Artificial intelligence in purchasing – Efficient design of purchasing processes
Purchasing and procurement are of central importance to the success of many companies. These areas contribute enormously. Responsibility with regard to cost development, liquidity and the development of supplier relationships. You are also responsible for ensuring stable material flows. Artificial Intelligence (AI) and new Data management methods enable you to significantly optimize the processes in purchasing and procurement.
- Automated review of contracts, such as payment agreements.
- Automated extraction and enrichment of master data
- Automated classification, such as impending supplier failure, taxonomy
- Duplicate detection and management
- Demand forecasts and recommendations for action
Typical AI use cases in purchasing
Supplier master data
Problem: Supplier data incomplete or outdated
Solution: AI analyzes supplier data, e.g., from external sources or email signatures, identifies relevant changes, and assists those responsible in updating the supplier master data.
Benefits: Optimized processes for purchasing, procurement, and master data management.
Analyze supplier quality
Problem: Supplier quality issues are often identified too late.
Solution: AI analyzes data, recognizes patterns, and provides recommendations for supplier discussions.
Benefits: Faster problem solving, less effort, and better quality.
Optimize purchasing strategies
Problem: Strategies such as single-, dual-, or multi-vendor strategies are often based on past experience.
Solution: AI evaluates materials and services to calculate the most economically viable purchasing strategy for each item or product group.
Benefits: Greater savings, reduced risks, and more efficient procurement processes.
Price forecast
Problem: Pricing is often a complex process dependent on many factors. New or short-lived products often further complicate price forecasting due to a lack of sufficient data.
Solution: AI analyzes market trends, comparable products and calculates precise price developments.
Advantages: Better planning, higher savings and improved negotiating position.
Improve demand forecasts
Problem: Forecasts are often based on heuristic and statistical methods with reduced complexity.
Solution: AI enables more precise demand forecasts through sufficiently complex models with a large number of variables.
Advantages: Forecast quality is on average 15% more accurate, lower risks of over- and under-provision, and reduces the workload for your experts.
Monitor payment flows
Problem: A concentration of large payments over time can lead to cash flow problems.
Solution: AI simulates complex payment flows and identifies potential risks early on.
Benefits: Improved liquidity planning, the ability to stagger orders in a timely manner, and early negotiations with banks and suppliers.
Automating material classification
Problem: Assigning articles to an EClass, ETIM, customs tariff number, storage location, or risk class requires experience. Rule sets are often used to minimize the effort. Maintaining these rule sets also requires expertise.
Solution: Automate classification using AI and supplement missing information by extracting information from a variety of data sources.
Advantages: Always up-to-date, correct and complete master data, relieving the burden on your experts.
Recommending substitute goods and forecasting demand
Problem: Supply bottlenecks often require short-term decisions regarding substitute products.
Solution: AI identifies potential substitute goods and forecasts the demand for them.
Benefits: Risk minimization, lower procurement costs, and proactive supply chain management.
Further use cases in the field of AI
Discover exciting use cases from various business areas and be inspired by how other teams find and implement innovative solutions. This exchange opens up new perspectives and creates valuable synergies for shared growth.
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