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


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.

The Path to Perfect Pharmaceutical Batch Quality Production

TQS Pharma Batch

Businesses within the pharmaceutical and life sciences sector must continuously ensure batch quality is maintained at the highest standards. After all, quality is the most critical metric in pharmaceutical manufacturing, nothing is more important than protecting patient health. However, the impact doesn’t go without also reaching bottom-lines and profitability. The numbers speak for themselves: The cost of a single batch deviation can range from $20,000 to $1M per batch, depending on the product.

Tight control of processes, inputs, and other variables is a necessity for successful pharmaceutical manufacturing. Traditionally, there have not been effective ways of looking at historical and time-series data to investigate deviations and variability besides spending painfully tedious hours of subject matter expert (SME) time in spreadsheets. Engineers look to create process parameter profiles to serve as guides for reducing process variability and increasing yield for all future batch development—also known as the “golden profile”.

But there are two problems with this. First, creating golden batch profiles repeatedly requires many hours spent manually sifting through years of data or delayed lab results that make it difficult to optimize process inputs to control the batch yield. And second, out-of-tolerance events will still occur, regardless of applying diligence in controlling the Critical Process Parameters (CPPs) of a recipe, as measured by a group of Critical Quality Attributes (CQAs). Often, it becomes clear the number of variables and the cause-and-effect relationships connecting these two aspects are more complex than originally assumed.

Find Your “Golden Batch”—Efficiently

The data is there. But it’s time to efficiently analyze it. The method of manually extracting production data from historians and various repositories within an industrial control system and creating graphs in Excel is outdated and doesn’t solve the whole puzzle of accurately finding the relationships mentioned above. There are many limitations on how a spreadsheet can actually be applied to understand complex process variability and provide actionable insights. Leading pharmaceutical companies have made the transition to advanced analytics to find their perfect batch parameters.

Applying Advanced Analytics to Make Data-Backed Decisions

The most efficient and intuitive way to lead your team to golden batch discovery and application is through advanced analytics. Applying the technology eliminates all manual work in spreadsheets and automatically cleanses, contextualizes, aggregates, and analyzes your process data in near real-time. It makes the manual connections that your engineers won’t have to—freeing up their time to apply the analysis to your process parameters and production methods to see improvements in quality and performance.

Seeq, the leading provider of advanced analytics, can be scaled applied across your entire organization, running on standard office computers and communicating directly with historians to quickly extract data and present results.

A Behind-the-Scenes Look

To visualize the application in action and for this specific business issue, assume you’re examining a production process with six CPPs connected to a single unit procedure. Using historical data from ideal batches with acceptable specifications on all CQAs, advanced analytics enables you to simply and easily graph these six variables from all the previous unit procedures. Curves representing performance from historical CPPs can then be superimposed on top of each other using identical scales to reveal new insights within the application.

It’s immediately seen if the curves tend to form a tight group, or if they are spread out, showing different values at various times. Seeq can easily aggregate these curves without the need for complex formulas or macros to establish an ideal profile for each CPP. Engineers can replicate this procedure, resulting in an updated reference profile and boundary for every variable. In the end, this process reveals new opportunities for process optimization.

In the screenshot below, Seeq’s advanced analytics is analyzing the cell culture process in an upstream biopharmaceutical manufacturer that is producing Penicillin. The technology is used to create a model for Penicillin concentration based on historical batches to find the CPPs that will produce the ideal batch. This model can then be deployed on future batches with golden profiles for CPPs to effectively track deviations and prevent them from occurring.

Batch Quality

In another example, a leading pharmaceutical manufacturer saved millions of dollars by gaining the ability to rapidly identify and analyze root cause analysis of abnormal batches via similar modeling techniques in Seeq. The team reduced the number of out-of-specification batches by adjusting process parameters during the batch and saved on the reduction of wasted energy and materials.

Additionally, Bristol-Meyers Squibb utilizes modern technologies, including advanced analytics, to capture the specialized knowledge needed to test the uniformity of their column packing processes. Seeq is deployed to rapidly identify the data of interest for conductivity testing to calculate asymmetry, summarize data, and plot the curves for verification by their SMEs. The entire team is empowered to operationalize their analytics by calculating a CPP and distributing it across the entire enterprise, providing reliable and fast insight as to when a column was packed correctly. In turn, this prevents product losses, product quality issues, and even complete losses of a batch.

Developing and deploying an online predictive model of pharmaceutical product quality and yield can additionally aid in fault detection and enable rapid root cause analysis, helping to ensure quality standards are maintained with every batch.

Across multiple use cases, one thing is clear—advanced analytics is the future of trusting batch quality to the highest extent for pharmaceutical and life sciences manufacturing. Combining the latest initiatives in digital transformation, machine learning, and Industry 4.0, it’s the technology that empowers your engineers to their fullest potential in making data-driven decisions to tremendously improve operations.

Applying Advanced Analytics to Your Operation

Are you ready to increase your batch quality and yield by incorporating seamless golden batch development cycles and application with advanced analytics? Make sure to watch this webinar from Seeq for insight on additional ways that advanced analytics can be used to capture knowledge from all parts of the product evolution cycle—from laboratory process design and development through scale-up and commercial manufacturing.

If you’re looking to see the technology live and in action, schedule a demo of the technology here.