The fourth industrial revolution is changing many things, but there are things that never change, for example, the KPI definition and the efficiency quest. In this context, tools and solutions play a very important role when collecting data straight from machines and delivering important information.
However, data collection is not enough. It is necessary to have a wide and clear panoramic for defining actions needed to achieve full improvement. A key step in this path is defining KPIs, for example, overall equipment efficiency or OEE, which is useful for measuring the time a machine is producing at its maximum capacity and with optimum quality.
This metric take into account three key factors: availability, performance, and quality. Keeping a record of these factors has a positive direct impact on OEE since it helps to identify where losses are happening. For this reason, IoT tools and solutions become allies that represent a big advantage. Here are some of them:
Availability: Moving from corrective maintenance to a preventive and smart one
It is impossible to avoid unavailability periods in machines since they need to be fixed once in a while, but it is possible to reduce those periods with preventive maintenance based on information extracted from machines like working records and waste derivated.
With solutions like those offered by DataGate, it is possible to collect data from machines and store and analyze it. This way, you can know in advance when it will be necessary to schedule maintenance and not to do a corrective one, which tends to take more time since it is necessary to find out what happened, request the necessary pieces, and then do the actual maintenance.
This is possible thanks to diagnosis based on machine learning done with data extracted from machines such as temperature, engine vibration, and other parameters. This way any deviation of parameters might work symptoms of a failure in the near future.
In the same way, IoT sensors can tell in real-time the actual amount of available materials, so the machines do not have to stop because of a lack of them.
Performance: Achieve and keep optimum performance levels
Besides regular maintenance, there are some other short stops in production that can be related to materials stuck or components dealignment. These stops use to be ignored because of their fast resolution and small impact in the immediate term. However, the accumulation of this kind of event might turn into performance reduction or hide a bigger problem.
With IoT, it is possible to know accurately where the failure occurred, and what parameter is out of range and this way find a timely and proper solution. Additionally, keeping a record of all regenerated data enables a full view of incidents with details like how frequently occur and how much time was required to solve them. This information helps teams to make better decisions, based on data, improving performance by eliminating or mitigating chronic issues.
On the other hand, IoT helps to keep speed production at adequate levels, because it is possible to know if any factors such as lack of lubrication, environmental temperature, or dust are slowing down machines’ performance. Offering and 100% accurate answer to the reduction of indicators such as OEE.
Quality: From beginning to the end
Quality is another element part of OEE, taking into account it depends on many factors it is very important to quantify them and see their impact on the final result. It is in this matter that DataGate delivers a remarkable value because allows measuring variables of the own machines and other externals like environmental temperature that may influence in product´s quality.
An important moment to implement improvements is when machines are starting production since usually defectus products are made within this first moment. These improvements can be designed from learnings generated from machines’ data analysis and collection.
In conclusion, working with a solution such as DataGate makes possible radical improvement of OEE because of the first-hand access to real-time and historical data, delivering relevant knowledge of different work fields. This knowledge is the fundamental base to make informed and timely decisions that contribute to reaching an OEE as close as possible to 100%.