Optimizing strategic procurement through artificial intelligence algorithms and big data analytics from Orpheus
- Ensure flawless, reliable procurement KPIs and big data analytics
- Derive savings and efficiency potential through intelligent analytics
- Define measures and activities from this potential
- Support an integrated approach to plan, manage, and measure success in procurement.
Flawless, classified, clustered spend data lay the foundation for successful procurement strategies. Companies need valid spend baselines and benchmarks in order to create intelligent, data-driven procurement or product category strategies. This allows them to measure the success of their procurement strategies at a later time.
Artificial intelligence BOTs (AI BOTs) automate many routine tasks for today’s busy strategic procurement managers. The software automatically suggests untapped potential or outliers in data. AI BOTs free up capacities so procurement managers can focus on their core tasks and leverage added potential.
Without semantic data management, strategic procurement managers lack comparable procurement data and a transparent, valid foundation of facts for product group and procurement strategies.
2. How strategic procurement benefits from artificial intelligence
Orpheus DataCategorizer uses a range of different artificial intelligence methods:
- Data Classification: The classification algorithms for procurement documents (invoices, orders/purchase orders) and master data (e.g. suppliers, materials) calculate spend transparency and valid baselines with high levels of automation. These methods turn the dream of price and spend comparisons and spend benchmarks into reality.
- Data Clustering: By combining same or similar parts and materials into groups across the enterprise, procurement professionals can identify starting points for standardization and specification changes. They can compare prices for similar parts through benchmarking, analyze prices and cost drivers, and create a data foundation for advanced methods of linear performance pricing.
- Cleansing & Outliers: Orpheus DataCategorizer identifies data errors and (KPI) outliers automatically. Clean data is essential to calculate valid, error-free procurement KPIs that drive savings and efficiency analysis, measure success, or simply serve as benchmarks. These methods also help establish price histories (e.g. predecessor/successor documentation).
Orpheus offers further artificial intelligence methods as an optional managed service. These analytic offerings conducted by our professional service division include:
- Predict & Simulate: These algorithms are based in part on artificial neural networks (ANN). They can be used in planning and simulation, for example, to forecast spend volume or simulate the effects of fluctuating material prices or exchange rates for planning and simulation. Other use cases include detecting cost drivers by analyzing cause-and-effect relationships.
- Consistency & Compliance: Our experts conduct a retrograde analysis of all (different) price attributes in the documents of a given process. Ideally, the prices in invoices, orders, procurement information records, contracts, and master records should be identical. The task here is to detect any price variances and, therefore, lost savings or unauthorized additional payments. Retrograde analyses of variances in the payment process or in payment conditions can also detect compliance breeches and, therefore, the accompanying negative liquidity and working capital effects.
- Process Mining: These algorithms analyze the actual procurement process to detect weaknesses and starting points for process improvements and automation. Some examples of processes include procure-to-pay, buying channel or procure-to-stock. The objective is to recognize suboptimal process variants, monitor service level agreements, or calculate and report process KPIs.
3. Real-world examples: Using artificial intelligence in procurement:
Text Mining and Rule-based Systems –
Make spend data and records transparent and comparable.
- Calculating detailed spend baselines
- Bundling spend into categories
- Creating benchmarks among companies in a corporate group
Cluster Analysis –
Enable intelligent comparisons of similar parts or materials.
- Preparing data for LPP (Linear Performance Pricing) analysis
- Standardizing materials
- Changing of material specifications
- Comparing or benchmarking prices of similar parts from different companies and suppliers
Artificial Neuron Networks (ANN) –
Forecast, predict, or simulate expenses, prices, and volumes.
- Forecasting and simulating invoice spend of indirect categories from orders of direct categories, exchange rate trends, or raw material developments
- Forecasting the time delay between orders and invoices per product category
Machine Learning & ANN –
Identify and learn from cause-effect relationships. Explain the origins of variances.
- Understanding the reasons behind a better/worse material-price variance (MPV). Which objects are primarily responsible for this difference?
Semi-automated Data Cleansing –
Identify data outliers and troubleshoot errors.
- Many procurement KPIs are meaningless because they were calculated using incorrect data. Modern algorithms are the optimal tool to automatically check millions of documents and master data for errors. The cleansing process is semi-automated.
Perfect data quality is the foundation for successful spend controls.
Automatic classification of procurement documents, suppliers and materials.
Please download the brochure DataCategorizer to gain a first impression.
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