Anyone searching the web for discussions on smart manufacturing and Industry 4.0 will find thousands of articles and countless blog posts on the topic. Many begin with an observation on how consumer tastes are evolving to where shoppers today demand products exactly the way they want.
When Sally Smith, for example, sets out to buy a new car, she doesn’t choose one off the dealer’s lot. She goes to the company’s website, and using the build-it feature, checks all the boxes to design the vehicle with exactly the options she wants. Then the assembly plant builds it just for her. This works because the whole production system is automated, and creating a one-off car is no trouble at all.
Most smart manufacturing discussions focus on the needs and operations of discrete industries — plants building things, which can be cars, smartphones or electric guitars. This is much different than pharmaceutical manufacturing, for obvious reasons. Mercifully, manufacturers don’t receive orders from individual consumers asking for tablets with the exact formulation and dosage wanted. Given these facts, does smart manufacturing even apply to the world of life sciences?
What Makes Manufacturing Smart?
All the elements of smart manufacturing do not apply the same way in every manufacturing environment, but there are some universal underlying concepts. It helps to think about what makes smart manufacturing smart, and how the concepts extend to life sciences.
Smart equipment: Taking the temperature of a product in a reactor can be done manually with a thermometer, or with an electronic instrument equipped with multiple redundant sensors and self-diagnostic capabilities. The latter, used in a smart manufacturing environment, can provide much more data about the product, about itself and about what’s happening inside the reactor.
Flexible production equipment: Getting to market quickly with new products combined with easily scalable production are central to smart
manufacturing. Products showing promise need fast ramp-up to accommodate demand, so the notion of building a production line to make only one type of product with a fixed production rate is fading fast. Whether building cars or active ingredients, new lines must be configurable and adaptable to a longer list of possible products with quick and easy changeover. Plant facilities are smaller so the equipment footprint needs to be minimized.
Plug-and-play connectivity: Any electronic devices used in a production unit — such as field instruments, valves, PLCs and so on — must be easy to connect and configure, or better yet, self-configuring. Designing more elaborate systems should be easy using a unified programming environment able to communicate with the individual devices. With this type of drag-and-drop programming, it’s easy to change the process software.
More sophisticated production equipment: Basic process instruments are giving way to more sophisticated devices. For example, determining the moisture content of granules tumbling in a dryer can be inferred by measuring the humidity of air coming out. But the same measurement can be done more quickly, directly and accurately by using a near-infrared spectrometer, which may also be able to measure and communicate other useful attributes at the same time.
Data gathering, analysis and distribution: The glue that binds smart manufacturing implementations together is data. While the realities of data wrangling are largely new to many kinds of manufacturers, life science companies are old pros. There is probably no other industry that has been creating and using big data as long. It is available in countless forms in a pharmaceutical manufacturing environment due to the range of regulations watching over every element, from cradle to grave.
The sheer volume of data generated has kept life science companies at the forefront of developing and deploying data retention and distribution methods. There are still technological backwaters to be found in some small areas where manual methods still exist, but these are increasingly rare.
Coordinated, not Isolated
Looking at these elements together in a group illustrates how different they are. For example, selecting instrumentation is not the same as designing a network, and developing them individually to achieve a common goal requires different approaches. The concepts necessary to make a CIP skid more flexible don’t look anything like those needed to reach to the cloud to support better data retrieval. Nonetheless, improvements done in any areas will deliver pockets of better performance to some extent. The real payoff comes with a more coordinated effort to connect the improvements.
Setting up the structure for data exchange is a basic element of smart manufacturing, because getting the right information to the right people at the right time is critical for success. Consequently, the mechanisms to support data flow connect all the elements, making manufacturing into a cohesive process rather than disconnected islands.
Making this work effectively depends on three strategies:
1. Effective process control and instrumentation
Understanding what is going on in a process depends on having the right kinds of instruments in the right places. A temperature sensor mounted in the wrong part of a reactor will not provide the desired information about what is happening in the process. It will create data, but nothing very useful. Processes must be evaluated to ensure the best technologies are being used and applied optimally.