IoT is changing the world of industrial manufacturing. And with this, manufacturers are shifting their focus on creating more sustainable industrial environments with an emphasis on customisation, remote data accessibility and company-wide data democratisation plus increased adoption of holistic technologies. Let us explore each of these items in more depth.
Europe’s Green Deal is targeting a 55% reduction of greenhouse emissions by 2030. The hope is to further the climate neutrality agenda to go beyond net-zero emissions. Lawmakers and governments are hard at work to ensure that these goals are met and that climate neutrality strategies remain high-priority topics. Industrial manufacturing is also picking up pace and the tables are turning. From one of the culprits of climate change, manufacturers are becoming vocal defenders of sustainability.
Transitioning to digital services for the production environment and embracing the efficiencies promised by the Internet of Things are part of the quest to alleviate the environmental and energy burdens of industrial manufacturing. An increasing number of manufacturers are participating in government programs and research initiatives to leverage IoT technology and so forward the agenda of sustainability. Sustainable services may span from IoT solutions for energy saving or increased operational efficiency to better predictive maintenance that reduces the need for human presence and thus carbon emissions.
In the long run, a clear focus on sustainability will help manufacturers remain competitive and continue to seek innovation. The obvious goals are achieving uninterrupted and efficient processes, cutting energy costs, and wasting less. But over and above these, the shift harbours the possibility of a more sustained focus on the planet’s well-being.
Demand for Custom Solutions
As we reiterate in a previous article on custom IoT, the transition to industry 4.0 is not a straightforward plug-and-play effort. Rather, it should be highly customized to truly deliver value. And in another piece, we answer the question of why IoT is not simply a “project”. Unlike projects, which are discrete entities, the process of developing IoT capability within an organisation is seldom finalised. So it should rather be viewed holistically, as a series of ongoing enhancements.
Rather than applying an overarching strategy, the rule of thumb is to carefully consider the factors endemic to a situation. Only then can you begin the journey towards implementation and setup. This means that customisation and the capacity to deliver custom IoT is crucial for successful IoT deployments. And as the focus shifts from tools to solutions and services, manufacturers are increasingly looking into the services of system integrators or full-service IoT platforms with comprehensive built-in tooling and workflow management options.
As we get to hear everywhere, custom IoT should be flexible. It should be built in such a way that it allows for instant scalability. So solution providers – especially platform vendors — are to make sure they are keeping up with the latest developments. This means the provision of full-on service and guaranteed openness.
Whitepaper: Utilising IoT & AI in Industrial Manufacturing
Demand for Remote Data Accessibility
With the rise of IoT, industrial manufacturers are increasingly expecting to reap the benefits of remote data access and the ability to look into the operations and performance of any industrial asset regardless of location. Whereas manufacturing is traditionally known as a site-specific and location-dependent industry, remote data access is slowly changing these perceptions.
Especially the ongoing shift towards cloud IIoT platforms and services allows for new ways to decentralise data. At the same time, data is made widely available across company functions and geographies. Data becomes available across plants. And even within isolated production facilities, different specialists can tap into the same pools of data. All this is possible because most platforms now come with granular user privileges. This way, staff, if given the right credentials, can break existing data silos easier than before.
The outcome is that asset tracking, condition monitoring, operational efficiency, and predictive maintenance can all be performed remotely, in real-time, and in bulk. There is less need for oversight. On-site staff can spend their time with more challenging tasks while IIoT platforms take care of routine monitoring and tracking. Further, IIoT platforms can run more sophisticated automations based on machine learning and send out alerts in a fully remote setting. In sum, this creates transparency and gives manufacturers the possibility to strategise in advance with an overview of all relevant data.
Holistic Tech Adoption
Manufacturers are also changing the way purchasing decisions are made. Today, we are seeing more involvement on behalf of tech leadership and more focus on business metrics. So the decision to adopt new tech is no longer in the hands of data scientists, data engineers or IT. Rather, the upper-level management is actively involved in the process. Critical questions regarding the projected efficiencies, cost-saving power, or future readiness of the adopted tech are being asked.
Why? The rise of Big Data and data-driven insight are making IoT all the more relevant for industrial manufacturing. The role of the business analyst is becoming just as critical as the role of the IoT engineer. Data democratisation, increased transparency into operations on the shop floor, and high data availability that extends company-wide are all making for a holistic buyer cycle that involves stakeholders at multiple levels. And among other things, this truly translates into the adoption of a company-wide, big-picture approach and a broader perspective when it comes to the adoption of new tech.