dotData Transforms Manufacturing With AI

Yield analysis is increasingly difficult due to shorter product cycles, advanced functionality, and broadening specifications. dotData helps leverage the vast amount of data to identify factors contributing to quality issues and allowing faster root-cause analysis.

dotData can leverage a wide variety of data types for yield analysis. Manufacturers can also leverage time-series data with varying degrees of granularity. dotData can identify conditions that impact product quality – like the relationship between product surface temperature and equipment power.

Improve manufacturing yield and boost efficiency and productivity of quality analysis.

Because they rely on multiple time series models, supply chain forecasts are notoriously hard to maintain and often too slow to respond to changing market conditions. dotData can automate and accelerate the process as data changes.

Develop supply chain forecast models at accelerated rates and adjust forecasts based on changing data. By relying on AI Automation, supply chain managers can combine human experience with data-driven analysis to build multiple forecast models across products and granularity.

Create and maintain better forecast accuracy, reduce stock-out periods as well as excess inventory, while lowering the cost of shipping costs by optimizing your supply chain.

Leverage historical data like contract rates as well as related information like sales activity and customer attributes to effectively create better sales forecasts and campaigns.

With dotData, you can arm your sales staff with key predictors of customer interest like customer visits as they relate to product interest. Factors like contract rate can be provided to sales and combined with BI dashboards to create dynamic performance indicators that help guide sales and improve win rates.

Improve sales performance and boost customer satisfaction by predicting more accurately customer interest in specific products.

Create failure prediction models by utilizing historical data from both operations as well as from sensor data in shipped products. Reduce maintenance costs and improve customer satisfaction by identifying high-risk products in advance.

Identify factors that impact product failures like operating mode and duration and provide maintenance personnel with critical information to manage customer expectations for maintenance of at-risk products.

Lower maintenance costs and improve customer satisfaction by decreasing downtime and improving maintenance processes.