It’s more exciting than before . . .

When Joe and I developed this function last year, we knew that we had to go way beyond just substantial improvement in current activities. We needed to link a wide range of activities together into a seamless global process in order to accomplish our objectives. Naming those objectives is a good way to start. They are to:

1. Develop and continuously improve a seamless tire performance predictive capability on a global basis. Elements of R&D included are GTC*A, GTC*L and Research. Functional groups include Testing, TVET, Advanced Product / Technology, Tire Engineering, Computational Mechanics and Materials Physics.
2. Integrate fundamental studies and project needs with Research – develop a project portfolio.
3. Manage the flow of technology transfer from the global technology platforms, teams, departments and individuals into the process.
4. Prioritize GTPP programs and resource needs.
5. Coordinate the Design Systems, Testing and TVET activities of SRI/Goodyear

GTPP is charged with improving the process of how we ultimately develop better methodologies and systems – and making an efficient transfer to the product groups. Tools you are all familiar with play a role. For example, predictive testing such as lab treadwear testing to replicate field performance. This is becoming accurate, quick and controlled when compared to field tests.

Another example of emerging technology is tire-vehicle modeling that will dramatically increase our ability to efficiently interact with OEMs. Here, we predict significant progress in ride, handling and wear, and new interaction with OEMs on systems technology, which will be led by the new vehicle systems group.

The expertise and the energy of our people is outstanding – the path which GTPP is taking is akin to fine-tuning a race engine to maximize its performance for the driver.

CAPE – computer-aided performance engineering – accesses our global database andprovides a vast amount of information available in a structured format for use by designers, compounders and analysts. This process continually captures information and provides feedback for model validations, performance prediction and knowledge capture. Data mining by “power users” who study and learn from the value in this data will be encouraged.
Tire modeling is rapidly maturing with the delivery of exciting new capabilities from the computational mechanics group - this combined with the expected release of CATIA V5 challenges us to constantly re-think the future process. This capability is being developed, validated and embedded in the development cycle at an ever-increasing rate.

One goal – which is becoming a reality - is to do major, up-front engineering before we commit to prototype tires. Once we do a prototype, however, we will emphasize characterization and predictive testing with those tires to capture information which is in turn fed back to develop design guidelines and to add formality to the knowledge retention process.

As we accomplished this, our goal is to continually minimize unnecessary road testing. We will not back away from final customer validation requirements and their ultimate satisfaction. (I’m sure our OE engineers will be pleased to hear this. . . ).

Ultimately, our goal is to bring these processes together and use them to develop future products that when built, generate the same performance characteristics as predicted. Along the way, we will reduce the potential for duplication – one of Joe’s main principles, as you all know. We simply can’t afford duplication of efforts.

You’ve seen by now that the emphasis here is on process. We are going to improve and maximize the efficiency of the process. I want to thank all of you for your support and forbearance in helping us achieve these goals. The end results will be better customer response time, increased efficiency, cost savings and compression of product development time.
-- Dave Glemming