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Slide One

Big Data

for Sales Forecasting

Slide One

Big Data

for Sales Forecasting

Demand Forecasting Analytics

Ideal Demand Planning

  • General Trade is the most widely adopted and longest running channels in India and most developing nation. The
    scale of the channel, and fragmented method of movement of goods from manufacturing plants all the way to the
    consumer make it one of the most challenging channels to truly optimize, despite its long-running nature.
  • Inefficient forecasting done manually using legacy tools or Excel has a ripple effect across the entire supply chain
    due to the inherent limitations and errors in such types of forecasting. AI BASED AUTOMATION is the most profitable
    way of tackling this issue.


Problem Overview

  • The scale of the channels brings a lot of complexity in the picture for supply chain. Lack of
    granular-level visibility adds to the issue of clogging the supply chain at certain locations, and stock-outs at some others.
  • The massive number of moving parts also makes it difficult and expensive to implement larger changes around logistics /
    network optimization. However, demand forecasting is the one area where massive improvements are possible using AI,
    and the benefits across the supply chain are immediately visible without having to make any operational changes
big data analysis

Forecasting Analysis

  • Forecasting Analysis offers to create a functional model based on the historical sales predicts the demand of a product for coming months and what if analysis of increasing or decreasing the inventory and analyze your savings.
  • Demand Planning: History of analysis and future prediction where the management can get clear visibility of how much to invest for the future stock and how much cost can be saved. The application will indicate how much the savings or investment lost from past history of data.


  • ARIMA, short for ‘Auto Regressive Integrated Moving Average’ is actually a class of models that ‘explains’ a given time series based on its own past values, that is, its own lags and the lagged forecast errors, so that equation can be used to forecast future values
big data analysis
big data analysis


  • The ETS models with seasonality or non-damped trend or both have two unit roots (i.e., they need two levels of differencing to make them stationary). All other ETS models have one unit root (they need one level of differencing to make them stationary).


  • Holt-Winters is a model of time series behaviour. Forecasting always requires a model, and Holt-Winters is a way to model three aspects of the time series: a typical value (average), a slope (trend) over time, and a cyclical repeating pattern (seasonality).
big data analysis