Advancing from Industry 4.0 to 4.1: traits for successful IoT platforms
Industry, in the last decades, has gone through a series of critical milestones. Historically, these have marked society as a whole, defining entire ways of life and the way people interact with their immediate and global contexts.
- The first great game-changer was mechanization, accompanied by the harnessing of steam and water as power sources. Locomotion and production were forever transformed, effectively multiplying human capacity.
- Then came the age of mass production, with the advent of assembly lines. The vast amount of materials now being transported from one location to the next could now be processed far more easily and quickly, making them readily available for markets at large.
- With the challenges of scale that mass production presented, automation of those lines was the next logical step, in what would be known as the third major revolution. No longer was production “limited” by human output… machines would work tirelessly (at the cost of energy and maintenance), promising more free time to workers for other endeavors. The work market was, and still is being, transformed. In many industries, this meant less manual labor, more executive decision-making.
- And nowadays, we are beginning to make the most of automated lines through their increasing autonomy and interconnection. “Internet of Things” (IoT) and “Cloud Computing” are now quickly becoming household expressions, fueled by a movement aptly dubbed Industry 4.0, the fourth major revolution in how things are produced...
Machines are now able to create a virtual model of their environment and exchange data between them and to a processing server, thus being able to make decisions on their own in accordance with the parameters of managers and/or operators. One thing consequently needs to be at the heart of any investment in this area:
Action is less dependent on human intervention, and far more reliant on accurate data. Decisions are context-based, providing quick response to fast-changing productive environments.
This is quickly becoming the standard. But that is not to say this revolution is without its challenges, as previous revolutions also faced their own specific barriers, before they were widely implemented. And that is the main focus of this article.
Accessibility & Ease of Prototyping
Even though the Industry 4.0 “trend” has been gaining traction for the past 5 years, the truth is that to many this is still an inaccessible reality, due to a knowledge barrier. And many are the questions raised by all levels of hierarchy:
What is it? What results can it help our company achieve? How much will it cost? What will the ROI be? How quickly can we achieve break-even? Will our workforce be able to adapt to this change? Will it play well with our existing structure? What steps should we take in order to keep up?
There is still a lot of uncertainty floating around. Additionally, the cost of hiring developers has skyrocketed. Speaking with directors of both Human Resources and Development/Innovation departments of several companies, it is clear that true capability in internal development is at a premium. And rightfully so: nowadays data has become ubiquitous, and the competitive edge lies in making vast amounts of contextual data work for the specific advantages you seek. Autonomously, preferably.
But alas, according to available global reports, code illiteracy among the general workforce is still high (and technological availability has been far from even across countries due to differences in educational and economic opportunities, but that is a topic for an entire discussion). This leads to an usual absence of internal specialized manpower to develop and implement these systems, aggravated by a reluctance from stakeholders to invest due to the absence of a fast and easy way to create a proof-of-concept.
In industry, if a technology is not adaptable enough to be swiftly implemented with predictable results it is as if it doesn’t exist at all (unless all the competition is already using it, then you either adapt or perish).
Consequently, the most adaptive of IoT solutions are already being built with this problem in mind, as a result of working closely with industries in diverse stages of ability.
Designed to be accessible to anyone by providing a low-code environment, they allow for custom applications to be easily outlined and configured through its intuitive graphical user interface. This means managers are able to collect data already available from the machines, and process it through secondary services, all within the same environment. The final result is not only able to serve as a prototype, but can subsequently be fully fleshed-out into an end-product.
Focus shifts from development (slow) to implementation (quick). And this is the difference between taking lead in a specific market or alternatively struggling to catch up: it becomes faster to experiment, adapt and thus find the right mix of solutions.
This ease of prototyping and its consequent value for production management is a key-point in the current state-of-the-art, being a fundamental plus when it comes to decision-making.
Communication & Collaborative Communities
Development and prototyping made easier, integration becomes the next vital feature.
In our current global market, great diversity of third-party software and hardware is the staple of any industry, and these services are expected to produce data that is paramount to management. And more often than not, multiple solutions need to co-exist in the same ecosystem, to provide the required results. Unfortunately, they seldom work well together, due to the nature of the competitive markets they navigate.
This makes it important to be able to communicate directly with multiple existing sources (like SAP, Odoo, among others), for an out-of-the-box IoT capability. Once again, this has been one of the concerns for current solutions, both to ensure specific drivers are constantly added and to create conditions for external developers to be able to create and share their own specific solutions with the community.
Only in this way can flexibility and continuity truly be guaranteed, by transferring capability to the people who know best what their specific expertise, limitations and requirements are. By keeping the tools updated in an dynamic environment where a lot can change in a single productive cycle.
Conclusion
All things taken into account, if the Internet of Things is at the heart of Industry 4.0 and contemporary alternatives are striving to make it available to a wider audience, then solutions that take these issues into account effectively make them an accessible 4.1, the next step in implementation.
As with its predecessor technologies, affordability and accessibility are two of the main necessary traits for IoT platforms going forward, paramount to help crystallize the value of I4.0 for future production value across industries. Flexible solutions that allow prototyping and experimentation through quicker design cycles are the key to individual success, as automated action is increasingly context-sensitive.
So, these elements become critical, toward the goal of adaptability:
- Accessible solutions that make the data readily available for use
- Inter-connectivity across multiple software & hardwares
- Openness to development by a wider audience, ensuring extended support
- Ease of prototyping, not only for proof-of-concept but also implementation
- Enabling of contextualized autonomy of analysis, decision and action
One size no longer fits all (in contrast with the previous principles of mass production), and that is a good thing… effective technology, like everything else designed by and for human benefit, is at its peak performance when adapted to the specific contexts to which it is meant to be applied.
