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Advanced Vehicle Performance and Fuel Economy Target Setting Strategy

IP.com Disclosure Number: IPCOM000007259D
Publication Date: 2002-Mar-07
Document File: 3 page(s) / 64K

Publishing Venue

The IP.com Prior Art Database

Abstract

To set realistic target ranges is the success guarantee of running a profitable vehicle program. Efficiency and quality of attribute target setting will be significantly improved using a Web-based futuring tool. Some features designated for performance and fuel economy, such as knowledge preservation, standard report-out process, customer satisfaction analysis, data sharing, and up-front target balancing, are enablers of the improvement.

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Advanced Vehicle Performance and Fuel Economy Target Setting Strategy

To set realistic target ranges is the success guarantee of running a profitable vehicle program. Efficiency and quality of attribute target setting will be significantly improved using a Web-based futuring tool. Some features designated for performance and fuel economy, such as knowledge preservation, standard report-out process, customer satisfaction analysis, data sharing, and up-front target balancing, are enablers of the improvement.

According to current performance and fuel economy target setting process, lots of decisions have to be made based on insufficient and incomplete data (attribute and non-attribute data). Target setting largely relies on subjective experience rather than objective data.

Method

Process flow of the advanced vehicle performance and fuel economy target setting strategy is shown in Figure 1. Two kinds of information need to be captured in order to implement a successful performance and fuel economy target setting: attribute data and futuring rules. Attribute data are collected through annual process. Resources of attribute data include JATO, GQRS (Global Quality Research System), J.D. Power, benchmarking, etc. Futuring rules are acquired from internal documents, public domain, and experts' experience.

A VEM (Vehicle Energy Management) futuring tool has been developed to provide a set of attribute data as complete as possible and fill data gap with expertise to support performance and fuel economy target setting. In this tool, four modules are available: Data Preserver, Report Generator, Trend Analyzer, and Customer Satisfaction Presenter. In the Data Preserver, a mechanism is built to preserve valuable performance/fuel economy attribute data. Only authorized personnel can contribute to the database. In the Report Generator, standard health charts are published on the Web in real time with pre-set flexibility. Two charts can be generated: Fuel Economy Health Chart and Performance Feel Health Chart. In the Trend Analyzer, the historical trend of individual sub-attributes is published in a professional format. Due to the nature of insufficient and incomplete data, advanced data mining technologies using fuzzy logic, neural nets and evolutionary algorithms are applied. In the Customer Satisfaction Presenter, the impact of fuel economy on overall customer satisfaction...