AI/Ml based intelligent demand forecasting, demand sensing and supply distribution planning solutionI

Demand Forecasting

The Demand Forecasting module uses a combination of classical statistical forecasting techniques together with modern ML/AI algorithms to generate accurate long-term, medium-term and short- term forecasts. The Auto modeling functionality and Hyper Parameter optimization ensures that the system offers you a ‘best-fit’ forecasting model for a given product, location, and planning level combination, empowering citizen demand planners to create accurate demand plans. This is suitable even in lean manufacturing environments, it supports Kanban, a progressive Operations Planning technique that optimizes inventory levels while reducing inventory costs.


The Solution enables automatic selection of a 'best fit' forecast model

Advanced AI/ML algorithms in addition to traditional techniques:

Demand Sensing

The long-term forecast generated by Demand Forecasting is next refined for higher accuracy over the immediate short-term future horizon using various internal & external variables that have a significant impact on sales demand. Historical data on sales, forecast, and other internal/external variables, such as promotions, price, Point-of-Sales, weather, extreme weather events, strikes, lockdown, or any similar occurrences are used to train the AI/ML models to analyze all events and highlight events that may have a significant impact on sales demand. The trained AI/ML models are then used to predict sales demand accurately over the near-immediate future horizon on a real-time basis.
Traditional methods of demand sensing are so laborious that it is not employed too often as it requires many man hours and the payback may not be regular enough to warrant the expense. The downside is that an event that may have a significant impact may go unnoticed. Our Demand Sensing module eliminates this problem giving the Planner greater accuracy and therefore greater profitability for the company.  The Demand Sensing application handles the complex processes of data preparation for external factor variables before they are used to train the AI / ML models and predictions. 

*Demand Sensing components and Process

Getting started

Successful implementation of demand sensing needs the right mix of data, technology, people & skills. It’s vital to start on a small but sound base and gradually iterate to expand to more variables and application areas across product categories. The key steps to successful implementation are broadly defined below:

Supply Distribution Planning

The refined accurate forecasts from Demand Forecast and Demand Sensing modules are used to generate accurate supply distribution plans throughout the supply chain network. A master-data based user interface allows planners to maintain parameter data for each SKU & location based on which the system generates a distribution plan over a medium-term planning horizon. The forecast demand is considered along with current inventory & other receipt elements from ERP systems to create an accurate supply plan. Advanced functionality like Dynamic Safety Stock and Re-Order Inventory Level calculation enables the supply planners to maintain a lean inventory level but ensure a high rate of demand fulfillment without any stock-out situations.

Supply distribution

Data Preparation & Mapping

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