5900-A35 IBM Maximo Production Optimization SaaS

IBM Canada/Latin America Sales Manual
Revised:  September 10, 2019.

 
Table of contents
TOC Link Product life cycle dates TOC Link Description
TOC Link Program number TOC Link Operating environment
TOC Link Abstract TOC Link Planning information
TOC Link Highlights TOC Link Publications

 

Product life cycle dates

Program NumberVRM Announced Available Marketing Withdrawn Service Discontinued
5900-A3500.00.002018/09/182018/09/26 2019/12/10 -

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Program number

  • IBM Maximo Production Optimization SaaS (5900-A35)

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Abstract

IBM Production Optimization has been renamed to IBM Maximo Production Optimization SaaS

IBM Maximo Production Optimization SaaS is a cloud service industry solution offering that is purpose-built for production floor users. It employs artificial intelligence and machine learning technology to enable manufacturing and production plants to implement Industry 4.0 use cases that can help optimize throughput, improve yield, and reduce cost of manufacturing.

Maximo Production Optimization SaaS enables data scientists and process engineers to customize prebuilt analytical model templates and adopt them for specific processes and assets. The offering also provides process engineers with the capability to configure plant information models, configure KPIs, map the analytical models that are specific to use cases to the plant structure, and deploy them in the cloud for runtime scoring and execution.



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Highlights

IBM Maximo Production Optimization SaaS prebuilt use cases include:

  • Predicting machine failure
  • Predicting quality failure
  • Processing anomaly detection for a set of co-related process parameters
  • Processing variability prediction and optimization of throughput, quality, and cost savings through recipe and set-point recommendations
  • Production task cycle-time efficiency prediction
  • Processing failure root cause analysis

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Description

IBM Maximo Production Optimization SaaS is a cloud service solution built on IBM Cloud. It combines IBM technology, such as IBM Watson Studio, IBM Watson Machine Learning, and IBM Watson IoT platform connection services, with several cloud services to address industry- specific use cases that optimize throughput, quality, and cost in production plants. With its prebuilt analytical models and templates, Production Optimization can help enable production plant users to quickly and reliably achieve plant-wide and enterprise-wide implementation of Industry 4.0 use cases.

Runtime capabilities for line-of-business users:

  • Monitor KPIs, metrics, and process variables across the plant hierarchy. Plant users can configure and monitor KPIs and key metrics at machine and station level; cell and line level; or plant, zone, and shop level. Thresholds can be set to create alerts. KPIs and key process parameters can be monitored.
  • Predict and pinpoint production losses. Production Optimization includes prebuilt analytical models that can be easily customized and applied to specific assets and processes in the plant floor in order to implement use cases. The use cases include:
    • Equipment-related losses:
      • Machine failure prediction. Production Optimization has prebuilt failure pattern analysis technology, tooling, and widgets that can be easily parameterized to predict machine failures. The primary inputs to the analytical model include control system events and senior data. The prediction outputs are predicted time to failure, with probability of failure, and confidence factor. Thresholds can be set for prediction metrics and alerts generated as the thresholds are crossed.
      • Quality failure prediction. The failure pattern analysis technology is also applied to quality failure prediction use cases where the previous history of process failure events has been recorded. The offering has prebuilt analytical model templates, tooling, and widgets to help users quickly and efficiently implement this use case. The input data required for this use case includes process parameters from the process that is being modeled. The prediction outputs are time to failure, probability of failure, confidence factor, and parameters responsible for failure. Thresholds can be set for prediction metrics and alerts generated as the thresholds are crossed.
      • Performance failure prediction. In discrete manufacturing where mechanical movements are involved, slow-downs, starvation, and blockages create significant loss to production. The prebuilt model performs anomaly detection on cycle-time data gathered from different process steps to predict potential slowdowns. The tasks that are likely to slow down are highlighted in the output of this prebuilt analytical model with a confidence score and time to breach threshold. Alerts are generated as the thresholds are crossed.
    • Process-related losses:
      • Process anomaly detection. In situations where it is not possible to capture or record quality failure events predicting anomalies in a set of processes can impact quality and will give advance notification on potential quality and process failures. Maximo Production Optimization SaaS has prebuilt, advanced, multi-variant algorithms that can be easily parameterized and customized to capture the relationship between variables and predict the anomaly patterns that arise from the related variables. The output is anomaly scored for each of the parameters that are being monitored. Alerts can be generated when the thresholds of anomaly scores are breached.
      • Process variability prediction and set-point optimization. Process variability results in reduced throughput, yield, or higher operational cost (for example, energy consumption). Maximo Production Optimization SaaS has prebuilt advanced analytical models that predict process variability in advance. Process engineers can configure optimization algorithms that calculate the set-points that minimize the variability and optimize throughput, yield, and cost. The input data is a set of observed and manipulated variables from the process that is being optimized. The output is the predicted value for target variables (variables being optimized) and optimized set-points for manipulated variables.
      • Process failure root-cause analysis. This use case enables process engineers to identify the set of variables that are responsible for a process failure. The prebuilt analytical models do advanced feature extraction and root cause analysis from a data set with several time series process parameters to provide the root cause of failure in the form of decision tree.

Project set-up capabilities for services and process engineers:

  • Analytical model authoring and customization. Maximo Production Optimization SaaS includes analytical model templates for the aforementioned use cases. It enables Watson Studio to be used to carefully parameterize and customize the use case analytical model templates with data from specific processes and assets. New use cases can be generated by using the analytical library and analytical model pipelines that are provided with the offering.
  • Information model configuration and analytical model deployment. Maximo Production Optimization SaaS enables project implementation teams to configure the plant hierarchy, map the data against each process and asset, configure KPIs to be monitored, set thresholds for alerting, map use-case-specific analytical models with processes, and deploy the entire configuration to IBM Cloud runtime infrastructure for runtime scoring and execution.

Accessibility by people with disabilities

A US Section 508 Voluntary Product Accessibility Template (VPAT) containing details on accessibility compliance can be found on the IBM Accessibility



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Operating environment

Software requirements

  • Internet connection
  • Supported browser

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Planning information

Customer responsibilities

Not Applicable



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Publications

No publications are shipped with this program.

Documentation is published in IBM Knowledge Center.
 

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Trademarks

(R), (TM), * Trademark or registered trademark of International Business Machines Corporation.

** Company, product, or service name may be a trademark or service mark of others.

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