Google Cloud and Siemens has announced a new cooperation to optimise factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud’s leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future.
Data drives today’s industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyse plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in “islands” across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.
By combining Google Cloud’s data cloud and AI/MLmachi capabilities with Siemens’ Digital Industries Factory Automation portfolio, manufacturers will be able to harmonise their factory data, run cloud-based AI/ML models on top of that data, and deploy algorithms at the network edge. This enables applications such as visual inspection of products or predicting the wear-and-tear of machines on the assembly line.
Deploying AI to the shop floor and integrating it into automation and the network is a complex task, requiring highly specialised expertise and innovative products such as Siemens Industrial Edge. The goal of the cooperation between Google Cloud and Siemens is to make the deployment of AI in connection with the Industrial Edge—and its management at scale— easier, empowering employees as they work on the plant floor, automating mundane tasks, and improving overall quality.
“The potential for artificial intelligence to radically transform the plant floor is far from being exhausted. Many manufacturers are still stuck in AI ‘pilot projects’ today – we want to change that,” said Axel Lorenz, VP of Control at Factory Automation of Siemens Digital Industries. “Combining AI/ML technology from Google Cloud with Siemens’ solutions for Industrial Edge and industrial operation will be a game changer for the manufacturing industry.”
Despite being a year of huge disruption, the year 2020 has accelerated change for many companies. Find out more in this article by Hitachi High-Tech Analytical Science.
There’s always an opportunity with a crisis. Whilst 2020 was a year of huge disruption, with industries having to cope with sudden changes in demand, issues with supply and restrictions on the ability to operate, it did accelerate change for many companies. Especially when it comes to big Industry 4.0 trends including connectivity, big data, smart factories, and sustainability.
Thanks to new technologies being deployed throughout companies, IIoT (Industrial Internet of Things) is enabling the collection of more and more information every day, including from manufacturing equipment.
Today, many analysers collect data on the instrument themselves. Our Hitachi handheld analysers, for example, are able to store measurements remotely. More models also have connectivity enabled, which is the real game-changer for enabling remote, real-time decision making. This, we predict, will be a key theme for 2021.
What Do We Mean By Connectivity?
The vision is that analytical instruments will have either Wi-Fi, Ethernet, USB, or in the future, 4G/5G functionality, depending on the industrial environment. The next step would be for analysers to have the ability to share and integrate operational technology (OT) data. But today, most of the connectivity is around data sharing and automation.
Connectivity in the future could also mean that analysers could integrate to process control systems and communicate with other machines and resources. Ultimately, the end goal is to speed up processes, optimise performance, reduce waste, and ensure product quality.
Leveraging Technologies to Make Manufacturing Greener
Industry 4.0 has uncovered an opportunity for positive action when it comes to sustainability, by leveraging technologies to make processes more efficient and greener.
Foundries, for example, have for years championed the green movement by being the ultimate recyclers of raw materials. However, many are also looking at what green technology can do to help reduce material waste. Each process step should have the right solution in place: incoming inspection, melt shop floor, central lab and outgoing inspection. Connected analytical instruments can feed data to a central point, where quality issues can be easily spotted and subsequently rectified to reduce wastage and save cost.
The same concept can be applied further down the supply chain within fabrication, but equally at OEM level. Ensuring each process step has a focused solution that enables data collection can help reduce wastage and deliver greener manufacturing.
Big Data is Power
One reason information rules in the metals industry is through its ability to make manufacturing quality assurance and control processes simpler and faster. However, whilst the quantity of data available is colossal, the question is how manufacturers turn this into something of value – recognising patterns and predicting behaviour to make informed decisions.
Even if thousands of measurements are taken each day, data from the analyser can help manufacturers optimise production in a number of ways, including:
Increased product quality by identifying defects at the earliest stage in the process.
Machine failure predictions and diagnostics leading to well-timed preventative work, reduced downtime and less risk of sudden failures that are so damaging to business.
Reduced costs through the use of big data for predictive analytics, shortening the quality assurance process.
Here’s how a quality data management software has helped Metabowerke GmbH to optimize its measuring and manufacturing processes. Article by ZEISS.
If employees at Metabowerke GmbH, a manufacturer of professional power tools and accessories, find a piece of chocolate at their workstations on the first workday of the new month, then everyone knows that the previous month was a profitable one. The chocolate is one way that management shows its appreciation, and this is hardly a one-off occurrence. Employees think so highly of their employer that, as the result of an employee survey, Metabo was named “Best Employer” amongst mid-sized companies in 2014 by the German magazine Focus.
Achim Schmid, Quality Coordinator in the Housing Technology Center, shares his colleagues’ high opinion of Metabo. For him, the approximately 1,100 employees in Nurtingen are always ready to give 110 percent because company management acknowledges everyone’s contributions.
“We know that at Metabo, our opinions and know-how matter,” says Schmid. The company’s flat hierarchies make sure that everyone is heard and can actively contribute.
There is an interdepartmental mindset at Metabo that encourages employees to work with their colleagues from different areas. This approach played a key role in the company’s decision to purchase ZEISS PiWeb quality data management software. After Control 2015, an international trade fair for quality assurance solutions held in Germany, Schmid was personally “more than convinced that Metabo would benefit from using ZEISS software. But first I wanted to make sure everyone else was on board.”
The software was presented to employees and discussions were held, some of which were attended by ZEISS representatives. The end result: Metabo ultimately decided to invest in ZEISS PiWeb sbs in the middle of 2016, and has been using this software in the Housing Manufacturing department at their site since December.
Wireless Systems Instead of Cable Spaghetti
When attending the trade event Control 2015, what initially impressed Schmid about ZEISS PiWeb was the possibility of transferring manually captured measuring and inspection data directly to the system via a wireless connection.
“I really liked that you could easily integrate manual measuring tools,” says Schmid, who has been working at Metabo as quality coordinator for almost 31 years. The feature was “ideal” because various manual measuring tools are in use at the measuring stations in the Housing Manufacturing area. Prior to the introduction of ZEISS PiWeb, these systems were connected via cables, which not only limit employees’ mobility but also broke quite frequently, making it impossible to transfer the data to the system. The technology was not very reliable, and uptime was limited because the measurement plan had to be processed at one go. This meant that captured data could not be saved in between measurements.
“But now, these problems are a thing of the past thanks to ZEISS PiWeb,” says Schmid. For example, if an employee is measuring the gearbox housing of a flat-head angle grinder, they just need to open the corresponding measurement plan in the ZEISS software. The employee moves the cursor to the appropriate field in the table comprising a total of 17 characteristics, which must be measured by hand. With the push of a button, the employee transfers these data from the manual measuring tool to the system. If the value is within the stipulated tolerance, then a green dot appears immediately. A red dot indicates that the workpiece does not meet the quality requirements for this characteristic. It used to be the case that the employee would have to change software and then search for the corresponding data set in the statistics solution. Now, they can view the measurement values for the previous 500 measurements just by using the report templates in ZEISS PiWeb.
“This way, our team sees immediately if this is an outlier or if there is a trend towards exceeded tolerances,” says Schmid. This knowledge enables Schmid and his colleagues to steer the production process more quickly than before.
Rolls-Royce has invited a group of leading companies to collaborate on Emer2gent, a new alliance of data analytics experts challenged with finding new, faster ways of supporting businesses and governments globally as they recover from the economic impacts of COVID-19.
Early alliance members are Leeds Institute for Data Analytics, IBM, Google Cloud, The Data City, Truata, Rolls-Royce and ODI Leeds. The alliance will be facilitated and co-ordinated by innovation specialists, Whitespace.
Together the initial wave of members brings all the key elements of open innovation; data publication, licensing, privacy, security; data analytics capability; and collaborative infrastructure, to kick off its early work and grow its membership.
Emer2gent will combine traditional economic, business, travel and retail data sets with behaviour and sentiment data, to provide new insights into – and practical applications to support – the global recovery from COVID-19. This work will be done with a sharp focus on privacy and security, using industry best practices for data sharing and robust governance.
Emer2gent models will help get people and businesses back to work as soon as possible by identifying lead indicators of economic recovery cycles. Businesses, both small and large, around the world, as well as governments, can use these insights to build the confidence they need to take early decisions, such as investments or policies, that could shorten or limit the recessionary impacts from the pandemic.
“We want the global economy to get better as soon as possible so people can get back to work. Our data innovation community can help do this and is at its best when it comes together for the common good,” said Caroline Gorski, Global Director, R2 Data Labs, the Rolls-Royce data innovation catalyst which started the alliance
“People, businesses and governments around the world have changed the way they spend, move, communicate and travel because of COVID-19 and we can use that insight, along with other data, to provide the basis for identifying what new insights and trends may emerge that signify the world’s adjustment to a ‘new normal’ after the pandemic, ” she continued.
Real-time data analysis is playing an increasingly critical role in improving overall productivity and manufacturing competitiveness. Here’s how the metalworking sector, through advances in metrology solutions, can create a smarter manufacturing environment that is simple to manage and deploy. Article by Hexagon Manufacturing Intelligence.
In today’s business climate, it is of paramount importance to reach consistently high levels of productivity, quality and cost-effectiveness.
For this reason, metalworking companies are harnessing new data-driven technologies to enhance and automate their design, production and inspection processes. And the change is taking place beyond sectors that are traditionally highly sensitive to quality, such as aerospace and automotive.
As today’s coordinate measuring machines (CMM) and their associated sensors and software become much faster and more sophisticated at collecting and exchanging accurate inspection data, real-time data analysis is playing an increasingly critical role in improving overall productivity and manufacturing competitiveness.
Many manufacturers have already equipped their CMMs with multisensor technology that allows them to perform contact and optical non-contact inspection interchangeably, while quickly and efficiently capturing data that can be checked for accuracy against original CAD data.
Measurement data collection and analysis is supported by the Q-DAS statistical analysis software, which can be used to visualise measurement data in real-time and monitor them statistically. Q-DAS’s statistical process control (SPC) function systematically informs the CMM operator of any violations of SPC alarm criteria, enabling operators to take corrective action in the manufacturing process before generating expensive scrap. At the same time the software’s high flexibility allows users to adapt the recording and visualisation of data to specific tasks.
Using Real-time Analysis to Improve Performance
But now, manufacturers are going a step further to increase their productivity by addressing the challenges of improving machine utilisation, throughput and uptime. This is leading them to adopt asset management solutions, such as the HxGN SFx Asset Management software, to monitor the status of one or several CMMs in real-time. The growing use of asset management software is partly due to greater automation, which requires operators and managers to keep a close check on the performance of unattended machines. By using asset management to efficiently monitor the status of CMMs remotely, manufacturers can maximise machine usage.
Asset management can help the metalworking sector raise productivity across multiple applications, such as the measurement of rotor or stator lamination stacks. Rotor or stator lamination stacks are composed of individual electrical steel laminations and are typically inspected in pallets using image-processing (vision) sensors. Hexagon’s OPTIV multisensor and optical CMMs come equipped with a vision sensor as a standard and are commonly used to simplify the measurement of large or palletised flat parts such as rotor or stator laminations close to the shop floor. Equipped with handle workpiece palletising, the Hexagon OPTIV enables a fast and automated inspection process, even for large batches of small serial parts such as clutch discs and fine-blanked parts.
Because speed and accuracy are of the essence when inspecting rotor or stator lamination stacks, operators often need to manage multiple CMMs that are running pallet measurement routines unattended.
The OPTIV’s extended measurement range enables prepared interchangeable pallets to be supplied semi-automatically by a palletising robot, reducing standstill times and increasing inspection throughput.
Hexagon’s Inspect software, meanwhile, makes it simple for operators to set up one pallet and then prepare and launch the next on a separate CMM. Asset management software offers a simple dashboard view of machine availability, which allows operators to save additional time by identifying which CMM has spare capacity.
The right asset management tool also helps manufacturers optimise maintenance schedules and plan for a more efficient use of manufacturing resources. As a result, quality departments can shift from managing assets as a cost centre to creating value by optimising equipment profitability.
If, for example, a manufacturer needs to increase production to fulfil new orders for a customer, information from asset management systems makes it easy to identify where spare inspection capacity lies either locally or at another site.
Raising Your Equipment’s Overall Effectiveness
Successful asset management, however, depends on having instant access to actionable insights. Notifications about the performance and status of metrology assets, for example, need to be available in real-time. And all alerts should be readily customisable so that operators and managers receive the information that is pertinent to their role and in a format that is easy to access, understand and use.
For this reason, Hexagon has ensured its HxGN SFx Asset Management platform provides a simple, accurate way to monitor and analyse how key assets are performing via a centralised, user-friendly dashboard. It works equally well whether the machines being monitored are on a single site or in multiple factories around the world. And in addition to a having a dashboard view of each CMM’s uptime and downtime, it’s possible for operators to see how each machine is being used, all while working remotely on a mobile phone or a PC.
Having real-time insight into CMM performance not only enables operators and managers to schedule work efficiently and respond immediately to operator errors. They can also use data analysis to understand and manage the productivity of their assets over the long term and to calculate overall equipment effectiveness (OEE) either for a single CMM or for several CMMs, across multiple sites and over various periods of time. OEE is calculated using data for quality, which is based on a CMM’s success in completing measurement processes during the scheduled time, performance and availability. Gaining a thorough understanding of a CMM’s OEE makes it easier for manufacturers to reduce spending on maintenance while achieving better overall performance and efficiency.
Getting the Right Data with Versatile, Multisensor Systems
Any analysis is only as good as the data it relies on, which is why Hexagon is investing in ensuring all its systems deliver accurate, real-time, relevant data to where it is most needed. The HxGN SFx Asset Management solution is an important part of this strategy, but it is not the only element.
When it comes to inspection, the choice of CMM, sensors and supporting software for a given application clearly plays a pivotal role in determining the quality and quantity of data that manufacturers gather, and at what speed.
Multisensor CMMs have grown popular because of their versatility when capturing data at the varying levels of detail and speed required by different applications and materials.
Having the Vision to Improve Processes
Vision sensors remain a key inspection tool in the metalworking sector because they are adept at quickly and automatically measuring large volumes of intricate metal parts. Designed to capture surface detail swiftly, vision systems are particularly useful when dealing with high production rates.
A vision sensor with a high-resolution digital CMOS colour camera and a programmable motorised zoom lens, for example, can offer variable illumination in the form of a coaxial LED top light, LED back light and multi-segment LED ring light to provide high contrast illumination of complex surfaces and edges, all while quickly capturing data that informs the entire manufacturing process. Hexagon further enhanced vision sensor speed and performances when it launched a Large Field Of View vision sensor for its OPTIV CMMs. The new vision sensor provided a field of view that is approximately four times larger than the standard vision sensor on today’s OPTIV CMMs.
When it comes to using multisensor systems to measure metal workpieces, Hexagon’s OPTIV Dual Z helps increases batch measurement throughput by enabling optical and tactile sensors operating in restricted measuring volumes to automatically reach more measuring positions within a single measurement cycle.
Whether a manufacturer’s applications best suit a vision sensor, a laser sensor or a touch probe, Hexagon is focused on providing data-based solutions that easily integrate with existing and future systems to create a smarter manufacturing environment that is simple to manage and deploy.
With Industry 4.0 and the rise of the Internet of Things (IoT), the heavy industries of manufacturing, construction, transportation and utilities are becoming ever more connected. Gartner forecasts that 25 billion connected things will be in use by 2021, capturing huge amounts of data information, including videos, images – all with the help of AI.
In order to do that, organisations are required to have the ability to understand data. In other words, they need to be data literate.
If organisations are to develop data literacy, the opportunity present is significant. Qlik’s Data Literacy Index states that if companies improve corporate data literacy levels, their enterprise value may increase between three and five percent, which equates to a staggering $324 to $534 million.
TOP INDUSTRY 4.0/AUTOMATION ARTICLES FOR THE MONTH
However, improving data literacy might be challenging for businesses operating in heavy industries. Leaders in the manufacturing, resources and construction, transportation and utilities industries admitted that they lack confidence in their organisation’s data literacy. In fact, businesses are falling short when it comes to data literacy. It is reported that although 78 percent of the global workforce are willing to invest more time and energy into improving their data skill set, only 34 percent of firms are currently providing data literacy training and just 17 percent “significantly encourage” employees to become more comfortable with data.
Part of the issue may be the value which leaders in heavy industries place on data literacy, or the lack thereof. Despite the ongoing conversation of Industry 4.0 and the increasing use of IoT, less than half of business leaders from the sector believe that data literacy is relevant to their industry.
This might be unlikely to change soon – just under a third of business leaders surveyed think data literacy is a very important factor when hiring, suggesting that it is not of priority for all.
This might seem like a contradiction – to have smart factories, meters, wind turbines, and connected housing; yet, limited desire to understand the data that all these innovations generate. One of the current issues is that, whilst leaders recognise the value of connecting production lines, processes and hardware, many are only looking at each part in isolation. So, for instance, that might mean investing in sensors to monitor heavy goods vehicles for maintenance, and separating the technology used for tracking locations, without thinking of how the two can work together. In effect, this creates data silos, where the lack of a complete view amounts to extracting a limited value.
The research results confirm this challenge, with few leaders expecting employees to be data literate – only 14 percent of heavy industry respondents significantly encourage their workforces to be comfortable with data, and less than a third (32 percent) provide data literacy training. Most tellingly, just a quarter of the sector communicated that they are willing to extend a higher salary to employees who are data literate.
That said, there are some areas which the heavy industries are focusing on. While the heavy industries reported a lower usage of data decision making than other sectors in the Index, they outperformed the cross-industry average when it came to data analysis. 37 percent of heavy industry respondents reported that data analysis influences the corporate performance measurement and demand forecasting, 11 percent ahead of the average. A deeper dive into heavy industries finds that 73 percent of engineers use data analytics, well ahead of banking (54 percent), the commercial sector (56 percent) or services (58 percent).
Is this light at the end of the tunnel, or merely a silver lining? With data analytics often being the first step in cohesive data comprehension, I hope the former over the latter. As with any industry, there are examples of manufacturers, utilities companies and logistics businesses understanding the power of data or rejecting it completely. Most sit somewhere in the middle, perhaps wrestling with contrasts and contradictions within their own organisation.
What is clear, though, is that ongoing investments are being made in technology to unlock new opportunities. In spite of that, true success will be out of reach without an increase of data literacy level. If these remain low, companies in the heavy industries run the risk of losing valuable ground on competitors and struggling to thrive in this data-driven era.
Article by Jeremy Sim – Senior Director, Industry Solutions, Global Manufacturing & HighTech and Retail
In an interview with Asia Pacific Metalworking Equipment News, Uwe-Armin Ruttkamp of Siemens Digital Industries talked about how digitalisation is helping machine builders and users, the utilisation of data to improve manufacturing processes, as well as how umati will help push the metalworking industry forward. Article by Stephen Las Marias.
One of the highlights of Siemens’ booth at EMO Hannover 2019 is the latest generation of its Sinumerik One, the first digital native CNC aimed at driving the digital transformation in the machine tool industry. Siemens has also extended its Industrial Edge offerings for Sinumerik Edge to include more new applications to help machine tool users improve workpiece and process quality, increase machine availability, and further optimise machine processes.
With Sinumerik One, machine tool manufacturers can virtually map their entire development processes, significantly reducing the product development phase and time to market for new machines. This helps machine builders significantly reduce the duration of actual commissioning. Its virtual model opens up new possibilities for manufacturers and operators—machine concepts and functions can be discussed even before real hardware is available.
Sinumerik One enables machine users the programming of workpieces in the virtual environment and the setup and operation of machines completely on the PC. Employee training can also be carried out using the digital twin instead of the actual machine. These hardware and software innovations help machine builders and operators speed up processing steps significantly.
In an interview with Asia Pacific Metalworking Equipment News, Uwe-Armin Ruttkamp, Head of Machine Tool Systems, Motion Control Business Unit, Siemens Digital Industries, talked more about the benefits of these new technologies and how digitalisation is helping machine builders and users. He also discussed the utilisation of data to improve manufacturing processes, as well as how umati will help push the metalworking industry forward.
When we look at the current potential for these technologies and all that they involve, are they more suited to advanced markets such as Europe or the US?
Uwe-Armin Ruttkamp (UR): I wouldn’t say so. You have all kinds of industries also in Asian countries. Not everything is low-cost and price-driven; they are also technology driven, especially aerospace, automotive industries, or the upcoming additive manufacturing.
So, there’s a lot of technologies driving the industries. In addition to this, labour is not staying on this low-cost level—in Asian countries, people want to earn more money as well—so saving time, and saving cost by saving time, is also an issue for Asian countries.
How does this technology play out in the smart factory concept?
UR: It plays perfectly into that concept, because with our Digital Enterprise (DE) Portfolio we offer a holistic end-to-end solution including industrial software and automation that allows the use of a seamless value chain. This value chain consists of five steps for the machine user, and five steps for the machine builder. If you build a machine, you start with a concept, mechanics, you go to electrical design, you go to engineering, you go to commissioning, and sometimes, it also needs service.
For the machine user, there are also several steps needed to build a part. Get the machine on the shop floor, create a part, build the part, check it for quality, and ship it. And this complete concept is the basis for running a smart factory.
In a lot of these steps, Sinumerik One brings great benefits. For example, in machine engineering, people in the offices can engineer the machine. You don’t need to have a test rack next to your desk, and you don’t need to go to the shop floor to test the applications. You can do it all in the virtual world. That’s one perfect example of an Industry 4.0 application that people will get from our Sinumerik One concept.
How do you see digital twins being implemented by customers in Asia?
UR: I see a lot of customers thinking about it. We talk to many customers, including those in Asia. We, for example, are customers of our customers. We have factories ourselves. And we only buy machines where we can get a digital twin beforehand. We make it a prerequisite for purchasing a machine, that it comes with a digital twin. And I believe in future many other users are going to do the same. The benefits are huge. You can train the people, who are going to operate the machine, before the machine is even delivered. And even more, you can also do the run-ins, do the first test of the programs, and know the cycle time of the production, before the machine is delivered.
Does siemens have a benchmark so that when machine users’ data are analysed, they will determine whether they are doing okay or they are falling short?
UR: We offer from our service department a digitalisation check. Together with our customers we examine their factories and give them advise what digitalisation measures are in place to get to another productivity level. It’s a consulting approach not a benchmark.
More and more people are talking about the lights out factory. how are you helping customers go into that level of manufacturing?
UR: Lights out factories are not new. When you go into an automotive factory, for example they produce the same part over and over, it is relatively an automated production. So, what they have done, of course, is to use a CAD/CAM chain, which, out from the design of the piece, create the program to build the piece, download it into the machine, and run it. Of course, this is something we support with our DE portfolio. You can put a program into the machine remotely, and then run it automatically. But of course, it requires in-feed of the materials and taking out the material and the pieces produced. But then again, you need automation, and the complete tool chain and software, like NX for example, or TeamCenter, to have a data backbone for all the production information about the part. But there are other companies focusing on job shops, so they produce many different parts every day according to customer specifications. For them it does not make sense run a fully automated line. So, a lights-out factory for them is not possible.
One of the highlights of emo 2019 is umati. How are you supporting this initiative?
UR: We support it 100 percent. We are part of the initiative and helped it to get to the point where we are today. At Siemens our solution to serve a universal interface for machine tools is based on our industrial edge concept. Edge computing is the perfect solution for this. For example, one wants to have a central dashboard, which shows the amount of cooling liquid used per hour. Cooling liquid per hour is not stored as one piece of information in all the machines in the same way. You need to have some sort of programming that knows where that data is stored in the machine and sends it out in a uniform way. Our Siemens industrial edge concept is perfectly suited for this, because OPC-UA is built into our edge devices. This allows the machines to communicate the data provided based on OPC-UA, and the user can program a little piece of code into it to acquire the data out of the machine.
The specifications for umati is still being finalised. during its early development, what were the challenges that you experienced, and are they still a challenge now?
UR: From a technical perspective, it’s not difficult, because it is OPC-UA, and it is a definition of data. It is basically a companion stem based on OPC-UA. The difficult part was to get an agreement among all parties which data they want to support, or which use cases they want to support. Once umati defines which piece of information has to be programmed, it’s done. It’s relatively simple.
Artificial intelligence (AI) is rapidly entering a new phase within the enterprise market, with an increasing number of businesses leveraging AI to turn the massive amounts of process, operational, and transactional data being collected into actionable insights that can improve the way they run their businesses as well as improve customer interactions, according to a new report from Tractica.
Based on the number and variety of pilot programs, proof-of-concept (PoC) demonstrations, and commercial deployments of AI technology already being publicised by enterprise customers around the globe, it is clear that AI is not a fad, but a key part of the technology landscape of today and tomorrow. Tractica forecasts that annual revenue for enterprise applications of AI will increase from $7.6 billion worldwide in 2018 to $107.3 billion in 2025.
“No longer is the discussion of AI limited to science fiction, autonomous vehicles, or Siri; AI is being deployed across a multitude of industries and use cases with enterprises leading the way,” says principal analyst Keith Kirkpatrick. “Thanks to the use of template-driven AI platforms, even a small pilot program can demonstrate real-world benefits. As enterprises are realising, the benefits of AI are even greater when the technology is scaled across the entire organisation.”
Tractica’s report, “Artificial Intelligence for Enterprise Applications”, examines the practical application of AI within commercial enterprises, providing a comprehensive analysis of use cases, business models, market drivers and barriers, technology issues, and the evolving market ecosystem.
Zebra Technologies Corporation has announced the results of its third annual “Intelligent Enterprise Index” which indicates a record 61 percent of enterprises worldwide are on the path to becoming “intelligent,” compared to only 49 percent in 2018.
This global survey analyses the extent to which companies connect the physical and digital worlds to drive innovation through real-time guidance, data-powered environments and collaborative mobile workflows. Their “Intelligent Enterprise” Index scores are calculated using 11 criteria that include Internet of Things (IoT) vision, adoption, data management, intelligent analysis and more.
Based on these criteria and driven by an overwhelming pressure to improve the customer experience, retail organisations have gained the most momentum in the last 12 months, graduating from the bottom of the 2018 vertical Index rankings to nearly the top of the 2019 list, second only to Healthcare.
The number of companies defined as truly “intelligent enterprises” by achieving a score of 75 points or greater on the Index has also risen year over year. Seventeen (17) percent of organisations with at least 250 employees crossed this threshold in 2019 versus only 11 percent in 2018. Interestingly, 37 percent of surveyed small to medium-sized businesses (SMBs) with 50-249 employees scored 75 points or greater on the Index, indicating SMBs with an IoT vision are in many cases more “intelligent” than large enterprises.
“When we launched the Intelligent Enterprise Index three years ago, many enterprises were trying to understand where and how IoT solutions could be best applied within their unique business environments,” said Drew Ehlers, Global Futurist, Zebra Technologies. “We now see more urgency to improve operational visibility and facilitate the delivery of actionable intelligence all the way to the edge of the enterprise. I believe that is why enterprises are now demonstrating a much greater commitment to executing their IoT plans and why we’ll likely see a surge in investments over the next few years.”
“The edge of enterprise is becoming increasingly connected, thanks to the continued adoption of mobility and IoT. Inevitably, this unprecedented, growing level of connectivity has generated large amounts of actionable data that that businesses can leverage on to improve their performance and workflows. Zebra’s Savanna is a revolutionary data intelligence platform that integrates IoT end-point connectivity, configuration management, data transport, data storage, analytics, and machine learning into a single platform which converts raw data into actionable insights that can afford businesses with greater capabilities in delivering new levels of service, productivity, and profitability,” said Fang-How Lim, Regional Director for Southeast Asia, Zebra Technologies.
The metalworking industry is entering a new era where new forces created by the explosion of data, robotics, automation, and artificial intelligence (AI), amongst others, are changing the dynamics for manufacturing companies. Article by Stephen Las Marias.
To be successful in this new environment, manufacturers should consider a paradigm shift, focusing on innovation, integration of new technologies, and collaboration with their partners—and even with the competition—to build new solutions and take their production to the next level.
And that’s where key transformative technologies such as big data, analytics and the Internet of Things (IoT) come in. Industry 4.0—our nascent industrial generation—represents digitalisation across all operational processes. Industrial IoT (IIoT), in particular, describes an integrated system of systems where sensors and actuators provide specific data such as measurements, timing, and equipment status, all connected and visible throughout the enterprise.
In this scenario, we’ll see the convergence of operations technology (OT) in the factory floor with information technology (IT) in the enterprise, all working together towards a single purpose—a more-efficient, profitable and successful manufacturing operation. With IIoT, companies will be able to view real-time data on their manufacturing processes, and compare performance across their plants, or even shifts within their plants. They can also quickly scale their production up or down; manage their energy consumption; and even manage, troubleshoot and fix their processes and plants, even when they are located in different parts of the world.
Data is what’s powering this. Seamless exchange of data between automated machine processes, including manual assembly, and testing, to name a few, provides clear visibility of the operations of this ‘smart factory’.
According to Mariano Kimbara, senior industry analyst for the Industrial Group of Frost & Sullivan, data will be the new value-multiplier for the factory. “Factory owners will strive to network various aspects of a plant, such as tools, assets, material, people, process, and services, on one digital platform. The level of integration and collaboration will offer customers unprecedented information visibility and subsequently generate value from domains that were generative before,” he said.
Making Use Of Data
Data plays a key role in all of these. As metalworking equipment manufacturers produce more and more sophisticated machines, with multiple sensors providing all the necessary data to ensure the health and performance of the machine, measurement, tracking, monitoring, inspection data—users today, on a daily basis, are grappling with more data than they ever did a decade ago.
With Cisco forecasting more than 50 billion connected devices by 2020, it is expected that there will be a deluge of data coming from these connected systems of systems. Which means a mindset shift is required when it comes to deciding what to do and how to leverage these data to improve your manufacturing operations.
Having all these data from your more sophisticated machines and tooling equipment is good, but the challenge is finding that relevant data that will provide you an actionable intelligence that you can implement to improve your processes.
According to Thomas Jakob of Bosch Software Innovations, this begins with sourcing for data creatively. He said companies can impel a more comprehensive look at information sources by being specific about problems they want to solve or opportunities they want to exploit. This means identifying and connecting the most important data for use in analytics, followed by a clean-up operation to synchronise and merge overlapping data, and then working around the missing information, according to him.
Once the information is in your hands, there are many ways how this will help you in your road to smart manufacturing. First is through preventive and predictive maintenance. Majority of the metalworking machines are now equipped with sensors that collect many different types of data, such as operating time and the conditions of the components and parts. Having these data provides users the knowledge on whether a part is no longer functioning efficiently and therefore needs replacement. Or, if a certain job is more tedious than the previous one, the machine will have to be rested for a slightly longer period than that required of the previous job. These things will help keep your metalworking equipment running efficiently, smoothly, and prevent machine downtime.
According to Bob Gill, general manager for Southeast Asia at ARC Advisory, the proportion of machine tool time actually taken up by cutting metal is generally less than 40 percent and can even be as low as 25 percent. He said that it can be difficult to accurately pinpoint the causes of all that non-productive machining time because most machine shops are performing manual, post-production data collection. In a smart factory, where your machines are constantly releasing production data, you will be able to gain intelligence on the impact of machine stops and tool changes in production, and as such plan more effectively and efficiently to improve machine uptime and utilisation.
Another benefit that you can get out of your smart data is energy usage. Smart machines provide data on their energy usage. Based on this, you will have the information when to ramp up production based on energy demand. Moreover, machines could power down when not in use.
And last but not least, quality control and assurance. With smart data, manufacturers will know whether a particular machine is not performing properly by comparing information about its performance to previous jobs. Or in case there are particular products that are not according to specs, users will be able to trace back where the process ‘faltered’, which resulted to production mistakes.
Industry 4.0 is here—and manufacturing operations can significantly benefit from utilising IIoT, smart data, and analytics. Building the right kind of technologies and expertise to apply them is critical for everyone to be competitive in this new industrial landscape.