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Title: Quantitative model for evaluating the quality of an automotive business process
Author: Zakarian, A; Wickett, P; Siradeghyan, Y
Abstract: One of the key issues to business process control is the identification of measurable process attributes. For manufacturing processes these are typically physical parameters of the process (e.g. temperature, set points) or physical attributes of the manufactured product (e.g. dimension, functional performance). However, for business processes the metrics are more abstract. The challenge has been to develop metrics that capture the contributing subtle and hard to measure factors for business process control. This paper presents an analytical model that uses the weights-of-evidence concept to convert answers to audit or self-assessment questions into a single numerical process quality index. This index is used to forecast process success or failure and monitor its performance from start to end. The application of the approach is illustrated with an automotive industry product development sub-process where the process performance metric is the field warranty data, i.e. incidents per thousand vehicles (IPTV). The analytical model converts process self-assessment (failure mode and effect analysis) questions into a single numeric process quality index. The validity of the model is reflected in the strength of the correlation between the index and the IPTV results. Also, in this paper a measure is developed for identifying critical process quality assessment questions. This measure quantifies the deviation in the automotive business process that should have more focus. The significance of the analytical model proposed in this research is that the project managers or quality assurance auditors may be able to use the metric to predict product quality at any point in the product development process.
Source: INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Publication Year: 2006
Volume: 44
Issue nr: 6
Pages: 1055 - 1074
Science Code: Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science
Tags: process analysis; quantitative models; FMEA
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