Procurement Glossary
Determining requirements: systematic identification and quantification of procurement requirements
November 19, 2025
Determining requirements is the foundation of any successful procurement strategy and includes the systematic identification, quantification and specification of material and service requirements. As the first step in the procurement process, it significantly determines the efficiency of downstream activities such as supplier selection and contract negotiations. Find out below what requirements analysis means in concrete terms, which methods are available and how modern technologies optimize the process.
Key Facts
- Determining requirements is the first and most critical step in the procurement process
- Differentiation between direct and indirect demand and between primary, secondary and tertiary demand
- Modern systems use AI and predictive analytics for more precise demand forecasts
- Incorrect determination of requirements can lead to excess stock or supply bottlenecks
- Integration into ERP systems enables automated and data-based demand planning
Contents
Definition: Determination of requirements
Determining requirements comprises all activities for the systematic identification, quantification and scheduling of procurement requirements in companies.
Basic requirement types
The needs analysis distinguishes between different categories:
- Primary requirements: finished products and saleable products
- Secondary requirements: assemblies, components and raw materials
- Tertiary requirements: Auxiliary and operating materials, wear parts
Demand assessment vs. demand planning
While requirements planning covers the strategic dimension, requirements determination focuses on the operational identification of concrete quantities and specifications.
Importance of determining requirements in Procurement
A precise determination of requirements forms the basis for efficient procurement logistics and optimal capital commitment. It enables well-founded decisions to be made when selecting suppliers and drafting contracts.
Methods and procedures
Various methods enable the systematic and precise determination of procurement requirements, depending on product type and planning horizon.
Deterministic determination of requirements
This method is based on specific orders and parts lists. It is particularly suitable for production companies with plannable production processes:
- Derivation from production program and parts lists
- Consideration of stock levels and lead times
- Integration in MRP systems (Material Requirements Planning)
Stochastic determination of requirements
Statistical methods are used for requirements that are difficult to plan. Requirements management defines the forecast parameters:
- Consumption-based forecasts with moving averages
- Seasonal adjustments and trend calculations
- ABC analysis for prioritizing critical materials
Hybrid approaches
Modern companies combine both methods and also use market data and expert knowledge for a holistic assessment of requirements.
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Important KPIs for needs assessments
Key figures make it possible to objectively assess the quality of demand assessment and identify potential for improvement.
Forecast quality key figures
These metrics evaluate the accuracy of the demand forecasts:
- Mean Absolute Percentage Error (MAPE): Average percentage deviation
- Forecast Accuracy: proportion of forecasts within defined tolerances
- Bias: Systematic overestimation or underestimation of demand
Inventory optimization
The capital commitment period and turnover rate show the efficiency of the determination of requirements:
- Storage range in days or weeks
- Obsolescence rate for unused materials
- Service level for critical components
Process efficiency
Operational key figures measure the efficiency of the requirements determination process and identify potential for automation using modern technologies.
Risks, dependencies and countermeasures
Incorrect determination of requirements can have a considerable impact on liquidity, delivery capability and competitiveness.
Forecast errors and their consequences
Inaccurate demand leads to suboptimal procurement decisions:
- Excess stock ties up capital unnecessarily and causes storage costs
- Understocking jeopardizes delivery capability and customer satisfaction
- Incorrect specifications require costly improvements
Data quality and system dependencies
The quality of the determination of requirements depends largely on the data basis. Materials management systems must be regularly maintained and validated in order to minimize sources of error.
Countermeasures and best practices
Systematic approaches reduce risks considerably:
- Regular validation of forecast parameters and models
- Implementation of plausibility checks and warning systems
- Setting up redundant data sources and backup procedures
- Continuous training of the employees involved
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Practical example
An automotive supplier implements an AI-supported system to determine demand for electronic components. The system analyzes historical consumption data, production planning and external market factors. By integrating sensor data from production, wear parts are precisely predicted. Implementation takes place in three phases:
- Data integration from ERP, MES and external sources
- Training AI algorithms with historical data
- Step-by-step introduction with continuous validation
Result: 25% reduction in stock levels with a simultaneous improvement in delivery capability from 94% to 98%.
Current developments and effects
Digitalization and artificial intelligence are revolutionizing the determination of requirements and enabling more precise forecasts with less effort.
AI-supported demand forecasts
The use of AI in Procurement enables the analysis of complex data volumes and patterns:
- Machine learning algorithms for improved forecasting accuracy
- Automatic adaptation to changing market conditions
- Integration of external data sources such as weather data or market trends
Digital supply chain integration
The digital supply chain connects all players and enables real-time data for determining demand. Cloud-based platforms synchronize demand information between suppliers and customers.
Predictive analytics and IoT
Sensor data from production and operation provides valuable information for predictive demand forecasting. Predictive maintenance reduces unplanned requirements and optimizes spare parts strategies.
Conclusion
Determining requirements is the foundation of successful procurement strategies and plays a key role in determining the efficiency of downstream processes. Modern AI-supported methods enable more precise forecasts and reduce manual effort at the same time. Companies that invest in systematic demand forecasting benefit from optimized stock levels, improved delivery capability and reduced procurement costs. The continuous development of methods and technologies makes demand forecasting a strategic competitive advantage.
FAQ
What is the difference between demand assessment and demand planning?
Requirements determination focuses on the operational identification and quantification of specific material requirements, while requirements planning covers the strategic, long-term orientation of procurement. Requirements determination provides the data basis for higher-level requirements planning.
Which methods are suitable for which types of requirements?
Deterministic methods are suitable for plannable production requirements with known bills of materials. Stochastic methods are used for consumables that are difficult to forecast. Hybrid approaches combine both methods for optimum results.
How can AI improve demand assessment?
Artificial intelligence analyses complex amounts of data and recognizes patterns that would be impossible to identify manually. Machine learning algorithms automatically adapt to changing conditions and continuously improve the quality of forecasts by learning from historical deviations.
What risks arise if requirements are determined incorrectly?
The main risks are capital commitment due to excess stock, supply bottlenecks due to understocking and quality problems due to incorrect specifications. These can lead to loss of sales, increased costs and customer dissatisfaction. Systematic validation and continuous improvement minimize these risks.
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