IIoT is Business Driven and vice versa

by Slavko Kastelic | Dec 13, 2019 | Blog

Increasing demands for mass customization of products, the complexity of global supply chains, the pressures of cost reductions, faster production, improved safety, and the “always around the corner” crisis prediction, which is already showing its teeth, especially in the automotive industry, are just a few of the challenges that force manufacturing companies to look for new and more effective ways to stay competitive.

The digital transformation of manufacturing is one way to improve the operations of the manufacturing facilities and supply chain and the Industrial Internet of Things (IIoT) is the path to this transformation. IIoT is based on a network of sensors that collect critical production data and uses software that transforms collected data into valuable insights and the efficiency of production processes.

Bsquare notes that 86% of companies have already started deploying IIoT solutions, with as many as 84% claiming IIoT to be extremely effective. Such companies soon build on their initial deployment by adding advanced IIoT applications such as advanced analytics and automation.

6 reasons for introducing IIoT

  1. Cost reduction
    Optimizing inventory management, reducing machine downtime, more flexible operation, and more efficient use of energy reduces operating costs and even generate new revenue streams (smart, connected products enable the transition from product sales to sales experience - e-scooter, smartwatches...).
  2. Shorter production time
    Faster and more efficient production and supply chains reduce the production cycle significantly. Harley-Davidson, for example, reduced the time to build a motorcycle from 21 days to six hours with the introduction of IoT.
  3. Mass customization
    Mass customization is a shift from serial to individual production. Its typically present in automotive manufacturing, where virtually every car that comes off the conveyor belt is different. These are products that are tailored to the needs of a particular customer while maintaining a large volume of production. Such production leads to an increase in inventories and more complex operations.
  4. Real-time data
    IIoT becomes a source of reliable, real-time data that enables thoughtful planning and organization of production and movement of materials in production.
  5. Improved security
    IIoT improves security on the production floor or in the office. Wearable devices monitor the health status of employees while performing risky activities, and sensors in workplaces monitor the situation and warn of potential hazards (such as monitoring gas leaks in the oil and gas industry).
  6. Industry standards and regulation
    Complete traceability of products and their components is required by industry standards, for example in the automotive industry, as well as regulators, for example in the food industry, and even more so in the pharmaceutical industry.

 

The effects of IIoT in production

The effects of the introduction of IIoT are many, such as transparency within the manufacturing plants, over remote locations, the supply chain and the external suppliers. IIoT bridges the gaps that are not covered by either modern ERP systems or MES systems, such as monitoring and managing the condition of equipment, machine utilization, real-time location of tools and materials, which can lead to up to 20% increase of production output on the same production lines.

IIoT also enabled a change in the monitoring of product quality through the production process, which goes from controlling individual product properties to controlling and managing machine parameters, which significantly increases process stability and, consequently, product quality. IIoT allows you to control the proper use of machines, devices, and tools, which extends their service life and improves their reliability. More advanced applications are moving from control to preventive maintenance systems. It is estimated that improvements in the management and maintenance of production assets could save companies more than 300 billion Euros per year.

 

And the biggest challenge is data engineering

When implementing IIoT solutions companies most often face the following challenges:

  • High investment costs and uncertainty about its profitability
    The introduction of IIoT involves several categories of investment, including hardware (sensors, networking, disk space), software (databases, integration platforms, analytics tools),human resources (technical support, administrators, developers, consultants) and others. The return on investment also depends on the speed of IIoT deployment.
  • Data security
    IIoT increases the risk of cyber-attacks, especially if devices and sensors are connected to the cloud, but half of the companies that have already introduced IIoT do not have a plan in place to prevent losses due to potential security threats. Based on an intrusion analysis, Verizon estimates that no business or industry is 100% secure against cyber-attacks. Gartner also predicts that the number of attacks will further increase and 25% of them will involve IoT.
  • Lack of trained workforce
    As in all industries, manufacturing companies lack experienced professionals in the fields of IoT deployment, data analytics, mass data, software development, IT security, and artificial intelligence, making it impossible for companies to leverage the full potential of IIoT solutions.
  • Integration with operating technologies and legacy systems
    The difficult part of implementing an IoT solution into the production ecosystem is a secure integration of IT and OT without data loss and security inconsistencies. Ensuring seamless convergence between IT and OT is difficult, as systems have had different goals in the past and have been built on different technologies and networks. Today, the rapid adoption of machine-level Ethernet protocols and the rapid deployment of web user interfaces are gradually facilitating the integration process, but the challenge remains particularly integrating the older machines into the system.
  • Providing the right architecture and infrastructure to provide trusted data in a timely manner
    This means fast and trusted data collection from all data sources (MES, SCADA, additional sensors, ERP, CRM, external data...), processing, storage and transmission of data. The data must be prepared in a format that is understandable and easily accessible to users that control and manage production, as well as to the devices and systems that operate on the basis of that data.

 

The complexity of data integration

Gartner points out that data integration is increasingly complex and requires more knowledge, and that proper implementation will be a key to the success of solutions. The complexity of data integration is enhanced by large amounts of data that are in the case of IoT at least one order of magnitude larger than data in conventional production. Automated production management requires the production and transmission of real-time data.

And then there is the challenge of heterogeneity of data sources. In manufacturing, we typically come across machines and devices that are more than twenty years old and that are extremely poor or not connected to the outside world in terms of data transmission. On the other hand, we have state-of-the-art robots that are already naturally connected to the company's data network.

IIoT, of course, does not end up in the factory yard but is also introduced into the products themselves - smart products. Product performance data is collected and analyzed in data clouds. Large product usage data also represents a huge potential for monetization of data, the introduction of new business models, such as servitization, but this is already a topic for another blog.

Are you ready for the digital transformation of your business? Contact us to arrange a meeting.

 

References

BCG: Time to Accelerate in the Race Toward Industry 4.0

Verizon’s 2017 Data Breach Investigations Report

Bsquare annual IIoT maturity survey

Gartner: Are You Ready for Multicloud and Intercloud Data Management?