Drive Better Business Decisions by Using Advanced Forecasting Algorithms

  • Consumer Packaged Goods

    Understand both the demand for a company’s products and the potential of the marketplace. This helps in building inventory, hiring staff for manufacturing, and planning marketing and distribution channels.

  • Banking, Financial Services, and Insurance

    Even a small increment in forecasting accuracy has a significant dollar impact. Advanced techniques are regularly used in quantitative finance, risk-assessment, and actuarial models.

  • Healthcare and Life Sciences

    These industries make regular use of forecasting techniques. For example, population health forecasting can improve preventive healthcare and reduce costs.

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    Accurately forecasting tomorrow's demand is critical to the success of many businesses. Fortunately, enterprises have a wealth of data to make this happen. Sophisticated forecasting algorithms can unlock data’s predictive power.

    This enables businesses to:

    • Understand business operations, markets, and competitors to stay ahead.
    • Smartly adapt to the ever-changing expectations of consumers.

    Fractal’s four ‘engines’ drive forecasting excellence:

    • EDA: Understand data to identify the right techniques to build forecasts.
    • Benchmarking: Create baseline accuracies that the models have to beat.
    • Modeling: Use sophisticated algorithms to drive better results and solve for data issues.
    • Confidence: Use multiple metrics to understand the model's coverage of the data.

    These drivers help enterprises achieve:

    • Accurate forecasting: The modeling engine uses state-of-the-art forecasting algorithms that overcomes the shortcomings of common techniques to drive more accurate forecasts, which reduces inefficiencies.
    • Success with hard to forecast cases: When data is sparse or volatile, standard methods fail because they are built on different assumptions. The modeling engine uses advanced techniques from machine learning and forecasting to work under such scenario.
    • Deeper Insights: The EDA engine provides deeper insights on data to enable action and decisions before the final forecasts.
    • Better benchmarks: The benchmarking engine provides better and higher benchmarks for the models to beat, which in turn improves forecast accuracy and usability.
  • Accurately forecasting tomorrow's demand is critical to the success of many businesses. Fortunately, enterprises have a wealth of data to make this happen. Sophisticated forecasting algorithms can unlock data’s predictive power.

    This enables businesses to:

    • Understand business operations, markets, and competitors to stay ahead.
    • Smartly adapt to the ever-changing expectations of consumers.

    Fractal’s four ‘engines’ drive forecasting excellence:

    • EDA: Understand data to identify the right techniques to build forecasts.
    • Benchmarking: Create baseline accuracies that the models have to beat.
    • Modeling: Use sophisticated algorithms to drive better results and solve for data issues.
    • Confidence: Use multiple metrics to understand the model's coverage of the data.

    These drivers help enterprises achieve:

    • Accurate forecasting: The modeling engine uses state-of-the-art forecasting algorithms that overcomes the shortcomings of common techniques to drive more accurate forecasts, which reduces inefficiencies.
    • Success with hard to forecast cases: When data is sparse or volatile, standard methods fail because they are built on different assumptions. The modeling engine uses advanced techniques from machine learning and forecasting to work under such scenario.
    • Deeper Insights: The EDA engine provides deeper insights on data to enable action and decisions before the final forecasts.
    • Better benchmarks: The benchmarking engine provides better and higher benchmarks for the models to beat, which in turn improves forecast accuracy and usability.

Case Studies

    Enable forecasting for global markets across regions

    A pharmaceutical manufacturer forecasts 450+ country-category combinations in 90 countries. The global analytics team of a large manufacturer of medical devices, pharmaceuticals, and consumer packaged goods was developing global-scale country-category forecasts using syndicated data sources. They wanted to build automated forecasting solutions for generating forecast numbers across 450+ country-category combinations of value and volume. To get a holistic view of the numbers, country, regional, and business heads were to consume the output of such forecasts through dashboards and...

Our People

  • Prashant Joshi

    Fellow