oee.ai is a system for minimally invasive Overall Equipment Effectiveness (OEE) analysis in manufacturing processes. It is a flexible addition to a potentially existing operating data acquisition thought. With oee.ai the OEE can be recorded and the causes of the loss can be analyzed in detail without equipment intervention, IT effort or investment. Typical users are CIP / improvement teams, plant operators / maintenance engineers or maintenance personnel who want to understand and optimize the causes of plant productivity loss or who want to display the OEE cost-effectively on an Andon display.
A Variety of OEE Evaluations is Possible
To increase the OEE, a detailed reporting of the status and causes of losses is irreplaceable. oee.ai was born out of the experience that PLC data is either not available at all or only after a lot of time and effort for process optimization. And even if the data exists, it must be laboriously evaluated in Excel pivot-tables. As a consequence, there is no targeted improvement process of the OEE based on numbers, data, facts. oee.ai puts an end to this.
oee.ai collects all OEE-relevant data with a standalone sensor without any system intervention, prepares it without manual intervention and makes it available to the user in an Internet browser for process optimization.
In the upper area, the data is limited to days and shifts or times. Below, the three OEE factors – availability, performance, quality – of the selected days are visualized in a bar chart. The solid line indicates the OEE for each day. In this case, the Q-factor is set to 100% because no separate sensor is connected to the reject line. However, the independent measurement of the Q-factor is possible.
In the lower third of the report are two so-called “heartbeat lines”. The lower line determines the zoom – visible through the colored area. This time period will be detailed. By way of example, this example is the progress of production between start of shift and 04:00 pm. It can be seen here that the system experiences both longer and many shorter downtimes. The equipment is rarely producing on its maximum level. Here’s the potential for productivity gains.
The next step is now to analyze the causes of the losses. Detailed reports are available for this.
In the example above, a very specific analysis time was chosen because a specific product ran across the plant between times. In the waterfall-chart (in Italian – the language of the customer) the loss reasons and their distribution are visualized. In this way it can be analyzed in detail, which loss times existed with which causes and at which time.
Furthermore, the OEE display can be visualized on an Andon board at any location (shop floor, meeting room, office). The website with the information is transmitted via a streaming dongle (for example, Chromecast Google) on a standard TV or computer monitor via Wi-Fi. Thus, the costs of such an Andon board are very low and the relevant information can be visualized in the important places.
To realize the display of the Andon boards, the existing standard can be used. However, company-specific solutions are possible with little effort.
Beyond the Andon Board, employees can be specifically informed about special plant conditions via push notifications. In this case, the employee receives an e-mail, an SMS or a WhatsApp if, for example, a down-time is longer than 20 minutes or the OEE is below 60% for more than 30 minutes. Company-specific configurations are flexible.
All raw data for the calculation of the OEE as well as the standstill data are available as a pre-formatted csv download, so that individual analyzes of the data are also possible if required.
Few Inputs for OEE Analysis are Necessary
oee.ai has been designed with the idea of simplicity and flexibility. The raw data for the OEE calculation is captured by a stand-alone sensor that only needs to be connected to a power supply. Alternatively and on a project basis, it is possible to directly access and visualize PLC signals.
For the input of the loss-reasons oee.ai relies an human interaction. Only he can decide in the complexity of the entire system, which is the actual cause for a standstill. That’s why we made it as easy as possible for the employee to make these entries.
Variant 1: Tablet for fault detection
If there is a central location where employees usually spend time during production, the use of standard tablets to detect the causes of availability, performance, and quality losses has been proven.
Company-specific loss-reasons can be configured on two levels, so that individual fault detection per sensor location is possible, and the employee must make a maximum of two clicks on the display to classify a fault. After how many minutes of plant standstill a prompt appears on the display is configurable. The prompt is supported visually as well as acoustically to attract the attention of the employees.
Variant 2: Smartphone for interference detection
If the employees are not at a central point on the system during operation, the fault detection via a native mobile app is more appropriate. In this case, the employee can carry the input display on the body and thus make the input in the place where he is currently. Android was chosen because devices with this operating system are very reasonably priced in the market.
Mobile Technologies and Standard Equipment
To implement the above functionalities is consistently set to mobile technologies and easily and inexpensively available equipment. Both the sensors and the input devices can be operated either in the GSM network (global mobile radio standard) or local WLAN. All input devices (tablet, Android mobile phone) can be ordered inexpensively on the Internet. This will take the entry barrier hardware.
For the detection sensors, all sensor types (photoelectric, inductive, capacitive, etc.) of the well-known manufacturers (Sick, Pepperl+Fuchs, …) can also be used.
When using OEE-as-a-Service, the data is stored in a European computer center that meets all data security standards. If a company policy prohibits data storage outside of its own data center, oee.ai can also be installed on company servers to operate within the corporate infrastructure.
We have sparked your interest. Contact us at email@example.com or call us: +49 (0) 241/401 842 75. We will gladly provide you with oee.ai sensors and access to the analysis cockpit for a trial period. Only then you decide. Also here we are easy!