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Mega-trends Driving Chemical Industry - Success Priorities Pt.2.4

SELL BUSINESS OUTCOMES INSTEAD OF JUST PRODUCTS

In Future most chemical companies will move from B2B push models to business-to-business-to-consumer (B2B2C) models. Digital technology and concepts such as Industry 4.0 will be leveraged to deliver sustainable, co-developed applications, services, and business outcomes. Companies that have identified an experience gap will gain a competitive advantage by engaging more closely with their customers and ecosystem partners. This engagement will enable them to deliver outcome-driven services and address customer expectations. This experience, along with developing customer relationships based on trust and shared values and risks, will be the new paradigm.

They will establish searchable intellectual-property databases to access relevant scientific information to create co-innovated products and solutions. Having established this foundation, they will extend into properties prediction and performance of new formulations to significantly shorten the development process and time to market while monitoring product and formulation compliance along the entire lifecycle. Furthermore, they will extend into their customers’ value chain, monitor process parameters, and allow in site quality control in real-time through sensors at customer operations. The implementation of Industry 4.0, where all sensors, devices, machines, and other equipment are connected in one single network, will provide an unprecedented amount of insight that eventually reveals new business opportunities, such as predictive maintenance, further improving the product experience. In terms of logistics, they will track and trace material flow and product integrity along the entire value chain.

Finally, they will collaborate on open innovation platforms, turn data into value-based services, and establish transformative business outcome-driven and customer-centric revenue models to improve quality and reduce costs and risks for customers.

OUTCOME-BASED CO-INNOVATION IN CHEMICAL COMPANIES

Traditionally, chemical companies have developed products in response to market needs driven by downstream industry sectors, such as consumer products, pharmaceuticals, engineering, and construction. In the future, chemical companies will strive for unprecedented levels of customer experience by anticipating market trends and needs, rapidly developing and manufacturing corresponding formulations and selling based on business outcomes, such as first-pass-quality semifinished parts or goods, instead of selling by quantity. Innovative technologies, such as machine learning, the IoT, artificial intelligence, and blockchain, enable these processes. A cumbersome, multistep development process and a traditional revenue model based on product quantity delay time to market and time to value.

Solutions offered in New World Scenario: Chemical Company simulates property and performance of new formulation using machine learning.

Top Value Drivers

Faster time     to market

Increased     Quality

Higher     Customer satisfaction

Reduced Costs     and Waste

Increased     Brand Recognition.

Summary

With growing competition, ongoing globalization, and blurring industry boundaries, companies can no longer afford to focus solely within their own four walls. Established business models and practices based on linear value chains are no longer sustainable in an era where companies are expected to be responsible for the emissions of their suppliers, customers, and other downstream business partners.

To survive and thrive in today's competitive environment, companies cannot rely solely on organic growth. Instead, more and more are turning to mergers, acquisitions, and divestitures to expedite portfolio adjustments and diversify into promising new markets or segments. The business model is being challenged by the mass commoditization of products and formulations, global competition, higher demand from consumers for sustainable products and operations, and exponentially increasing regulatory requirements. In conclusion, those who are willing to evolve will be welcomed by the market, while those who are not willing will be lost in the crowd.

To Read full article click https://www.linkedin.com/pulse/mega-trends-driving-chemical-industry-success-priorities-solulever-1f/?trackingId=%2F%2FRj8SuQSiSch4u0x4FcWg%3D%3D

Solulever, a Dutch Technology Startup, is based on the principles of Industry 4.0 and delivers top industrial connectivity platforms to help manufacturers in taking up the digital transformation of their plant. Solulever's Brabo Edge Platform® is a platform that allows seamless connectivity to different tools and equipment on the shop floor. It performs data mashups that are therefore available to the development teams on a real-time basis

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Unleashing the Power of Digital Manufacturing in Today's Industry!

Digital Manufacturing uses digital technologies to optimize manufacturing operations, creating a well-connected, fully integrated, and networked environment. It enables manufacturers to capture and also analyses real-time data, optimizes plant processes, and increases productivity. Implementing Digital Manufacturing helps improve output quality, reduce inventory, eliminate bottlenecks, reduce time to market, increase product portfolio and volumes, also deliver rapidly to customer needs, and much more. The demand for complex tailor-made solutions delivered quickly and the exiting of the skilled workforce puts companies into dire straits. Companies are now harnessing Industry 4.0 practices to their advantage to remain competitive.

 Digital Manufacturing includes using predictive efficiency and predictive maintenance to prevent expensive bottlenecks and breakdowns, making the entire production line more prolific, real-time monitoring and tracking of the assets within the plant, reducing the use of raw materials and labour, adjusting production to cater to changing consumer needs, and including a continuous flow of materials by using smart conveyance. Digital Manufacturing is becoming the most coveted solution for the Manufacturing Industry, especially for moderate-sized companies, as it is becoming more accessible than ever.

 Digital Manufacturing is becoming more prevalent in the manufacturing industry because it helps improve production output, factory capacity utilization, and labour productivity while reducing operational effort, time, waste, and costs. According to a report by Deloitte, investments in digital plants increase production output by 10%, factory capacity utilization by 11%, and labour productivity by 12%.

 By 2030, with the use of Digital Manufacturing, labour productivity is expected to triple. Despite hardware challenges due to vendor lock-ins providing a fixed set of features, start-ups such as Solulever provide digital manufacturing solutions such as Brabo, a platform of platforms that help manufacturers collect real-time operational data, normalize the data, run analytics on top of it, and make it readily available for development teams.

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Mega-trends Driving Chemical Industry - Success Priorities Pt.2.3

IMPROVE STRATEGIC AGILITY IN RESPONSE TO MARKET DYNAMICS

The company's future growth will be directly proportional to its ability to adjust its strategy in response to changing market dynamics. Continuous improvement of its product and service portfolios, as well as expanding into different markets or segments to realize the synergies of mergers, acquisitions, and spin-offs, will drive overall performance, profitability, competitive position, and growth.

Chemical companies will begin by analyzing the profitability of their existing product and service portfolios using real-time, granular data that range from raw material costs and overall production to development costs, logistics, and order fulfilment costs. In a follow-up step, they will embed external market and company data into ad hoc simulations of strategic scenarios, such as mergers, acquisitions, and divestitures, to assess overall corporate KPI and company performance. They will also proactively address customer and market concerns regarding mergers and acquisitions (M&A) by collecting and acting on stakeholder sentiment. In the final stage, after the merger, acquisition, or divestiture, companies will restructure themselves around their revised product portfolios, using consolidated financial data to measure and monitor the impact of their restructuring in real time.

By 2026, 25% of G2000 will use AI to accelerate innovation in products and services by identifying new operational capabilities to drive at least a 10% increase in annual revenue for those companies.

ENABLE M&A FOR RAPID DIFFERENTIATION AND DIVERSIFICATION

Enhanced speed and agility would be required to adjust company strategies and refocus product and service portfolios on an ongoing basis. Portfolio optimization is the true force behind M&A activities in the chemical sector, and this is often the case with divestitures as well. M&A and divestitures allow companies to grow their business where they can do better than the competition and carve out a business that is no longer strategic.

Problems in Traditional Scenarios

Long-lasting and expensive system consolidation doesn’t deliver value quickly enough, due to missed opportunities to take advantage of synergies.

Solutions offered in New world scenario

Early financial consolidation provides the visibility required to take advantage of synergies while accelerating the process of integrating acquired business units and spinning off divestitures.

Top Value drivers for Rapid Diversification are-

Increased     market share

Realized     Synergies

Increased     Revenue growth

 To Read full article click https://www.linkedin.com/pulse/mega-trends-driving-chemical-industry-success-priorities-solulever-1f/?trackingId=%2F%2FRj8SuQSiSch4u0x4FcWg%3D%3D

Solulever, a Dutch Technology Startup, is based on the principles of Industry 4.0 and delivers top industrial connectivity platforms to help manufacturers in taking up the digital transformation of their plant. Solulever's Brabo Edge Platform® is a platform that allows seamless connectivity to different tools and equipment on the shop floor. It performs data mashups that are therefore available to the development teams on a real-time basis.

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Different Industrial Revolutions.

Right now, we are going through the fourth industrial revolution or Industry 4.0. But, the era of technology might not have the same shape and size as of today but for their time, indeed, it was something for people to look at. Although the industrial revolution is considered a single ongoing event that started in the late 18th century, it can be better understood as four industrial revolutions. Each revolution was built upon the innovations of the prior revolution and led to more advanced forms of manufacturing processes.  

 The first Industrial Revolution: The first industrial revolution started in the late 18th and early 19th centuries when the biggest changes came in industries in the form of benefits of mechanization wherein for the first time in history. Some animal or even human labor could be substituted by mechanical labor due to mechanization. Agriculture started to be replaced by industry as the backbone of the societal economy. 

 The second Industrial Revolution: Almost a century later the second industrial revolution started in the late 19th and early 20th century which relied on the application of the principle of mass production along assembly lines, which was able to scale up manufacturing output with higher coordination between labor, tasks, processes, and machines. To this day, the Second Industrial Revolution is considered the most important one as the inventions of the automobile and the aircraft began in the 20th century. 

 The third Industrial Revolution: The third Industrial Revolution began in the second half of the 20th century, wherein we see the emergence of yet another source of untapped source of energy which is the Nuclear energy! Also third industrial revolution brought forth the rise of electronics, telecommunications and, the computers. It opened the doors to research, space expeditions, and biotechnology through the new technologies using machines that are able to repeat a series of tasks under a relatively well-defined parameter and require minimal supervision. It also relied on the development of information technologies, initially with the digital computer, and then with information technologies by the end of the 20th century. This led to the formation of the internet. 

 The fourth Industrial Revolution: The 4 Industrial Revolution is taking shape the worldwide economies are based on them. The manufacturing processes, the scale and scope of the output, and the customization of the products. Machines are therefore getting similar to the flexibility of human labor. They involve more than simple and repetitive tasks. Therefore the prime focus shifts to global value chains, which are a circular process of gathering resources and transforming them into parts, and products and distributing the finished goods to the markets, and finally again making these resources available through various recycling and reuse strategies. 

 Solulever, a Dutch Technology Startup, is based on the principles of Industry 4.0 and delivers top industrial connectivity platforms to help manufacturers take up the digital transformation of their plant. Brabo Edge Platform is a platform that allows seamless connectivity to different tools and equipment on the shop floor. It performs data mashups that are therefore available to the development teams on a real-time basis. 

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Merge IoT and AI to take smart decisions.

We are in the era of Industry 4.0, and with that, comes the threat of increasing global competition. To be more competitive, it is crucial that organizations especially in the manufacturing industry can apply the tools of IoT and AI, which are at the forefront of aiding organizations to meet their long-term goals. IoT or Internet of Things is the name given to the network of physical devices that are connected to the internet. This network of connected objects includes cloud-connected devices, sensors, and industrial equipment. IoT or the Internet of things is a network of technologies or sensors that consists of some advanced technologies, that are embedded into it. It helps in interacting and communicating with their data. This process involves receiving and transferring data through the network without any human intervention. These data from the devices or the sensors can be stored in the cloud and again it can be made available for real-time analytics. Thus, IoT collects this data from different environments which is generally vast, due to the ever-rapid growing of devices and sensors. These big data are important only when it is transformed into a piece of valuable and actionable information within a specified time period. Here comes the role of artificial intelligence or AI. It collects the data by applying analytics and it converts these collective data into applications to extract the meaning from it. When this big data is fed from IoT devices and into an AI system, it reviews and analyzes the data, and therefore it produces decisions made either by machines or humans. The importance of big data is significant only when it's transformed into relative, valuable, and actionable information within a specified time period. Artificial Intelligence is used for learning and thus making self-decisions by using the process of complex organized data and also unorganized data. The IoT and AI play a crucial role in the future as it has a growing need for technologies in both government and private sectors, especially in the manufacturing industry. At this point, edge analytics is presented by Brabo Edge Platform, a manufacturing connectivity and intelligence platform developed by Solulever, a Dutch technology startup.

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Preventive Maintenance and Predictive Maintenance

Maintenance is an essential element for operating manufacturing equipment. The key difference between Preventive Maintenance and Predictive Maintenance is that preventive maintenance is scheduled on a regular basis, from time to time, while predictive maintenance is scheduled as needed, based on the asset’s condition. Both maintenance methods aim to increase the reliability of assets and reduce the likelihood of machine failure, thereby being cost-effective and reducing unplanned maintenance costs.

 Preventive maintenance uses a schedule of inspections and tasks to find and fix small issues before they have a chance to develop into big problems like unexpected failures occurring in the future. It’s possible to stray over into over-maintenance, where doing more than that is needed.

Preventive maintenance comes with the challenge to balance the cost with returns.

Preventive maintenance can prevent equipment failures and extend the functional life of the assets and also keeps the production lines up and running, so as to be more profitable as well.

Predictive Maintenance tries to predict failure before it actually happens by monitoring the machine during normal operations by drawing real-time data collection and analysis of machine operation to identify issues at the nascent stage before they can interrupt production. Investing in a computerized maintenance management system will give the measurements and data required to make smart decisions and can develop a strong overall maintenance program to reduce unneeded maintenance tasks, minimize maintenance costs, and monitor the equipment and systems to keep the facility up and running.

 In order to implement predictive maintenance, automated condition monitoring is need to be installed on the target equipment and machines. Brabo Edge platform, establishing a strong mesh of interconnected machines and equipment developed by Solulever, the company aims to achieve smart manufacturing for most manufacturers in the manufacturing industry.

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Industrial IoT: Benefits, Applications, and Challenges Of Wide-Spread IIoT Implementation

A Quick Look Into the Concept of IIoT

2023 is said to be THE year for the manufacturing industry. Backed by the strong forces of the fourth industrial revolution, the manufacturing sector seems to be all set for improved productivity, newer launches and higher efficiency.

But what is the hype about Industry 4.0 all about?

Industry 4.0 is the fourth industrial revolution in the realm of the industrial sector which provides data insights, streamlines processes, lowers operating costs, and much more, all at once. All of this is made possible through interconnected shop floors, most popularly known as IIoT.

Now what is IIoT

Industrial internet of things is interconnecting smart devices together to monitor, automate, and forecast future outcomes, processes, and preventive measures in an industrial setup. Through the many shop floor monitoring systems, IIoT delivers countless opportunities ranging from enhanced employee protection to predictive maintenance of equipment. Even though Industrial Internet of Things has empowered factories to produce more and better than they ever could, it is fraught with multiple issues which are highlighted in a table below.

Advantages

Disadvantages

Reduced costs

Lack of IoT  experience

Ability to take  improved decisions

Security Threats

Greater energy  efficiency

Inability to  align KPIs with clear business goals

Better quality  outputs and results

Improper  organizational alignment

Reduced  equipment downtime

Implementation

IIoT can be applied to multiple areas of which three main ones include predictive maintenance, remote monitoring and automation. Predictive maintenance is one of the most important aspect as it eliminates wastage of both time and money. This is because even before the machine and equipment reach a point of their breakdown, their maintenance is performed. This way, no time is wasted during the maintenance procedures, which enables the workers to continue their usual processes, and doesn’t even cause any wastage of material.

These practical implementations can help in improving the overall output and quality of manufacturing units, with lower wastage and operational expense. IoT can be an extremely powerful technological tool with numerous advantages for any industry. Yet, reaping the benefits from the networked devices required proper monitoring and coordination.

There are various technology companies that make the power of IIoT reach to the manufacturing setups, such as Solulever, which is a Dutch Technology startup. The company offers a manufacturing connectivity and intelligence platform solution, Brabo Edge platform that establish a strong mesh of interconnected machines and equipment. The platform’s ability to capture data spontaneously right from the point it is originated, mash it with business insights and apply advanced analytics helps in achieving smart manufacturing.

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How data fuels the move to smart manufacturing

Why Humans are Chasing Data Driven Manufacturing?

Smart manufacturing has become the buzzword of the decade and will continue to prevail as the NEXT best thing of the future. But why does it matter?

With so many vast manufacturing industries being set up, each having a complex set of processes, it is next to impossible for humans to go through all the different manufacturing and design scenarios.

But how to fuel the digital transformation move?

Powered by a combination of artificial intelligence and industrial data, the move of smart manufacturing is on its way. To get the job done, AI comes to aid with a potential to transform the manufacturing continuum, end-to-end. This includes all the design and implementation stages such as early stage ideation, flexible production, predictive maintenance, and finished goods. To develop a seamless workflow, both data and computational models must be deployed together using AI, to reach optimal outcomes. There are four major ways to combine data and AI to power the movement of IIoT.

Using AI for     process design, planning, and execution

After setting specific goals or parameters for a certain aspect of the process, countless possibilities can stem out for the right manufacturing process or product design from it. For instance, an engineer may provide different designs for a dashboard based on core requirements, to create a conceptual design. But in the real world, the engineer may face multiple limitations in creating a number of designs. Contrastingly, in the world of AI, engineers can simply outsource their tasks to the software to do the heavy computations and chalk out different designs. The expert only needs to provide the different parameters, such as weight, costs, strength targets, etc., which are used by the software to produce variable possibilities of the design with different viable candidates.

AI can do the same for process planning and execution methods that are used to create the dashboard. Teams can use AI-based technologies to run through various simulated events and scenarios to identify which systems and materials will deliver the items in the most efficient and productive manner by building full-scale 3D and behavioral models of shop-floor machinery.

The main task for the engineers is to set the goal and let the AI run its analysis so that an automated process of conducting the manufacturing event in the most efficient manner is panned out.

Collecting     data from its source of origin

With the advent of the movement of the Fourth Industrial revolution, the aim was to connect manufacturing equipment and machines together through IIoT to let the machine-driven communication lead the automation and insights process. Yet, much effort is needed due to ambiguity around the shop floor goals and data complexity.

According to experts, edge platforms must be integrated right at the shop floor to spontaneously collect industrial data and mix it with business data for analysis. The data store in different locations, in silos, must then be normalized along with providing a context for it. This forms a crucial step in the smart manufacturing process as it makes the data more readable by the shop floor leaders. By doing so, the level of efficiency in process analytics increases to greater levels.

Marry     business requirements with the shop floor requirements

The main obstacle in accomplishing digital transformation is faced due to the large gaps in the expectations of the C-suite leaders and the factory managers. On the one hand, the executives look at data-driven manufacturing to develop new business models. On the other hand, the shop floor managers look at resolving specific challenges such as transitioning over a line or increasing the uptime.

The gaps majorly exist as leaders at these different levels do not interact often, which leads to missed opportunities for continuous improvements close to the areas where the problems arise. Simply marrying the right skill sets of shop floor experts to the right technology can create enhanced ROIs.

Establish a     future with machines and humans working in a collaborative manner

Within a plant, the tooling and fixtures are numerably variable and the parts are geometrically complex which makes it practically impossible for workers to go through all possible events on their own. Also, with the increasing demands of flexibility within manufacturing, humans can’t possible run the entire system without the help of AI or manufacturing intelligence and connectivity platforms. With each passing day and increasing manufacturing needs, the operations need to become hybrid in nature.

The dynamic duo of machines and humans is going to be a must as humans will bring in judgement, dexterity, and flexibility, while machines will bring in precision, repeatability and strength. The combination will not only lead to developing things better, but also creating new things in a better way at the same time.

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The 10 Most Valuable Metrics In Smart Manufacturing Manufacturing Made Smart with Metrics

The last few years have been witnessing the revolution of Industry 4.0 as many manufacturers are taking up digital transformation to level up their factories. According to a survey conducted by Capgemini, 43% of smart factories were taken up in 2017, which rose to 68% in 2022. It was also found that by 2024, discrete manufacturing would eventually lead to smart factories. The investment in smart factories will increase by 40% in the coming 5 years, which means an increase of 1.7 times the annual investment in comparison to the last years.

Yet only 14% of the manufacturers agreed on being successful. This suggests that each of the digital manufacturing projects needs better clarity, accuracy, and precision to measure the progress. To build a successful smart factory, laying a strong foundation of analytics is a must. The desired outcomes can be achieved simply by changing the behaviors, that is, being on top of each incident in the plant. This can be attained by making use of metrics and analytics that help reinforce and create ownership of outcomes. Metrics measure the complete production process to suggest improvements, with a strong focus on product lifecycle approach and quality. To make the manufacturing process more efficient and productive, the right metrics need to be selected that focus on team accomplishments and collaboration instead of the success of a siloed department.

Some of the key metrics used by manufacturers to plan, pilot and launch digital transformation projects are as follows:

Customer     Satisfaction Levels- Customer satisfaction rating is measured     through regular customer satisfaction audits and is a metric that should     be developed to measure the performance of the end-to-end manufacturing     process. Manufacturers building smart factories bank on building customer     satisfaction metrics and calculating order shipment dates.

Inventory     Turnover-     It is the frequency of a particular facility's inventory being consumed to     produce a merchandisable product and renewed over a particular period of     time. Inventory turns are commonly determined using the "Average     Sales by Inventory" factoring for a given accounting period. An     alternative way is to divide the cost of goods sold (COGS) by the average     inventory for a given accounting period.

Perfect Order     Performance-     It is the measure of a production facility’s efficiency in delivering     undamaged and accurate orders to customers before or on the delivery due     date. It is measured as the (Percent of orders delivered on time) *     (Percent of orders complete) * (Percent of orders damage free) * (Percent     of orders with accurate documentation) * 100.

Order Cycle     Time-     It is the total elapsed time from the point a customer makes an order to     the point it is received. Job cycle time shows how collaborative the total     production team is. Smart factory pilots use this metric to assess the     contribution of inventory management, supply chain, manufacturing, and     fulfilment performance.

There are various other metrics which are powerful in enabling a strong and successful smart factory. The real capability of metrics in smart factories is helping manufacturers understand their importance and their contribution to planning, manufacturing, selling, and maintaining products. Analytics form the foundation for keeping smart factories strongly focused on clients and their changing needs. Their true potential is to deliver meaning and purpose to production teams to relentlessly work to improve manufacturing process performance, product quality, and customer sa

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Industrial IoT: Benefits, Applications, and Challenges Of Wide-Spread IIoT Implementation

What is the hype about IIoT?

The present age crowned as the fourth industrial revolution, or Industry 4.0, has unprecedented potential for the manufacturing sector. Mainly due to the Industrial Internet of Things or IIoT, which streamlines multiple processes, provides data-driven insights, and reduces operating costs at the same time. Yet IIoT may face numerous challenges in these wireless times where cybersecurity risks are on the risk. So let’s find out what are the different advantages, challenges, and applications of IIoT’s implementation.

What is IIoT?

Industrial internet of things is interconnecting smart devices together to monitor, automate, and forecast future outcomes, processes, and preventive measures in an industrial setup. With the widespread use of IIoT in different manufacturing units, the functioning of manufacturers, warehouse managers, supply chains and operations becomes more effective and seamless. The technology provides endless possibilities which range from predictive maintenance to enhanced worker protections via shop floor monitoring systems.

The Upside of IIoT

IIoT is one of the biggest gifts to the technological ecosystem as it enables greater results based on insights that are data-driven. What can translate into:

Reduced costs

Ability to     take improved decisions

Greater     energy efficiency

Better     quality outputs and results

Reduced     equipment downtime

All in all, when factories are automated, they become more efficient through better data-gathering capabilities. Through this, better use of energy is also made as the processes become more streamlined. As metrics are analysed, product efficiency also increases as there are reduced breakdowns. IIoT can yield considerable savings as it reduces downtime incidents as each incident translates into an average loss of seventeen thousand dollars. For this reason, the technology becomes extremely desirable for many plants across industries.

Implementation of IoT

Even though the applications of IIoT are limitless, there are 3 main categories where it can be applied to deliver high-value output. These areas include predictive maintenance, remote monitoring and automation. These three categories give sectors efficiency and precision with the right implementation on a large scale.

Predictive Maintenance

Within the power sector, the use of a drone has increased, especially to maintain increased security. These devices are integrated with sensors and equipment monitors which continuously evaluate risks and monitor powerline networks. Through this equipment, incidents such as falling of a tree on a power line can be prevented, along with preventing unnecessary maintenance and repair costs. This way, any damage is known before it can be prevented from occurring. Through predictive maintenance, cost-effective repairs are done even before any damage occurs.

Remote Monitoring

To monitor local displays, radar level sensors are made use of. To make it easy for the operators, a single dashboard providing all the displays in one place is helpful. Using a single dashboard, measuring points on rotating and moving machines become easier, which helps the operators in continuously fetching real-time data. The real-time insights into the complete equipment life cycle help in predictive maintenance and repairs.

Automation

IoT automation has its application in industrial farming as smart irrigation. To ensure proper plant care and maintain a consistent water supply, farmers have to manually keep a check on the precious water resource. By automating this process, not only is water conserved but the moisture levels in the soil are also continuously checked which enables water supply only when needed.

The Downside

Like most connected devices, IIoT is also prone to cybersecurity threats. To implement IIoT, pre-planning and analysis is required. Some of the barriers to successful implementation of IIoT include:

Lack of IoT     experience

Inability to     align KPIs with clear business goals

Security     threats

Improper     organizational alignment

Not considering the above challenges can mean much more than financial threats as employee safety can be compromised. For example, an automated machine can be infected with malware or can be hacked, which can further impact the personnel. It becomes essential to eliminate security risks by giving proper training to the staff, and maintaining period checks on the system.

Concluding Notes

In the end, it can be said that IoT can be an extremely powerful technological tool with numerous advantages for any industry. Yet, reaping the benefits from the networked devices required proper monitoring and coordination. For a brighter future, automated machines and sensors can provide high value to the company by overpowering any cybersecurity risks.

 c

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What does hybrid data integration really mean?

Hybrid data integration for Real Digital Transformation

In the world of technology, hybrid integration has gained immense popularity over the years and is continuing to do so. It is the ability to connect data, software, business processes, and files, across both the edge platforms or the cloud. Yet, the concept is much larger as the integration spreads across various deployment models, endpoints and various other domains.

According to the real definition of hybrid integration, not just two, but four different components can be integrated together. These elements include integration models, deployment models, and endpoints such as IoT devices, or stakeholders. This integration is named as HIP or simply Hybrid Integration Platform, which is a tool used to simplify applications and data across the different deployment models. Through this HIP, organizations can convert data into actionable insights which helps them in taking quicker and more result-oriented decisions. For this reason, Gartner has predicted that by the end of 2022, over 65% companies in the world would have implemented HIPs in their systems.

Over the years, the need for IoT devices and higher mobile connectivity is increasing which is making the implementation of digital transformation an increasingly complex process. At present 12 million IoT devices exist in the technology ecosystem, which is bound to increase even further, which will further put a load on smart manufacturing integration. As a result, the capacity of new integrations within organizations has exceeded their actual capacity. To handle these integration complexities, enterprises often go for cloud solutions. But these systems do not account for the existing rigid legacy systems, which the companies can not get away with.

To implement digital transformation within the factories, new integration tools are needed to meet the complexities. Sending every piece of data to the cloud is not feasible, for which, a solution is needed that can help streamline processes, restructure integration architecture, spread the technical expertise over a broader range of personnel, streamline the processes, and many others, all while making use of the latest technologies. The use of these technologies and their tools can make the integration more efficient and simpler. In such cases HIP, prove to be the hero!

Hybrid integrations is extremely valuable as it pairs up horizontal as well as vertical integrations to meet the requirements of all aspects of the plant. Horizontal integration works to make sure that the IoT devices, machines, processes, equipment, cloud systems, and edge systems, are all interconnected seamlessly. While, vertical integration allows data to be used by the management to take actionable decisions by moving the data right from the sensors to all the business levels of the organization.

Having a hybrid integration platform is simply not enough. It must also ensure intelligent workflows spread throughout the system so that they can assess and take action upon advanced criteria by making use of machine learning and artificial intelligence. They must ensure seamless connectivity to edge platforms, so that spontaneous processing happens exactly where the data originates.

The HIP must also act as a bridge between the IT infrastructure and the physical world, so that integrations work between offices at all production levels of the supply chain. Another key aspect to keep in mind is to make sure that the platforms are event driven. This is crucial as it gives the company an opportunity to spontaneously act on any data that may have been modified, streamed, or even recently created.

One such strong HIP has been developed by Solulever, a Dutch tech startup. The company has created a manufacturing connectivity and intelligence platform, Brabo, that has been developed with great expertise of the in-house technology enthusiasts. The Industry 4.0 platform promotes smart manufacturing and delivers the true meaning of digital transformation, by combining the power of on-premise, cloud, and edge platforms. Brabo maintains a smooth data flow across the entire system through its centralised system, which makes it easier to maintain and monitor it. Companies must realize the true potential of integration platforms such as Brabo to enable intelligent modelling and smart decision making.

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Leveling up the game of IoT using AI

The past decade has realized that the way manufacturers view their machines can be transformed if they use artificial intelligence coupled with real-time data analytics and communication. When artificial intelligence is combined with the power of IoT, the generation of smart manufacturing can be unlocked as these technologies have been creating waves in the sector individually. By combining all these powers, the advantages become manifold. According to Nick Bostrom, a Swedish philosopher, "machine intelligence is the last invention that humanity will ever need to make." For such machines, AI can act as its brain, which collects, processes, and uses vital information retrieved from devices connected through IoT, which acts as the nervous system for sending and receiving signals. By using both technologies, the system becomes intelligent enough to make decisions for itself on its own, which also forms the AIoT or Artificial Intelligence of Things. By 2026, the global AIoT sector is expected to grow by more than 78.3 billion US dollars, accounting for approximately 40% compound annual growth rate, as reported by Research and Markets. Integration of AI with IoT IoT includes technologies such as enhanced connectivity, cloud computing, machine-tomachine (M2M) communication, and various others that enable the connectivity of the machines, provide storage for data, and convert it into meaningful insights. On the other hand, AIoT is used to enhance the capabilities of IoT, which offers numerous benefits to the markets that make use of technologies. Previously, manufacturers were satisfied with their machines' ability to perform optimally and make their own decisions upon the data stored and processed. But with the integration of AI into these machines, the manufacturers can close the loop as the devices can spontaneously take action on their learned processes to perform in the best manner. But to make AIoT more comprehendible and viable, data management systems need to be added along with the machines so that they can support speedy decision-making. Storing data on the cloud seems feasible as data is analyzed near its source, but AIoT can up this game as the data analysis would be done at the edge itself, the point where the data is collected. Reaping the Benefits of AIoT To be able to make use of AIoT at the edge, the development of its offline model needs to be done, along with training it using the existing stored datasets. This training would help ensure that the model meets the requirements and expectations. Once the offline model passes the conditions, the sector leaders can implement it by exporting it online and using live data fed on a real-time basis. But easier said than done, testing the model based on stored data could produce different results from testing it on live data. This is because live data may not be categorized or filtered and may lead to a chaos of knowledge as each data set may arrive at different time intervals. For this purpose, data filtration needs to be done before AIoT ultimately uses it. At this point, edge analytics is presented by Brabo Edge Platform, manufacturing connectivity and intelligence platform developed by Solulever, a Dutch technology startup. The Industry 4.0 platform helps collect the data in real-time from the connected IoT devices or edge devices and prepares them before being fed to AIoT. The data is made scalable after being received in different formats from multiple sources and then analyzed. By harmonizing the data, the devices become more intelligent to make their own decisions and even act upon them on their own, which results in maximum output and reduced wastage. Even though individually, all of these technologies render great power to the manufacturing industry, when combined, they become indomitable. Suppose enterprises start integrating AIoT and Edge technologies from companies like Solulever. In that case, they can uncover these technologies' full potential and benefits to make the processes optimized, fast and efficient

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Smart Manufacturing Made Easy with AI mixed into IIoT

With each passing year, the amount of data is increasing rapidly. Reports suggest that by the year 2025, the number of IoT devices will increase to over 64 billion. Even McKinsey Global Institute predicts that in the next five years, IoT can contribute a total of 4 to 11 trillion US dollars in economic value. This means that the amount of data generated and collected by these devices would also be monumental. The amount would further be voluminous for the manufacturing sector. To manage the influx of data from the shop floor, AI and IIoT can work together to create a powerful tool.

IIoT devices or Industrial Internet of Things can deliver an improved performance due to greater visibility into data and better opportunities for automation. IoT sensors are also used for predictive maintenance in the manufacturing industry as they regularly monitor equipment and process conditions, and provide spontaneous insights for robust decision-making.

With the integration of artificial intelligence or AI, IIoT is expected to advance by leaps and bounds. The integrated combination can become extremely powerful as the smart sensors are enabled to collect voluminous data from the manufacturing environment. This ability, clubbed with emerging technologies, can provide novel insights into ways to manage and perform smart manufacturing.

Implementing an actual IIoT system can pose an extremely complicated task, much different from the blueprint, due to the deployment of various sensors, machines, tools, and equipment, spread across the entire shop floor. The main reason is that the data sources need to be identified and then the data collected from them must be validated. Performing such a task can be tedious and time-consuming, if done through traditional means.

To ease the manufacturing processes, AI systems are becoming a necessity. The powerful duo of AI and IIoT can bring about a digital transformation revolution to the sector, and the way interactions between human operators and machines take place. AI can help identify and study the most critical aspects of the machines and operations, such as tooling, configurations, and status. Post-analysis, the data can be organised in a proper and more readable manner. Following this, the normalised data and its insights can be applied across all the operations in the plant. This way, the shop floor operations can become more intuitive and productive.

To gain the power of IIoT and AI together, manufacturers are adopting one of the unique Industry 4.0 solutions available in the market, Brabo. Brabo is a manufacturing connectivity and intelligence platform developed by the in-house experts of Solulever, a Dutch technology startup. The Netherlands-based company aims to bring digital transformation to every manufacturer’s plant through their edge platform, Brabo. The platform provides robust OT-IT connectivity, which enables smart manufacturing for manufacturers and allows them to be more productive.

Looking at internet-connected devices alone, it can be seen that, they provide an array of benefits. But when these devices are clubbed with AI, they can move on the path of automation, easing the operations for the human operators. While IIoT can help in collecting streams of data rapidly, AI systems ensure that the flow of data according to the solutions is processed smoothly, which can enable manufacturers to transform data into meaningful and actionable insights.

 #IIoT

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The Roadmap to success for Smart Manufacturing

There is no contention that digital transformation has become a necessity for the manufacturing industry to climb up the ladder of success. However, a value-first plan is essential for the transformational process to lay down the foundation for significant organizational success and ROI. The plan or a digital transformation roadmap is developed based on industry research and experience to guide the organizational leadership on their path of achieving true digital transformation. It is an outline which can be unique for different organizations.

Stage 1: Create the route

Before proceeding with the outline, a well defined goal for digital transformation must be identified. For most companies, the main goals are outlined to financial goals which influences the decision making at all hierarchical levels of the company. The primary factors may include operating margins, asset efficiency, and revenue targets. Having identified these primary goals, the companies can move forward in the right direction in an effective way.

Some of the prominent factors that drive decision making include:

Increasing     availability of the equipment for increased revenues

Establishing     improved revenues with improved value chain collaboration

Improving     asset efficiency by addressing first-time fix rates.

Increasing     equipment performance for better operating margins

These factors act as a guiding light for the leadership to establish the right goals for creating a proper roadmap. Identifying the aims and the value that needs to be brought to the company, sets up the very first and most important step for success, in the pursuit of smart manufacturing.

Stage 2: Moving ahead on the path

Once the goals are identified, the best to reach to it should be approached, which would bring the maximum value. For this, it is important to remove the traffic delays so that time to value is not delayed. Even dead ends need to be eliminated or prevented so that digital transformation projects are not scaled. To increase the overall success of transformation and its value, the leadership must maintain a fine selection of use cases.

According to a survey performed in 2021, there was a stark difference between the digital manufacturing programs that highly exceeded the expectations and fell extremely low. Based on this study, it has been analyzed that the programs that miss the return on investment goals attain about 30% of ROI, while the ones that exceed the expectations attain the ROI goals by at least 50%.

On an average, these programs have nearly eight pilots, of which at least 3 to 4 fail to scale. The key is to start small but to scale rapidly so that the ROI and time to value is attained quickly. For this reason, the most common use cases must be used first. Further, technology that allows high grades of cross functional integration must be picked first so that a strong foundation for scalable programs can be laid.

Stage 3: Make use of expert advice

To reach to the identified goals faster, organizations must seek the expert advice and support from the right partners to succeed in their digital transformation efforts. Such partners bring along expert knowledge from the industry based on their years of experience, along with knowledge of technology solutions. The right fit of technology is something that simply can’t be missed as it can create a bottleneck at the very start of the process.

The criteria is to find partners that meet the company needs and are also aligned with its long term strategy of smart manufacturing. The partner must possess the abilities to scale up with the company’s efforts. For instance, value can be rapidly enhanced when scaling is made a part of the review.

Additionally, organizations must consider the Industry 4.0 platforms they choose to apply various use cases in different manufacturing plants. These platforms may include cloud solutions, on-edge platforms, or hybrid platforms. One of the most after sought solution in the current manufacturing landscape is the combination of a cloud’s scalable infrastructure and SaaS products.

Stage 4: Selecting the right approach

After all that is set and done, organizations must create a nurturing work culture that is inclusive, collaborative and innovative. According to a research, an imperative at this stage is to have an internal buy in from all levels of the organization to make the transformation process highly successful.

To bring about a cross-functional change, for a significant transformational impact, both a bottom up and a top down approach must ne followed. While the top management aligns the transformational efforts with a strong focus on strategy, the departmental leaders identify the most beneficial use cases by providing a visibility into the operational benefits.

Stage 5: Paving your way forward

To bank on the benefits of Industry 4.0, it is pertinent to identify that digital transformation is a continuous process where focus must be laid on the change that must be integrated in your industry. The focus areas must include type of change, growth aspects, types of disruptions, and innovation aspects in your industry.

Once the use cases are marked as successful, they must be applied to other factories and units, along with building on other digital initiatives. To further develop ease in future implementations, a playbook can be created for unique use cases and by avoiding any roadblocks.

While a roadmap outlines the best practices on how to achieve smart manufacturing, organizations must lay strong emphasis on their individual financial outlook, bottlenecks, unique needs, and structure to design the most effective path to gains. Some of these emphasis can be quickly achieved with Brabo, a manufacturing connectivity and intelligence platform which is one of the best Industry 4.0 platforms. The edge platform is developed by a Dutch technology startup, Solulever, which helps organizations in achieving their dream of digital transformation.

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This Is Why Companies Are Looking to Take the Edge Off Industry 4.0!

According to a report published by Gartner, it is estimated that by 2025, over 75% of organizational data will be generated, processed, and stored outside the conventional cloud or data center. For this reason, companies must ensure that they make edge analytics an integral part of their IIoT strategy, to receive data in a more rapid, cost-effective, and secure manner.

Even according to a report published by Forrester in 2019, 69% of decision-makers look at prioritizing edge analytics for IIoT processes to achieve their IoT objectives in an enhanced manner. This is because storing and monitoring all the voluminous data on the cloud would turn into a big challenge. This is exactly where Edge Analytics steps in and offers an olive branch.

Why must you make Edge Analytics a part of IIoT

The manufacturing industry is already witnessing the many benefits of Edge or Hybrid platforms, in terms of having access to real-time data and insights, spontaneously. As the edge platforms are placed next to the equipment and machines at a plant, the distance between the end-user and the server is shortened. This allows the strategy makers to perform faster data analysis, leading them to make quicker and more insightful decisions.

Further, edge analytics is turning into a necessity with the increasing number of digital data interactions per user. The need is amplified as we realize the number of IIoT devices present in any smart manufacturing factory. International Data Corporation or IDC suggests that within each 18 seconds, each individual will have a minimum of 1 online data interaction.

Making edge the most effective

To get a competitive edge, digital manufacturers must manage their machines and equipment in a way that best suits their operations. This is because, in smart factories or similar Industry 4.0 environments, there is an availability of many devices that are interconnected, making them benefit from edge analytics. The reason being each device produces data that is processed on a real-time basis to be acted upon spontaneously, to promote efficient and lean decision-making.

The enhancement of plant operations is possible through Edge platforms, such as Brabo, which is an Industry 4.0 platform developed by Solulever, a Dutch technology company. Brabo, manufacturing intelligence and connectivity platform collects insights from machines and equipment at the plant and sends immediately to a central cloud at a fast pace. This way the managers are able to respond and react to the data immediately, making them take enhanced strategic decisions while saving huge maintenance and other costs. In fact, organizations can cut down their maintenance expense by half annually, simply by aligning the maintenance investment to asset condition.

The technology is extremely versatile as it can be easily integrated or taken up by manufacturing plants of any age or type. For instance, edge analytics can also be suited to normalize data at a plant to make it more compatible with different types of machines. This works best for plants with legacy systems as there the data is collected from different equipment of different types. By adopting such edge analytics to harmonize data, all types of tools, either old or new, can be easily integrated into the edge system.

It is predicted that companies can improve their targets to achieve their Industrial Internet of Things objectives rapidly and more efficiently, only by adopting edge analytics. As the technology supports manufacturers’ IIoT objectives, it has the ability to bring more and more manufacturers on board. Edge platforms not only bring insights closer to the machines but also help the manufacturers in gaining faster insights into their operations, to be able to produce more efficient output with enhanced quality, at a much better pace. All in all, edge analytics is the way forward to not only gain a competitive edge but also save on huge operational costs.

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Why Smart Manufacturing is Important for your plant?

Of all the industries, the manufacturing sector has been lagging behind in adopting digital technologies such as artificial intelligence, robots, machine learning, nanotechnology, etc. Industry 4.0 is one such technological advancement that has completely changed the game for manufacturing as it provides the vision for smart and interconnected plants where all the tools and equipment are connected using a common platform and are capable of making intelligent decisions on their own. The explosion of data from various connected platforms has made it imperative for various enterprises to rapidly evolve from a physical to a digital world. As the manufacturing industry moves to mass customization from mass production, it needs to ensure to walk on the path of smart manufacturing by digitizing the different components within its supply chain and operations. Digital transformation allows factories to create a fully networked, integrated and connected environment with access to real time manufacturing data to yield high product design, productivity gains, and reduced waste and operational expenses. Solulever, a Dutch Technology Startup is based on the principles of Industry 4.0 and delivers top industrial connectivity platforms to help you in taking up digital transformation of your plant. One of its key products, Brabo Edge platform®, enables holistic and flexible digitization of the different manufacturing factories. It is a manufacturing connectivity and intelligence platform that allows easy and quick access to production data, operational data, and quality data using big data analytics. The Industry 4.0 platform overcomes some of the most common entry barriers faced by the manufacturers which include adoption barriers, increased capital expenditure, data connectivity, and many more. Brabo is a platform of platforms which allows seamless connectivity to different tools and equipment at the shop floor, through which it performs data mashups which is available to the development teams on a real time basis. Based on a microservices architecture architecture, Brabo relies on an open source environment that gives the manufacturers the flexibility to opt for their desired hardware and software solutions only, that saves them a fortune from investing into yet another cloud heavy solutions. The platform is designed in a manner that it can easily communicate with the existing platforms at the plant, without having to bring in another mediator. The data and communication is normalized using Brabo which makes it easy for the developers to perform real time calculations at a fast pace. Integrating the latest Industry 4.0 platforms, manufacturing sites can reduce their downtime and operational costs considerably. As the machines get smarter, occurrences of predictive maintenance are reduced, resulting in higher quality output. Optimizing the supply chain and using the resources smartly lead to higher plant productivity and cost efficiency, allowing manufacturers more room for innovation. Take your manufacturing business a step ahead with Solulever and digitize your shop floor to have more flexibility in the manufacturing processes to respond better to your customer demands. With smart manufacturing, you are not simply a mass producer, but an innovator for every customer's individual needs.

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Overhaul of the chemical industry

Technology has been advancing rapidly over the years which has also led to a great rise in the customer demands. The increasing customer wants and their technological solutions put a strong pressure on the chemical industry which faces multifaceted challenges. The complexities may arise due to complicated supply chains, uneven demand patterns, regulation and compliance pressures, and even from the lack of digital transformation. Such issues result in a low operational output and performance which also translate to a reduce asset utilization and reliability. The end result being, an ineffective use of the business’ resources and a capital, due to addition of undesirable operational costs.

Smart manufacturing is the most coveted solution to such challenges. To make the best use of the organizational operations and resources with least amount of wastage, plant digitization becomes necessary. Digital transformation of the chemical plants remove the need to perform the operations in-silo, and enhance the visibility on the shop floor procedures. This leads to better quality operations and output for the business. This is made possible as the equipment and tools are interconnected with a digital platform which collect, normalize and analyze the data to be converted into meaningful insights. Such insights prove to be useful in applying predictive maintenance programs to the shop floor processes which reduce the instances of machine breakdown. It also helps in reducing the process variations which means there are less occurrences of rework performed by the operators.

In the absence of digital transformation, there is a low level of digital maturity for the plant which obstructs its scope of continuous improvement. But, Solulever, a Dutch technology startup, is here to save the day, for most of the present day chemical manufacturers. The European technology company has developed a strong Industry 4.0 platform, Brabo. The manufacturing intelligence and connectivity software platform provides a wholesome solution that can be quickly being adopted by well-known chemical giants. It adds multiple layers of technology to the shop, connected to the IT systems to create meaningful information, deliver quickly, and generate the desired business impact. Brabo works as an integrated manufacturing intelligence system making the customer benefit from areas of asset utilization, resource efficiency, OT service management, process control and improvement, and much more. The OT-IT connectivity software provides real-time data transfer capabilities for the shop floor operators to take preventive and a corrective decisions and measures during production processes. It also combines the power of edge and cloud computing which provides the clients a drag-and-drop procedure to create, analyze, and visualize enhanced SCADA systems that replace the need for SCADA system from a third party.

Leaders of the chemical space are fast moving to adopt Solulever’s Brabo to make smart manufacturing a faster reality for them to achieve.