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Originated in Germany, Industry 4.0 translates as “a holistic automation, business information, and manufacturing execution architecture to improve industry with the integration of all aspects of production and commerce across company boundaries for greater efficiency”, according to Automation ISA.

From this starting point, the source article delves into the history of Industry 4.0 initiative. The Industry 4.0 initiative is part of a 10-point high-tech German strategic plan, created in 2006 and continued in 2010 by introducing the High-Tech Strategy 2020. Science and industry need to cooperate, in order to turn knowledge into skills.

Creating networks that incorporate the entire manufacturing process and convert factories into smart environments involves linkages. Linkages such as “smart machines, warehousing systems, and production facilities that feature end-to-end integration, including inbound logistic, production, marketing, outbound logistics, and service”.

Real-time data, a key element in the Industry 4.0 chain of operations

Real-time information enables strong decisions, based on insights. When it comes to production, it enables a superior reactivity and responsiveness in what production chain materials and operations are concerned.

Also, Industry 4.0 is based on asynchronous manufacturing.  This requires that the components in the production flow are able to use “auto identification technology to inform each machine and operator what needs to be done to produce the customized end product at each step of the production process.”

Based on real-time field data, the machines can be rapidly configured so they would adapt to customer specifications and other commands inherent to the production operations.

The collected data also serves as prime material for a range of analytics. These provide a bonus post real-time advantage to those in charge. By analyzing the data, such systems provide recommendations on improving performance and productivity.

Using embedded intelligence at all levels, the advanced cyber-physical systems (CPS) save “significant cost, providing greater flexibility and improved reliability.”

What binds together this new, developing industrial landscape is a series of software solutions that allow for the data to be collected and processed. Furthermore, the insights become recommendations. The decisions then turn into commands that go back into the cycle, driving modified, optimized operations.

 

Standards emerge, as well as a worldwide agenda

With separate, but similar Industry 4.0 agendas in countries such as Germany, China, Japan and/or others, a sum of specific standards emerge. These standards require harmonization, if they are going to work seamlessly at a global level, regardless of the physical area where the industrial processes involved take place.

Future automation systems must adopt open source multivendor interoperability software application and communication standards similar to those that exist for computers, the Internet, and cell phones. Industry 4.0 demonstrations acknowledge this by leveraging existing standards, including the ISA-88 batch standards, ISA95 enterprise-control systems integration standards, OPC UA, IEC 6-1131-3, and PLCopen.

Process automation is at the core of this plan. It becomes more and more present with each industry entity that joins this trend. The Industry 4.0 vision of the future factory joins together advanced technology hardware and brilliant software solutions, for a flawless functionality.

We are getting there with each software solution. Producers can unload more and more of their operations onto the digital, be it monitoring operations, management, coordination or others.

Call us for software solutions partnerships – we build the software you need for going one step further into the Industry 4.0!

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Tech

LASTING Software’s expertise in Big Data empowers you and your customers to optimize the processes & operations necessary to succeed in today’s emerging market sectors.

Remember that we always put ourselves in your clients’ shoes and deliver solutions that fit their actual needs. User experience is one differentiator that will set you apart from the competition when you partner with us.

 

The bigger the data, the more important is meeting the challenges

Harnessing Big Data may not be as easy as it seems, even in the age of hopes and technological progress we are currently experiencing. Luckily, those who paved the way in this field serve as both an example, and a cautionary tale.

Big Data is a topic of great interest – at a global level. Big companies, researchers and software engineers share their findings, and the digital community benefits from this environment. Innovation spreads around, and unproductive redundancies are less likely to take place when keeping an ear to the ground.

 

The quality of the input greatly determines the speed and accuracy of the results

 

Pete Warden, lead of the Google AI TensorFlow Mobile/Embedded team, approaches in detail the necessity of improving the training data in this post on his blog. This “garbage in, garbage out” blunt truth about Big Data is already familiar, as the online media circulates breakthroughs at a fast pace. Perhaps less important than who discovered it first is to extract the maximum of wisdom out of this nugget of tech gold.

What it means for all those developing and using Big Data solutions is that analytics accuracy depends on the quality of the input. Tailoring the criteria for input data selection is extremely important. Depending on what you need to find out when using the software solutions of choice, the processing method needs to get the right information to begin with.

As simple as it sounds – an obvious truth, perhaps, turning this into code and building apps and platforms that would select, recognize and process precisely what is relevant takes passion, experience and a restless, inquisitive mind.

 

Takeaway:

Technical details are available in the source article, so we won’t replay them here. It suffices to say that we pay attention to what the biggest players in the tech game share with the world. Their input may not come in big datasets, but in advice or recommendations, yet it holds a much-necessary relevance.

When R&D is backed up by important financing, the tech community benefits from the shared results. Our software engineers like to continually update their knowledge and expand their skills.

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