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Automated Data Logistics and Advanced Machine Learning Techniques
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.
A Top 5 retailer requires digital analytics for 4000 feature releases annually.
Their analysts would require 8 weeks to provide analytics on the impact created by a website feature release. These were manually computed, based on just a differential between impressions and conversions. The FS incrementality computation included seasonality, attribution, and statistical significance. Business users could gain instant insights through a conversational interface. Additionally, forecasts were generated with a 22% uplift compared to internal accuracies for all product conversions to detect anomalies after a release.
Forecasting of 8 real estate parameters for 24 months with over 90% accuracy.
The biggest challenge for a real estate asset management company in the US was forecasting market conditions for investment and divestiture decisions. By forecasting rent, occupancy, and value with over 90% accuracy for 24 months, the company was able to use the forecasts for making financial and investment decisions. Additionally, an economic downturn forecast was conducted to determine the probability of a recession in 6 months, 12 months, and 24 months with 94% accuracy.
B2B logistics company with over 26,000 vehicles in service. Forecast daily demand of vehicles at 27 locations.
Challenge:
Japan's premier transportation company sought to significantly lower transportation costs through precise demand forecasting. The objective was to maintain operations only in stores where there was a clear projection of future demand.
Solution:
Leveraging the advanced multi-algorithm time-series forecasting capabilities of Findability Platform, the company was able to accurately predict: A 45-day advance forecast of package volumes to be transported across 18 specific location pairs. The overall volume of packages to be moved throughout Japan. These accurate daily predictions of package volumes enabled the client to efficiently allocate their fleet, optimizing both vehicle usage and store operations. This strategic approach not only enhanced customer satisfaction but also ensured high accuracy in forecasting for both specific routes and nationwide operations.
Optimized SKU Level Price.
51,000+ materials to forecast, across 35 plants and sales demand for individual material. Over 80% forecasting accuracy.
Challenge:
A top-tier microelectronics component manufacturer aimed to refine SKU-level pricing strategies for trade sales. Their existing price prediction algorithm was plagued by bugs, leading to extended processing times. The goal was to suggest price points that enhance the chances of winning transactions, thereby maximizing revenue without sacrificing profit margins.
Solution:
Findability Sciences deployed its machine learning technology to analyze historical data on revenue, quantities, price points, deal success rates, and forecasted demand. This enabled the recommendation of optimal price points for each SKU. To facilitate seamless integration, a custom middleware consisting of SAP application connectors and SAP BAPI was developed. This solution bridged the client's SAP systems with the Findability Platform, ensuring efficient communication and data exchange.
Demand forecasting and inventory optimization for 6600+ SKUs across 350+ locations. Over 90%+ accuracy.
Challenge:
A premier HVAC solutions provider in North America aimed to refine inventory planning and enhance sales predictions across more than 360 locations. The complexity was heightened by a diverse product range exceeding 6,600 SKUs, where stock-outs were leading to elevated warehousing and holding expenses.
Solution:
To address this challenge, a specialized forecasting solution was engineered, leveraging both machine learning and deep learning algorithms. These techniques were combined in an ensemble approach to achieve superior accuracy over traditional time-series forecasting methods, offering a strategic advantage in inventory management and sales forecasting.
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
Discrete Target, 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.
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Multi-Model Predictive Analytics
Employs both supervised and unsupervised machine learning algorithms, alongside advanced multi-model prediction and time series forecasting techniques. It finds multiple patterns in the data, creates multiple models, and selects the best model for each record from Prediction data. This methodology allows for a broad range of analyses, from univariate to multivariate, ensuring that the predictive outcomes 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 for Least 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
Enterprise Forecasting 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 uses Sequential Additive Ensemble, a proprietary algorithm which provides higher accuracies.
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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.
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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 both supervised and unsupervised machine learning algorithms, alongside advanced multi-model prediction and time series forecasting techniques. It finds multiple patterns in the data, creates multiple models, and selects the best model for each record from Prediction data. This methodology allows for a broad range of analyses, from univariate to multivariate, ensuring that the predictive outcomes 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 for Least 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
Enterprise Forecasting 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 uses Sequential 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.
