IIoT - How manufacturing plants turn smart

IIot and Smart Manufacturing

What is IIoT and Smart Manufacturing

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.

Manufacturing Plant Operational Structure

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.

Scenario 1: Optimizing Production and Quality

Conventional Manufacturing – No IIoT (Industry 3.0)

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.

Smart Manufacturing – Using IIoT (Industry 4.0)

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.

Scenario 2: Equipment Maintenance

Conventional Manufacturing – No IIoT (Industry 3.0)

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.

Smart Manufacturing – Using IIoT (Industry 4.0)

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.

Scenario 3: Adding a New Device

Conventional Manufacturing – No IIoT (Industry 3.0)

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.

Smart Manufacturing – Using IIoT (Industry 4.0)

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

Summary

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

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.

How to Achieve Today’s Sustainability Goals While Improving Operational Performance

Striving for improved sustainability goals with advanced analytics can have multiple benefits to your operation.

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.

Defining our Goals: United Nations SDGs

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.

Sustainability Doesn’t Happen in Spreadsheets

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.

Enter: Advanced Analytics

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.

Applying Specific SDG Measures

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.

A Sustainable Future

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.

How to Transform Data into Insight

Advanced data analytics is empowering process manufacturing teams across all verticals.

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 Up from “Data Janitors”

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.

The Difference Between Spreadsheets and Advanced Analytics

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.

How It’s Done

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:

The Impact of Advanced Analytics

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.

Which is better? On-Premise or Cloud Based Industrial Internet of Things Data Flow?

Which is better? On-Premise or Cloud Based Industrial Internet of Things data flow.

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.

Industrial Internet of Things data flow.

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.