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Extending possibilities of Spend Analytics with AI

July 19, 2019 - Procurement Insights Team

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In today’s era of digital transformation, procurement leaders across the world have been pulled into various discussions on how to align procurement strategies with the enterprise as a whole. Never in the history of procurement has it been so important to harness the power of data and transform it into insightful intelligence resulting in performance excellence. As procurement moves from being a mere support function to playing a strategic role, business leaders across verticals have begun to acknowledge the role of Artificial intelligence in driving procurement performance.

As per the 2017 Deloitte Global CPO survey — which polled 480 procurement leaders from 36 countries — the leaders believed that the impact of robotics and automation will increase from 50 percent to 88 percent by 2020, and up to 93 percent by 2025.

However, industry experts say that procurement has still got a long way to go. They believe embracing intelligent technologies can help procurement teams generate more business value. With procurement teams always under tremendous pressure, it only makes sense to harness the power of AI to reduce some of their workload. Towards achieving this goal, procurement leaders are constantly looking for AI-powered solutions to streamline processes and improve decision making. This has provided wider acceptance for software such as spend analytics and contract analytics, which enable procurement teams to identify areas where cost can be saved. Most enterprises today have deployed Spend Analytics Software, to give their procurement teams more visibility and control in managing contracts.

Use of AI in procurement can be seen as a major paradigm shift in the way procurement functions are performed. They provide a clear-cut advantage to enterprises vis-a-vis traditional methods, helping procurement organizations gain new insights and shape new strategies, which weren’t possible before with standard spend analytics approaches.

Data is power when it comes to taking strategic and tactical decisions and machine learning is an AI capability that helps transform data into insights facilitating more accurate and informed decision-making.

Leveraging AI for procurement functions can actually give a greater control over vendor and spend management. However, currently most of these technologies are restricted to processes of collecting, classifying, reviewing and analyzing spend data. Though they help identify areas where savings could be made, it is still at a very basic level and not leveraging the ultimate power of AI. When leveraged to its peak, organizations can streamline their supply chain, which involves data from inventory, brand perception, risk mitigation, advertising, vendor management among others.

Impact of AI on Spend Analysis

Spend analysis is an important step in establishing an effective procurement organization. Providing a recurring spend visibility could be the driving force behind a long-term cost optimization strategy. However, with limited time and resources, it also becomes a major challenge for organizations. In spite of significant investments in technology solutions, procurement functions often lack the quality, caliber and analytics required to function strategically.

This can be addressed with AI, particularly machine learning, which helps increase the speed to spend analysis results, ensuring accuracy with limited manual effort.

Such enterprise-level spend visibility helps drive strategic planning, refine operational focus and improve business results.

Strategic Sourcing: Strategic sourcing teams around the world are required to provide detailed spend analytics that inspires procurement insights. The advent of automation and upcoming cognitive technologies help predict and guide analyzing unstructured data. Also, many organizations have deployed Cognitive Procurement Advisors, Virtual Personal Assistants, which use Natural Language Processing and Natural Language Generation to provide insights from the available data aiding strategic sourcing.

Spend Classification: Traditionally, spend is classified manually or with the help of rule-based software with supplier names and keywords. Not only are these processes labor-intensive and time consuming, they are also inaccurate, which limits sub-categorization due to higher dependence on supplier names than product descriptions. Machine learning helps automate at a granular level of categorization from varied fields including product, supplier or line-item. This way, ML helps enterprises improve their spend visibility up to 95% by capturing data in real time and making complete sense of it.

Supply Management: Negotiating with a supplier can be tricky with incomplete knowledge about the supplier and market trends. For procurement teams worldwide, striking a deal with a supplier can be an elating situation. However, once sourced, as the supplier joins the list of various others, it becomes difficult to monitor or keep a track of their performance to ensure they bring value to the enterprise. The basic Spend Analysis helps determine the spend factors such as what, with whom, how much etc. But the AI-powered version extends analytics beyond the confines of traditional approach and provides insights into operations and logistics as well. This is possible with enhanced Lachine learning analytical services which ingest semi-structured data and others quickly and accurately. For instance, apart from purchase orders and invoices, ML capabilities can run reports on inventory turnover and warehouse utilization, helping procurement teams determine inventory overhead costs and predict stockouts.

Risk Management: Not only do AI-powered analytics provide accurately classified data in less time, they also enrich this data with external content and help arrive at an insight. This can help integrate risk scores, sustainability and other scores related to risk. This information can then be used by procurement to analyze the spends with a particular supplier and reduce the risk of falling into a jeopardy due to the supplier’s misdoing. With this data and more, Machine learning can help discover trends, reducing risk and carry predictive analytics to determine price and negotiate with suppliers.

Conclusion

Though AI and Cognitive procurement are relatively new, spend analysis is definitely going through an accelerated transformation with advancements in ML and NLP. Exploring these technologies will only further help spend analytics, by interpreting structured and unstructured data, understanding descriptive and predictive outcome.

In short, use of AI in procurement can result in enhanced delivery commitment, as it reduces manual intervention. It optimizes processes and enhances productivity, thereby accelerating processes. All of this together provide quick insights through dynamic dashboards, helping procurement leaders make informed and strategic decisions.

Enterprises using AI for procurement are enabled to buy smarter and manage their suppliers better. AI also helps businesses automate their everyday operations, backend ERPs, purchasing and accounting systems. All of this in turn enable employees to provide customers a faster and better experience while ensuring better control and visibility over spends for the business. Procurement leaders are empowered to make better informed purchasing decisions in collaboration with complete spend visibility, automated processes, and data driven insights.

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