Advanced Science Delivers Greater Returns On Inventory Assets

Even the most regular-selling item can be difficult to forecast. Add in slow sellers, intermittent items, new item introductions, and promotions, and forecasting future demand can be extremely arduous. Multiply that problem by a massive network of SKU locations, and the problem becomes even more daunting. However, predicting future demand is the first step in any inventory strategy, regardless of the industry or type of items being managed.

Manhattan’s demand forecasting solution provides visibility into network demand and combines innovative forecasting techniques with demand cleansing, seasonal pattern analysis, and self-tuning capabilities to accurately anticipate demand even in the most complex scenarios. Using machine learning to constantly evolve and adapt the science of demand forecasting, our customers benefit from higher degrees of forecast accuracy, without heavy user intervention.

Manhattan's demand forecasting solution simplifies the complex science behind Demand Forecasting by focusing the analyst on managing just those key exceptions that the system itself cannot reconcile. It becomes easy to manage an infinite combination of locations and products with differing time horizons and aggregation to enable assortment, financial, and merchandise planning, in addition to replenishment.

• Improve forecast accuracy
• Model demand for slow and intermittently selling items
• Forecast demand for both enterprise planning and daily replenishment activities
• Scale to meet the needs of very large networks

• Generate and maintain forecasts at different levels of product and location nodes
• Forecast by selling channel to ensure a granular view into omnichannel fulfillment strategies
• Leverage built-in demand cleansing and seasonal profiling to properly account for repeatable patterns
• Detect and self-correct destructive seasonal profiles
• Sense and respond to trends and demand patterns with the self-adapting Unified Forecasting Method™
• Filter out non-essential forecast exceptions
• Detect and self-correct when demand patterns change with Advanced Exception Management
• Benchmark accuracy of manual forecast changes against system-maintained demand forecast
• Audit management of forecast exceptions
• Protect the integrity of demand history when exceptional events occur that have a negative impact on demand
• Compute promotional event lift expectations based on similar events executed in the past
• Track promotional impact on demand separately from baseline demand