Being first to market with products that consumers clamor for is the Holy Grail for cosmetics and personal care companies. It’s no secret that organizations spend a great deal of time and resources trying to understand what customers want, and then race to translate this intelligence into the design of new materials and formulations that will form the basis of next-generation products. Getting it right results in winning innovations that grab market share and maximize profitability. Getting it wrong can result in costly and time-consuming mistakes.
In fact, innovation and the technologies that support it are big drivers for just about any industry; a recent Gartner survey1 of more than 2,300 CIOs cites enterprise growth, attracting new customers and product innovation among the top 10 business priorities for 2012.
Increasingly, demand-driven innovation depends on how efficiently companies can leverage the many sources of data that contribute to the design-develop-test-manufacture value chain. The goal is to speed and streamline everything from early research and the chemistry that builds on that first glimmer of a new idea, to the development, scale-up and production processes critical to getting the final product to market. To achieve this objective, cosmetic and personal care companies need informatics technologies that have the breadth to reach across the many diverse disciplines and departments that touch the product lifecycle, as well as the depth to handle the highly complex data that powers groundbreaking science.
To create the next generation beauty product, researchers
must leverage their data sources.
In today’s hyper-digitized and hyper-social world, relevant market data has never been more available. From Twitter feeds to blog posts to online reviews and more, a wealth of information is being generated (beyond focus group research and surveys) about what consumers like and dislike and what features they want. As a result, the challenges facing cosmetics and personal care companies have shifted. While it may be easier to gain insight into what customers want, figuring out how to efficiently and cost effectively satisfy constantly changing market demands is tough. To take advantage of potentially profitable opportunities, R&D organizations must rapidly design novel materials and systems, build prototypes, test lead candidates and quickly move products on to production. A few bottlenecks and delays are all it takes for the competition to get there first.
Further impetus for the increasing velocity of innovation is the fact that product complexity is reaching an all-time high. As consumers demand advanced personal care capabilities specifically tailored to their unique needs, advanced chemistry and materials science, as well as multiple formulations, computational models, experiments, QA/QC test results and more, form the basis of an ever-growing data pyramid leading to the new product. The volume of information that R&D requires to capture, analyze, share and report on is staggering, and unlike the structured data that is commonly processed through business information management systems, like PLM and ERP, it’s exponentially more diverse. Beyond standard row and column-based data sets, it may include scientifically meaningful text, images, multi-scale models and more, and be generated by a multitude of software systems, laboratory equipment, sensors, instruments and devices.
Traditional chemistry is being aided by computer modeling.
Speeding the demand-driven innovation cycle thus requires an end-to-end, enterprise-level informatics platform capable of capturing and integrating diverse sources of information. It should also be able to analyze and run processes across the data, and report it in a way that makes sense for multiple users, whether a chemist adjusting a formulation, a processing engineer calibrating plant equipment or a business executive making decisions about where to invest development resources.
Thanks to the advent of cloud computing, service-oriented architecture, the use of web services and advanced search and data mining, technology that streamlines demand-driven innovation, yet respects the complexity inherent in it, is now a real possibility. Web services can, for example, be used to support “plug and play” integration of multiple data types and formats without requiring expensive, customized IT intervention. As data previously scattered throughout the global R&D enterprise is made accessible through a central, integrated and open informatics framework, it provides a “single version of the truth” that drives a number of time, cost and efficiency benefits.
First, information, no matter where or how it was generated, can be used across the product development and commercialization value chain, to enhance collaboration. Toxicologists can make their history of assay results available to formulators developing formulas for a new lipstick color, for example, or chemists trying to address consumer demand for a more effective anti-aging cream can collaborate earlier in the process with sourcing experts to ensure that the compounds they are developing in the lab are actually viable candidates for large-scale production.
Second, processes, such as product specification management, that were previously disjointed due to critical data being locked within isolated databases and proprietary systems, can be streamlined and automated, speeding innovation cycle times. And third, institutional knowledge garnered in the discovery stage that could be instrumental in responding quickly to consumer feedback is more easily captured and accessible, promoting re-use and reducing unnecessary rework.
Consider all the data and processes that go into clearing a new formulation out of discovery and into development. At a big cosmetics company, this may involve multiple steps that touch numerous information sources and applications. Product safety and efficacy tests must be reviewed, which includes data from three to four different systems. The new recipe must be compared with historical data and scenarios. Conclusions and recommendations are written and formatted so that the next contributor to interact with the information can actually use it. Without an integrated informatics framework in place, a reviewer will likely jump back and forth between several different screens and information systems to get through a single step in the clearance process, with each step taking several minutes. Multiply the number of steps required, the number of systems involved, and the number of times the process must be completed (sometimes a clearance review needs to be done for each of the 20-30 ingredients that make up a new formulation) and it’s easy to see how just this one activity alone can eat up an enormous amount of time and resources.
An enterprise informatics platform, on the other hand, can compress innovation cycle times by freeing project participants from tedious manual tasks such as searching for data, prepping information for analysis, formatting reports from multiple applications and systems for different users. When all the scientific data related to product discovery, design and development is captured within a single, open information framework, processes and workflows can be automated across disciplines, applications, systems and departments, which in turn speeds the flow of information and reduces costly errors along the R&D value chain to market. This also ensures that critical data is not lost or missed.
Finally, a central informatics platform helps ensure that all individual contributors are on the same page. Bringing a new product to market requires domain-specific expertise, ranging from the business units which must hit product profitability targets and need insight into ingredient costs early in the cycle, to the chemists working on compounds, to the toxicologists who screen for adverse reactions. If, for example, consumer data shows that women want a lighter feeling yet high SPF sunscreen, then every domain expert should have this objective in mind, whether they are designing molecular compounds, studying skin cell cultures for efficacy, costing the ingredients with suppliers or carrying out toxicology tests. When they all have immediate and unfettered access to the same information, they can not only better understand the bigger picture, but also work on their individual pieces in parallel, rather than waiting for information to be handed off serially.
Extensive reliance on trial-and-error experimentation is another potential bottleneck factor for cosmetics and personal care companies that want to rapidly capitalize on consumer demand. Multiple rounds of experiments and testing adds significant cycle time and cost, especially for increasingly complex personal care systems that can be impacted by even small changes in chemistry, composition or processing steps. Companies must reduce the number of lab experiments needed, while at the same time increase their ability to quickly investigate a growing number of leads.
Obtaining and using the right data is critical.
Combined with a platform for integrated informatics, modeling and simulation software is even more powerful. Through a centralized, enterprise data platform, the learning gained through modeling and simulation can be shared more quickly and on a much broader scale with other stakeholders throughout the organization—even non-experts who may have not been able to access advanced modeling software previously without expert help. For example, once potential formulation ingredients are identified through modeling, procurement specialists might use the information to confirm whether those ingredients are viable candidates for use on a large production scale.
If not, the early research and discovery team can look at other viable candidates that may not have been selected at first pass, saving resources that might be wasted experimenting on compounds that work in the lab, but are not economical, stable or effective in bulk.
R&D, PLM and ERP
As a final step, demand-driven innovation must be closely aligned with the nuts and bolts activities that are involved in bringing a great idea to life. So in addition to accelerating innovation processes and enhancing collaboration from product ideation through production scale up, an enterprise approach to R&D informatics must also be able to hand off critical data, in a usable, structured format, to PLM and ERP systems that govern business analytics and supply chain management. When complex research information can be pulled into these more structured practices (and vice versa) bringing a new product to market will become more effective, global and streamlined.
In a climate where budgets are tight, competition is fierce and time-to-market pressures are high, today’s cosmetics and personal care companies must channel all their expertise and resources to the correct areas. This requires the ability to quickly, efficiently and cost-effectively translate market demands into winning products. The data to help organizations do this is more available than ever, it only needs to be inter-connected with an underlying framework for scientific informatics that allows them to speed innovation by closing the gaps between consumer feedback, R&D and the final product.
1. Gartner Executive Programs, January 2012 “Amplifying the Enterprise: The 2012 CIO Agenda.”
Michael Doyle, Ph.D., is director of product marketing and principal scientist at Accelrys (Accelrys.com), a leading provider of scientific informatics software and solutions for the life sciences, energy, chemicals, aerospace and consumer products industries. His blog can be found at: http://blog.accelrys.com/author/michael/