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Enterprise Forecasting

Automated Data Logistics and Advanced Machine Learning Techniques

Data Logistics

Data Logistics Wide Data: The collection, analysis, unification, and preparation of data assets, encompassing both structured and unstructured categories from Internal and external sources.

Data Scenario: DiscreteTarget, Continuous Target, Time Series Target 

Data Exploration: Employing sophisticated algorithms to sift through data to  identify patterns, features, trends, and insights that are crucial for informed decision-making through statistical tests and visualizations. 

Data Quality:
Employs advanced algorithms to ensure appropriate quality of data through handling missing values, outliers, bias, drifts, etc. 

Feature Engineering : Utilizing cutting-edge machine learning techniques to enhance data attributes through encoding, normalizing, scaling, balancing, etc. to remove noise from the information utilizes a mix of internal, external, structured, and unstructured data sources to provide a holistic view of the forecasting landscape. This approach ensures that all relevant factors are considered in the predictive models, offering a more accurate and nuanced understanding of future trends and outcomes.

Advanced Analytical Techniques

Multi-Model Predictive Analytics:  Employs bothsupervised and unsupervised machine learning algorithms, alongside advancedmulti-model prediction and time series forecasting techniques. It findsmultiple patterns in the data, creates multiple models, and selects the Bestmodel for each record from Prediction data. This methodology allows for a broadrange of analyses, from univariate to multivariate, ensuring that the predictiveoutcomes are highly accurate. 

Discrete Target Variable Data: Enterprise Forecasting handles the Binary as well as Multi-class scenarios equally well. In case the target variable data is binary it offers three modes of modeling as Model forLeast Frequent Value, Model for Most Frequent Value, and Model for Both Values(BV) The Least Frequent option is generally suitable for targeting applications like propensity to pay, Loan default, employee churn, etc.

Continuous Target Variable Data: Enterprise Forecasting identifies if the target variable data is continuous and models the data for multi-model estimation. This is suitable for all regression like applications.

Time Series Data: EnterpriseForecasting can handle time series data and perform modeling and forecast. The data may contain multiple time series and Enterprise Forecasting handles each time series independently. It usesSequential Additive Ensemble, a proprietary algorithm which provides higher accuracies.

Tailored Insight Delivery

Model Insights: Provides model performance metrics including statistical measures as well as visualizations appropriate for various types of applications to make informed decisions, variable importance to understand the influencing factors, local or model level explanations for deeper understanding and statutory compliances. 

Customizable Insight Dissemination: Results are delivered through an array of customizable mediums—be it custom dashboards, triggers, alerts, simple reports, or API integration with existing legacy systems. This flexibility ensures that actionable insights are accessible in the format that best suits the operational and strategic needs of the business, facilitating easier decision-making and integration into business processes.

Enterprise Forecasting: Play the Video

Case Studies

Findability Platform's Enterprise Forecasting Case Studies are powered by automated data logistics which maximizes efficiency and reduces pre-modeling time by up to 75% with our intuitive, no-code statistical data analysis platform.

With a comprehensive range of options for univariate, bivariate, and multivariate analysis at your fingertips, our platform simplifies and speeds up data pre-processing, paving the way for swift progression in your machine learning endeavors.

It elevates your strategic insights by forecasting vital business metrics, including product sales, revenue, quantities, and pricing trends with our advanced multi-model prediction and time series forecasting tools.

Our automated, no-code prediction platform streamlines the AI model development and validation process, saving substantial time and effort.Craft and deploy rapid, low-latency solutions tailored for real-time or near real-time applications, enabling prompt and informed decision-making.

Retail Analytics

Digital analytics for ~4,000 feature releases annually on the website

Realty Indicators Forecasting

Forecasting of 8 real estate parameters for 24 months with over 90% accuracy

Demand Forecasting for Logistics

B2B logistics company with over 26,000 vehicles in service. Forecast daily demand of vehicles at 27 locations.

Price Predictions

>51,000 material to forecast, across 35 plants and sales demand for individual material. Over 80% forecasting accuracy.

Demand Forecasting

Demand forecasting and inventory optimization for 6600+ SKUs across 350+ locations. Over 90%+ accuracy.