Demand Planning
SOLUTIONS DESIGNED TO SYSTEMATICALLY ESTIMATE MARKET DEMAND
Be it New Product Development, Planning, or Promotions, implementing collaborative Demand Forecasting provides you with statistical / ML / AI data to support the course of action in the medium term.
Description
Demand Planning Module is a collaborative tool that allows for a consensus demand to be arrived at across the organization. The first step towards Demand Planning is arriving at the statistical and ML/AI based forecast.
The Module applies various methods of forecasting on the historical Sales data to arrive at the forecast in the coming months. The best fit is selected as the method that offers least error.
Forecasting can be done at various levels such as SKU-Depot / Brand-Region / SKU Region etc. and tool works in conjunction of our consensus Planning tool to allow for Bottom-Up updation by Salesperson and Top-Down rationalization by Sales Head
Benefits
Features
Statistical and ML/AI Forecasting Techniques
Implement advanced statistical methods and machine learning algorithms to forecast at multiple hierarchies, providing more accurate predictions across various levels of granularity.
Choice of Different Error Measures
Utilize a range of error measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Forecast Bias to assess forecasting accuracy and identify areas for improvement effectively.
Classification Basis: Runner, Repeater, Stranger | 9 Box Analysis
Classify products based on their performance characteristics using frameworks like Runner-Repeater-Stranger or 9 Box Analysis, enabling tailored strategies for different product groups.
Multi UOM Forecasting
Forecast demand in multiple units of measure (UOM) such as units, value, tons, etc., allowing for a comprehensive view of demand across various metrics and facilitating better decision-making.
Promotion Data Impact Through Elasticity
Analyze promotion data and its impact on demand elasticity to adjust forecasts accordingly, ensuring accurate predictions during promotional periods and minimizing inventory issues.
Outlier Detection, New Product Development, Cannibalization Impact, Consensus Planning
Detect anomalies, consider new product effects, address cannibalization, and integrate consensus planning for robust forecasting.
Some Customer Cases
On the quantitative side, we have integrated the powerful Statistical and Graphical Modelling libraries of Python. We have also connected to ML / AI libraries of WML / Google / AWS to enables the application of a variety of statistical and ML/AI techniques for different situations
Consumer Electrical Company
Enhanced visibility and transparency via S&OP suite for a consumer electrical manufacturer
Speciality Tyre Company
Implemented Demand Forecasting and Demand Aggregation for the smooth function for the manufacturing unit
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