Industry 4.0

so it all starts with the first industrial revolution which happened in the late 1700s and this is where water and steam power fueled a lot of growth in agriculture and textiles back in the time then 

about 100 years later in the late 1800s we had the second industrial revolution and this revolution was driven by electrification and the advent of electricity and more importantly the advent of modern mass manufacturing so production lines became more common and everything that was enabled by electricity in the economy that was enabled by electricity and 

then finally in the 1970s is when the advent of the computer and supercomputer really fueled that third industrial revolution so this is where supercomputers and enterprise resource planning and other sorts of technologies really started to digitize businesses and economies throughout the world which leads us to industry 4.

 which is an extension of the third industrial revolution but certainly a big shake-up and a big change and pivot towards where we're headed in the future and that's what the third industrial revolution really laid the foundation for industry 4. and the computerization digitization of businesses throughout the world really gave us the building blocks and the pieces that are leading us to industry 4.

the key difference though with industry 4.

 is focusing on connectivity and tying together all this technology and all this data that's been accumulating over years and decades so one of the things that's being fueled by this connectivity are the end-to-end business processes that is the foundation for industry 4. 

Instead of having separate systems and silos and processes throughout the organization industry 4 is really focused on how we pull together all those business processes and all the data and the information in the workflows throughout the entire internal organization but it's not just about internal operations that we're connecting we're also connecting suppliers the suppliers and vendors that we work with making sure that we capture data and integrate our business processes and workflows with them and most importantly integrating with our customers so capturing data at the customer level in ways that we haven't been able to do in the past and one of the keys to all of this integration when we talk about the internal integration of business processes as well as the integration to suppliers and especially that integration to customers is the whole technology of devices so in other words devices and sensors that are capturing data throughout the entire value chain from the customer order through the internal operations to the vendors as well and some examples of devices and that sort of connectivity that's being created is on a manufacturing shop floor for example you have robotics and machines that are producing materials or producing finished goods and their sensors on those machines that are capturing data about what they're actually doing and the volume of production the quality it's also capturing data around potential predictive maintenance things of that nature you also have on the consumer or customer level probably the most commonly understood device is something like an apple watch or a wearable device that's capturing data about me personally and you personally every day and that's capturing a ton of data that's now connected to the modern enterprise and that is a key part of industry 4 is that whole connectivity of the entire value chain technology and data behind it as well as the devices that enable it one of the key components of industry 4 is artificial intelligence so i mentioned before how the third industrial revolution led us to digitization and the capturing of data throughout organizations and for decades now organizations have been capturing data but they haven't had good information and they haven't been able to make good use of that data for the most part until now with industry 4 though we have emerging technologies like artificial intelligence that actually can make use of that data and give us some predictive analytics and some deeper understanding when it comes to customer behavior for example and really understanding and anticipating what our customer needs might be and what their purchasing decisions might look like and what their overall consumer behavior might look like ai can also be used for things like predictive maintenance so when you have machines on the shop floor you have sensors on them that can predict when those machines might need to be maintained or repaired and so it's a way to anticipate potential problems so you don't have to wait until they break but you're also not investing too much money in maintaining or repairing things that don't necessarily need it yet so ai is a key component of industry 4  and really making organizations smarter and making better use of the data that they already have and also introducing new data via some of the smart devices and sensors in addition to connectivity internet of things artificial intelligence some of the other things we've talked about there's a number of other key components that are important to understand about industry 4  first of all are the systems and devices which we've briefly talked about the enterprise technologies the systems and devices on the shop floor your suppliers your customers all connecting via sensors and devices that's all an important part of industry 4 and a prerequisite to realizing the benefits of industry 4 in addition you also need big data you need to have the data that's used as an input into artificial intelligence and some of the other smart factory type of thinking and the smart factory type of functionality that we've talked about so far so having that data captured in systems and more importantly being able to use that data via artificial intelligence and other analytical tools is very important another intangible prerequisite to industry 4  is operational and technological innovation so in other words you as an organization need to have a culture of innovation and a willingness to try new things and a willingness to think outside the box of the way things have always operated so it's really looking at ways that you can use technology and operational improvements to really leverage the full potential of industry 4 and finally and most importantly you need to have organizational change management you need to manage the organization through the change because industry 4 is not just about technology and process improvements and inner connectivity internet of things and all that stuff i've talked about more importantly and more than anything it's a cultural shift it's a different way of operating as an organization your employees are going to be affected in pretty significant ways more so than they've been affected by computers in the past so it's important to have a very solid change management strategy in order to be successful in your industry 4 initiatives Ethical Implications there's also a dark side and that is the ethical implications of industry 4 and some things to think about are what does this mean to your workforce if we're going to displace workers because we're now automating their jobs or replacing them with machines and sensors and data that can do some of the work that they were doing what does that mean to you as an organization how are you going to manage that change and more importantly is that the right thing to do is that an ethical thing to do and i think that's something that every organization has to answer for themselves and that's more of a societal question as well even if employees don't lose their jobs though you also have the question of their purpose if computers are doing a lot of the jobs that humans once did and even if that doesn't necessarily mean i lose my job does that undermine my sense of meaning and purpose within the organization that's another ethical question or dilemma that needs to be answered and then finally it begs the question of government regulation is this the type of thing that should be regulated by government does there need to be additional safety nets does there need to be additional fallbacks for organizations and individuals that might be affected negatively by industry 4 so we're still a ways out from that becoming a reality but it is very real risk that becomes more and more apparent over time as more organizations move to industry 4 so i hope this has given you some things to think about as you think about your transition to industry 4 and if you're going through any sort of digital or business transformation i encourage you to check out some of the links i've included below including a link to our digital transformation report which provides best practices around how to manage transformation within your organization including things like industry 4

How do we move those bits more effectively, more cost effectively, thinking about different patterns

and different things that we could do. So I'm happy to tell you that we've thought about this. We've launched something called adaptive ingestion

that we believe will reduce the cost of moving data to the cloud by about 70%.

And that's tested. So we've thought about this in terms of creating a warm tier where data is immediately accessible.

We still have our low latency instant access time series streaming as well for any of those. 

And we still have real time events, real time alarms . 

But a lot of your data doesn't need to move in real time. A lot of that data is there for audit purposes. It might be there for daily production max mins.

 So when we think about 15 minute interval data, we can send that data to a warm store.  We can do that 96 times a day instead of thousands of times a day. We can do all these roll-ups in the edge hanks to SiteWise Edge. We can move all that data in a very, very optimized fashion to reduce the cost overall.   And as I said, our results are around 70%,  

it was actually kind of difficult to get data back out of SiteWise if you weren't using one of our own tools. we thought harder about making sure that this is available through simple query interfaces.

So we now have a SQL-like interface that you could use to pull data from SiteWise.  this year, we've launched around 15 new enhancements to SiteWise overall.

The vast majority of equipment downtime in Toyota comes from unplanned machine breakdowns.  That's our unplanned corrective work orders up there. Where we are going, through a solid predictive maintenance system, is that we can do the correct

planned maintenance on non-production time to prevent downtime during production, increasing ROA,  and reducing downtime. So this is where AWS came in. So we had a lot of data.

 how many abnormalities are detected within a production  line.

data

That's an enormous amount of data. Unfortunately, most of it is locked down. What I mean by locked down essentially means that the data is in silos, typically in either systems that are hermetic to external connection, so unable to extract the data, or it's simply very expensive, and takes a long time to implement a solution

we have equipment that's simply too old to be connected. So there are solutions for that as well. The point being, there's a lot of data  that resides on shop floors, but that data is not accessible in most cases. So over 90% of it being locked down.

https://www.youtube.com/watch?v=cJ4dSAUsTKg&ab_channel=AWSEvents

OPC Unified Architecture is a cross-platform, open-source, IEC62541 standard for data exchange from sensors to cloud applications developed by the OPC Foundation.

OPC Unified Architecture (OPC UA) is a machine-to-machine communication protocol used for industrial automation and developed by the OPC Foundation. The OPC UA platform in an platform-independent service-oriented architecture that integrates individual OPC Classic specifications into an extensible framework.