Artificial intelligence (AI) is increasingly reshaping all aspects of procurement, including contract management, supplier selection, and strategic sourcing, with its data-driven insights and automation capabilities. The technology is transforming the procurement function from only a transactional process that involves sourcing critical services and goods and effective supply chain management across organizations into a strategic tool that can unlock crucial business insights, enhance supplier relationship management, and lead to substantial cost savings. This article discusses the growing importance and application of AI in procurement processes.
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Importance of AI in Procurement
Procurement primarily involves optimizing supplier relationships, mitigating risks, navigating dynamic market conditions, and managing vast amounts of data. AI can be employed to effectively improve and automate different procurement processes to enable organizations to increase operational efficiency, optimize allocation of resources, and make more informed decisions.
AI can quickly and efficiently analyze large amounts of unconnected and repetitive data/millions of data points from diverse data sources, such as industry baselines, historical pricing, market information, commodity pricing, inventory turnover, warehouse utilization, product stock-outs, supplier fulfillment, contract terms and rates, consumption and usage data, inventory records, and transaction details, to identify anomalies, correlations, and patterns.
Procurement professionals can use AI techniques to obtain extensive insights from complex and vast datasets and automate time-consuming tasks. AI can reveal meaningful patterns from procurement data through advanced data interpretation and predictive analytics to improve purchasing decision quality and enable risk mitigation, effective spend management, and strategic sourcing.
AI can also detect latent opportunities that have been missed by human analysis. Specifically, the technology can effectively identify underutilized suppliers, cost-saving opportunities, and market trends. Enterprises can use AI to align procurement strategies with their primary business objectives through intelligent analytics and process automation to refine and accelerate procurement operations and promote operational efficiency and synergy throughout the organization.
AI allows the procurement team to focus more on the strategic aspects of procurement, such as procurement planning, intelligent sourcing, and supplier relationship management, by assuming the responsibility of repetitive tasks in the procurement cycle, which improves overall productivity.
New suppliers can be identified by AI by leveraging extensive external data. For instance, machine learning (ML) algorithms can analyze supplier data based on several factors, such as delivery times, reliability, and cost, to assist businesses in identifying the most suitable suppliers based on their requirements.
AI promotes data-driven interactions to improve supplier relationships. Natural language processing (NLP) can facilitate effective communication with suppliers, while predictive analytics can predict supplier performance. AI enables suppliers to align with the business requirements of the enterprises by providing them with crucial insights.
AI Techniques Used in Procurement
ML: In procurement, ML models, such as neural networks (NN), decision trees (DT), and regression models, are utilized to extract critical insights from the procurement data, such as real-time and historical information on delivery times, invoices, purchase orders, suppliers, and pricing, which is a high-dimensional and complex data involving non-linear interaction between several variables.
The ML models can be trained on this procurement data to detect relationships and patterns between variables. The insights generated by ML models can improve decision-making and efficiency of the procurement process.
For instance, an ML model can identify a relationship between a supplier’s delivery times, frequency of delayed deliveries, and location, which can assist in formulating procurement strategies, such as negotiating delivery terms and selecting suppliers.
ML models continue to learn and improve over time to adjust their internal parameters to reduce the difference between the actual outcomes and their predictions. Moreover, AI can learn the most effective implementable action in a specific situation based on the concept of punishment or reward using techniques such as reinforcement learning.
Robotic Process Automation (RPA): RPA in procurement involves leveraging software robots/bots to automate rule-based and repetitive tasks that are conventionally performed by humans. For instance, RPA bots can process purchase orders/invoices automatically by extracting necessary data from these documents, feeding the extracted data into the procurement system of the organization, and forwarding the data for approval to the appropriate person.
In complex scenarios, RPA can be coupled with other AI techniques, such as NLP to respond to and understand emails, or optical character recognition (OCR) to extract and read data from scanned documents. Moreover, RPA bots can significantly improve productivity and cost-efficiency as they can operate 24/7 and reduce instances of errors, leading to higher accuracy in the procurement process.
NLP: In procurement, NLP can be used for text analysis that involves examining procurement-related documents, such as supplier correspondence, purchase orders, and contracts. NLP algorithms can tokenize the text into smaller parts, such as phrases or words, and identify crucial elements through named entity recognition and part-of-speech tagging.
Sentiment analysis can be utilized for supplier evaluations as this NLP technique can effectively interpret sentiment in supplier feedback or reviews to provide meaningful insights for relationship management. Similarly, machine translation can enable seamless translation of procurement communications or documents from one language to another language.
NLP can also be employed for information extraction as the technique can extract specific details, such as delivery dates or contract terms, from the unstructured text data to transform it into structured data suitable for analysis, and for automated procurement report generation in natural language to simplify data interpretation for humans. Virtual assistants and chatbots leverage NLP to respond to and understand inquiries from suppliers and procurement professionals.
Major AI Applications in Procurement
Spend Analytics: Spend analytics is critical for effective spend management and sourcing strategies. Currently, procurement occurs across several geographies, business units, and functional departments, and the data is stored in several systems, which increases the challenges of obtaining an enterprise-wide view for mid and long-tail spending. The major challenge is identifying new opportunities to reduce spending.
AI can create new opportunities to streamline operations and reduce spending by providing full visibility into recurring spend in all business units, identifying suppliers with significant spend growth easily, flagging major transaction outliers to ensure legitimate spending, and identifying duplicate transactions that indicate supplier invoicing errors or potential fraud.
Most software solutions use supervised ML for automated new spending data categorization into procurement taxonomies, with classifiers suggesting the categories and providing confidence levels for every category, which allows human experts to validate and review AI-classified data to improve future classifications and spend analysis cycle times.
Contract management: Contract management involves complex legal interactions, which must be handled efficiently to prevent a significant loss of business value. AI methods can be used to achieve higher precision and automation in contract management.
AI techniques, specifically NLP, can facilitate automatic management of compliance monitoring, deadlines, and contract monitoring to reduce the reliance on human intervention and errors. AI-enabled contract management software can efficiently extract and scan critical information from several contracts using text parsing, while OCR can be employed to interpret and digitalize text from previously non-digitized documents.
The application of AI in contract lifecycle management (CLM) tools increases efficiency by identifying risky contract language, managing negotiation workflows, automating initial drafts, and standardizing templates, enabling organizations to obtain a comprehensive audit trail, including necessary approvals and escalations.
Guided Buying: ML algorithms can recommend new services and goods based on current needs and past transactions to assist organizations in strategic buying decisions. AI can also identify non-compliance in buying activities to reduce fraudulent spending. Specifically, AI-powered guided buying platforms can analyze and collect past business data to offer actionable insights, negotiate better deals with preferred suppliers, and suggest the most suitable option to employees.
Procurement teams can use AI to improve supplier negotiation outcomes by identifying opportunities to achieve favorable terms through re-negotiation of renewals before the fiscal year of the supplier ends, aggregating all supplier spend under a single contract to ensure the best pricing, and proactively identifying suppliers operating without a contract and aggregating demand throughout the enterprise.
Other applications of AI in procurement include manual task automation, chatbots/virtual assistants, supplier selection and risk management, inventory management, anomaly detection, insightful/strategic sourcing, accounts payable automation, intelligent supplier performance evaluation, and invoice data extraction.
Reference and Further Readings
Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023). The role of artificial intelligence in the procurement process: State of the art and research agenda. Journal of Purchasing and Supply Management, 29(2), 100823. https://doi.org/10.1016/j.pursup.2023.100823
How Leveraging AI in Procurement will revolutionize spend management [Online] (Accessed on 30 October 2023)
Takyar, A. AI IN PROCUREMENT: REDEFINING EFFICIENCY THROUGH AUTOMATION [Online] Available at https://www.leewayhertz.com/ai-in-procurement/ (Accessed on 30 October 2023)
Kushner, A. (2023). Artificial Intelligence in Procurement [Online] (Accessed on 30 October 2023)
AI IN PROCUREMENT [Online] (Accessed on 30 October 2023)