How AI Can be Used to Improve Procurement Data Quality
- Quality procurement data is essential but often suffers issues like duplication and inconsistency, leading to losses.
- AI analyzes unstructured data, enriches incomplete information, maps processes, and monitors changes to boost data quality.
- Complete, high-quality data then drives smarter decisions and maximum efficiency.
December 07, 2023 | Procurement Software 2 minutes read
High quality data is essential for efficient and cost-effective procurement operations in today's data-driven business environment.
However, procurement data often suffers from issues like inaccuracy, duplication, and inconsistency. These challenges stem from manual data entry, disparate systems, and lack of standardized processes. Poor data quality can lead to misguided decisions, inefficiency, and financial losses.
The answer lies in leveraging artificial intelligence (AI) and automation to dramatically improve procurement data quality
Here's how:
Structured and Unstructured Data Analysis
A wealth of valuable data resides in unstructured formats like emails, attachments, scanned documents, and contract notes. AI tools equipped with optical character recognition (OCR), natural language processing (NLP), and machine learning can rapidly ingest these data sources. They identify suppliers, parts numbers, prices, contracts terms, and other vital information. This data can automatically link to existing structured data stores.
As a result, procurement has a more compete view of sourcing events and negotiations, with few gaps. This prevents maverick buying and drives better category analysis.
Data Enrichment
Even when data is structured, it often lacks consistent formatting or contains incomplete supplier or part number information. AI techniques can identify these gaps and inconsistencies. Then automatically enriches the data by tapping into internal and external data sets. This includes reference data for standard industry categorization codes, supplier master records, and market pricing data.
The enriched data drives smarter category and supplier analytics to identify savings opportunities. And with a complete view, maverick buying is prevented.
Process Mining
Organizations often have broken and inefficient procurement processes due to legacy systems and changing supplier and part landscapes. Process mining uses AI to automatically map actual processes end-to-end.
It identifies broken steps, automation opportunities, and bottlenecks.
Procurement leaders can see where key data is missing or decisions stall out. This drives process re-engineering and digitization for maximum efficiency.
Ongoing Data Monitoring
Finally, AI techniques can provide ongoing monitoring of data changes and trends. It identifies new suppliers, unusual orders, price changes, early contract expiration, and risk patterns. Procurement is automatically notified of changes and can act quickly to drive savings and mitigate risk.
Get More Insights: How Can You Make Your Procurement Data AI-Ready?
The Bottom Line
High quality, complete data is the fuel that powers procurement analytics, drives savings, and prevents maverick buying. AI and automation in procurement provide breakthrough technologies to tap into new data sources and enrich, clean, and monitor information. The result is informed procurement leaders who find efficiencies while controlling costs and risks. That’s the key to procurement and supply chain success in the digital age.
Learn more about GEP’s AI-first approach to procurement.
