Smart objects are everywhere, especially in the home, where connected devices now have considerable market penetration. But what is called the Internet of Things, Internet of Things, has also been marking considerable growth in the industrial sector for some years now, with manufacturing leading the way. In this context we speak precisely of Industrial Internet of Things.
While starting from similar assumptions, the Industrial IoT has very different developments and significantly more structured implications.
What Industrial Internet of Things means and how it is changing the market
The concept behind it is suggested by the name itself: to use IoT concepts in the industrial field. In this area, the main goal of this set of solutions and technologies is to facilitate communication machine-to-machine, i.e., the direct connection between machines, and process automation, while relying on analytics and prediction disciplines such as Machine Learning and the use of Big Data.
The goal is to Improve the efficiency of companies, both in terms of productivity and spending.
In the most canonical declination, machines are (or are being) equipped with sensors and actuators that collect data of various kinds, are deployed within structured analysis systems. The benefits gained go far beyond simple optimization. Real-time data exchange and information gathering, in fact, can lead to strategic improvements in workflow within a company.
IIoT and the first ally of Industrial Analytics
We know that data collection by itself is not sufficient to provide adequate support for industrial processes. But when this data is analyzed, for example by a system of machine learning, can initiate predictive analysis and management mechanisms unprecedented in the history of technology.
For example, by analyzing data from a machine, it is possible to reliably predict whether it is about to reach a critical condition.
The simplest scenario is overheating. In that case, in a connected supply chain, management systems will temporarily divert production to similar machinery, slow down the pace or schedule maintenance. All while minimizing the need for operator intervention.
In the field of predictive maintenance, it is possible to predict when a piece of machinery is about to reach a critical state, and plan the necessary interventions. Reducing both inefficiencies due to any redundant planned maintenance, and the possibility of production downtime due to failure.
The Industrial Internet of Things in production processes
The adoption of industrial IoT devices, however, provides productivity-level support in contexts more related to day-to-day supply chain management. Below are some of the main areas of application:
Procurement: also leaning on the communication chains introduced by Industry 4.0, a connected machine can interface with its supply chain, internal or external, to manage the storage and supply of raw materials and consumables in an optimized way. This is also based on inventories in the company. Data can be saved and converge into a predictive system to optimize subsequent purchases.
Optimization: an interconnected supply chain can dramatically reduce inefficiency times, allowing maximum utilization of company assets and reducing downtime, while maintaining optimal maintenance status through predictive analytics tools.
In some cases, the level of optimization can be as high as 40 percent, as in this Keysight case study.
This technique has been used, for example, by the German ski manufacturer Blizzard which leveraging the Industrial Internet of Things reduces inefficiencies, production drops and manufacturing defects.
Cost optimization: In addition to forecasting aspects related to machinery, data collection and processing can lead to the implementation of the production model called Digital Twin. In this, thanks to the use of increasingly realistic mathematical models, every aspect of the supply chain can be reproduced digitally: from the simple testing of a prototype, through simulation tools, to the replication of the entire production activity. Thus it is possible to identify critical issues, room for improvement and potential malfunctions before starting or modifying the production process in the real world, allowing a considerable reduction in costs and lead times.
Tool and material retrieval: another area where, particularly in complex production chains, IIoT provides valuable support is the location of tools and materials within the company. For example, in the case of tools shared among several work groups, operators can know immediately where a piece of equipment is located and whether it is available, reducing downtime in retrieval and usage queues.
