Demand Planning is a multi-step operational process that is essential for a company to create a reliable supply chain forecast. Effective demand planning can increase the accuracy of revenue forecasts in tactical, operational, and strategic business plans.
To ensure the accuracy of your forecasts Olapsoft provides multiple classiacal forecasting methods :
- Linear and non-linear regression;
- Simple moving average;
- Hyperbolic regression;
- Variations of exponential smoothing;
- Croston method;
- Auto-regressive integrated moving average models (S)ARIMA(x).
ML-driven Demand Forecasting
Decision trees and ensemble:
- Decision trees;
- К-nearest neighbors;
- Random Forest;
- Gradient Boosting.
Clustering: К-means and DBSCAN
Statistical sales and demand forecasting
- Forecast products’ quantity, prices, demand of raw materials needed.
- Plan for shifting demand and seasonality.
- Compare multiple scenarios for each product and product family against each other.
Marketing events planning
✓ Trade promotions. Compare different trade promo strategies and budgets, raise promotion plan accuracy. Maximize your promotion plans effectiveness.
✓ New product launches. Build a granular forecasting model to see what pirce would be most advantageous for different market segments in different regions.
✓ Store openings. Compare different possibilities taking into account wide range of influencing factors.
Product portfolio management
Product portfolio management provides comprehensive understanding of products and their lifecycles.
Product portfolio management on the Olapsoft platform can show how shifting demand can affect different products. Olapsoft helps your planners to better fit new products into the existing product portfolio, predict cannibalization, conduct end-of-life planning and the analysis of attachment rates.
✓ Creating a multi-week statistical forecast based on a defined-week grouped calendar.
✓ Flexible time intervals for the demand and forecast period, allowing you to view the forecasts in days, weeks, months, fiscal quarters, calendar quarters, etc.
✓ Options to analyze historical demand data for each combination of product and location to determine the appropriate class of demand (e.g. seasonal, unseasonal, volatile or irregular).
✓ Selecting the most appropriate model from the group of algorithms and separating normal demand from advertising or unusual demand streams.
✓ Seasonality and trend analysis.
✓ Options to forecast in alternative units of measure (cost, price, cases, global sales unit, weights and sets).
✓ Demand forecasts can be adjusted to anticipated supply based on the latest supply chain planning capabilities.
✓ Allows users to easily import and export data from/to database and/or external data sources
✓ The power to create different scenarios and forecasts, then to compare them to one another.
✓ Identify and mark as exceptions the forecasts exceeding the parameters set by the user.
✓ Allows users to create scenarios using various parameter settings to understand how changes in such parameters (forecasted product price, expected growth) affect the organization’s forecast, stock position, revenue, and profit.
✓ Users can manually adjust demand history by exception so that demand anomalies (e.g. promotional) may be removed from the data to ensure statistical validity.
✓ Ability to add new statistical algorithms/models.
✓ Provides cross-functional capabilities to view forecasts in different divisions or at various levels of hierarchy within an organization.
✓ Forecasts across hierarchy levels (distribution center level, plant level, market level, customer level, product level).
✓ Generation of sales and operations planning reports with the option to conduct operational planning via “what-if” scenarios.
✓ Introduction of new production and option to phase-in and phase-out at the end of life, identifying similar elements and planning based on historical patterns.
✓ Classifying and grouping similar products into multiple categories (statistically significant, slow/no action/highly volatile) and applying different forecasting techniques to them.
✓ Tracking and monitoring of forecast accuracy shifts with varying lag times.
✓ Users can analyze order structure for each customer to determine the proportion of monthly or weekly forecasts in derived or estimated daily level forecasts.
✓ Exception management reports and alerts to inform the demand planner of any anomalies, outliers, abnormal patterns or trends.
✓ Long-term demand forecasts are used and developed as the product lifecycle ensues.
✓ Development, maintenance, monitoring, and evaluation of business plans, replenishment programs and forecasts.
✓ Synchronizes production, replenishment, forecasting and promotion plans throughout the extended network.
✓ Supports end-to-end planning and execution workflows, including sharing of orders, forecasts, logistics, inventory, and capacity.
✓ Power to define approval step workflow in the context of collaborative planning processes.
✓ Real-time data entry (cancelled orders, unusually large and/or unexpected orders, shipment notifications) are used to adjust expected demand.