Rise above uncertainty in the CPG industry with the power of AI at scale.

TAKE FULL ADVANTAGE OF MACHINE LEARNING FOR SMARTER CPG MAINTENANCE AND QUALITY CONTROL

Extract actionable quality
control insights

SparkCognition solutions provide layers of new insights driven by data you already have, allowing you to monitor snapshot metrics and asset health continuously, validating areas of improvement across plants, product lines, runs, and timelines—with intelligent analysis.

Manage for the KPIs that
matter most

With run-over-run comparisons, plant-over-plant comparisons, and continuous KPI insights on a granular level, SparkCognition solutions have been used to significantly reduce operating costs and improve efficiency.

Maintain proactively,
not wastefully

Predictive analytics software solutions use artificial intelligence and machine learning to help plant managers stop burning time and wasting resources on assets that don’t yet actually require maintenance. Predict and prevent hard-to-find asset failures before they occur.

According to Allied Market Research, the global market for artificial intelligence in manufacturing is projected to reach almost $15.3 billion in 2025.

Reduce unexpected downtime

SparkCognition solutions provide CPG plant operators with faster insights into asset failure prevention. Reduce unplanned outages by analyzing large volumes of data to identify anomalous behavior and show causal relationships using unsupervised learning techniques.

Minimize operational costs

Our solution allows CPG plant managers to make better use of the sensor data they already have. Predict asset failures with enough lead time to take corrective actions before the equipment has a chance to break down while leaving assets that do not need maintenance to continue running.

Optimize asset performance

Advanced behavior modeling helps you flag operational inefficiencies and underperformance from actual observations and system behavior within the contextual environment. Natural language processing (NLP) reduces machine troubleshooting time by automatically classifying fault codes and recommending optimal corrective actions.

Streamline decision making

SparkCognition supports engineers and analysts with real-time predictive insights to automate the most time-consuming steps of the data science process.

Prevent zero-day cyber attacks

Preventing 99.9% of never-before-seen attacks, SparkCognition provides highly awarded protection against zero-day ransomware, viruses, malware, and more.

Rapidly scale AI

Using unsupervised learning techniques—with models that adapt and maintain themselves dynamically whenever components or assets may change—our solutions scale efficiently across systems, plants, and geographic regions.

Use Case Library

CPG plant operations and assets tend to deteriorate over time. But most of the industry still relies on preventive maintenance to avert failures. SparkCognition offers an advanced approach with predictive analytics software solutions to help plant managers stop churning valuable resources on assets that don’t yet actually require maintenance, and start predicting and preventing hard asset failures before they occur.

Read Use Case

Products that do not pass quality standards waste valuable resources, money, and operating time. But if they make it to distribution, they pose a material threat to the producer’s reputation. SparkCognition develops scalable machine learning solutions to assess operational performance and address inefficiencies in production processes proactively.

Read Use Case

Find out how SparkCognition can protect, monitor, and optimize your operations.

Upcoming Webinar

The Age of Digital Transformation:
Enabling Organizations with Artificial Intelligence During Market Transitions

This webinar will discuss the influence of artificial intelligence transformation and the most significant financial opportunities for the companies that apply this technology. Key topics include how to quickly capitalize on the fast-moving technology curve and how to develop an AI strategy based on previous implementations at some of the largest companies in the world.

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