Data-driven production optimization

  • Greater visibility: From the data gathered throughout the organization, both shop floor and office specialists can better determine the performance of the enterprise. The data provides them with insight into not only individual results, but also operations as a whole. Such possibilities help decision-makers to identify weak points, such as: worse working shifts, repeated machine downtime or other production bottlenecks.
  • Artificial Intelligence and Machine Learning: With large datasets, manufacturers are able to run machine learning algorithms to help solve complex problems and apply advanced practices such as predictive maintenance. Without access to data and the possibility of their processing, even simplified forms of decision-making based on data would not be possible.
  • Automation: There are two categories of automation that can be used in data-driven strategies. The first is automatic data collection. If the enterprise is equipped with the appropriate data collection devices and software to process it, the process no longer requires manual work. The second element is the use of data for automatic decision making. As manufacturers develop, their analytical pathway moves from descriptive to predictive analysis. This means that they first use the data to understand what has happened or is happening at the moment, but eventually mature to understand what might be, so they can react well in advance.
  • Savings: Data also provides manufacturers with the information they need to improve production processes and minimize waste. Without hard data, it is difficult to accurately measure the improvement in production and be sure that the changes brought real savings for the company.
  • integration with other systems — many systems and devices have problems with effective integration and communication with each other. They can result from separate operating systems in different departments or from older hardware. Therefore, aggregating data in different systems can be a big challenge and reduce the value of the collected data. This problem is helped by the solutions of the IoT platform, which connects many levels of systems and makes old equipment available online.
  • security threats — the IIoT security problem arose for two main reasons. First, the more devices are connected to the network, the greater the chance of creating security holes. Second, security at the computer level was not dealt with before, which means that no standards or protocols have been developed, so manufacturing companies should also invest in security solutions to mitigate this risk.
  • data storage — the more devices and systems are connected in a production plant, the more data will be collected. Seemingly, this is a big advantage, as the user can gain a wide insight into the collected data. However, their dynamic growth also requires space for their collection and processing, which can be a large and costly challenge, especially if the user has to store data locally, which speaks for the use of cloud computing.
  • Inaccurate or incomplete data — Incomplete production data can have a huge impact on situational awareness and, consequently, decision making, especially in critical projects where data is the basis for success. It also means a great deal of work, a waste of time and effort to complete incomplete records or to make sure that they are authentic and correct.

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