Stop/Downtime Analytics
Analytics of downtimes in Factbird include pareto charts, scatter plots, and hourly charts (see Graphic above). In the pareto analysis, the system will show at a glance what the biggest issue (in terms of time loss) on the line/asset is. By default, this will be shown on an OEE category basis, and then the user can navigate to the lower levels, i.e., stop cause categories or stop causes. Once the lowest level, i.e,. The individual stop cause is reached, Factbird recommends using the scatter plot visualization to identify how the stops related to the selected issue develop over time in terms of frequency and length.
Below the graphs, a list of all stops is shown that can be filtered/sorted by OEE category, stop category, stop cause, start time, stop time, and duration. In addition, all comments of operators are also shown, and if a Factbird View is installed, users can also watch video footage of detected stops to identify the cause, even in hard-to-see areas (e.g., high up on the conveyor belt, inside the safety doors of the machine, etc.).
Factbird can also display the stop causes in a scatter plot, allowing users to see how a certain stop cause or category is developing over time. Sometimes, a fix on the line will move the stops from infrequent but long-duration downtimes to frequent and short-duration.
Hourly count is a different way of visualizing and analyzing downtime. This way is frequently used in metal processing industries where machine stations are isolated. Here, the operator will be able to view the hour-by-hour performance and register stops at the same time. For each hour, it is possible to register the countermeasures performed to make the next hour better, and to get a manager to sign off on the actions.
OEE
Factbird offers a comprehensive OEE (Overall Equipment Efficiency) functionality, providing both real-time OEE dashboards and detailed OEE reporting.
Based on our experience, customers tend to deviate from the original formula to calculate OEE (Availability x Performance x Quality). Hence, Factbird strives to implement a solution where users can pick and choose the most appropriate KPI to report on. Therefore, our OEE dashboard (see Graphic below) includes 4 “types” of OEE measurements:
- OEE1: Valued operating time/operating time
- OEE2: Valued operating time/production time
- OEE3: Valued operating time / manned time
- TCU (Total Capacity Utilization): Valued operating time / total equipment time
The calculation flow for each of these OEE types is visualized in a waterfall chart, so that users can quickly identify what stop reasons have contributed to which type of OEE loss, and how much time was lost on them.
Factbird offers to hide some of the OEE types, if wanted, and allows you to configure whether scrap and speed loss should be taken into account for OEE1 or not. In addition, users can manually set targets for each OEE type. Typically, those targets would be determined by company-wide policies or using the trend function to identify what the highest OEE reached so far has been. In addition, Factbird also provides an in-depth breakdown following the original OEE formula, detailing which losses have occurred under the categories of availability, performance, and quality. It should be noted that quality can only be taken into account if scrap is registered in the system, either via sensors or through manual user input.
The OEE data is available in real-time and historically to receive an immediate view of or identify trends in performance. Users can utilize a convenient pre-set time picker to analyze data within their desired time range, batch, or shift, which allows customers to see how efficient specific batches and shifts run over day, weeks and months, and, for example, evaluate whether a morning shift would perform better than an evening shift, or end of week performs better than the start. Following on from the batch selection, Factbird also allows users to filter OEE data by product, providing a closer analysis of OEE for specific products over time to see how they compare in performance.
Users can view OEE data at various consolidated levels, going beyond the individual machine data, e.g., at the line level, division level, and plant level, enabling a broader perspective on equipment performance. To achieve this, assets can be grouped together accordingly, and the overall OEE of such a group can be viewed in the “Consolidated lines” feature.
KPI Trends
For each KPI that is shown on a line’s live page as well as the OEE, Factbird offers trend analytics, where users can select a time period and Factbird will show the development of that KPI over time, including a trend and an average line (see Graphic below). That way, users can easily detect whether their line performance is increasing or decreasing.