Predictive Maintenance for Consumer Packaged Goods (CPG)
SparkCognition offers a significantly more advanced approach for CPGmaintenance. Predictive analytics software solutions—using artificial intelligence and machine learning—help plant managers stop spending time and wasting resources on assets that don’t yet actually require maintenance, and focus more on predicting and preventing asset failures before they occur.
By switching to an AI-based predictive maintenance approach, CPG plant operators can make the best use of the large volumes of data already generated by sensored assets in place, and proactively mitigate equipment failure in CPG plants at scale.
It delivers the power of expecting the unexpected.
McKinsey & Company research suggests that predictive maintenance can reduce machine downtime by 30-50% and increase asset life by 20-40%.
Improve operational efficiency
SparkCognition’s predictive maintenance solution for CPG optimizes production efficiency at scale, with continuous learning from a variety of operating conditions. With run-over-run comparisons, plant-over-plant comparisons, and continuous KPI insights on a very granular level, SparkCognition’s solution has been used to reduce operating costs and improve efficiency by 2-5%.
Minimize asset downtime
Predicting anomalous behavior in plant components is the precursor to taking action to prevent unexpected failures. By extracting facts, figures, entities, and contextual data from an asset’s maintenance history, our solution helps CPG operators determine causal patterns that indicate potential failures, even in so-called dark subsystems that lack sensors.
Increase Lead Time to Mitigate Failures
SparkCognition predictive analytics software can alert plant managers about suboptimal operations and impending asset failures days or weeks in advance, with minimal false positives. With the advantage of natural language processing technology, our solution also optimizes the capture and application of recommendations to expedite maintenance processes.
Optimize resource usage
Proactively maintain equipment, but only when and where it’s needed. Preset maintenance schedules and/or waiting to observe a failure are both highly expensive in terms of worker resources and lost production time. By leveraging predictive insights and root cause analyses, plant operators can dramatically reduce unplanned equipment downtime to avert possible plant shutdowns and save valuable resources.
SparkCognition solutions have been used to reduce operating costs and improve efficiency by 2-5%.
Machine learning is the future of maintenance for CPG. McKinsey & Company forecasts that AI adoption in the sector will likely create $200 to 500 billion of additional value each year, through optimizing supply management and management processes.
SparkCognition’s predictive maintenance solution uses machine learning algorithms to ingest large volumes of historical data alongside knowledge from subject matter experts, building a model profile of what normal operations look like for a total plant or fleet of plants. The normal behavior model can then analyze asset sensor data in real time, and flag any values that deviate from this established norm. The solution delivers predictive visibility, resource optimization, and anomaly management for actionable insights.
Most CPG operations are already heavily sensorized and monitored. But most are not taking advantage of the true potential of that data.
With traditional predictive models, a change in even a single variable, such as a replaced part, necessitates reworking the entire model. AI and machine learning alleviates—and can even eliminate—many of the difficulties associated with predictive maintenance.
It’s time for a new maintenance paradigm. Leading CPG companies will gain a wealth of benefits by taking a predictive maintenance approach powered by AI and machine learning.
Contact us to learn why machine learning is the future of maintenance for CPG.