Experimentation Matters - Unlocking the potential of new technologies for innovation

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Millennium Pharmaceuticals (MP)

MP, a Cambridge, Massachusetts based pharma company started its operations with an investment of $8.5 million. The company made efforts to build a technology platform where molecular biology with automation and informatics can play a major role. To obtain best results and make the optimum use of new technologies, MP decided to rework on its product development process. It appointed Michael Pavria, a pioneer in combinatorial chemistry as a chief technology officer to review the entire product development process.

Developed by the California based Altera Corporation and Xilinx Incorporated during the mid 1980s, FPLDs allow companies to program and perform a particular experiment on computer. This gives more flexibility to the companies as well as to the chip supplier as they do not need to involve themselves in the custom design process. Companies can themselves prepare the physical prototype needed for testing. The gate-array-based chips that was designed by LSI Logic Corporation has also made possible to cut costs during the customization process. These chips allow programmers to design and test chips before committing to a physical prototype. Other new technologies like field programmable gate arrays (FPGAs) have also made companies possible to erase and reprogram a chip according to their needs without incurring additional costs.

From Experimentation to Learning Innovation

Whatever may be the final result of any experiment, whether it's a success or a failure, it is sure that it will generate enough information for the scientists to study. Experiments are the major source of information and this information finally leads to learning since the next series of experiments are based on the information gathered from the past similar experiments. Sometimes, the learning from the experiments can transform the knowledge itself into an industry. For example, the experiment with the custom chips ended with the formation of multidollar programmable logic industry. The various stages of experiments from where the learning can be retrieved include Design, Build, Run and Analysis. All these stages involve close interactions between all the product development team members. It involves a good amount of information transfer from one person to another in the form of discussion; debates, presentations or question answers sessions. These steps form the basic part of learning from experiments. There are some factors that are common to all sorts of experiments and learning depends on these factors. These factors are:

The Technology Affect

• The gap between the actual and virtual testing conditions. The learning depends on up to what level or condition the organization is conducting experimentation. Does the experiment represent the final product, the processes or series under the test conditions?

• The cost of experimentation is another major factor associated with experimentation. It includes designing, building, running and analyzing costs. It also includes expenses for prototypes, laboratory and its usage.

• The time factor in getting the experiment results, analyzing the output and then based on analysis the time gap between another experimentation.

• The capacity factor i.e how many times the same experiment has been carried out to gain more accurate data.

• The learning factor also depends on whether the organization has gone for parallel or series of experiments and for how many times.

• The manipulation factor that implies how many times the input data has been manipulated and whether the change is an incremental or a radical one.

Bank of America

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