What is the Future of Industries eXcellence?
The Journey Ahead...
There is still a long way to go for companies looking to achieve the Digital Enterprise as it is defined today. While the end-to-end concept of the Digital Thread is well-established and while most forward-thinking manufacturers have accepted that digitalization is both an inevitable and critical aspect of their market strategy moving forward, the journey towards Industry 4.0 is and will continue to be just that – a long and challenging journey. At the same time, certain enabling technologies will grow in their significance to this space in the years to come.
The Reign of the Digital Twin
While already a topic with a lot of buzz around it across a number of industries, the concept of the Digital Twin originated within the manufacturing environment. It can come as no surprise then, that it is in the manufacturing context that it will be most readily adopted in the years to come. The Digital Twin is a virtual copy of an organization's entire business process workflow, covering all manufacturing processes, products, machines, resources and the entire existing IT ecosystem (as well as how systems within it interact).
Essentially, the Digital Twin provides a risk-free environment for manufacturers to study their processes, test changes and evaluate the impact certain decisions would make to their operations and their business. In the future, the Digital Twin will be a staple for every organization. Everything in manufacturing is going to be tested and simulated first before any physical changes are made in either the plant or the process. Decisions regarding the hiring of new resources, deciding if a new design for a model car should be introduced, running cost and supply chain analyses to determine cost-effectiveness – all these will soon be done in a simulated environment first in order to ensure smart decision-making, optimize processes and drive continuous improvements throughout the enterprise.
(Big) Data Problem & Maintenance 4.0
A competitive advantage will be achieved by manufacturers that know how to invest in the one resource with the greatest potential: data. Unlike many valuable resources, data is not limited, and it is already out there, waiting to be analyzed and harnessed for the greater business good. But the increasing volumes and variety of available data do not necessarily translate into value for the business. Manufacturers must understand what data they need, what data is relevant to them and what data can be utilized to drive real improvements. Therefore, the ability to transform data into intelligence and to use this intelligence to make timely decisions will become a primary competitive advantage.
But even this is still not enough if we do not use technologies capable of handling high volumes of data to derive a systemic view, which dominates the intrinsic complexity of modern production processes and production support. Only in this way will it be possible to link the performance of individual assets to the value generated for the company during their lifecycle. Maintenance 4.0, of which predictive maintenance and analytics are a core component, defines a machine-assisted digital version of all the things we have being doing for the past forty years as humans to ensure our assets deliver value for our organization. Maintenance 4.0 includes a holistic view of sources of data, ways to connect, ways to collect, ways to analyze and recommended actions to take in order to ensure asset function (reliability) and value (asset management) are digitally assisted. Maintenance 4.0 will therefore mean evaluating and anticipating the impact of individual maintenance policies not only on the individual asset but on overall corporate margins, taking into account all the factors involved and the complexity of the ecosystem as a whole.
Collaborative Robots & Smart Manufacturing
"Lights out" manufacturing touts the idea that through advanced automation and robotics, we are soon going to have factories running themselves with minimal or no intervention from humans. But this sounds, and most likely is, more in line with the futuristic world portrayed in sci-fi movies than with reality. Yes, the principles of Industry 4.0 – interoperability, information transparency, technical assistance and self-organizing factories – do paint a picture of smart systems aggregating and analyzing data to autonomously make informed decisions, solve problems, even conduct tasks that before were executed by human workers. But the goal of Smart Manufacturing is not the elimination of resources or humans from the process. Rather, the goal is the allocation and utilization of resources in the right way.
The future of Smart Manufacturing, then, is the automation of monotonous, repetitive, hazardous, data-heavy and precision tasks and the re-allocation of humans towards more business-driving, value-add activities. This is the true end goal of the Industry 4.0 paradigm – intelligent networks connecting and enabling communication between people, things, machines and systems to help manufacturers become smarter, more efficient and more profitable.
AI & Machine Learning
With the trend towards digitalization well under way in the manufacturing sector, we are harnessing and gathering more data from the production floor than ever before. Advanced computational capabilities and analytical tools are enabling us to put that data together in order to expose trends, issues and opportunities for improving operations. But more than that, data is allowing us to advance the way machines operate by programming decision-making power within the machines themselves.
A Machine Learning model is a software application that has been designed to mimic the way that human beings think about and process information. Deep Learning models are more flexible than their traditionally developed counterparts. Imagine that your production machines had the capability to determine when a defect might occur. Imagine that, before a defect was likely to occur, they could automatically take corrective action, thereby preventing the defect from ever happening. What if machines could think about their performance and automatically determine when they might need maintenance? With Machine Learning and Artificial Intelligence (AI), this becomes a definite possibility.
A Final Note (on Engineering)
Our mission at Engineering has always been to deliver technology solutions and services that advance the way the manufacturing world works. Our team seizes every opportunity to share experience and continuous improvement with our customers, partners and community. We are proud to join them in their efforts to develop new systems that drive innovation and help enhance the quality of their products. By designing and delivering the Digital Thread, our global Industries eXcellence group helps manufacturers across all sectors and in all parts of the world realize the enterprise of the future.
