Predict Consumer Demand in COVID 19 with a Short-Term Demand Forecasting Model Using ML — ITC Infotech

  • Slowdown in production due to gathering restrictions and slower movement of goods
  • Conventional just-in-time inventory & replenishment not able to handle the disruption.
  • Shortage of raw material
  • Unplanned shutdown of plants for sanitization following infection among workers and
  • Increased worker absenteeism
  • POS Data: Point of Sales data for a few of the Key Accounts/Online Vendors with respective market share will help in deducing the new behaviour of these accounts.
  • Weather: Consumer-based businesses should be paying greater attention to the weather as about 5% of their total annual revenue directly impacted by the weather, with much higher sales variability in seasonal categories. Based on research by the American Meteorological Society, current U.S. economic output varies by up to $630 billion a year (about 3.4% of 2016 gross domestic product) due to weather variability.
  • Consumer Sentiment: Information like Consumer Confidence, Consumer Credit, Consumer Spending, Disposable personal income, Households debt to GDP and Consumer Savings can contribute a lot to understanding the consumer situation.
  • Macro-Economic Data: Information like Consumer Price Index (CPI), Producer Price Index (PPI),Economic Activity Index, Unemployment Rate, GDP from various sectors, GDP Growth Rate, GNP, Labour data and Housing Index will provide information on the economic situation of the region.
  • Disease spread / mortality rate: Information on the number of Coronavirus Cases, Deaths and Recovered will be a strong influencer on how the customers will react to this outbreak. For example, in case of a sudden large spike of the COVID-19, customers might be motivated to stockpile items.
  1. Multiple Linear Regression model with ARMA errors,
  2. Box-Jenkins ARIMAX (Auto-Regressive Integrated Moving Average with suitably estimated parameters from the data)
  3. More sophisticated algorithm like Dynamic Linear/Non-Linear Models — Gaussian State Space Models with Kalman filters which essentially includes models like UCM — Unobserved component Model, Bayesian Structured Time Series Model, Prophet
  4. Highly computationally intensive models from the class of Deep Learning Models like LSTM using daily historical POS data in the presence of other data sources as mentioned earlier along with the data representing consumer behavior.
  1. Downstream Data Integration: Analyzing point-of-sale data from different regions, markets, brands and distribution channels to better understand consumer behavior.
  2. Measuring the Impact of Demand Shaping Actions (DSA): Recording and determining the impact of so-called demand shaping events like COVID19 in this case.
  3. Latency Reduction (LR): Model demand more frequently — weekly, or even daily
  1. https://www.nielsen.com/us/en/insights/article/2009/h1n1-impact-implications/
  2. https://www.numerator.com/resources/blog/impact-covid-19-consumer-behavior
  3. https://www.mckinsey.com/business-functions/risk/our-insights/covid-19-implications-for-business

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ITC Infotech

ITC Infotech

ITC Infotech is a global technology solution and services leader, providing business-friendly solutions, that enable future-readiness for clients.