Augmented Intelligence for Smart Factory

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Smart Factory Management

Based on our Smart Factory Management concept, real value will be gained by implementing analytics and machine learning. Once you have connected all the data into a single virtual data lake, you start to optimize your processes and production. You do not need to jump directly into predictive maintenance type of applications, instead you start from optimizing low hanging fruits before moving into the more advanced application areas. For example just the simple understanding of what is happening and why it is happening usually brings important improvements in efficiency.

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Augmenting the OODA loop with Intelligent Technology

Agility and the ability to respond to sudden changes in the environment is important in complex manufacturing plants. The Observe-Orient-Decide-Act –loop is one way of addressing the human and organizational side of this. We use technology to enhance decision-making and achieve Augmented Intelligence, which enables workers to make faster and more intelligent decisions.

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AI tools are readily available

AI for smart factory is not science fiction, all the tools are available for practical use today.

  • Real-time data gathering from sensors and other IT systems for enhanced possibilities to observe
  • Anomaly detectors (software algorithms for pattern recognition, comparing current data feeds to historical records) for helping to allocate limited human attention to most important changes
  • Prediction models, both at subsystem level and as system-level simulation, for helping to anticipate effects of alternative courses of actions
  • System level view of all factory operations; help humans to understand the full context and key interactions

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Make better decisions

Machine learning in Smart Factory is a tool for processing available information for better human decisions.

There are many areas where machine learning can be implemented. All factories are unique and thus have their unique development opportunities. 

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Shift from reactive to proactive operations

Holistic real-time data is the first step. You need to have full visibility to your production. Only after that you can start to optimize your production, but you still need a paradigm shift from reactive to proactive mindset. 

You cannot only react to upcoming faults and errors, but must stop them from happening in the first place. Having the line produce low quality products or machine being shut down costs many times more than the investment into predicting them beforehand.

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Typical use case: Predictive maintenance

With real-time information and algorithms you can react to upcoming faults before they actually occur. We analyze the behavior your equipment and processes and predict if there will be problems.

You avoid breakdowns of the machines which cause delays to your production. On the other hand you also avoid unnecessary maintenance, which comes from sticking to the traditional methods of using manufacturer guidance or regular maintenance (e.g. weekly, monthly or yearly maintenance) regardless of how that machine has performed. Moving into predictive mode brings you cost savings and less disruptions to your production. You minimize disruptions and schedule your activities when they are actually needed and  useful.

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Smart quality improvement system

With predictive analytics you maximize product quality in multi-stage manufacturing processes. By connecting data from all the machines and products, significant quality variation patterns are studied and critical variables and conditions are found. Models are then refined to establish zones of best practice operations, and  ‘quality failure’ risk models of each process stage are developed and evaluated.

Based on the data-derived models, you establish best operational practices for individual process stages. The deployed models are dynamic and continuously adapting to changes in the process or manufactured products. This can be applied to different kinds of environments from process to discrete manufacturing.

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The full range of benefits of advanced analytics in factory operations come from learning at multiple levels
  • Capture the behavior of individual pieces of equipment, to continuously improve specific process
  • Benchmark similar pieces of equipment and processes, to find and transfer best practices
  • Ultimately simulate whole factory operations, being able to optimize production across multiple facilities