As chief executive officer of Accelrys, one of the things I most enjoy is speaking with customers and potential customers about their businesses. Because we serve a range of industries, their successes and challenges are pretty diverse. Engineering an aircraft is very different from finding the right formula for a shampoo, or being in the midst of an FDA approval for a potential blockbuster drug. What comes through clearly, however, is that operating a successful innovation-based business today is an increasingly demanding endeavor, requiring varying degrees of fast action and deliberation, often under the scrutiny of regulators and shareholders.
Today's business environment requires that organizations respond quickly to change in order to capture new market opportunities and gain competitive advantage. Take, for instance, the food and beverage company that wants to capture first mover advantage in a new geographical region by introducing a soda flavor that meets local tastes. The window to do that could be relatively small. In the life sciences industry, a premium is placed on regulatory compliance and hasty mistakes can result in costly fines or even worse, product recalls.
Across industries – from aerospace, to oil and gas, to life sciences, and consumer packaged goods– there is the business imperative to do more with less, keep costs down and profits up – all the while accelerating innovative new product development. However, businesses are really struggling to meet these objectives. According to IDC Manufacturing Insights, almost half the resources allocated to new products are wasted, only 25 percent of projects in key industries – from pharmaceuticals to aerospace – result in the commercialization of new products and of those 25 percent, 66 percent fail to meet original expectations.
Now more than ever, the strategies of IT and scientific innovation must converge – businesses need to consider an enterprise IT strategy that is tailored to their scientific innovation lifecycles.
Organizations' IT strategies and solutions have simply not kept pace with changes in the business climate or the rapid scientific advances that have lead to, for example, the development of new materials for lightweight planes or personalized medicine to better treat cancer. New technology coupled with advances in scientific discovery are generating an unprecedented amount of data, both structured and unstructured. And with the trend toward globalization and externalization, that data is more distributed than ever before. Businesses work with partners and employees in different facilities around the world from research through commercialization. No longer are outside partners relegated to contract manufacturers, but research is conducted among groups from various locations. In scale-up manufacturing, a research scientist may discover an ingredient in a newly developed drug is not reacting as it should, making it imperative that the company quickly identify the chemical compounds that could be causing the problem, how they interact with other elements of the drug and possible alternatives. If part of that data is stored on a research scientist's notebook in one department in Boston and another element is in a completely different program used by a chemical process development engineer in a different department, and another with a CRO scientist in Korea, precious time and resources are wasted.
Moving a product from the research and development stage through scale-up and commercialization demands bi-directional sharing of information and knowledge across these sites. But collaborating and sharing information alone is not enough. Organizations must learn from the knowledge they are gaining through the experimental and development process and apply that in sight to continually optimize new product development.
In many cases, we are fortunate to have a wide array of IT solutions at our disposal. Electronic laboratory notebooks can digitally log key research data and modeling and simulation software allows exploration and experimentation before embarking on a project, reducing the risk of more costly failures down the line. Laboratory information management systems (LIMS) allow samples to be tracked and inventory to be managed.
Many organizations have implemented electronic systems alongside paper-based systems and LIMS and ERP software, leading to silos of data throughout an enterprise. A fundamental hurdle to innovation is that important data is compartmentalized, making it difficult to search and retrieve. Knowledge is vested in individuals and not corporations. Many companies cannot access the data to distill insights about or the context of discoveries. Information about development and manufacturing and the connection to research is lost and never transferred to the commercialization stage. Many companies will re-measure information rather than even try to find it in their organizational mazes. Therefore there is a productivity gap that spans the entire lab-to-commercialization value chain. The scientific innovation lifecycle slows and new product development falters.
In an attempt make data more accessible and actionable, there has been a trend in software development toward broader solutions that try to tackle multiple problems for a variety of users within one system. LIMS is a good example of this. Once designed to accomplish a couple of very specific tasks, many LIMS now are large, complex systems that require significant company resources to implement and oftentimes are abandoned before they provide their full value.
A Flexible but Structured Solution
In 2013, businesses have the opportunity to overcome these obstacles by aligning their IT strategies with their specific scientific innovation lifecycles. This requires both a change in mindset as well as the implementation of new technologies that can help close the productivity gap that is stalling innovation.
Here are key criteria to consider while evaluating a solution that serves your organization's business goals:
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