Machine Learning

Industrial Data Help Overcome All Business Challenges

How Can Industrial Data Help Overcome All Business Challenges

Today, if we see the ongoing competition between industrial companies, we can easily underline the challenging hurdles they face to become the best, primarily in operational objectives and in understanding the immense amount of data available to them to decide how best they are achieving those goals.

To meet this objective, industrial data management strategies must be adopted to leverage existing assets and systems to unlock the full potential of their plants and drive their businesses forward.

Currently, the flooding industrial data is mostly wasted. In fact, as per the European Commission, 80% of industrial data gathered is never utilized. Asset-intensive organizations need a holistic and integrated solution that offers seamless connectivity across all data sources while providing real-time monitoring capacity to ensure no data is wasted.

With such a broad framework, these companies can maintain asset reliability through predictive equipment failure analysis, reducing maintenance costs and improving overall plant efficiency. Yielding on this vision is a big task today as a flooding amount of data is present. Companies across these sectors have recorded and captured large amounts of data for decades. These data have incredible potential, and using them to good use is far easier than expected.

Unclosing high-potential value use cases that utilize this data in production optimization, machine learning, or emissions tracking needs potent data management strategies. After all, industrial data and systems have traditionally been located in organizational silos, having different pockets of functionality developed by various dealers at different times. This has made data management more difficult and rendered most data unusable at scale.

Going through the Data Lake confusion

To counter the challenges highlighted above, businesses often choose to construct data lakes in which data from different sources is collected.

These data lakes work as potential reservoirs that swiftly accumulate vast amounts of information.

Nonetheless, it is not easy to potentially utilize these data lakes as it requires a workforce skilled in data handling and analysis, ultimately creating a considerable challenge to industrial business. Hiring such highly skilled personnel becomes even more intimidating due to the promptly evolving workforce, where specialized expertise is at a compensation.

Going through this complex system requires a strategic approach, allowing businesses to unveil the full potential of their data lakes and secure a competitive benefit.

The need for real-time data platforms suitable for commercial use

An asset-intensive business offers potential solutions; however, traditional data historians remain key, allowing industrial organizations to access data, know what is relevant, place it into workflows, and make it usable. The market for these assets remains on an evolutionary path globally. As per Mordor Intelligence, it will grow from US$1.15 billion (€1.05 billion) in 2023 to US$1.64 billion (€1.49 billion) by the end of 2028, at a compound annual growth rate of 7.32% during the projection period. 

Today, plant operators and engineers use historians to monitor operations, analyze process efficiency, and look for new opportunities. These are target-oriented systems customized for the operation teams’ benefit. 

With time, there has been an increasing demand for cloud-based applications to aid advanced analytics and quickly scale up. Meanwhile, on the IT side, digitalization teams and products need to be structured, clean, and contextualized data to produce usable insights and expand use case volumes. 

However, different data sources, including historians, offer at-a-glance analyses; their customized nature makes it hard to automate consistency in contextualizing and structuring data.

Enforcing a new solution

The collaboration of plant-level historian solutions and enterprise data integration and management technology allows a uniform confluence of IT, that is, Information Technology, and OT, which is Operational Technology functions. Along with this, we are also noticing the rise of next-generation real-time data platforms, supporting industrial organizations in collecting, consolidating, cleansing, contextualizing, and analyzing data from their operations.

This data foundation shows the beginning point for the industrial organization to optimize processes using machine learning and AI and develop new working methods based on data-derived insights.

Such organizations will be competent in developing current data systems to gather, merge, store, and retrieve data to boost production operations with data-driven decisions or backing performance management and analytics across the business.

This new data consolidation strategy prints a key moment in the evolution of data management. An organization can unveil unimaginable efficiency, innovation, and visibility by centralizing information from different sources into a unified, cloud-based, or on-premises database. The collaboration of batch and event processing delivers track and trace capabilities and authorizes organizations to search into batch-to-batch analysis quickly.

Driving ahead positively

Today, industrial companies face umptieth challenges, including meeting operational objectives, comprehending large amounts of data, and improving asset reliability.

They need a data management approach that uses legacy assets and systems to manage these issues. This approach should have an integrated solution that enables organizations to connect all data sources, access real-time monitoring, boost asset dependability, and increase overall plant efficacy.

Conventional data historians are still crucial to this strategy but must be integrated with cloud-based applications, enterprise data integration, and management technology. This will help companies gather, consolidate, cleanse, contextualize, and analyze data from their operations. This real-time data platform has grabbed a competent place worldwide as companies seek solutions to enhance their operational efficiency and decision-making capacity. Not just this, companies will also be able to update current data systems to gather, store, merge, and get back the lost data. This will ultimately improve production operations with data-based decisions and help in performance management and analytics across the system.

Along with this, companies will also get access to real-time asset performance, track material progress through complicated processes, and interlink people, data, and workflows to support compliance.

How can Artificial Intelligence Boost the Manufacturing Industry?

Today, most of the Giant industries, around 83 percent, believe that AI delivers better outcomes; however, only 20 percent have embraced this technology. It is pretty clear that a stronghold on the domain is important for successfully adopting artificial intelligence in the manufacturing industry.

Domain expertise is important for successfully adopting artificial intelligence in the manufacturing industry. Jointly, they form Industrial AI that uses machine learning algorithms in domain-specific industrial applications. AI can be potentially used in the manufacturing industry through machine learning, deep learning, and computer vision.

Let’s check out some of the critical needs in artificial intelligence technologies in the manufacturing industry to obtain a better picture of what one should do to keep the business up-to-date and seamless.

AI Is a Broad Domain

Artificial intelligence is not the correct way to describe all the technologies, and we’ll discuss that they have applications in manufacturing industries. AI is a big subject with different methods and techniques that fall under it.
There are robotics, natural language processing, machine learning, computer vision, and many other technologies that also need attention.

Keeping this in mind, let’s begin with artificial intelligence applications in the manufacturing industry. So here are some industrial uses of AI.

The Goal of AI in Manufacturing

Artificial intelligence studies how machines can process information and make decisions without human interference. The best way to understand this is that AI aims to mimic how humans think but not necessarily. We all know that humans are better and more efficient in performing certain tasks, and in certain tasks, they are not. The best type of AI is one that can think and make decisions rationally and accurately. The best way to explain this is that we all know that humans are not efficient enough to process data and the complex patterns that appear within large datasets.

However, AI can easily do this job using sensor data of a manufacturing machine and pick out outliers in the data that provide information about the machine that will require maintenance in a few weeks. Artificial Intelligence can perform this in a fraction of a human’s time analyzing the data.

Robotics: The foundation of Modern Manufacturing

Many applications of artificial intelligence include software in place of hardware. However, robotics is mainly focused on highly specialized hardware. As per Global Market Insights, Inc, the industrial robotics market is expected to grow more than $80 billion by 2024. In many factories, for instance, Japan’s Fanuc Plant, the robot-to-human ratio is approx 14:1. This reflects that its possible to automate a large part of the factory to reduce product cost, improve human safety and enhance efficiency.

Industrial robotics demands specific hardware and artificial intelligence software to assist the robot in accurately performing its tasks. These machines are specialized and are not in the business of making decisions. They can run under the supervision of technicians, and if not even, they make very few mistakes compared to humans. Since they make very few mistakes, the overall efficiency of a factory improves when integrated with robotics.

When artificial intelligence is integrated with industrial robotics, machines can automate tasks like material handling, assembly, and inspection.

Robotic Processing Automation:

Robotic processing automation, artificial intelligence, and robotics are among the most familiar. It is important to understand that this process is not related to hardware machinery but software.

It involves the principles of assembly line robots for software applications like data extraction, file migration, form completion and processing, and more. However, these tasks do not play very important roles in manufacturing; they are significant in inventory management and other business tasks. It becomes more important if the production operation requires software installations on each unit.

Computer Vision: AI Powering Visual Inspection

Quality control is the manufacturing industry’s most significant use case for artificial intelligence. Even industrial robots can make a mistake, though the possibility is less than humans. It can be a huge loss if a defective product reaches the consumer by mistake. Humans can manually monitor assembly lines and identify defective products, but no matter how attentive they stay, some defective products will always slip through the cracks. Therefore artificial intelligence can help the manufacturing process by reviewing products for us.

Adding hardware like cameras and IoT sensors, products can be interpreted by AI software to catch defects automatically. The computer can then automatically decide what to do with defective products.

Natural Language Processing: Improving Issue Report Efficiency

Chatbots driven by natural language processing is an important manufacturing AI trend that makes factory issue reporting and helps requests more efficiently. It is a domain of AI that specializes in mimicking natural human conversation. Suppose workers can access the devices to communicate and report their issues and questions to chatbots. In that case, artificial intelligence can support them in filing proficient reports more promptly in an easy-to-interpret format. It makes workers more accountable and decreases the load for both workers and supervisors.

Web Scraping:

Manufacturers can use NLP for an improved understanding of data collected with the help of a task called web scraping. AI can check online sources for appropriate industry benchmark information and transportation, labor, and fuel costs. It can help in boosting business operations.

Emotional Mapping:

Machines are quite poor when it comes to emotional communication. It is very challenging for a computer to understand the context of a user’s emotional inflection. However, natural language processing is enhancing this area through emotional mapping. This brings a wide variety of opportunities for computers to understand the feelings of customers and operators.

Machine Learning, Neural Networks, and Deep Learning

The three technologies used in the manufacturing industry are machine learning, neural networks, and deep learning, which are artificial intelligence techniques used for different solutions:

  • Machine Learning: It is an artificial intelligence technique in which an algorithm learns from training data to decide and identify patterns in collected real-world data.
  • Neural Networks: Using ‘artificial neurons,’ neural networks accept input in an input layer. The input is passed to hidden layers that increase the weight of the input and direction to the output layer.
  • Deep Learning: It is a machine learning method where the software mimics the human brain like a neural network, but the information goes from one layer to the next for higher processing.

Future of AI in Manufacturing

What will be the next role of artificial intelligence in manufacturing? There are so many thoughts and visions coming from science and technology. The most visible change will be an increased focus on data collection. AI technologies and techniques used in manufacturing can do so much work independently. As the Industrial Internet of Things grows with increased use and effectiveness, more data can be gathered and then used by AI platforms to improve different tasks in manufacturing.

However, with the advancement in AI in the coming years, we may observe completely automated factories and product designs made automatically with less human interference. However, reaching this point is only possible through continuous innovation. All it requires is an idea- it can be the unification of technologies or using technology in a new case. Those innovations alter the manufacturing market landscape and help businesses stand out.

Which solution is best for your Connected Device- Edge or Cloud Computing_

Which Solution is Best for Your Connected Device – Edge or Cloud Computing?

If you have adopted IoT and are developing an IoT-connected device, you may wish to do some valuable computation to resolve the important issues that have been hindering growth. You might be desiring to install sensors in remote locations, create a device that can do data analytics to watch a renewable energy source, or develop health-related devices that can detect the early signs of diseases.

While creating the IoT-enabled device or IoT solution, at some point, you might get into a dilemma where you have to choose between edge or cloud computation. But what would be best for your device? Where should your device do the valuable computations in the cloud or at the edge?

Selecting between computing on edge or cloud can be an impacting decision, like it can influence a device’s efficiency or cost. Therefore, everyone does great research and thinks twice to avoid the cost of making the wrong decision and then the money spent correcting it.

What is Cloud Computing?

Cloud- It is a collection of servers accessed over the internet. Some renowned cloud providers are Microsoft Azure, Amazon Web Services, and Google Cloud. 

These servers offer on-demand computing resources for data processing and storage purposes. You can easily say that cloud is a centralized platform for storing your files and programs, and you can easily connect any device to the cloud to access the data. Some of the cloud-based services are Dropbox or Google Drive etc. 

Cloud computing is the process of doing computation in the cloud. These computations include data analysis and visualization, machine learning, and computer vision.

What is Edge Computing?

Edge is described as the “edge” of the network that includes devices at entry or exit points of the cloud, but it is not a part of the cloud. For instance, a server in a data center is part of the cloud; however, smartphones and routers that connect to that server are part of the edge. 

Edge computing can be defined as the process of performing computations on edge. In this, the processing is completed closer or at the location where data is collected or acted. 

One example of an edge computing process is object detection attached to an autonomous vehicle. The vehicle processes the data from its sensors and utilizes the result to avoid obstacles. In this process, the data is processed locally rather than sent to the cloud.

What are the points to be considered?

Before opting between edge and cloud computing, a few key questions must be considered.

Quality of Your Device’s Network

Conducting computation on the cloud can be beneficial if you have high bandwidth, low latency, and a sturdy connection to the internet, as you’ll have to send your data back and forth between cloud servers and your devices. If you have to use your device, for example, in an office or home with a steady internet connection, this back and forth can be done seamlessly. In most cases, if computation is conducted on edge, it won’t be affected by the bad or lost internet connection in a distant place. The processing can continue as it is not performed in the cloud. You would never want your vehicle’s objection detection to be failed while driving on the road. It is one of the reasons why autonomous vehicles perform computations like object detection on edge.

How Swift and How Often Does Your Data Need to be Processed?

Edge computing can be best suited in cases where customers demand response times from devices prompt than waiting for it in a decent network connection, such as monitoring components of the device.

The latency of the travel time between the cloud and the device can be minimized or eliminated. It means data can be processed immediately. It implies that if data processing is quick, one can achieve real-time responses from the devices. Cloud computation is also useful when device use is unsteady. For example, smart home devices running computation in the cloud allows sharing of the same computing resources between multiple customers. This decrease costs by restraining the need to provide the device with upgraded hardware to run the data processing.

What Part of Your Data is Crucial to You?

Computing on edge is helpful if you are only concerned about the result of your data after it has been processed. One can only send only important things for long-term storage in the cloud, which may cut down the expense of data storage and processing in the cloud. Suppose you are developing a traffic surveillance device that needs to inform about the congestion situation on the road. You could pre-process the videos on edge- instead of running hours of raw video in the cloud-one can send images or clips of the traffic only when it is present.

Do you know Your Devices’ Power and Size Limitations?

If you think your device will be limited in size and power, provided it has a strong network connection, sending the computing work to be done on the cloud will permit your device to remain small and low-power. For example, Amazon Alexa and Google Home capture the audio and send it to the cloud for processing, letting complex computations run on the audio as it can not run on the small computers inside the device themselves.

Data Processing Model Your Intellectual Property?

If you are creating a device for costumer and the methods you are adopting to process data are part of Intellectual Property, you must rethink the plan to protect it. Placing your IP on your device without a proper security plan can make your device vulnerable to hacks. If you are unaware of resources to secure your IP on edge, it is best to opt for the cloud, which already has security measures.

Final Reasons for Choosing Between Edge and Cloud Computing

Hence, we can conclude that one must consider a few things when choosing between computing on edge or the cloud. In complex issues, you might find the combination of both very beneficial by leaving some parts of processing on the cloud and rest on the edge.

How To Build Smarter Apps Using Mobile Artificial Intelligence?

Mobile artificial intelligence is already revolutionizing the mobile app development game. In 2020, the mobile AI sector crossed the valuation of 2.14 billion dollars, and this number will possibly grow 4.5x by the year 2026. It is quite apparent that mobile artificial intelligence holds a great future so let’s not waste time and know this innovative technology and its use in mobile app development.

What are the benefits offered by Mobile Artificial Intelligence?

Mobile artificial intelligence endeavors to make mobile technology smarter and more functional for users. Amazon’s Alexa Shopping product is a very popular example of mobile Artificial Intelligence. It has reduced countless hours of customer support work for Amazon. At the UX level, it has also brought prominent quality of life improvements to end-users.

It is expected that the most significant growth will likely come from AI virtual assistant technology. The remarkable success of last-generation AI assistants like Alexa and Siri shows the power of the technology. 

AI-capable processors in next-gen mobile devices are featured with various intelligent solutions such as language translators, AR and VR enhancement, context-aware AI assistants, and better security attributes.

The fortune of these advanced apps and on-board solutions is highly extensible, and its integration with the third-party mobile application provides developers with a full-fledged AI development ecosystem.

It is also projected that sectors such as smartphones, cameras and imaging, drones, robotics, automotive, and cloud computing also show incredible growth from mobile AI technology.

The government of the United States and other western countries are trying to prohibit restrictions on consumer drone technology; the drone sector will expand steadily in the presence of AI-capable mobile processors.

Next-gen drones offer an amazing home, and enterprise user features like AI-assisted photography, surface mapping, GPS, AI autopilot and navigation, and many more applications.

Eventually, it is impossible to ignore the potential of next-gen AI to reduce numerous human hours using the AI app development pipeline. AI aids programmers in crushing barriers that consume a lot of time and money in processes like porting software across platforms and removing manual error-checking and troubleshooting once done by human testers.

How AI Makes Your App Smarter?

The increasing number of mobile users and change in trend is shifting the demand toward more customized features.

Earlier, UI was managed in a first-party way by app developers; now, many app developers use on-board UI from smartphone manufacturers to offer an interface for their users. These manufacturers include AI-capable processors, smartphones can interpret user behavior and conduct real-time customization of the app interfaces for a better user experience.

Thus we can say that  Artificial intelligence fetches remarkable new possibilities for mobile development via machine learning, biometrics, recognition technologies, and voice technologies.

Machine Learning:

Today, many businesses are investing so much money into machine learning development as it can predict and optimize user behavior, leading to upsells and cross-sells.

Machine learning improves a better user experience and ensures users keep returning by delivering appropriate content to drive up total usage hours.

The advanced technology has stirred up the competition in the app market. Machine Learning helps companies keep users engaged and entertained, ultimately improving their rank and rating on google play and other App stores.

Online retailers use ML to create customer profiles based on various data like customer purchases and their relationship with other users, the customer’s behavior on the app or website, and many other contributing factors. Using the data, retailers offer recommended products based on the customer’s interest.

For example, Amazon extensively uses machine learning to connect customers with products they might be interested in buying. 

Transport providers like Uber also use this latest technology in their logistics apps to provide drivers with updated information on the road. 

ML solutions predict the fasted possible route for drivers to avoid traffic jams.

Recognition Technology:

The addition of recognition supported by Mobile AI has changed the outlook of the entire mobile utilization pattern. Image recognition technology like Google Lens and other similar apps have revolutionized the way of interaction between people and the world. This image recognition app allows users to recognize the specific plant varieties, and OCR powered by ML can change the foreign language into the native language without delay.

Financial institutions are adopting the same technology in their mobile apps to process checks without needing the customer to visit the bank for the same purpose. Pharmacists are employing this tech to scan medical prescriptions and import them into software to know the exact place of the medicine or its availability in the store.

Next-gen mobile AI improves the existing facial recognition technology by using technologies like artificial neural networks to boost the process of detecting human faces.

AI biometrics boost the level of protection of mobile applications ensuring better privacy for storing sensitive data. This feature also increases the use of mobile applications in the sectors like finance, healthcare, government, etc.

Voice Technologies:

Highly advanced text-to-speech technology provided by mobile artificial intelligence provides clear voice functionality generated from text input. Better text-to-speech empowers visually impaired users to navigate apps and websites, changing static text into clear and understandable voiced content.

AI assistant technology uses voice recognition provided by mobile artificial intelligence to converse with the user without any latency. Commands by the users are processed into actions by the virtual assistant, offering a smooth experience.

For instance, our very popular Alexa and Siri of Amazon and Apple, respectively, can execute different user requests.

The Future Transformations

Mobile artificial intelligence is holding a great scope in the coming years. Many industries are embracing technology and facing rapid transition. Integrating mobile processors with AI- friendly features will enhance the AI capabilities of first and third-party applications.

The key technologies contributing to the changes are machine learning, recognition technology, biometrics, and voice technologies. Mobile AI optimizes the process, removes obstacles for users and providers, delivers relevant content, enhances end-user engagement, and improves the development process. AI-integrated mobile apps are more extensible, modular, dynamic, and offer superior performance for developers and users.

How is IoT Helping The Procurement Team in Improving Productivity

How is IoT Helping The Procurement Team in Improving Productivity?

Today, almost every device is connected; whether it is your smartwatch, air conditioner, or television, we can say it’s a world where devices are more connected than people. No, doubt these connected gadgets present around us make our lives easier by working systematically. This is possible because of the most popular concept known as the Internet of Things, which can also influence the procurement team.

IoT, a.k.a Internet of Things, can be defined as a network of interconnected computing devices, either mechanical or digital machines. This technology allows transferring data without human-to-human interaction or human-to-computer interaction. Communication is possible using networks and cloud-based systems.

An IoT ecosystem includes web-enabled smart devices that collect, send and work on data collected from their surroundings utilizing embedded systems such as CPUs, sensors, and communication hardware.

IoT devices can exchange sensor data stored in the cloud for analysis purposes or examined locally by interlinking to an IoT gateway or other edge devices.

Besides this, these gadgets can connect with other related devices and respond according to the information they receive from one another. Even individuals can operate the devices for the beginning setup, give instructions, or recover data; the device can perform most of the tasks without human interference.

The Role of IoT in Procurement

Procurement is an important part of the business. It demands the implementation of new technologies to boost productivity, enhance customer service and save costs. As of now, the procurement process is also embracing automation; IoT in this process is one of the most exclusive things happening in the era of digital transformation.

The inclusion of the Internet of Things will provide greater spending visibility and understanding of the supply and equipment used for the procurement process. So, with a proper understanding of what is being used and the requirement specified, the procurement team will have access to optimize catalogs and manage expenditure. Forecasting demands more closely using analytics can significantly improve budget and contract management. This also helps in improving budget and contract management. Despite this, the data generated through IoT sensors and other devices can assist in making informed decisions.

Let’s know how IoT works in procurement.

Traceability of Materials:

A study done by a McKinsey Global Institute shows that by the end of 2025, the Internet of Things’ possible contributions to inventory management, logistics, and supply chain management would reach 560 billion to $850 billion per year. This shows the possible IoT-oriented future awaiting us. Most of the time, IoT contributes to these sections by tracking. IoT sensors can help in making inventory management systems more effective.

For instance, RFID tags connected with IoT devices can track physical inventories and eliminates the need to scan barcodes or labels. In fact, businesses with vast inventory can track the days before items expire using interlinked IoT devices, saving the business from huge losses. IoT also prevents product theft by enabling businesses to know the location of their products.

With the use of machine learning, procurement teams can manage products per demand.

Supply Chain Visibility:

In this process, the procurement team can also potentially use IoT technology. Supply chain visibility, items are documented as transported from the manufacturer to the customer. An IoT-enabled system can read data from various devices like smart tags and sensory data like surrounding temperature and humidity, vehicle speed, and geolocation and accordingly follow the supply chain when connected to it.

The adoption of IoT devices to track inventory and route planning provides the details about where and when items are delayed in transportation. This allows emergency planning and identification of other options to accelerate the supply chain.

Stock Management:

Along with smart shelves and storage bins that inform about the stock levels in real-time and how long the product has been on the shelf, IoT also assists in detecting the pattern of consumption.

For instance, if a product named X is on shelf A and has been the quickest utilized item, IoT sensors will monitor the usage rate and suggest its economic order quantity (EOQ).

This clears how essential procuring an item is, which products are needed, and what amount. Procuring the right inventory quantity reduces costs by lowering waste and the menace of shortage.

Monitor and Alert Maintenance:

The sudden breakdown of equipment in a production unit is the most horrifying dream as it disrupts the business. If the condition of the equipment is not known, things become more difficult and result into process disturbance, indefinite downtime, and even business loss. Regular monitoring of the equipment’s condition through IoT sensors permits the team to watch indicators like vibration, oil, temperature, and performance.

When these indicators go out of range, the sensor alerts the team via computers.

In fact, smart sensors also alert when a machine’s working pattern changes or is about to fail. So this allows teams to schedule the maintenance, decrease the chances of sudden machine failure, and ensure seamless productivity.

Better Decision Making With Predictive Data Analytics:

Procurement teams can predict the future using predictive data analytics and spend analytics. These predictions assist in making critical decisions for designing and executing business techniques. Continous flow and accumulation of data with IoT devices also help create more robust and relevant historical data.

Infact, joining IoT data with additional data coming from other sources can boost business growth.

For example, knowing what quantity of a product is needed can help send accurate requisitions for approvals and create error-free purchase orders.

For example, having information on what quantity of a product is being used can help in sending accurate requisitions for approvals and generating error-free purchase orders. This results in an efficient and effective purchase management system. Data collected by IoT can also be used for onboarding suppliers with supplier management solutions to get new products based on previous performance metrics and set criteria.

IoT Procurement Takeaway:

The Internet of Things has become a sensation and is impacting almost every industry. So, it will be smart to invest in this technology and unheave the existing business model.

The procurement team requires a comprehensive IoT framework consisting of machine learning, artificial intelligence, and embedded technologies. These technologies, all together, can bring holistic change and offer maximum benefit.

Generating Continuous Value for IoT Using Ecosystem Approach

Generating Continuous Value for IoT Using Ecosystem Approach?

The emergence of the COVID-19 pandemic disrupted almost all sectors. Still, on the other hand, it opened a plethora of opportunities to improve the existing business culture by showing us the path of Digital Transformation. Today, the industry stands on the doorsteps of its much-awaited renewal. It is evident that manufacturing leaders have to adopt digital transformation but have to accelerate innovation while managing crucial processes like enhancing capacity without compromising product quality.

Thus, digital transformation is the new focus in the manufacturing industry, and no doubt, effective collaboration will be the best way to keep both things smooth and productive at the same time. However, this will not be easy as workforces have gone and are still mostly remote.

Pandemic Impact:

As the virus blanketed the globe, it became pretty clear that there would be a fight for survival among industries. There would be winners and losers. Before the pandemic situation, the manufacturing sector had been slow in adopting the digital transformation and lacked a data-centric mindset that has already transformed other industries. Even those who embraced multimillion-dollar Industry 4.0 or IoT initiatives were not receiving any excellent results to showcase their efforts. Unfortunately, when the pandemic knocked the globe, resources to support implementations went at the edge.

Not just they lost the data they needed to adapt at the moment but also potential value..

Digital Transformation:

Today the most asked question is why invest in digital transformation at the corporate level when there is no usable data from the factory floor? 

Well, Smart manufacturing does not demand to have an entire organization devoted to its success. In manufacturing, it can begin with capturing insights from the very initial operation- the machine assets that make products and people handling the machine. The assets are one of the most significant capital investments for any manufacturing industry, and it produces thousands of data points every second. Still, these valuable data are not captured and analyzed to improve the efficacy leading to no improvement or growth. Today’s factories are based on manual processes that result in massive inefficiencies and disturbs every part of the organization.

Insights along with correct action-driven from this data can lay the foundation for manufacturers to grow their business and stand above the competitors. Even the chances of errors and inefficiency are negligible.

Machine Data Infrastructure:

As we already know, there were many manufacturers, organizations, consultants and system integrators who attempted to rebuild the machine data infrastructure from scratch and produced varying degrees of achievement as a part of large IoT initiatives.

Even while leveraging a horizontal IIoT platform, the whole setup requires months or years. Once it is completed, the mechanism for capturing and contextualizing machine data has to build, and it needs regular maintenance. Not only are the expenses of sustaining these solutions limit, but the missing opportunity and value affiliated with misallocating resources to produce something that already exists causes a competitive disadvantage for the manufacturer.

Accurate real-time data automatically collected and transformed from machine assets produce a solid base for driving bottom-line value. When joined with visibility and actionability via alerts, analytics and automation triggered by the data, one can observe a 15-20% improvement in utilization performance in months.

Once this is over, the value achievement can be fast and multi-directional by integrating the data into other siloed data on enterprise factory and industry systems, i.e. from product designing to production, product quality, maintenance and logistics to run endless automation and accomplishment of exceptional value.

This enables an ecosystem of manufacturers and partners to speed up value attainment and reduce the risk of initiative failure by optimally adjusting the entities having individual skills, in particular IIoT initiatives.

IIoT Ecosystem:

IIoT Ecosystem includes manufacturers, machine builders, machine builder distributors, technology and solution providers, service providers, software providers, system integrators and consultants. Each has its unique skill, expertise, or intellectual property that can be used to drive a successful IIoT initiative. When the resources mentioned above are disarranged or sub-optimized, IIoT initiatives fail to deliver on the insured value proposition or fail entirely.

So, the question is, where should the manufacturer focus? Analytics, including both Machine Learning and Artificial Intelligence algorithms, can be developed and applied at the edge as well as in the cloud using analytics technologies. The correct alignment of skills and technologies produces the optimal formula for the manufacturer’s speedy and regular value generation.

Successful IIoT initiatives need selecting the right technologies and perfect alignment of the different entities in the IIoT Ecosystem that participate in the industry. In the IIoT Ecosystem, the alignment should be done based on each participant’s unique technology, IP and domain expertise to extract maximum benefit and reduce risk.

The emphasis should be on quick data transformation, excellent application, integration and automation into other best factory systems.

Pivot, Respond, Adapt:

As I already shared that many manufacturers suffered a lot during the pandemic times, and no doubt much of that suffering was out of their hands. But who were the ones who surpassed all the challenges and succeeded? Who were winners when the whole world was encountering losses at different levels? Well, the organizations that can pivot, respond and adapt at the tough times. It wasn’t easy, but they were prepared with the data, the tools and the mindset to win.

For manufacturers who had to spend a lot on difficult-to-implement should pump the breaks in favour of vertical solutions that can benefit immediately.

It’s time to switch to the new world of digital transformation. Are you ready for it?

How can Edge Computing Change the Outlook of Manufacturing Industry?

IoT, cloud, AI, ML and Edge have been quite familiar terms for technology lovers. There has been a wrong idea or approach that Edge and Cloud are mutually independent. Though they may operate in different ways; leveraging one does not prevent the utilization of the other. In fact, they powerfully complement each other.

Edge Computing in Manufacturing

With the growth and penetration of the Internet of Things in different sectors, the edge computing framework is also findings its way in several sectors. Today, the most promising edge computing use cases are present in the manufacturing industry as it welcomes new technologies, and these advanced technologies effectively improve performance as well as productivity.

IoT is already providing its best for the optimal result in the manufacturing industry; manufacturers are looking for some platform to boost the responsiveness of their production systems. To accomplish this, companies are adopting smart manufacturing with edge computing as its leading enabler.

Smart manufacturing indicates a futuristic factory where equipment can make autonomous decisions based on operations going on the factory floor.

The new technology allows businesses to integrate all steps of the manufacturing process like design, manufacturing, supply chain, and operations. This provides better flexibility and reactivity at competitive markets. But no doubt, this whole vision requires a combination of related technologies like IoT, AI, ML and Edge computing.

One of the critical reason for gathering analytics at the edge of the network is that it enables us to analyze and execute on real-time data without bandwidth costs that come with sending data offsite for analysis.
We all are well-aware that manufacturing is time-sensitive in terms of avoiding the production of out-of-spec components, equipment downtime, worker injury, or death.

In fact, for more complex, longer-term tasks, data can be transferred to the cloud and coupled with other structured and unstructured forms of data. Thus, this supports that the application of these two different computing frameworks is not mutually exclusive but its a symbiotic relationship leveraging the benefits provided by each.

Why businesses need Edge for Manufacturing?

In the manufacturing sector, the purpose of edge computing is to process and analyze data near a machine that require prompt action in a time-sensitive manner. It demands a quick decision right away without any delay. In traditional IoT platform set up, data produced by a device is collected through an IoT device is sent back to the central network server (cloud).

In the cloud, all the collected data is processed in a centralized location, usually in a data centre. This implies that all the devices which need access to this data or use applications associated with it should be connected to the cloud. Thus everything is centralized, and the cloud is easy to secure and control even if it allows for reliable remote access to data. Well, data processing is completed in the cloud; it can be accessed through IoT platforms in several ways, i.e. via real-time visualization, diagnostic analytics, reporting to support better decision making based on real data.

Now, the question which triggers is that, if everything is quite favourable, then why do we need edge computing. The main problem is that the whole process takes time, and the situation turns complicated when there is a need to take prompt decision based on data.

In the traditional process, the data travels the distance from the edge device back to the cloud, and a slight delay can be critical for taking a specific decision like stopping a machine tool from avoiding breaking. In fact, these IoT connected machines produce a massive amount of data and all the data travelling back and forth between edge and cloud disrupts the communication bandwidth.

The only way to achieve real-time decision making is to adopt edge computing. Edge enabled machines to collect and process data in real-time at the edge of the machine that allows them to respond promptly and effectively.

Edge Use Cases in Manufacturing:

Let’s now check the practical reasons to add edge computing as a necessary thing in manufacturing. There are many business benefits to ensure that all networks are correctly connected to the cloud while providing on-time delivery of powerful computing resources at the edge.

1) Updated equipment uptime:

The adoption of edge computing in manufacturing predicts failure in a subsystem, component or impact of running in a degraded state in real-time. It regularly refines as more data is analyzed and is used to boost operational purposes and maintenance schedule.

2) Decreased sustenance costs:

Better analysis of data for required maintenance means that maintenance can be completed on first visits by providing mechanics detailed guidance about the cause of the problem, required action, what part requires extra attention which ultimately deduces repair cost.

3) Lower spare parts inventory:

Edge analytics models are business-friendly; they can be tailored as per the need of an individual device or system. This implies reading sensors directly associated with specific components/subsystems.

Thus, the edge model describes how the system should be optimally configured to accomplish the business goal, making spare parts inventory more efficient at a minimum cost.

4) Critical failure prevention:

By collecting, analyzing and monitoring data related to components, edge analytics detect a cause for future failure before it affects actualize. This enables early problem detection and prevention.

5) Condition-based monitoring:

The convergence of I.T. and O.T. has allowed manufacturers to access machine data to know the condition of their equipment on the factory floor; either it is new or legacy equipment.

6) New business models:

This is an essential point because edge analytics helps in shaping new business models to catch opportunities. Let’s check an example; edge analytics can enhance just-in-time parts management systems using self-monitoring analysis to predict machine component failure and provides parts replacement notification throughout the value chain. This affirms for a needed maintenance schedule to reduce downtime and parts inventory and ensures an efficient model.

In the CNC machine tool, in-cycle stoppages to the tool are edge decision, whereas end-of-cycles can be a cloud decision. The reason behind this is that in-cycle stoppages require a very low, near-zero, lag time whereas end-of-cycle stoppages have a more lenient lag time. Thus in the former scenario, the machine would have to leverage edge analytics when in-cycle to adapt and shut down the machine automatically to avoid potential costly downtime and maintenance.

Edge and cloud computing

As we already know that IIoT aims to apply the latest analytics to large quantities of machine data to reduce unplanned downtime, reduction in the overall cost of machine maintenance and potentially utilizing the machine learning capabilities. The cloud has been responsible for making this kind of massive data acquisition, transfer, and analysis.

So, if data speed is high and connectivity should be stable and then adopting edge solution is the best option. Therefore it is clear that edge computing will not replace cloud computing but it will complement each other for the optimal result. Thus, integration of edge computing with cloud computing capabilities can enhance efficiency and maximize the productivity of the business.