Within the chemicals manufacturing industry, many systems or tactics exist for finding the source of production variability and identifying changes that could reduce it. But all-to-often, the legacy system at the plant is an intertwined ecosystem of moderately-capable equipment, custom-specified unique raw materials, product formulations that are hypersensitive to processing conditions, and possibly government-regulated compliance requirements. Too many companies have been painted into a corner by product/process design decisions made years ago.
Fortunately, the world of speciality nanomaterial full scale manufacturing is practically brand new by comparison. Inside many of these companies, the ‘plants’ and product formulations are still conceptual in R&D, or possibly have made it to pilot scale. Many more degrees of freedom exist because everything’s new. Now is the time to co-design products and manufacturing systems to build in robustness so companies don’t wind up pulling together that batch to batch defect-reduction team three years down the road.
So what are the key points to nip production variability in the bud?
- Take the Researchers to the plant.
- Take the Engineers/Operators to the lab.
- Break the product formula.
- Break the plant.
- Fail Fast!
Take the Researchers to the plant
The research team needs to understand how the commercial operation does, or will, work. When faced with the challenge of creating a new product for a customer, a material scientist can unleash his or her inventiveness in all kinds of directions, but the real (affordable) world is made of components and processes which are as industrially standard and available as possible.
By having the researcher familiar with the realistic capabilities of the plant, the researcher will be able to self-prioritize paths of investigation that have the best chance of working at full scale. Only if those options don’t meet the product requirements does it move up the ladder of complexity and robustness risk.
Take the Engineers/Operators to the lab
Having plant engineers and operators see the lab batches being made allows them to mentally overlay the equivalent manufacturing process onto each step in the lab batch. From this, engineers can identify where new process capabilities need to be created at the plant to control critical sources of variability.
It’s also an opportunity to ask researchers, “Why did you do that step that particular way?” If the answer is, “It doesn’t have to be that way,” it is possible that highly-capable equipment is already available which accomplishes the same function.
Break the product formula
The materials scientist has successfully created a candidate product formulation which meets the customer’s particle size, morphology, and reactivity needs and generally uses a reasonable process for producing it.
Congratulations. Now try to break it!
Once the researcher appears to have created a product with the required performance, the formula should be written up and processed with all the instructions and parameters the researcher ostensibly believes are needed. Then it should be handed to another researcher to run – or better yet to a technician or plant operator to run on development equipment which at least somewhat models the manufacturing environment. Every time during the product re-creation process the technician has to stop a batch to ask a question because knowledge is missing, and every time a batch turns out bad because a critical tweak wasn’t made, more and more of what is truly required to produce in-spec product will be uncovered.
Break the plant
Now that plant engineers are familiar with the proposed product formulation and manufacturing process, a batch needs to be run through – mentally, using process modeling software, or even cardboard cutouts and string if that works.
Using process engineering experience, ask what would happen if each component of a combined system didn’t perform as expected due to wear, incorrect set-up, or outside influences.
Even with a great research team, if no one is told what the likely, and even non-obvious, variability sources are, the right experiments cannot be run to determine what happens to product quality when those sources vary in the plant. The engineering team needs to model and feedback these issues as soon as each new candidate formulation/process is available.
Fail Fast
Scaling up an invariant high-tech nanomaterial in the first design iteration will fail, so at least fail early and often, when it’s cheap and correctable.
Very few people will resist change if work has proceeded only a short distance down an idea path when it is discovered that a course correction is required. If a business model depends on manufacturing revenue, letting a non-manufacturable product slide through could result in a serious failure far too late in the game.
