Real-Time Quality Management in the Automotive Industry:
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Table of Contents
I. Continuous Improvement in the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 II. Statistical Process Control: A Scientific Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 III. A Case Study in SPC for Continuous Improvement: Cooper Tire . . . . . . . . . . . . . . . . . . . 4
I. Continuous Improvement in the Automotive Industry
Over the last 30 years, the manufacturing industry has undergone a notable shift in terms of pushing geographic and cultural boundaries. An increased dependence on global trade, offshore labor and a worldwide supply chain are the determining factors for where, what, when and how produced goods reach consumers in an increasingly level global playing field. This shift has been particularly prevalent in the automotive sector, as automotive manufacturers obtain parts from hundreds of suppliers, and the standards for quality are becoming more stringent. A complex organizational structure is therefore required to line up the end-to-end logistics of supply chain management, financials, customer relations and human resources. With a centrally developed and coordinated manufacturing strategy, individual facilities must execute the various tactics for quality management. In this environment, opportunities for business success can be fleeting. Even under ideal circumstances, an unforeseen, outlying factor can determine whether a company wins or loses an important contract. It is critical to establish a competitive advantage in order to simply maintain profits, let alone increase revenues. Businesses can simultaneously reduce costs and remain competitive by investing in process improvements that increase quality. For example, identifying and implementing efficiencies in production methods can result in reduced scrap, rework and even labor costs. Automotive manufacturers are rapidly adopting technologies for the automation of not only processes, but quality control functions. Methodologies such as statistical process control (SPC), six sigma, lean manufacturing, and total quality management (TQM) have arisen out of the steadily emerging culture of continuous improvement. They are key aspects of the operations management strategies that help manufacturers gain the competitive advantage needed to remain profitable. In the automotive industry, focusing on comprehensive process improvements leads to the creation of more precise parts with less variability. Many companies have adopted a hierarchy system to organize and execute six sigma and continuous improvement efforts according to Kaizen.1 The hierarchy is built around a champion, whose responsibility is to define and coordinate business objectives and provide the necessary resources to team members. The champion organizes team responsibilities and determines the scope of involvement necessary to execute tasks. The “black belt,” an expert in engineering process improvement, works in conjunction with the champion to identify innovations that contribute to quality initiatives.
II. Statistical Process Control: A Scientific Methodology
More and more manufacturers are implementing automated Statistical Process Control (SPC) systems 1
Kaizen is Japanese for improvement. In business, the term applies to the culture of applying continuous quality improvement functions.
as part of their continuous improvement efforts. Simply stated, SPC uses statistical equations and graphs to create acceptable limits for process variation—“control limits”. Control limits fall well within product specification limits so that unstable processes can be identified before problematic product characteristics are produced. With real-time SPC, operators monitor processes on the production floor....