Use cases
AI use cases in purchasing and procurement: Efficient process design in purchasing
Purchasing and procurement are of central importance to the success of many companies. These areas bear enormous responsibility with regard to cost development, liquidity and the development of supplier relationships. They also have the task of ensuring stable material flows. Artificial intelligence (AI) and new data management methods enable you to significantly optimize processes in purchasing and procurement.
- Automated checking of contracts, such as payment agreements
- Automated extraction and enrichment of master data
- Automated classification, e.g. impending supplier failure, taxonomy
- Duplicate detection and management
- Demand forecasts and recommendations for action
Typical AI use cases in procurement and purchasing
Problem: Supplier data not complete or up-to-date
Solution: AI analyses supplier data, e.g. from external sources or the signature of an email, identifies relevant changes and supports those responsible in updating the supplier master data.
Benefits: Optimized processes for purchasing, procurement and master data management.
Problem: Supplier quality problems are often identified too late.
Solution: AI analyses data, recognizes patterns and makes recommendations for supplier meetings.
Benefits: Faster problem resolution, less effort and better quality.
Problem: Strategies such as single or multi-supplier approaches are often based on experience and are not optimal.
Solution: AI evaluates materials and services to suggest the best purchasing strategy.
Benefits: Higher savings, reduced risks and more efficient procurement processes.
Problem: Pricing is often a complex process that depends on many factors. New or short-lived products often make price forecasting even more difficult due to a lack of a sufficient number of observations.
Solution: AI analyzes market trends, comparable products and calculates precise price developments.
Advantages: Better planning, higher savings and improved negotiating position.
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 on average 15% more accurate, lower risks of over- and under-coverage, less workload for your experts.
Problem: Concentration of large outgoing payments can lead to cash flow problems.
Solution: AI simulates complex payment flows and identifies potential risks at an early stage.
Benefits: Improved liquidity planning, possibility for timely equalization of orders or early negotiations with banks and suppliers.
Problem: Assigning items to an EClass, ETIM, customs tariff number, storage location or risk class requires experience. Sets of rules are often used to minimize the effort involved. Maintaining these sets of rules 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, relieves your experts.
Problem: Supply bottlenecks often require short-term decisions on substitute products.
Solution: AI identifies potential substitute goods and forecasts the demand for the substitute good.
Benefits: Risk minimization, lower procurement costs and proactive supply chain management.
Further use cases
Discover exciting use cases from different areas of the company and be inspired by how other teams find and implement innovative solutions. This exchange opens up new perspectives and creates valuable synergies for joint growth.
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