oee.ai is a lean Manufacturing Execution System (MES) for Overall Equipment Effectiveness (OEE) analysis in manufacturing processes. With oee.ai, causes of loss in production can be recorded and analyzed in detail – with the help of artificial intelligence, among other things. Typical users are COOs, production managers, foremen, CIP / improvement teams, production engineers or maintenance staff who want to understand and optimize the causes of loss of plant productivity.

The Advantages of oee.ai at a Glance

Modern Industry 4.0 OEE Management for production processes quickly installed and for rent. oee.ai prepares equipment data and additional employee knowledge to increase shop floor productivity through advanced analytics. Data is visualized in real time and analyzed using the latest technologies and algorithms:

  • The widget-based reporting can be configured individually. There is a large selection of evaluations: top availability, performance and quality losses separated by categories, various KPIs from OEE to MTTR or MTBF as well as time series, for example, can be selected.
  • Employee interactions such as the selection of production orders or input of the reason for losses are carried out in the simplest way via tablets or other web interfaces.
  • All data is available on Andon boards, tablets, industrial smartwatches, shop floor management boards or any other web-enabled device.
  • The data streams are evaluated with the help of AI algorithms: Anomalies (e.g. accumulations) are identified, losses are predicted, suggestions for action can be made. An algorithm library enables quick results. Individual algorithms, developed in-house or by our data science team, are possible.
  • Gamified visualizations and messages are available to motivate employees to act on the basis of the data insights.
  • Benchmarking within your own organization or with other companies is available to evaluate optimization potential.

Almost all manufacturing companies can be covered: mechanical processing, injection molding, filling / packaging, steel production and processing, electronics, woodworking, paper / cardboard production and processing and many others – in the units of measurement piece, liter, (square) meters or kilograms.

Various Analytics

The received data is graphically processed in oee.ai and made available in an internet browser. The reporting is completely widget-based and can be configured individually. The user has the option of selecting and displaying the data according to various criteria, such as shift.

Image: Productivity Cockpit

In the upper area, desired KPIs such as the Meantime between Failures, Meantime to Repair and the No Touch Time between microstops are shown. The section below includes configured diagrams, in this example in shift resolution. Again below that, is the so-called “heartbeat line”. The number of units is visible there in minute-by-minute resolution. The red solid line visualizes the current set speed, the light red areas mark availability losses, whereby the cause of the disturbance is displayed with a mouse-over as additional information. Areas with a yellow background mark times with performance losses.

A detailed analysis of the loss of availability follows as a further analysis example:

Image: Availability losses

The availability losses are analyzed in terms of number, duration and a percentage distribution. In the waterfall diagram, the loss reason and their distribution are visualized. In this way, it can be analyzed in detail, which losses with which causes and at what time were present.

The same methodology is used to evaluate the reasons for the loss of performance, which usually receives little attention in day-to-day business. oee.ai also captures them and makes them transparent.

The central statement about reporting is that it can be individually configured and displayed using various visualization forms such as tables, waterfalls, heatmaps, etc. In addition, all data can be accessed via API, so that all data can also be read out and used in other systems.

Simple Loss Reason Entry

With the input of the loss reasons oee.ai sets on humans. Only humans can decide in the complexity of the entire system, what is the actual cause for a standstill was. That’s why we made it as easy as possible for the employee to make these entries. Commercially available Android or iOS tablets have been proven to detect the causes of availability, performance and quality losses.

The tablets also require internet access and are usually attached to a workbench or shelf with a gooseneck. If an input is required, the tablet receives a push notification and displays the loss catalog, whereby different catalogs can be configured for availability, performance and quality losses.

Image: Loss entry via tablet

Company-specific loss reasons can be entered at a maximum of 9 levels, so that individual loss detection per sensor location is possible, and the employee only has to make a few clicks on the display to classify a loss. After how many minutes of equipment standstill or reduced equipment speed a prompt appears on the display is configurable. The prompt is supported both visually and acoustically to attract the attention of the employees.

Real-time Andon-Boards

Andon boards visualize the current status of the production. oee.ai displays live the status of the equipment, the current shift OEE and the course of the equipment performance for the respective shift. This information can be visualized on any display with web access. Displays without a built-in browser can e.g. inexpensively and easily be upgraded with a Raspberry Zero. This means that all relevant information can be displayed in important locations such as the shop floor directly at the equipment, in the meeting room or in the office. The presentation of one to a maximum of eight equipments on a large display has proved its worth. Of course, the display can also be used on any laptop or tablet.

Image: Multiple-Andon in foreman’s office

OEE-Analysis by Artificial Intelligence

Anomalies are hidden in the OEE data stream: After some set-up procedures, the system quickly returns to the crest line, while it takes longer to do so in other conversions. Some products produce more micro-stoppages than others. Whether an installation should rather be run faster – or slower and thus with fewer micro-stoppages – can be calculated: These analyzes are usually not performed as of today. However, manufacturing according to Industry 4.0 principles does just that – capturing data, intelligently evaluating it on a large scale and presenting it on-line to people for decision-making.

oee.ai provides an anomaly cockpit for the analyzes. OEE data is prepared for analysis in the cloud database and AI algorithms analyze it in real-time so that anomalies – and productivity losses – can be detected as they occur.

Image: OEE anomaly cockpit

It is important to know that no data scientist is necessary for AI use. All configuration and training tasks of the algorithms and the neural networks are performed in the background after a period of data collection by the expert team of oee.ai.

More information on artificial intelligence can be found here.

Realtime Shopfloor Workflows with Industrial Smart Watches

Industrial smart watches move into the factories. Employees wear a sturdy Smart Watch on their wrist and receive information from the systems. In this way it can be ensured that e.g. in multi-machine operation, even in hectic phases, no activities are lost. oee.ai establishes the direct connection to the systems without them having to fulfill a technical condition. So the employee gets informed when a system comes to a standstill or whether it runs slower than expected. It is also possible to enter loss reasons via the Industrial Smart. Events start one workflow each, which can also be passed on from employee to employee. This is how fast and efficient communication on the shopfloor of the future looks like.

Image: Industrial Smart Watch on the shopfloor

You can find more information about smartwatch deployment here.

Employee Engagement through Motivating Analytics

It is not enough just to focus on improving the technology. We are sure that the employee will continue to play an essential role in the production process in the future. Therefore, with the advancement of Industry 4.0 and digitization, the human factor must also be given significant attention. The goal of oee.ai is that machine operators are motivated and happy to get involved in the (improvement) process. We therefore address the interaction between people, organization and technology to support companies in terms of motivation and participation of employees. For example, an intelligent algorithm – we call it the “Good News” algorithm – finds positive events and trends in the data history and makes them available for display on Andons, smartwatches or tablets.

Image: Motivational andon with a dynamic goal

The evaluations make the good performance of employees more transparent and confirm their actions. This promotes intrinsic motivation and willingness to collaborate and improve. From our point of view, such motivational elements represent an elementary component of OEE management, and thus also of the oee.ai solution.

Connecting the Machines

The connectivity to equipment from different manufacturers of different ages has been a challenge in the past. However, this hurdle has been overcome with the emergence of more and more new tools – machines can now be easily connected. There are four archetypes of connectivity:

ArchetypDescriptionEquipment age and requirementsPossible Use Case Complexity
Plug-& -Play RetrofitSupplement of e.g. quantity or vibration sensorsNo requirementsLow to medium
Integrated RetrofitSupplement to an industrial PC or a “connector box”PLC must be availableMedium to high
Software layerUse of a “data layer” via Industrial Service BusPLC must be availableMedium to very high
Control nativeAvailability of a new generation of controls with OPC UA or REST APIOnly new equipmentLow to very high
Table: Four Archetype of Equipment Connectivity

We offer solutions for all archetypes, sometimes together with partners. Further information on system connectivity can be found here.

Flexible Use and Licensing

oee.ai is available on-premise or in the cloud from a digital data center in Germany. Thus, German or European data protection law is applied. If required, all operating and safety certificates can be viewed.

The data transmission is encrypted via the Internet. The use is billed on the basis of connected equipments for a rental period starting from one month. If a contract is concluded for several systems and/or a contract period of one year or more, the user charges are discounted. The amount is a flat rate for all users of a business, including usage, data transfer, maintenance and updates.

Increase Equipment Productivity through OEE Management

The two founders of oee.ai, Prof. Dr. Markus Focke and Jörn Steinbeck, wrote a book in Springer Verlag in 2018 titled “Increasing Equipment Productivity through OEE Management” which is available in German.

Image: Increasing Equipment Productivity through OEE Management

The book describes the basics of OEE as well as the steps to a professional digital implementation. The book can be obtained via Amazon.

In addition, a video learning course with the contents of the book and other information is also available on Udemy.

Image: On-line course OEE-Management on Udemy

The course is structures into 10 sections…

  1. Presentation of learning objectives and the speakers
  2. OEE as a representation of true equipment productivity
  3. OEE as a tool for identifying losses
  4. Ability to record the OEE and the reasons for the losses
  5. Industry 4.0 and artificial intelligence to increase OEE
  6. Analysis of the OEE and the loss reasons, directed OEE improvement
  7. Employee motivation for OEE management
  8. Company maturity level and evaluation of OEE improvements
  9. Summary and other sources of information
  10. Bonustrack oee.ai Manufacturing Intelligence

… and is concluded with a certificate of participation.

Please note: The course is currently only available in German. An English translation is planned for May 2021.