Data-driven manufacturing challenges
Data-driven manufacturing challenges and the emergence of mass customization.
The Internet of Things (IoT) is here, with Internet-connected devices across the enterprise and consumer devices generating data that is constantly being captured, analyzed, and acted upon all around us. The insights gained from this data are allowing for many industries to be more efficient and productive, putting more pressure on operations staff to ensure they are staying ahead of the curve. However, the industry that is being asked to store the most data is often overlooked. Surprisingly, it’s not health care, retail, or government-it’s manufacturing.
The first Industrial Revolution was characterized by the mechanization of production using water and steam, the second by the introduction of mass production using electrical power, and the third by using information technology (IT) to automate production. However, most recently we’ve entered into the Fourth Industrial Revolution (also known as Industrial Internet of Things, or IIoT), which is characterized by Internet-connected devices that enable a new level of Big Data and analytics.
By 2020, Gartner predicts that the IoT will consist of 26 billion devices and will generate more than $300 billion in incremental revenue, the majority of which will be generated by services. We already know that the industrial manufacturing process creates a ton of data, and that more devices are becoming connected and networked.
As a result, we’re seeing a massive explosion of data that has enabled new business models and practices to be put into place that are transforming the global manufacturing playing field and allowing organizations to compete on smarts, not costs. Because manufacturing is one of the largest economic drivers, this massive shift brought on by IIoT creates tremendous benefits even beyond the plant floor.
The emerging mass-customization model
For manufacturers, one of the biggest capabilities emerging from the rise of the IIoT is the mass customization of products and services. The appeal of mass customization is the potential to help manufacturers reduce costs and gain a competitive advantage in the market.
In the past, organizations that tried to accomplish this mass-customization model failed because the technology wasn’t able to keep up. Instead, manufacturers focused on mass-production because it was most profitable to produce a large quantity of the same product.
However, today’s technology is allowing manufacturers to slowly step away from the mass-production business model and move into the mass-customization one. Many of today’s manufacturing systems are able to produce make-to-order products that are inexpensive and personalized, which is giving them a leg up over competitors.
In the manufacturing industry, it is not enough to be able to create branded or personalized items. For organizations to produce a wide range of unique products, they need to also create flexible automation processes, machines, and manufacturing systems integrated with business systems and consumers.
Essentially, manufacturers need to move beyond a process control system that has a "central brain" and understands everything about the assets in the factory, to a model where each asset knows everything about itself-and knows how, when, and why to communicate directly with other components of the manufacturing process not necessarily going through a central computer. By combining this business model shift with the data and insights gained from the machines, manufacturers are now able to empower a factory’s assets to optimize their own productivity and efficiency within the broader context of the process control system. To put it simply, the plant assets need to become "smart."
Building the "Smart Factory"
Internet-enabled connectivity of devices and analytical capabilities have pushed forward new models of manufacturing. The development of self-organizing and -reporting systems that have moved away from centrally orchestrated processes to a more distrusted one is key. With the machines and other assets of a plant communicating independently of one another to make decisions, there is more room for flexibility-or customization of products and services. To achieve this new model of manufacturing, organizations need to think about what makes their assets smart.
For example, adding smart objects to your plant allows facility managers or engineers to create a digital model of physical assets within the virtual environment. These smart objects will now have the ability to attach to different data sources and access the information that is available on a particular asset.This data, combined with different dimensions and parameters in the context of one another, gives new and deeper insights into the performance of an asset.
Additionally, it is vital to have connectivity and accessibility with smart objects. Without a level of connectivity, smart objects are not able to be fully tapped into the framework of a factory, therefore they are not able to provide insights to those in the field or at the enterprise level of the organization. Building a fully connected network from the field to the enterprise that pulls insights from power, process, building management, and IT is essential to contextualizing data and making it both visible and accessible.
Finally, to actually be considered a Smart Factory, a facility needs to generate intelligence from its smart assets. This will allow for plants to move past the traditionally passive automation process to a more intelligent, semi-autonomous decision-making system. By adding intelligence to a control system, assets are better able to leverage their available data sources. Plant intelligence is helping to evolve the plant from managing production targets and quotas to managing profit performance.
Many manufacturers are being pressured to decrease their time-to-customer delivery, as well as offer more unique products with an unpredictable demand. The rise of IIoT and its impact on the creation of the Smart Factory is a huge win for this shift, allowing for the flexibility, automation, and data sharing needed to make the mass-customization business model a reality, in a cost-effective and efficient way.
Greg Conary is senior vice president of strategy for Schneider Electric.
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