IIoT refers to industrial IoT, or the Industrial Internet of Things. Standard IoT describes a network of interconnected devices that send and receive data to and from each other through the internet.
IIoT and Smart Manufacturing is the usage of connected devices for industrial applications, such as manufacturing and other industrial processes. It involves the use of things such as machine learning and real-time data to optimize industrial processes through a connected network of sensors, actuators, and software. The implementation of IIoT is referred to as Industry 4.0, or the Fourth Industrial Revolution.
Currently, most conventional industrial processes are still using Industry 3.0 practices. However, with the ongoing development and implementation of IIoT across industries, we are trending towards Industry 4.0 – with manufacturing plants being one of the major recipients of this change.
In order to understand the impact that Industry 4.0 and IIoT and Smart Manufacturing have on manufacturing plants, it is necessary to understand the existing structure that allows a manufacturing plant to operate.
A manufacturing plant has an operational structure of several levels; each of these levels has a certain function and is comprised of equipment, software, or a mixture. This is known as the automation pyramid.
• Level 0 is the field level, containing field devices and instruments such as sensors and actuators.
• Level 1 is the direct control level, containing PLCs (programmable logic controllers) and HMIs (human-machine interfaces). HMIs display parameter values and allow remote control of devices through stop and start instructions, as well as set point adjustment. HMIs are connected to the PLCs, which are then connected to the field devices.
• Level 2 is supervisory control, and contains the SCADA system (supervisory control and data acquisition). The SCADA is a system of software and hardware, and is used for real-time data collection and processing, as well as automatic process control. SCADA collects its data from PLCs and HMIs over communications protocols such as OPC UA and Modbus.
• Level 3 is the planning level, containing the MES (manufacturing execution system). The MES is responsible for monitoring and recording the entire production process from raw materials to finished products.
• Level 4 is the management level, containing the ERP system (enterprise resource planning). ERP is responsible for centralizing all of the information within the organization. It’s used to manage accounting, procurement, and the supply chain, among others – and is more focused on the business aspect rather than the manufacturing aspect.
With an IIoT and Smart Manufacturing system in place, there is an additional layer: the cloud, which is above all the other layers, and implements analytics such as machine learning. the field devices are referred to as edge devices. An edge device has no physical connection to the PLC – it’s instead connected through Wi-Fi. These devices communicate with the PLC over the native protocol, where all the process control is done.
During production, human operators observe the MES system to monitor parameters such as availability, performance, and quality – which are multiplied to give the OEE (overall equipment effectiveness). An OEE of 100% shows perfect production – the goods are manufactured as fast as possible and at the highest quality possible.
If one of the parameters is low, such as the performance (production speed), the operator can instruct the SCADA system to increase the machine speed; this will result in goods being manufactured faster – and a higher performance value.
However, while goods are being produced faster, there also tends to be more waste – so the quality will drop. The operator will have to decide exactly how much to set the machine speed in order to find a good compromise between quality and output. To find the exact balance that maximizes profitability is a difficult task – one which is almost impossible for a human to accomplish.
IIoT and Smart Manufacturing enables all of the devices and systems to be able to send and receive information to and from the same place, in real time, without human intervention. This allows the machine learning to make optimal decisions regarding equipment and parameter set points to make the manufacturing process as efficient as possible.
With this system in place, no humans are required to make complex decisions. This results in optimized decisions to be made as quickly as possible – and conditions that result in the greatest profitability for the manufacturing plant.
The primary method of maintenance is condition monitoring, also known as condition-based maintenance (CbM).
Condition-based maintenance relies on real-time parameters measured by an equipment’s sensors such as temperature, pressure, speed, vibration. Each of these parameters is given a particular range for which the values are acceptable for a given piece of equipment. These parameters are actively monitored, and once a value is measured outside of the acceptable range, maintenance is scheduled.
The issue with condition-based maintenance is that the equipment’s fault is detected after a certain amount of degradation has already taken place. Depending on the rate at which degradation is taking place, this may not leave enough time for timely maintenance to be carried out. The amount of degradation may have also caused damage which is more costly to repair than if it were addressed earlier. The reverse could also be true; a parameter has exceeded a certain boundary, leading to maintenance to be performed immediately. However, there could’ve been a more convenient time, or maybe the machine could’ve carried on running for a considerable amount of time before maintenance being necessary – leading to excessive, unnecessary costs.
With IIoT, the method of maintenance can evolve to predictive maintenance (PdM).
Like condition-based maintenance, predictive maintenance also uses sensors to continuously monitor parameters. However, predictive maintenance also continuously collects and analyzes both historical and real-time data using statistical methods and machine learning. Because data trends are being analyzed instead of absolute values, problems can be detected much earlier, and an accurate failure time is determined – allowing maintenance to be scheduled at the most convenient, effective time.
Without IIoT, every time a new field device is installed in the plant – such as a pressure transmitter, flowmeter, control valve – it needs to be manually wired into a PLC. Then, its tag needs to be added to the PLC, HMI, OPC server, SCADA, and MES. This is a costly and time-consuming process.
When a new device is installed, no complex engineering is required to connect it to the cloud and the existing devices.
The edge devices, PLCs, HMIs, SCADA, MES, ERP, and machine learning all publish their tags and data into the unified namespace – a centralized data repository.
The machine learning allows continuous, real-time collection of data from all of the devices. It can then use this data to run algorithms and publish additional tags into the namespace
In essence, IIoT and Industry 4.0 allow manufacturing plants to address many of the inefficiencies and solve a lot of the challenges that they face. The use of interconnected sensors and machines, along with free-flowing data enables smarter decisions to be made regarding all aspects of production and operations – leading to reduced downtime, faster production, higher-quality production, and increased profitability.
TQS Integration is a global technology consulting and digital systems integrator. We provide you with expertise for the digitization of your systems and the digital transformation of your enterprise.
With clients across the pharmaceutical, process manufacturing, oil and gas, and food and beverage industries, we make your data work for you – so you can maximize its potential to make smarter business decisions.
Please contact us for more information.
In today’s world, virtually no industry is operating without consideration of their impact on the environment. It’s no secret that process manufacturers have been scrutinized for contributing to greenhouse gas emissions and excessive energy consumption, so striving for sustainability can sometimes be seen as a forced hassle. In contrast, however; achieving enhanced sustainability at a process manufacturing organization can actually result in better operational performance and efficiency, saving time and money for the manufacturer. Two birds, one stone.
In recent years, the UN created a set of seventeen Sustainable Development Goals (SDGs) to reach by 2030 in an effort to universally protect the planet. Of these are four that pertain particularly to process manufacturing companies and how they can contribute to the global effort:
These four goals have become a guideline for the industry as a whole to craft corporate sustainability goals, and it’s evident that these have become a top priority. Besides the obligations that require companies to invest in sustainable processes, it’s also become known that changing operations to better align with these goals results in many other positive impacts.
There are many challenges that process manufacturing teams face today in terms of meeting these sustainability goals. The first stems from the lack of tangible and actionable direction provided to team members at the plant, with broad guidelines set at a corporate level. Subject matter experts (SMEs) often do not have the resources within traditional data technology and methods to analyze their process data and make insight-based decisions to push their operation towards KPIs for improved sustainability.
The reality is that spreadsheets don’t provide any tools for efficiently contextualizing, cleansing, and analyzing data. Many teams spend numerous hours inside of spreadsheets trying to organize data for insight, leaving no time for actually making connections between the data that can lead to a reduction in waste, materials, or money spent.
Additionally, this method does not empower process manufacturers to make reliable predictions based on rapid and historical data. If an environmental violation happens, actions taken to correct it can only happen after it has occurred, and the opportunity to see what caused the problem can be missed.
With advanced analytics applications, process manufacturing operations can generate compliance reports automatically, with up-to-date data from disparate sources, freeing up time to focus on environmental impact.
Beyond monitoring of data as it’s happening and opening a world of insight during incident investigation, the accessibility, presentation, and correlation of data contributes to effective predictive analytics. This can give teams insight into when unproductive downtime may occur and lead to wasted resources.
Advanced analytics can also provide SMEs with a better understanding of how process changes will affect the environment by reporting on KPIs geared towards specific sustainability measures and creating models to compare process performance and operating conditions to ideal levels.
Beyond this, advanced analytics makes it easy to share insight across an entire team—leaving the days of spreadsheet-sharing in the past. Results are error-free and accessible for the whole organization to maintain the same mindset, so regardless of level, everyone knows the company’s progress towards improved sustainability.
Here are a few examples of ways that process manufacturers are utilizing advanced analytics to strive towards a better sustainability goals with lower impact on the environment, while also improving their operational performance.
The term “sustainability” can mean a lot of things, such as monitoring and controlling green house gas emissions, optimizing energy efficiency, implementing alternative energy sources, reducing waste and so on. These examples highlight the flexibility of Seeq; wherever you have environmental process data and would like to optimize your environmental performance, Seeq can be used.
SDG 6: Clean Water and Sanitation
Operations can avoid over-cleaning in clean-in-place (CIP) processes where sanitation materials can be unnecessarily used.
SDG 7: Affordable and Clean Energy
Process manufacturers are currently using advanced analytics to develop energy models and decrease total energy consumption, with minimal required capital expense.
SDG 12: Responsible Consumption and Production
Mass balance equations can be run continuously to track historical changes, providing an opportunity to find points where material is wasted.
SDG13: Climate Action
Many organizations are increasing generation of renewables and adopting smart grid technologies to mitigate carbon emissions through advanced analytics. Aggregation of methane emissions from various data sources through the use of advanced analytics can identify or predict places where methane is leaked, down to detailed micro-levels within the operation.
It’s simple: Investing in a sustainability goals strategy is good for business. The efficient use of raw materials, less waste, and lower energy consumption both directly lead to an improved environment and your bottom-line. In addition, sustainable practices such as these can boost your reputation above the competitors in your industry. See how advanced analytics can work for your operation today.
Enhanced accessibility into operational and equipment data has surged a transformation in the process manufacturing industry. Engineers can now see both historical and time-series data from their operation as it’s happening and at remote locations, so entire teams can be up-to-speed continuously and reliably. The only problem with this? Finding their team is “DRIP”—Data rich, information poor.
With tremendous amounts of data, a lack of proper organization, cleansing, and contextualizing only puts process engineers at a standstill. Some chemical environments have 20,000 to 70,000 signals (or sensors), oil refineries can have 100,000, and enterprise sensor data signals can reach millions.
These amounts of data can be overwhelming, but tactfully refining it can lead to greatly advantageous insights. Many SMEs and process engineers’ valuable time is filled with sorting through spreadsheets to try to wrangle the data, and not visualizing and analyzing patterns and models that lead to effective insight. With advanced analytics, process manufacturers can easily see all up-to-date data from disparate sources and make decisions based on the analysis to immediately improve operations.
Moving data from “raw” to ready for analysis should not take up the majority of your subject matter experts’ time. Some organizations in today’s world still report that over 70 percent of their time involved with operational analytics is only dedicated to cleansing their data.
But your team is not “data janitors.” Today’s technology can take care of the monotonous and very time-consuming tasks of accessing, cleansing, and contextualizing data so your team can move straight to benefitting from the insights.
For an entire generation, spreadsheets have been the method of choice for analyzing data in the process manufacturing industry. At the moment of analysis, the tool in use needs to enable user input to define critical time periods of interest and relevant context. Spreadsheets have been the way of putting the user in control of data investigation while offering a familiar, albeit cumbersome, path of analysis.
But the downfalls of spreadsheets have become increasingly apparent:
All of these pain points combine to an ultimate difficulty to reconcile and analyze data in the broader business context necessary for profitability and efficiency use cases to improve operational performance.
With advanced analytics, experts in process manufacturing operations on the front lines of configuring data analytics, improvements to the production’s yield, quality, availability, and bottom-lines are readily available.
Advanced analytics leverages innovations in big data, machine learning, and web technologies to integrate and connect to all process manufacturing data sources and drive business improvement. Some of the capabilities include:
Simply put, advanced analytics gives you the whole picture. It draws relationships and correlations between specific data that need to be made in order to improve performance based on accurate and reliable insight. Seeq’s advanced analytics solution is specifically designed for process manufacturing data and has been empowering and saving leading manufacturers time and money upon immediate implementation. Learn more about the application and how it eliminates the need for spreadsheet exhaustion here.
Applications around the Industrial Internet of Things (IIOT) have mushroomed and each one comes with a different set of capabilities and features. So how do you compare different applications or services? And how does the new solution fit into your existing data architecture?
In general, industrial internet of things architectures fall into three categories: (1) on-premise, (2) cloud based or (3) a hybrid of the two. In the on-premises solution, data are never leaving the manufacturing network, whereas in the cloud solution all data are directly send to the cloud. In the hybrid solution, a subset of the data is replicated to the cloud and used for analysis.
Today, many industrial internet of things applications fall into the hybrid category and lead to a scenario where some applications will execute on-premise and others in the cloud. To choose the right blend of on-premise and cloud functionality, let’s consider the following key metrics:
For regulated industries, there is often a requirement that the compressed timeseries is identical between two components.
For a sequential system, the calculation is as follows:
R=R1×R2×R3× ... ×Rn=ΠRj
As an example, if a system has four components with a reliability of 95% each, the overall reliability drops to 81.4%.
Making the same system redundant increases the overall system reliability to:
R=1-(1-R1)×(1-R2)×(1-R3)× ... ×(1-Rn)=1-Π(1-Ri) or 96.6% using Ri=81.4%
Highbyte is providing in flight data contextualization on the edge. This opens the door for very flexible and dynamic solutions.
Most of the protocols are equipment centric, missing relational information (one-to many and many-to-one) and time segmentation. Microsoft’s Digital Twins Definition Language (DTDL) is a relative new approach that has the potential to bridge the gap.
Summary
Industrial internet of things apps range from pure on-premises to all cloud-based solutions. On-premises architectures typically provides a higher system reliability and lower latency, while cloud-based solutions offer scalability, flexibility, and wide range of readily accessible data analytics. As a result, manufacturing IT will most likely have a blend of both, where process level analysis will run on premise and enterprise level analytic in the cloud.
Current connectors do not provide a complete manufacturing process model, industrial strength data compression, and redundancy necessary to seamlessly integrate into existing on-premises data architectures. But this is changing quickly and new approaches of in-flight contextualizer are closing the gap quickly. The goal being to better understand and utilize industrial internet of things data.
Please contact us for more information.
Seeq software applications, Seeq Workbench and Seeq Organizer, enable manufacturing organizations to rapidly analyse, predict, collaborate, and distribute insights to improve production outcomes. Seeq is designed to run on-premise, on Microsoft Azure or Amazon Web Services cloud platforms, or in mixed on-premise and on-cloud deployments encouraging digital transformation.
“We are proud to confirm our partnership with Seeq that brings together expert authority in advanced process analytics. Together we can empower our customers with smart operational insights which improve process manufacturing efficiency for them”, comments Alan Bourke, Global Head of Business Development for TQS.
Being certified partners of Seeq further enhances TQS partner network. This allows them to collaborate with clients to help define their data infrastructure and digitalization roadmap, the cornerstone to any Pharma 4.0 initiative.
“Thought leaders like TQS Integration with local customer and vertical market expertise are critical to the success of Seeq,” adds Will Knight, Head of Partner Sales for Seeq. “Our customers rely upon them to deliver advanced analytics solutions that provide insight and drive results”.
“We have also invested in building out our Data Analytics Team to bring Data Modelling capabilities to all our customers, so they now have the all-encompassing, best-in-class, suite of products, and services to ensure that they overcome their data challenges in their organization through innovative solutions that promote digital transformation”, says Paul Power, Global Head of Engineering for TQS.
Seeq provides in-depth and advance process data applications which are key to deliver accurate diagnostics and predictive analytics. With the help of the right partnerships, TQS is committed to improve process performance and drive operational excellence for their customers.
About TQS Integration
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0 and preparing them for digital transformation.
For information, please contact us.
About Seeq Corporation
Founded in 2013, Seeq publishes software applications for manufacturing organizations to rapidly find and share data insights. Oil & gas, pharmaceutical, specialty chemical, utility, renewable energy and numerous other vertical industries rely on Seeq to improve production outcomes, including yield, margins, quality, and safety. Headquartered in Seattle, Seeq is a privately held virtual company with employees across the United States and sales representation in Asia, Canada, Europe, and South America.
For more information about Seeq, please visit www.seeq.com.
TQS Integration is proud to be data partners with four of the global pharmaceutical companies currently at the forefront of the development and manufacturing of a Covid-19 vaccine.
The rapidly-deploy, scale-up and produce model has emerged over the past few months, to facilitate the efficient production of the Covid-19 vaccine. This is a major step forward in the evolution of vaccine technology. At the heart of this evolution is sound data, and a patient centric approach, which includes product quality, safety, and data security. All this rely upon the integrity of vast amounts of big data generated throughout the product lifecycle. “TQS is a key enabler in automated data collection, then getting the relevant data to our scientists.” comments senior Pfizer engineer.
TQS have data intelligence specialists who provide expertise in all areas of data integrity and data analytics to the Life Science sector. They also provide OSI PI software engineering integration services, software development services, and data solutions to each of the top 12 pharmaceutical companies worldwide.
“We are extremely proud to play a part in this fight against the global pandemic. We see our role as fundamental in the quick and efficient manufacturing of these wonderful vaccines which will help protect people all over the world. Our expertise in data and manufacturing excellence has helped to ensure that our clients can deliver on quality, price, and speed.” Says Alan Gallagher, Global Account Manager at TQS.
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
For information, please contact us.
TQS Integration, an international software consultancy company specializing in the provision of system architecture and application design, engineering, and system integration, today announced that they have been certified as a Google Partner with Google Cloud Partner Advantage.
Achieving Partner status means that TQS and Google can now share their combined knowledge and advanced technologies to provide the perfect end to end experience for their customers, from edge computing to automation, data historian to advanced data analytics and more.
“Obtaining Partner Status is a huge step into the future. It means recognition of our unique advanced technical expertise, and the ability to mutually share our learnings across a larger customer base with the Google team” says Rory Sheehan, Global Strategic Account Manager.
As a start, TQS and Google will focus their partnership on the Pharma / Lifesciences Industry where they have many mutual customers. Combined, their expertise and systems will allow customers to access manufacturing data, R&D data, operational data, and predictive data to streamline operations and quality.
“Our services will allow data sharing in a much wider capacity, both within the business and externally where required. With this partnership program and all our ongoing TQS developments, we are bringing customers, industries and partners into the future of manufacturing and that future is fuelled by data.” says Rory.
TQS has always been recognised for providing customers with an unrivalled service and advanced engineering, and this new Google partnership will continue to advance them into the future of Industry 4.0, IIoT and Edge Computing.
TQS Integration is a global data solution company specializing in the provision of system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with extensive client base in the Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
For information, please contact us.
Following the successful launch of the Madrid office in 2018, TQS Integration is proud to announce further expansion of their new Madrid office - No 30, Paseo de las Delicias,2nd Floor, Madrid 28045, Spain.
Due to achieved customer and partner growth across Europe, they see great potential in the Spanish market which has resulted in significant growth in their customer portfolio. “Part of our business plan in 2021 will be to focus on growing and developing the Madrid office to accommodate customer demand” says Alan Bourke, Global Head of Business Development at TQS.
Growing the Madrid office reflects the increasing global demand for data analytics, intelligent data, data integration and customer on-site engineering support. The aim is to help businesses on their journey to achieve successful data integration while stimulating collaboration with partners which will enable them to work with every sector for data integration and compliance.
As more and more businesses come to the realisation that Data is vital to ensure achievement in Industry 4.0 goals, the expansion in Madrid allows TQS even greater ability to provide solutions for their clients.
About TQS Integration
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
For information, please contact us.
