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Enterprise Forecasting

Automated Data Logistics and Advanced Machine Learning Techniques

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Enterprice Forecasting Use Cases

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.

Demand Forecasting
Demand Forecasting
Sales Forecasting
Predictive Maintenance
Price Optimization
Realty Indicators
Churn Prediction
Retail Analytics
Data Migration & Logistics
Transportation Demand Forecasting
Commodity Price Prediciton
Anomaly Detection In Accounts Payable

Demand Forecasting

Problem

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.

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Result

95%+ Accuracy
6,600 SKUs
360 Locations

Sales Forecasting

Problem

A major U.S. retail department store faced high inventory costs and inefficiencies managing 7,000+ products. Inaccurate demand forecasting frequently resulted in overstocks or stock-outs, impacting profitability.

Solution

Historical sales data and external market insights were integrated using NLP-driven predictive AI. Self-learning, multi-model technology provided dynamic, highly accurate weekly, monthly, and quarterly sales forecasts.

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Result

99% Forecast Accuracy
Significant Cost Reduction
Real-Time Insights

Predictive Maintenance

Problem

A Japan-based automotive components manufacturer used IoT sensors to track asset health, analyzing data on power, position, and stoppages to predict machine failures and optimize maintenance.

Solution

‍Findability Sciences structured graphical IoT sensor data into CSV files, built ML models to predict breakdown timestamps, and used time series analysis to identify failure patterns and key influencing factors.

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Result

90% Accuracy
Significant Cost Savings
Increased Operational Efficiency

Price Optimization

Problem

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.

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Result

98% Accuracy
MSE 0.4
Reduced time from 24 to 1.5 Hours

Realty Indicators

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.

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Result

>90% Accuracy
1M+ Forecasts
Recession Prediction

Churn Prediction

Problem

A Japan-based multinational aimed to reduce employee churn and improve mentorship but struggled to identify root causes of voluntary attrition despite development investments.

Solution

‍Created a predictive model analyzing performance scores, supervisor relationships, assessments, managerial feedback, and work patterns to forecast churn likelihood.

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Result

Accurate Churn Prediction
Optimized mentorship
Improved talent allocation

Retail Analytics

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.

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Result

+22% Accuracy
>90% Stat Sig
4 Weeks Saved

Data Migration & Logistics

Problem

A top Latin American sugar producer with operations across 5 continents needed to migrate data from SAP HANA Cloud and external sources to Google Cloud—ensuring unified processing, security, governance, and real-time accessibility.

Solution

Built a robust, scalable data pipeline using Airflow, BigQuery, and other GCP-native tools to automate extraction, categorization, governance, and access control—streamlining the entire data lifecycle.

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Result

Centralized Data Lake
Automated Pipelines
Role-Based Access

Transporatation
Demand Forecasting

Problem

Japan's leading B2B logistics provider needed robust forecasting to slash fleet idle time and significantly enhance operational efficiency across 27 locations.

Solution

Implemented Findability.AI’s sophisticated multi-algorithm forecasting, utilizing historical data, external variables, and targeted event indicators for accurate 45-day demand predictions.

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Result

95% Forecast Accuracy
7-Digit Cost Savings
Increased Customer Satisfaction

Commodity Price Prediction

Problem

A leading global MNC faced significant risks due to unpredictable aluminum market prices, impacting trading profitability. Traditional forecasting methods lacked precision, limiting effective risk management.

Solution

Implemented a customized commodity price prediction solution, providing accurate daily forecasts for aluminum prices (high, low, close) on the London Metal Exchange (LME), leveraging historical data, macroeconomic indicators, and business news analytics.

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Result

99% Prediction Accuracy
Continuous Functional Optimization
Real-Time Monitoring

Anomaly Detection In Accounts Payable

Problem

A leading US-based manufacturer set out to strengthen oversight of employee-generated SCM and inventory transactions. Existing controls missed near-duplicates and subtle irregularities. With 2 million records processed monthly and 2,000 flagged as fraudulent, the challenge was clear: uncover fraud, pinpoint procedural lapses, and expand anomaly detection across multiple categories to safeguard operations and scale resilience.

Solution

A rule-based and ML-driven system was designed to detect anomalies—near-duplicates, odd posting times, excessive transactions, and suspense clearing. Integrated dashboards empower managers with actionable insights. Even in pilot stage, it has uncovered previously undetected fraud, proving ROI potential, validating business impact, and setting the stage for measurable gains in fraud detection, operational efficiency, and executive oversight.

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Result

2M+ Records Monitored Monthly
2K+ Fraudulent Transactions Flagged
1 Undetected Fraud Exposed

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.

1
Demand Forecasting
95%+ Accuracy
6,600 SKUs
360 Locations

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.

2
Retail Analytics
+22% Accuracy
4 weeks saved
>90% Stat Sig

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.

3
Realty Indicators Forecasting
>90% Accuracy
1M+ Forecasts
Recession prediction

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.

4
Demand Forecasting for Logistics
95%+ Accuracy
7 Digit Savings
45 Days Advance

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.

5
Price Predictions
98% Accuracy
MSE 0.4
Reduced time from 24 to 1.5 Hrs

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.

What is Enterprise Forecasting?

Data Logistics
Analytical Techniques
Insight Delivery

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.

Data Logistics

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.

Analytical Techniques


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.

Insight Delivery

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.

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