An effective vendor ranking solution helps minimize subjectivity in assessing suppliers, facilitates better communication with vendors and provides overall control of the vendor base.

Machine learning could help in forecasting vendor performance by evaluating areas like risk, pricing, delivery, quality and service. ML algorithms can consider factors such as price stability, price accuracy, billing, compliance with purchase order, contract, etc to evaluate vendors and improve the quality and timeliness of delivery of items.

Manual contract review can significantly slow down the contract management process in the source-to-pay cycle. Generally, contract management involves the ERP system collecting and storing contracts from various sources across the organization (emails, scanned paper documents, SharePoint, etc.) and populating them in the Source-to-Pay system.

An RPA bot can review these contracts, compare them against standard templates, and point out nonstandard terms and conditions. The bot can then send a summary to the reviewer for use in supplier negotiation.

Effective source-to-pay automation solutions can empower procurement teams with actionable insights and maximize compliance while reducing the process cycle time; enabling enterprises to meet their procurement objectives, thereby significantly impacting organization’s bottom-line performance. It is recommended to reconsider the source to pay strategy in the light of the current crises to quickly determine actions to be taken to reduce risks, costs and create a more predictable supply chain.