Digital technology has changed the way we live!
In fact, technology has not only changed the way we live, but it has also changed the way we think about and perceive the world!
From communication to health, there is really no area of society that has not been affected by this era’s digital landscape.
Business and manufacturing industry are also seeing the effects of digitalization.
Commonly referred to as Industry 4.0, the adoption of modern-day technology, much of it smart technology, to enhance every aspect of business and make many of today’s industrial systems autonomous is no longer viewed as a marketing pitch but a reality that is changing the way companies operate.
When we say every aspect of a business, we mean every aspect, including production.
What has Industry 4.0 done for manufacturing and production?
…and this version of the technological revolution has only just begun!
Industry 4.0, as it pertains to manufacturing, differs from its previous automation-centered versions because it is centered on big data, constant connectivity, and human-machine interactions.
These focal points have made autonomous manufacturing systems a reality on a massive global scale.
What are the technologies that have spurred on the fourth version of this industrial-technological revolution?
How have these technologies specifically advanced production and manufacturing systems and procedures and changed the workforce profile of today’s corporations?
There are 16 technological advancements that have helped usher in a new modernization of industrial manufacturing and created an irrevocable change in the way businesses produce their goods and compete within their given industry.
These 16 technologies often overlap in their scope and application as we design them to integrate fully with one another to produce a cohesive manufacturing unit.
The most recent mobile communication technology is 5G, and it surpasses its predecessor, 4G with greater data capacity, data speed, and lower network latency (delayed data).
This benefits both consumers and industries alike.
5G network coverage has already begun this year (2020) but how it will affect the various industry sectors that will take advantage remains to be seen.
We expect it, however, to spur a wave of new and innovative services in several of them.
As far as manufacturing is concerned, 5G will change the traditional role of wireless communication as the formerly limited connectivity that most plants, factories, and warehouses experienced in the past will now be a thing of the past.
Since 5G will offer a more reliable and consistent connection with little to no delays, manufacturing plants can now rely on autonomous machines to perform their duties without interruption.
Ultimately, this non-stop connectivity will allow for higher production levels with the need for little supervision, if any at all save for regular maintenance checks.
Also, 5G will facilitate the Internet of things (IoT), which keeps all machines, computing devices, and communication apparatuses constantly connected with each other.
Again, as 5G will supply a more consistent and constant Internet connection, production lines and manufacturing plants will employ more and more modern technology that uses IoT to make their systems faster, more efficient, and more secure.
Some specific manufacturing areas which will see benefits from 5G include:
Although 5G is just being rolled out in industries in the west, there are some eastern countries that are looking towards 5G to revamp key industries within their borders.
Specifically, Africa and the Middle East are already using 5G technology to further advance their Smart cities and agriculture industry.
Faster speeds, lower latency levels, and a massive communication network will not only to change industries and production processes & capabilities but also the entire economic potential of countries around the globe.
Can mobile apps really make a manufacturing plant more efficient?
8 out of 10 CEOs believe that mobile technology is strategically important for their operational efficiency.
Manufacturing facilities are chaotic.
Massive amounts of inventory, many assembly-line employees and various inspectors and technicians are all packed into one large production center trying their best to get everything organized and moving ahead as scheduled.
Customized mobile apps can help minimize this chaos by facilitating data collection and asset tracking, which will ultimately increase productivity and make for a smoother-running production plant.
And those are just two of the many ways in which manufacturing apps can expedite a production facility’s operations.
There are several other possibilities that this technology helps with:
Some manufacturing plants and warehouses are so large they require golf carts to get from one place to the next without exhausting oneself.
This makes order processing and fulfillment a difficult task and time-consuming task.
Mobile apps can help bridge the gap between time and space in such facilities by integrating with CRM software, the result of which is constant communication between customers and plant workers, managers, and officials, making it easier to process orders in a more timely and flexible manner.
Traditionally, employees at a manufacturing plant would use various hand-held scanners and devices to process orders and track inventory.
Mobile order fulfillment apps, however, have made the need for multiple tracking and processing devices obsolete.
Order-fulfillment apps can be accessed via a single device (smartphone, laptop, or tablet) where all manufacturing tasks can be handled.
Such tasks include:
The best part is that while all these tasks are being easily completed from one device, the app will automatically update the plant’s database, and the company’s tracking software and website.
How important is it to provide a potential customer with an accurate price quote?
Some manufacturers claim a 70% sales conversion rate when they provide a price quote either during or right after the sales pitch or customer query.
A configured price & quote (CPQ) app allows sales reps to get accurate price quotes while engaging with potential customers and so increases the chances of a sale.
Shorter production time increases customer satisfaction and revenue.
The ability to program a production run from a remote location is now possible through mobile apps and IoT software integration.
All you need to do is input the mockup manufacturing data into your app via your smartphone or tablet and it will immediately relay the information to the IoT equipment at the production plant where production will start immediately.
A company can even charge an extra fee for such expedited production runs increasing its bottom line.
Mobile apps can instantly notify a manufacturing facility maintenance team of any malfunctioning equipment or offline machines to cut down on any losses due to downtime.
Manufacturing apps can also scan for service and maintenance updates using installed sensor devices on IoT equipment.
This helps make sure that the required maintenance and servicing of all the machines at a manufacturing facility are on schedule reducing wear and tear and early retirement.
Industry 4.0 and the Internet of Things (IoT) have become phrases we use simultaneously to reference the impact technology is having on modern-day industries but also the impact “smart” technology is having on manufacturing.
While they are not the same thing, Industry 4.0 would not be possible without IoT.
The Internet of Things uses Internet networks and sensory devices to make machines multi-connected and so work more autonomously and more efficiently.
Think of smart thermostats throughout a house that use sensors in each room and which can relay messages back and forth to each other through your smartphone via an Internet connection.
Industry 4.0 refers to the automation and data communication of manufacturing processes through technology-driven machines, devices, applications, and software.
How though do these two connect and why does one rely almost entirely on the other to function properly?
IoT connects the nine technologies that automate the entire Industry 4.0 manufacturing process.
As all the robots, tools, and devices that make up these nine core technologies have sensors and data processing & collection capabilities, we can connect them via IoT technology and bring them together in a harmonious loop.
The result is one large cohesive manufacturing network that can respond in real-time to the demands of the market it serves.
Although IoT and IIoT refer to the same technology, it should be clear that the term IoT refers to consumer goods and IIoT (Industrial Internet of Things) for industrial processes such as manufacturing.
The main point of IoT and IIoT is that smart machines connected via the Internet will be the norm for manufacturers in the years to come.
We expect global manufacturers to invest $70 billion in IoT solutions by next year (2020).
As of right now, IIoT is going through a digitization process we expect will be followed by “smart” capabilities such as predictive maintenance and predictive or smart production.
Connecting an entire manufacturing network of machines, tools, and devices is not without its obstacles but the opportunities it provides in the long run far outweigh the difficulties.
Just what opportunities can IIoT provide?
Harley Davidson, originally found it very difficult to retrofit their IIoT sensors within their manufacturing plant but could cut down their build-to-order cycle and increase productivity by 3-4% once their fully functional IoT production facility was completed.
Using robotic technology in manufacturing is not a new concept.
We have used roots in industrial settings since 1954 to increase production, produce higher quality goods, and replace human workers.
What is new, however, is the robots, commonly referred to as collaborative robots or cobots, which fill in the gaps between robots and human workers.
It is not always possible to fulfill certain manufacturing activities with automated robots or “caged-off” workers. This is where cobots come in.
They are smaller than their automated production line counterparts and so can work alongside human laborers and assist them instead of replacing them.
Cobots are designed to assist humans in a manufacturing plant in 4 distinct ways:
Ford Motor Company has been using cobots in their German manufacturing plant since 2016 to fit and insert shock absorbers into their cars.
Not only have they increased their employee satisfaction by doing so, but they have also increased their production output and factory space efficiency.
Wearable technology is just as its name implies – technology you can wear.
Smartphones, glasses, watches, or clothing can now all contain chips or applications that give us real-time data without us having to search for it.
We have used wearables on a consumer and entertainment basis but industrial uses for such a technology are being explored further.
As a manufacturing plant is an environment where we need real-time data as quickly as possible to ensure efficiency and safety, the ability to wear technology that can alert an employee of relevant information is invaluable.
Wearables can provide alerts for the following safety situations:
Some examples of how wearable computing is being used today are:
Users can use this device to locate data and detect broken sensors without having to search for it – it appears right in their field of vision!
Smart glasses are being designed to stream both video and audio in real-time.
This will enhance the ability of maintenance repairs where a technician can provide detailed instructions to a production line employee wearing the glasses without having to be next to them to do so.
Such technology can also relay real-time data regarding warehouse parts, pickups, and other inventory alerts without the employee having to go anywhere or access any device to retrieve it.
We can now wear sensory devices as patches to identify employee production levels based on movements, gestures, and body proportions.
This is a wonderful tool in determining when a human worker needs a break to refresh themselves and come back stronger to complete their tasks more efficiently and effectively.
Some clothing tech that is being used in manufacturing plants worldwide includes temperature controls which can either cool or heat the user’s body as needed.
Not only can such technology provide constant comfort for production line and manufacturing plant workers but can also detect dangerous levels of heat and cold and therefore protect the user from potential harm.
Another application of clothing technology for manufacturers comes in the form of exoskeletons.
Exoskeletons for manufacturing are the most accessible technology in that market today, according to Dr. Joseph Hilt of the Wearable Robotics Association.
Exoskeletons provide three distinct advantages to manufacturers:
They help to cut down on medical expenses, increase productivity, and optimize the potential work-life of the employee.
Some of the main categories of exoskeleton wearables include:
Many big-name brands already use the above wearables to increase productivity and promote worker safety within their plants but BMW has taken this technology a step further with the unveiling of their new virtual factory where quality controllers can point to any part of the factory and analyze & document flaws through the use of a wearable.
We mentioned predictive maintenance in an earlier section as being a major benefit of IoT technology.
However, it is being mentioned here again as its impact on manufacturing should not be undervalued.
Predictive maintenance itself is not a technology but a set of techniques that are greatly enhanced by technology.
Predictive maintenance (PdM), uses machine data to uncover particular patterns that alert maintenance manufacturing workers to issues that may occur.
The main benefit of such techniques is that a maintenance crew does not have to wait until a machine breaks to fix it, as they can make minor adjustments before a major problem occurs, especially during planned downtimes which almost always require lower costs.
The main technology which assists in PdM is IIoT.
Incorporating such technology within a manufacturing facility to improve predictive maintenance has increased ROI by tenfold, reduced maintenance costs by 30%, reduced downtime by 35-45%, and decreased equipment breakdowns by over 70%.
Deep learning, commonly referred to as machine learning, is one of the most important technological factors for Industry 4.0 as it makes gathering and storing data simple and cheap.
Not only that, but smart machines can self-analyze the data to create higher-quality products at lower costs – the most important goal of manufacturing!
Deep learning machines are intimately tied to AI (artificial intelligence), which we will discuss further in a later section.
For now, the focus will be on how machine learning increases productivity, boosts product quality, and ensures employee safety.
We have discussed already much about the predictive maintenance aspect of Industry 4.0 but another benefit that this technological revolution is having on manufacturing plants worldwide comes in the form of predictive quality and safety through machine analytics.
Smart machines can accurately predict quality deterioration with the same ease it can prevent unwanted downtime by predictive maintenance analysis.
Once a machine “understands” that the quality of a product is about to enter a downward spiral through analyzing product data it has collected, it can halt the production of such products and offer solutions before restarting it again.
The same goes for employee safety.
The machines are installed with sensors able to detect data from the environment and assess whether a potential hazard is looming on the horizon.
There are two main methods a machine uses to learn about relevant data: Supervised and Unsupervised.
Supervised Machine Learning: Here the target is already defined – input and output data & the desired outcome is known. The only thing the machine needs to do is match the two to come up with the necessary prediction for the desired scenario.
Unsupervised Machine Learning: With this deep learning, the machine is given free rein to collect and analyze the data as the input, output, and outcome is not known.
The outcomes of both styles of machine learning are:
A great example of machine learning in action is Siemens’; a German conglomerate, use of machine learning in the form of neural networks – unsupervised machine learning – to both monitor and improve its steel plant efficiency.
Siemens says that its investments in machine learning networks are the main reason it could improve its gas turbine emissions to the degree it has – better than any human could have done, according to the conglomerate.
The company continues to invest in machine learning and AI technology to improve its manufacturing facilities and says it will continue to add upon the $10 billion it has already invested in US software companies over the last decade.
Cognitive manufacturing refers to how cognitive computing can handle the “load” of Big Data brought about by recent technologies such as AI and IoT.
The old way of processing, analyzing, and optimizing manufacturing data is now irrelevant to Industry 4.0 technology. It can not ‘keep up’, let alone ‘scale up’, with the ever-increasing amounts of data smart machines can collect and store.
This is where cognitive computing comes in.
Cognitive technologies, which are built upon the foundation of IoT, can fully use massive amounts of data across many systems, processes, and equipment to come up with insights into the entire supply chain – beginning with design and ending with customer support.
Cognitive manufacturing technology can do this in three ways:
IBM surveyed 140 electronic executives across the globe to see how cognitive manufacturing was affecting the electronics industry.
What they found was that many of the electronic companies were already using cognitive manufacturing technologies in full swing and were actually experiencing greater ROI due to higher productivity levels by using such technologies.
Hybrid manufacturing refers to the combination of two technologies working in unison within a manufacturing setting, namely additive manufacturing (i.e., 3D Printing) and subtractive manufacturing (i.e., Computer Numerical Control – CNC milling).
3D printing is used for the building up phase of a production while CNC milling is used to fabricate and polish the final product.
The main benefit of using both these technologies together is a more unified and precise manufacturing environment where greater design freedom can efficiently and create intricate and flexible parts no matter how complicated or radical their designs may be.
Other benefits of hybrid manufacturing include:
Design freedom and precision brought by the combination of additive and subtractive manufacturing help design a higher-quality end product.
Distributed ledgers are comprised of databases that are spread throughout a broad range of locations to make transaction transparency clearer and so make it very difficult for cyber attacks to occur as each transaction is publicly witnessed throughout a synchronized network.
Besides the core transactions made throughout the synchronized databases, any change made to the ledger will be noted and distributed to all interested parties in a matter of moments.
All interested parties are kept informed of what is going on at all times.
Blockchain, the main technology underlying the famed cryptocurrency Bitcoin, is an example of a digital ledger technology (DLT).
It is said that DLT will be the driving force behind all the technologies fueling Industry 4.0 soon.
Why?
By keeping the entire manufacturing process trackable and transparent, production becomes cheaper, faster, and more secure.
Using DLT increases the visibility of every area of the manufacturing process, increasing the efficiency of the entire supply chain.
Increasing manufacturing visibility to all the areas of manufacturing beginning with suppliers an ending with customer delivery produces the following specific benefits for manufacturers:
The only problem with using DLT in a manufacturing setting is getting all the different Industry 4.0 technologies synched up with a chosen distributed ledger.
There are few DLTs designed for manufacturers at this moment but one, in particular, is showing great promise.
IOTA is tailor-made for Industry 4.0 as it can record and execute transactions between plant equipment/machines and the Internet of Things quickly and securely.
Fujitsu, a Japanese IT company, believes IOTA blockchain technology will be the missing piece that will link the various Industry 4.0 technologies together and play an integral part in the creation of the smart factories of the future.
What we refer to as analytics here is not your run-of-the-mill data but manufacturing data which requires a different analysis.
Manufacturing analytics is unique because the only way to analyze such information is with smart technologies, the kind that Industry 4.0 supports.
Knowing the effects of machine downtime and production scheduling is important but gives a complete picture of what is happening in the overall supply chain.
As many of the traditional BI tools helped in creating predictive measures for the situations described above, they can not spot quality defects in real time and offer solutions to the design team to rectify them.
What exactly is new analytics?
For that matter, what is smart analytics?
For manufacturing data, it means augmented intelligence which can learn, adapt, and execute in a moment’s notice according to environmental and market conditions.
Such augmented technologies include:
One such BI tool that can deliver smart analysis – manufacturing analysis- is the revamped IBM software known as Cognos.
The software includes machine learning capabilities, auto pattern detections, simple reporting, and interactive & intelligent dashboards.
Before data analytics and technological connectivity, a company’s supply chain comprised various distinct and separate departments – manufacturing, logistics, and procurement, to name a few.
Each of these departments contributed to the whole but were not ‘whole’ in their interactions and dealings with each other.
Industry 4.0 technology, specifically IoT and integrated data systems, have made this chain more holistic and managing it much easier with constant information being bounced back and forth from department to department so that one department’s decision-making does not affect another part of the supply chain adversely.
The benefits of a more holistic supply chain management system include:
As of right now, 8 specific technologies have been identified as assisting in bringing about a more collaborative and connected supply chain among modern-day manufacturing facilities.
They include:
Holistic supply chain management is not an idea intended to come to fruition but a current reality that many companies are already having a great deal of success with.
Between 2007 to 2016, Home Depot unified each of its stores’ logistics management departments into one centralized unit as their workers were more busy managing and replenishing inventory than helping customers.
The company now uses advanced data software solutions within its headquarters to track and replenish each of its stores’ inventory levels in a more efficient and effective manner.
Machine-to-machine (M2M) communication is the collection of data from machinery via electronic sensors to the transference of such data via networks to special software that can accurately interpret it.
The transference of data between machines and software either becomes the product of human evaluation or direct imperatives that are transferred directly to other machines and processes in order for them to complete their tasks.
M2M method and technology are the cornerstones of Manufacturing 4.0 as the amount of data collected in today’s manufacturing facilities is often too large to interpret using traditional data collection and analysis methods & tools.
The amount of data that Industry 4.0 can generate is way too much for human consumption and is one reason for so much of it going unused.
Manufacturing machines have not only the ability to collect massive amounts of data but also can store, transfer, and interpret such data so humans can make sense of it and use it to improve the production process.
Some main production areas which can become more efficient and transparent by the use of M2M technology include:
Ultimately, there are two major end results of M2M technology: better network communication & enhanced human capabilities.
Traditionally, M2M communication was a product of hard-wired networks, which limited both the amount of data processed and data processing speed because of close network proximity and lack of protocols.
Today’s wireless technology and the cloud have offered M2M networks a broader range of connectivity and more standardized protocols.
Also, advanced machine sensory equipment and software have connected floor operation with office management and allowed them both to access and transfer data simultaneously in real-time which helps the entire production line keep track of recent market conditions.
The great thing about M2M technology is that it is not intended to replace humans but to help them advance their creative possibilities.
As machines can now automatically collect, monitor, and interpret data, and decide and adjustments by themselves, production workers are left with extra time to come up with better product designs, processes, and systems.
Artificial intelligence, also referred to as machine learning, in a manufacturing setting includes ‘smart tools’ like pattern recognition software and robots that use sensors to collect data for analysis.
Such AI technology, however, is not solely limited to robots and other production machines as the ‘smart’ factories of today are already using various AI technology within their production processes, manufacturing systems, and other tasks that are not linked directly to the production line.
The following are just some of the areas where manufacturers are using AI-infused technology to produce more efficiently and cut down on operating costs and production time:
AI can help optimize the production process by reducing manufacturing costs.
The various manufacturing areas where AI can boost cost optimization are:
With a minute of downtime costing manufacturers approximately $22,000, predictive maintenance is even more important in keeping a healthy bottom line.
Much was discussed earlier about predictive maintenance, yet it should be noted that it is not possible, at least not possible in today’s manufacturing environment, to do it successfully without AI.
Humans & rule-based systems cannot compute the amount of data that smart machines can collect without the guide and help of intelligent algorithms and applications.
Basically, AI systems can capture an entire strand of machine data and process and translate it for human consumption.
The result of this is increased uptime, reduced maintenance costs, and less maintenance planning.
No doubt almost all manufacturers’ supply chains have become more complex with the addition of various sensors, systems, and gadgets being added to various products every year.
To reduce the complexity of an increased supply chain, we can use AI to create accurate demand predictions and automate many of the procurement activities required by manufacturers today.
The biggest area where AI has helped increase quality assurance (QA), is in visual inspection.
Today, the most accurate QA measure is visual recognition. Machine learning algorithms can now detect product faults almost instantly and stop production before low-quality products hit the market.
Such technology not only helps improve fault detection but also product quality.
All manufacturing facilities require resources in the form of staff, electricity, and cooling & heating systems.
Such resource costs can be optimized, however, through AI technology.
Google, for instance, has given its Deepmind AI full control over its data center’s cooling system.
The result of such a trust was a 30% energy savings for the company!
Manufacturing yield does not just reflect productivity but also production waste.
As much as 70% of materials can be wasted and scrapped during the manufacturing process!
As AI technology is not just capable of collecting but also combining data from every process and machine within a production facility, it can use and correlate such massive amounts of various data to reduce yield detraction.
AI has been shown to reduce yield detraction by up to 30% within the semiconductor industry!
Cyber-Physical Systems (CPS) refer to the bridging of the physical world with computing & communication technology. The systems themselves are monitored and controlled by computer algorithms and are connected to end-users via the Internet.
Industry 4.0 can be considered a Cyber-Physical System as both technology and industrial labor are both integrated and harmonized within modern-day industrial facilities.
When a CPS model is used for a manufacturing process, it becomes ‘cyber manufacturing’.
There are many benefits to manufacturers who adopt such a system, some of which are given below:
The main advantage of CPS, or cyber manufacturing, over traditional manufacturing management systems (e.g., experience-based management systems) is that it relies entirely on evidence to keep the real-world and digital world connected to manage assets and assess risks and opportunities.
The main technology driving CPS is sensory-based communication tech such as wireless sensors that monitor the environment of a manufacturing facility.
Other kinds of technology which are used in a CPS include:
Coupled-Model is one of the more recent manufacturing approaches that is based on CPS.
The couple-model approach to manufacturing uses a cloud-based simulation of physical machines with the help of analytical algorithms.
The beauty of this model is that managers can access all the simulations and collected data for analysis and future predictions even when access to real-world machines is limited.
While Industry 4.0 technology has changed the entire manufacturing game for the better, it has also brought with it a lot of exposure issues that need to be addressed.
As more tech enters manufacturing, so does the susceptibility to cyber-attacks.
Since 2017, the manufacturing industry has been the most susceptible to cyber-crime.
The main areas where cyber attacks like data breaches are likely to occur include:
While many manufacturers cite the fact they have a lack of trained cybersecurity staff and the budget to hire them, there are various low-cost and easy-to-implement security measures that can help keep sensitive data safe.
These include:
Besides data breaches, there are many other ways cybercriminals can disrupt the manufacturing process such as IP theft and industrial espionage.
Smart manufacturing plants must make it a priority to protect their data, processes, systems, and networks with cybersecurity technology.
One of the best ways to do this, if the budget allows, is to hire an IT Managed Service Provider (MSP).
They offer a host of cybersecurity measures such as:
Industry 4.0 promised to revolutionize the way companies produced their products.
Looking at the above technology, methodologies, and systems that make up Industry 4.0, and the real-world examples and case studies that accompany them, it appears that it has.
These technologies and systems have interconnected the entire production line, from start to finish, in such a way that massive amounts of data from various sources and departments can now be used by any member or team within a manufacturing facility to communicate and analyze information in a virtual environment and then translate it back into real-world applications.
The result is a more streamlined, cost-efficient, cost-effective, transparent, and flexible manufacturing process that keeps customers happy and boosts the bottom line.
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