Formula for success

Fast, focused learning speeds formulated products from design to production

Jacqui Griffiths
30 July 2018

3 min read

Incompatible IT infrastructures, language barriers, different methodologies – these are just some of the many factors that make it challenging to transfer product and process knowledge from development to manufacturing. Compass spoke to Graeme Cruickshank, director of the UK Centre for Process Innovation’s National Formulation Centre, about the secrets behind getting great formulations to market.

COMPASS: Why do organizations find technology transfer difficult, and how does the National Formulation Centre help to overcome these challenges?

GRAEME CRUICKSHANK: Joining up the knowledge between different organizations or divisions within a large corporate company can involve teams in different geographies, with different cultures and speaking different languages. We need to find a common ground to understand where each side is coming from initially, or misunderstandings and confusion will stop any meaningful progress.

Even within the same organization, software systems often don’t talk to each other. Cost reduction pressures mean that companies don’t want to replace the entire system. Even if they did, they would still face the challenge of communicating with the systems of external organizations.

In principle, these entities often need to join up the systems they’ve already got. This is where open-access innovation centers for advanced formulated product design and manufacture can help. At the National Formulation Centre, for example, we bring together industrial knowledge, research and technology infrastructure so that companies can accelerate the commercialization of formulated products.

How do trends like personalization add to the challenges of tech transfer?

GC: Personalization exists in almost all categories, from stratified medicines to the different drinks available in coffee shops or the various powders, liquids, tablets and combinations in the supermarket laundry aisle. As a result, production is becoming more fragmented and scale-up of formulations more localized as companies seek to make smaller quantities to meet increasingly dynamic consumer demand.

Seamless information transfer between R&D systems and multi-center factory systems is more important than ever in this environment, so that knowledge learned in one geography can be applied in another.

Graeme Cruickshank, Director, UK Centre for Process Innovation’s National Formulation Centre

Can knowledge from other sectors help smooth the path to production?

GC: One of the fastest ways to help one sector of the industry is to give it a solution from a different sector. In formulations, most challenges exist across a range of products, but organizations haven’t necessarily realized that. They’ve kept trying to invent their own solutions. For example, issues with a deodorant can be like those in paint formulation – both are about spraying stuff on quickly and evenly, having it dry quickly without feeling tacky, and self-healing to cover the bits the user has missed.In a highly competitive industry, this horizontal technology transfer is popular with organizations because it helps them find solutions while avoiding the intellectual property issues and defense mechanisms of knowledge transfer between competing companies or divisions.

What role does predictive modeling play?

GC: We are developing more predictive tools, we’ve got better data capture systems and we can better harvest our knowledge, so we’re more in control of our product. But there’s still a long road ahead because when you’re dealing with liquids and pastes that flow and mix, and which might be sensitive to time, temperature and process, the results are not easy to control or predict.

Is there a secret to creating a smooth transition from design to production?

GC:The key is to learn small, learn fast and learn thoroughly.Ultimately, successful technology transfer is about sharing the right information with the right people. A lot of the skill is working out which attributes you need to measure and getting good data quality and information-sharing on those. Big organizations are now looking for neutral testing grounds where they can quickly screen through all their technology leads and make objective decisions about which one they should focus on scaling up. There is a limit to how many big, expensive trials you can do and how much you can learn from them. But using a high-throughput robotic system on small-volume samples can generate thousands of data points. When you have that data density and data quality, you can use it to develop predictive models that will help with your next-generation products.

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