Improve availability with more accurate demand predictions
Pick the best forecasting technique
Parts demand is often irregular and difficult to predict, especially in MRO and spare parts environments. Lanza supports multiple forecasting techniques that match the characteristics of your parts portfolio.
Forecasting methods include support for:
- Slow-moving parts
- Intermittent or lumpy demand
- Seasonal demand patterns
- Trend-based demand
- Planned vs. unplanned demand
Lanza helps you select the most appropriate method based on forecast accuracy, ensuring reliable input for your planning decisions.
Improve your forecast by removing outliers
Reliable forecasting starts with reliable data. Lanza allows you to quickly identify outliers that distort demand patterns. One-off modifications, recalls, or simply typos. They should not be included in your future demand.
By correcting or excluding exceptional demand values, you improve forecast accuracy and create more accurate planning parameters, e.g. safety stocks and reorder points.
Combine Production and After Sales Demand
For OEMs that deliver service to their end clients, spare parts demand often originates from two distinct sources: production requirements and after sales service demand. Lanza enables you to combine both demand streams into a single, integrated forecasting approach.
By consolidating internal production demand with field demand from the installed base, planners gain a more complete and realistic view of total spare parts requirements. This helps prevent shortages caused by competing demand streams and enables more effective coordination between manufacturing, service, and supply chain teams.
Combining Production and After Sales demand supports better capacity planning, improved availability for customers, and more consistent inventory decisions across the product lifecycle.
Enrich forecasts with reliability figures
Historical demand alone is not always sufficient for spare parts planning. Lanza allows you to enhance forecasts with additional operational data, such as:
- Planned maintenance schedules
- Bill of Materials or asset structures
- Installed base changes
- Running hours
- MTBF or MTBUR data
By combining statistical forecasting with engineering insights, Lanza creates more realistic demand predictions for complex spare parts environments. Techniques like Machine Learning enhance the demand forecast even more.
Improve planning confidence across your organisation
More accurate forecasts help you:
- Improve parts availability
- Reduce unnecessary safety stock
- Increase confidence in planning decisions
Demand forecasting becomes a reliable foundation for inventory optimisation.
See how more accurate forecasting improves your parts performance
Discover how Lanza helps you predict demand more reliably and create better planning decisions across your organisation.
