エンタープライズ予測

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

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.
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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.
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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.