Demand Forecasting


Efficiently Generate Scientific Forecasts with Automation

Demand forecasting analyzes historical data to forecast the potential of sales in the pre-determined future. Demand Forecasting helps Automotive companies make crucial business projections for project turnover, profit margins, revenue inflow, expenditure, risk assessment, mitigation scenarios, and capacity planning. AI-powered predictive analytics is used to predict customer demand and operationalize supply chain and business management.
The Problem Canvas
  • No Pre-trained Demand Forecasting Model
  • Datasets are not suitable, no story will be generated, or functions
    will be limited as there are only two columns
The Data Question
  • By applying ML to market data, product specifications, and sales trends, you can predict future sales more accurately. This helps to plan better and optimize inventory, production capacity planning & scheduling

Solution Canvas


Rubiscape’s demand forecasting leads to effective inventory management and evaluation of proposed holding cost. It helps map uncertainties and looks at how hidden undercurrents in present-day data change the business track at the organizational level in particular and the global level in general. Thus, you can identify all possibilities without making limited predictions.

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RubiStudio – Merging and Joiner, Expression Evaluation, Descriptive Stats, Hypothesis, Code Fusion, Writer
RubiCast – Data exploration, Data preparation, ARIMA, Auto ARIMA, Random walk
Rubisight – Exploratory and Demand Forecasting Dashboard with filters

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Collaboration Platforms
Business Reporting
Machine Learning
Domain Knowledge
Data-driven Problem Solving

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The dataset contains month-on-month car sales data for a period of 9 years.

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Business Impact
  • Better Bin Planning for Inventory Management, reduction in the shortage of the spare-parts
  • Improved after-sales service, and enhanced customer satisfaction
  • More visibility on Supply Chain and more control to reduce inventory costs leading to improved cashflows
  • Higher precision on capacity expansion planning
  • Achieve Incremental Savings and higher-order fulfillment
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