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How does collecting equipment data affect production efficiency?

14.07.2021

Manufacturing data monitoring is not a new management method, but the nature of the process has changed significantly over the past decade.

There are three main trends:

Digitalization. The ubiquitous digital transformation is a significant step towards unification of production and the most affordable way to optimize business processes. By eliminating legacy metrics recording methods, you can open the door to powerful programmatic analytics.

Centralization. By creating an integral enterprise ecosystem based on IIoT technology, you provide all participants in business processes with the necessary information: production controllers, planning department, maintenance and repair service, logistics center, marketers, accountants and directorate. The centralized data collection method removes unnecessary links in the decision-making chain, reducing the bureaucratic burden.

Automation. By eliminating the human element from the data collection process, several benefits can be achieved. First, overcome the physical limitations of recognizing the condition of the equipment. Second, to improve the analysis of emergency situations. Third, provide monitoring 24/7/365. Fourthly, to optimize the time and labor costs. And most importantly, automated systems can directly transmit data to scheduler programs.

For each category, you can choose a scalable solution that meets the current needs, technical and financial capabilities of a particular enterprise.

In particular, SmartEAM develops products that help to work effectively with data. These can be local monitoring tools or complex IT-systems for automated production control.

Arsenal of tools

Asset management plays an important role in Industry 4.0 philosophy. There are several areas that provide an IT system with equipment data:

  • IIoT / ICPS is a solution that combines the means of monitoring the physical and virtual parts (hardware behavior in real time and computational optimization processes). IIoT / ICPS provide adaptive management of various platforms within one IT system, work with vertical and horizontal streams of dynamic data.
  • WSAN is a complex of specialized sensors and instruments that provide registration of the physical conditions of the production environment (temperature, vibration, pollution level, air humidity, presence of electromagnetic interference). The technology is used to create scalable dispatch networks and data collection systems.
  • NCS is a technology that connects equipment control loops with a decision-making center through communication networks (CAN, LON, IP / Ethernet). Systems based on this technology collect data over significant distances.
  • Industrial Robots – Robotic manipulators and mobile robots can also be a means of collecting production data. In the extractive industry, such systems are used to circumvent the physical limitations of personnel. They provide valuable insight where only machines can.
  • Assembly Line – systems that carry out a step-by-step analysis of the process of assembling a product from semi-finished products. To obtain reliable and most accurate information, flexible tools are used that provide object recognition and resource identification, collection of quantitative and qualitative data, and executive feedback.
  • M2M Communication – solutions for creating deeply integrated IT systems using wired and wireless channels for data collection, as well as optical monitoring tools. An integrated approach to construction and a wide choice of M2M network configurations make such a solution optimal for ensuring the smooth operation of resource-intensive industrial programs.
    In Eastern European countries, different approaches and combinations of methods are used to collect data. This is due to the fact that in many enterprises you can find modern equipment next to outdated machines with one HMI / PLC port. Flexible software solutions allow combining all these units into a common ecosystem.

What will it give in the end?

Making decisions is easier when you know the answers to important questions:

– how many resources you have at your disposal and when is the best time to replenish stocks;

– how to plan production in order to meet deadlines or reduce the cost of the product;

– in what position the equipment is and how efficiently it is used;

– what challenges and crises await you in the near future;

– how to solve problems quickly and without additional costs;

– how to ensure the maximum return on investment in production.

In conditions of fierce competition, the value of information increases, and hence the means for its monitoring. Also, without a systematic process of collecting data in production, it is impossible to implement solutions based on such promising technologies as Big Data, Machine Learning and AI. And behind them, as we know, is the present and future of the industry.

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