Single version of the truth. Whether you need to manage the forecast in days, weeks, or months; by SKU, category, brand, customer, warehouse, region; in units, pallets, kilograms; by revenue euros/dollars, or by profit contributed. It doesn’t matter how different stakeholders need to work with the forecast, Demand Planning provides a single version of the truth available in any dimension.
Collaborative. Demand Planning facilitates a consensus demand plan that combines the statistical forecasts with multiple functional perspectives and events from the sales, marketing, and finance teams.
Multi-enterprise. Demand Planning transcends fiscal entity boundaries ensuring the best possible end-to-end picture of future demand across multiple enterprises.
Data cleansing. Provides automated detection and correction of outliers, and other analysis of historical data using machine learning algorithms.
Baseline planning. Automatically segregates historical promotional activity from organic demand ensuring a promotion free baseline for statistical forecasting.
Statistical forecasting. Supports complex seasonality, external factors, trend analysis, auto-selection of best-fit algorithm; an expert library of forecasting methods for every possible sales profile including machine learning (intelligent forecasting clustering).
Forecasting for complex history. Provides forecasting capabilities using machine learning techniques that can accommodate short history, periodic history, and step changes.
Multi-echelon forecasting. Detailed or aggregate forecasts, top down, bottom up and middle out forecasts. Reconciles forecasts based on different input (products, customers, markets).
Demand shaping. Provides your organization the ability to simulate price/volume trade-off scenarios.
Product life cycle planning. Manage product lifecycle information ensuring demand expectations align with product life stage. This includes product introduction planning, growth tapering, pipe-fill volumes, functionally rich supersession management, and early obsolescence identification.
ABC classification. Perform pareto analysis to classify the portfolio using rules to identify fast movers, benchmark items, high profit and revenue contributors.
Inventory optimization. Determines optimal inventory policies (safety stock) based on service levels, inventory costs and supply and demand variances.
Trade promotions management. Provides for all types of promotional activity including special offer profiles, repetitive special offers and multi-item special offers. It determines promotional, cannibalization and halo impacts of a promotion across the buy-in, sell-out periods of the promotional calendar.
Cloud deployment. Demand Planning is available in the QAD Cloud and AWS public cloud. Both options provide a secure, reliable and extensive cloud infrastructure.
Mobility. The Demand Planning user experience supports Web, mobile and touch screen user interfaces.
Analytics. As IoT and machine learning deliver a greater number of data points, supply chain solutions must have a best-in-class capability to translate data into trends and decision grade analytics. Demand Planning seamlessly embeds capability from Qlik, a leading business analytics provider. This provides an intelligent and intuitive user experience supporting responsive and accurate decision making.
In-Memory. Demand Planning uses a highly scalable, rapid in-memory data model enabling real time simulation planning and effective decision support.
Integration. QAD DSCP supports integration with QAD, SAP, Sage, JDE, Oracle, Infor, Microsoft and many other ERP and enterprise applications. It uses a data hub approach to exchange supply chain information across the organization and includes a tool for building custom integrations.
