Predict variances and optimize plant performance

Harness the expertise of your most skilled operators

IBM Production Optimization learns from the actions of expert operators by correlating the actions with outcomes for any situation. The knowledge gathered is then used to calculate the right set points for various operating conditions and guide everyday operators on the right actions that will maximize throughput, optimize quality and minimize energy.

Unlock insights hidden in your plant floor data

Production Optimization uses advanced machine learning techniques to bring insights out of plant floor data collected. Process engineers can compare process efficiencies across several plants and baseline performance to derive process improvement steps.

Monitors a variety of KPIs and process variables

The solution is configurable so you can choose specific KPIs for optimization. In the case of cement, the fineness is an important criteria and has to be optimized at a narrow tolerance even if it takes more energy. Or vice versa, the energy consumption has to be minimized even if fineness has higher variance.

Supports on-premises vs as-a-service deployment flexibility

Production Optimization offers a number of deployment scenarios depending on requirements. Clients seeking to accelerate time to value and avoid upfront infrastructure costs can deploy the solution as a service. The solution can also be deployed on-premises for clients with unique security requirements, disconnected operations, or specific performance needs from near-edge processing.

Fast deployment with pre-built analytic models and templates

Production Optimization has pre-built advanced analytical pipeline for early anomaly detection, anomaly scoring, regression/prediction, auto classification and optimization optimized for various use case and processes in discrete manufacturing plants. This reduces upfront data scientist’s effort and accelerates time to value.

Out-of-the-box integration

Two way integration with EAM system is possible including strong integration with Maximo EAM. Plant Maintenance Supervisors are able to send EAM tickets after evaluating predictive maintenance alerts. They are able to monitor the status of the maintenance ticket throughout the maintenance workflow. The dashboard also gives consolidated metrics on the maintenance ticket status of all the work orders sent.

How customers use it

  • Reduce production loss due to unplanned downtime

    Reduce production loss due to unplanned downtime


    Unplanned downtime can be the most significant factor affecting overall equipment effectiveness. Estimates indicate approximately 89% of machinery failures are random, yet many organizations still practice "run to fail" or "time based maintenance."


    Asset instrumentation, connectivity, IoT, and analytics capabilities provide visibility into unplanned downtime to significantly reduce impact through predictions that allow operations and maintenance to proactively address impending asset failure.

  • Reduce loss due to poor quality

    Reduce loss due to poor quality


    Even in a thriving company cost of poor quality is estimated to be 10 to 15 % of operations. The earlier quality problems are identified in the manufacturing process the greater positive impact on overall equipment effectiveness and cost savings.


    Identify when production equipment is trending away from prescribed calibration(s) and is in danger of producing defective parts. Identify the source of the problem and recommend steps to mitigate process variances.

Technical details

Software requirements

There are no specific software requirements for IBM Production Optimization

    Hardware requirements

    There are no specific hardware requirements for IBM Production Optimization