Demand planning

What is demand planning? – Definition and basics

Demand planning forms the foundation of any efficient materials management system and refers to the systematic planning of material quantities at specific points in time. This process determines precisely which goods are needed when and in what quantities to ensure that production processes run smoothly. Companies use this methodology to minimise capital formation while ensuring delivery capability.

Demand planning in materials management involves more than just quantity planning. It takes into account complex relationships between primary, secondary and tertiary demand as well as external factors such as batch sizes and transport costs. While primary demand results directly from the sales programme, secondary demand arises from the bill of materials breakdown for assemblies and components.

Successfully implemented demand planning significantly reduces both shortages and excess inventory. Companies benefit from optimised cash flow cycles and improved delivery times. The challenge lies in combining different demand planning methods in a way that is appropriate to the situation and adapting them to specific business requirements.

An overview of the different types of demand planning

Deterministic demand planning

Deterministic demand planning is characterised by accurate demand forecasts based on known parameters. This method is ideal for companies with stable production programmes and predictable sales patterns. Planning reliability is achieved through mathematical models that calculate future demand precisely.

Typical areas of application include series production with standardised products and long-term supply contracts. Automotive suppliers often use this technique because manufacturers’ call-off schedules provide a reliable data basis. Cost optimisation is achieved through precise quantity planning without safety buffers.

Limitations become apparent in fluctuating market conditions or unforeseen changes in demand. Flexibility suffers from rigid planning logic, which is why combinations with other methods are recommended.

Heuristic demand determination

Heuristic demand determination is based on experience and expert knowledge rather than mathematical formulas. This method is particularly useful for irregular demand patterns or new products without historical data. Decision-makers use rules of thumb and industry knowledge to forecast demand.

Heuristic approaches are characterised by their ability to adapt quickly to market changes. Sales staff can identify short-term trends and incorporate them into planning. Personnel experience compensates for a lack of data, especially in the case of seasonal fluctuations or product launches.

However, subjectivity poses risks to planning accuracy. Systematic distortions due to personal assessments can lead to significant forecasting errors. Documentation and performance measurement of heuristic decisions improve the quality of forecasts in the long term.

Consumption-based demand forecasting

Consumption-based demand forecasting analyses historical consumption data to forecast future demand. Time series analysis and statistical methods identify trends, seasonal patterns and cyclical fluctuations. This technique works best for items with regular, predictable consumption.

Mathematical smoothing methods such as exponential smoothing or moving averages reduce random fluctuations in consumption data. Forecast accuracy increases with the quality and quantity of historical data. Automated systems can forecast thousands of items simultaneously and significantly reduce planning effort.

However, structural breaks in consumption patterns pose major challenges. Product changes, new sales markets or changing customer habits distort historical data bases. Regular review and adjustment of forecast parameters ensures consistently high planning quality.

Programme-oriented demand determination

Programme-oriented demand determination derives material requirements directly from planned production programmes.

Bills of materials and work plans form the basis for accurate demand calculations for raw materials, parts and components. This method guarantees maximum precision for complex product structures. ERP systems automate bill of material explosion, taking into account lead times, batch sizes and scrap rates.

Changes in the production programme are automatically propagated through all planning levels. Capacity planning and scheduling supplement pure quantity planning with time components.

However, flexibility requires up-to-date and complete master data. Incorrect parts lists or outdated lead times lead to significant planning errors. Continuous data maintenance and regular system validation are therefore essential for successful programme-oriented planning.

Order-oriented demand determination

Order-oriented demand determination is based on specific customer orders and is particularly suitable for individual production or customer-specific products. Each order triggers specific material requirements that exactly match customer requirements. This method minimises inventory risks, as only materials that are actually needed are procured.

Make-to-order strategies benefit from this planning logic, as variant diversity and customisation can be mapped precisely. Project business with one-off products or plant engineering uses order-oriented approaches for cost control. Customer deadlines directly determine procurement planning.

Challenges arise from fluctuating order intake and unpredictable delivery times.

Suppliers must be able to respond flexibly to short-term requirements. Framework agreements and call-off options create the necessary planning security for critical materials.

Demand determination process: plan-driven vs. rule-based approaches

Plan-driven demand determination

Plan-driven demand determination follows systematic planning cycles and integrates different areas of the company into coordinated demand planning.

Master production schedules and sales & operations planning form the basis for medium-term material requirements. This methodology ensures coordination between sales, production and purchasing. Rolling planning continuously adapts requirements to changing market conditions. Planning horizons vary depending on the type of material and replenishment time. A-parts require longer planning horizons than C-parts with short delivery times. Integrated systems automate planning processes and reduce manual effort. Planning quality depends largely on the accuracy of data and forecasts. Delays in data collection or incorrect entries propagate throughout the entire planning system. Regular planning rounds and exception handling correct systematic deviations in a timely manner.

Rule-based demand determination

Rule-based demand determination automates procurement decisions using predefined scheduling rules. Reorder points, maximum stock levels and order quantities are parameterised for each item. This method is ideal for items with constant consumption and short replenishment times.

Kanban systems and order point procedures implement rule-based logic in practice. When the reorder point is reached, order processes are triggered automatically. Safety stocks buffer demand fluctuations and delivery delays. Disposition is decentralised without a central planning authority.

Parameter optimisation requires regular adjustment to changing consumption patterns.

Static rules can lead to overstocking or understocking when trends change. Modern systems use machine learning to automatically adjust parameters and continuously improve planning quality.

Options for determining demand in practice

Determining demand in purchasing

Determining demand Purchasing coordinates procurement activities with suppliers and optimises purchasing conditions. Collaborative planning with suppliers enables joint demand planning and reduces bullwhip effects in the supply chain. Suppliers receive demand information at an early stage and can plan their capacities accordingly.

Framework agreements and call-off agreements create flexibility while ensuring cost certainty. Volume discounts and transport optimisation significantly reduce procurement costs. Category management groups similar items together and exploits synergies between different product groups.

Supplier relationship management integrates strategic suppliers into demand planning. Long-term partnerships are created through transparent information exchange and joint optimisation projects. Risk management identifies critical dependencies and develops alternative sources of supply.

Demand determination in materials management

Demand determination in materials management optimises inventory levels and improves material planning. ABC analysis classifies items according to their value share and controls differentiated planning strategies. A items receive intensive individual consideration, while C items are planned using standard procedures.

Inventory optimisation balances service levels and capital commitment using mathematical models. Economic order quantity and safety stock formulas take into account demand fluctuations, delivery times and ordering costs. Warehouse key figures such as turnover rate and range continuously monitor planning quality.

Cross-docking and vendor-managed inventory reduce inventory levels through alternative logistics concepts. Suppliers take responsibility for inventory and optimise replenishment processes. Digital technologies such as RFID and IoT enable real-time transparency of material flows and inventories.

Demand determination example: Practical implementation in various industries

The automotive industry demonstrates complex demand determination using multi-stage planning processes. Vehicle manufacturers use programme-oriented processes for main components such as engines and transmissions, while C-parts are planned using consumption-oriented methods. Just-in-sequence delivery synchronises material flow with assembly cycles and eliminates intermediate storage.

Retail companies cope with seasonal fluctuations through combined planning approaches. Fashion items require trend-oriented forecasts, while basic assortments are planned based on historical consumption patterns. Category management optimises assortment breadth and depth based on customer behaviour and market trends.

The chemical industry coordinates continuous production processes with discontinuous demand. Campaign planning groups similar products together and minimises setup times. Raw material availability and minimum shelf life have a significant impact on production sequencing and demand scheduling. Integrated planning optimises the entire value chain from raw materials to the end product.

Challenges and best practices in demand forecasting

Data quality is the foundation of successful demand planning and requires continuous attention. Incomplete or incorrect master data leads to systematic planning errors and increases procurement risks. Master data management establishes uniform data standards and maintenance processes for all information relevant to planning.

Integrating different types of demand determination requires well-thought-out system architectures and interface design. Hybrid approaches combine the strengths of different methods and compensate for individual weaknesses. A-items benefit from detailed individual planning, while mass items are efficiently scheduled using automated processes.

Technological support from advanced planning systems and artificial intelligence continuously improves forecast accuracy.

Machine learning identifies complex patterns in consumption data and automatically adjusts planning parameters. Predictive analytics anticipates market changes and enables proactive adjustments to the procurement strategy.

Conclusion: The optimal demand determination for your company

Successful demand planning requires the selection of methods appropriate to the situation and continuous optimisation. No single method can solve all challenges, which is why intelligent combinations of different approaches are necessary. Company-specific factors such as product complexity, market situation and supplier structure determine the optimal planning strategy.

Digitalisation opens up new possibilities for demand forecasting through real-time data and advanced analysis options. The Internet of Things and Industry 4.0 create transparent supply chains with automated demand detection. Blockchain technology enables trustworthy data integration between business partners.

Future-proof demand planning is evolving from reactive to proactive systems. Predictive maintenance and demand sensing identify needs before they arise. Continuous learning and adaptation ensure long-term planning excellence in dynamic market environments.

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