Creating Value from Smart Manufacturing

Companies implementing smart manufacturing often find themselves awash in data; manufacturing data store concepts can turn these raw numbers into actionable information

By John Clemons, Director of Manufacturing IT, Maverick Technologies

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Whether it’s called smart manufacturing, Industry 4.0 or Industrial IoT, even a casual observer of the industrial landscape can see how manufacturing is changing, driven by new technologies and rapidly evolving customer demand. Buyers expect a greater range of choices and manufacturers have responded with mass customization - the concept of building flexibility into mass production.

The pharmaceutical industry is experiencing these changes in its own way. The number of products continues to grow, and the range of choices in mature categories is vast.

Most smart manufacturing implementations start with smart instruments. Advances in electronics have created the ability to gather far more information from sensors, actuators and analyzers. A flowmeter from 20 years ago typically supplied a single flow process variable. But a modern smart flowmeter can supply hundreds of measured and calculated data points related to internal diagnostics, secondary variables such as temperature, noise signatures from the process, self-calibration functions, and other parameters (see Table 1).

As instruments got smarter, so did the controllers connected to them. Programmers were able to incorporate additional information from smart instruments into the process control strategy, using it to gain new insights and optimize processes.

For the pharmaceutical industry, it’s been a mixed bag. In general, pharmaceutical manufacturers use sophisticated instruments due to the high degree of precision necessary in most manufacturing processes. On the other hand, the closely regulated nature of pharmaceuticals makes it more difficult to adopt new technologies without going through the steps to have a process re-validated. Process analytical technologies (PAT) have made this easier, but difficulties remain.

Most attempts to implement smart manufacturing succeed or fail based on what the user does with the data. Instruments and low-level controllers can create data. Raw materials moving around a plant can be tracked constantly using RFID, creating data. Individuals performing manufacturing tasks can have their actions recorded, creating data. But it takes higher-level systems to convert these waves of data into useful information.

Diagnostics can support highly effective maintenance programs and new instrument readings can help optimize a process, but simply packing process historians with numbers has no value in itself. Effective enterprise-level networks and data analysis platforms are necessary to make it useful, with these implementations often referred to as industrial IT.

The automation systems used to control batch processes common to pharmaceutical manufacturing often don’t extend to the enterprise level or offer tools to help bridge the gap. So enterprise platforms must reach down to engage with these automation systems to bring the data to where it can be useful.

Any consideration of pharmaceutical manufacturing has to deal with the extensive regulatory environment in which it operates:

• Plants must use specific types of equipment and undergo extensive inspection

• Processes must be qualified and validated in detail

• Meticulous manufacturing records must be maintained, and

• There must be extensive traceability within the supply chain, both for raw material in and products out, often to the point of sale.

Some of these functions happen on the plant floor. Anything related to actual manufacturing processes, such as maintaining temperature in a reactor, will be controlled where it is happening, but process variables must also be maintained in batch records. Eventually all the data moves up to a higher level where it is stored and used for whatever purposes are necessary.

With smart manufacturing implementations, the amount of data for a given process increases substantially. Smart instruments developed over the last few years have extensive native networking capabilities, so collecting data is much easier. If used properly, this data can be instructive and offer critical insights into the process.


Given the amount of data collected in a manufacturing facility of any size, finding the information necessary for a given application is not always simple. Accessing the data can be made simpler by using a manufacturing data store strategy to extract the slice of information needed to fulfill a regulatory requirement, help improve a process or answer a question. A manufacturing data store is a selection and retrieval tool able to help users find and extract the information they need. The data store approach is used in many data-heavy applications, but has not seen extensive use in pharmaceutical manufacturing.

A manufacturing data store can help gather all the data related to a given lot of product including incoming ingredients, manufacturing steps, and handling and movement within the facility. It can connect upstream to suppliers and downstream to customers, providing details on how the products were transformed from raw materials to finished pharmaceuticals.

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