Detecting, Identifying and Managing Sources of Variation in Production and Product Development
Doktorsavhandling, 2006

It is well known that customers have varying needs and wants and thus expect to have a broad spectrum of products to choose from. However, they hardly tolerate variation in products produced to the same specifications. This variation, which originates from multiple sources and adversely affects product quality, should be detected, identified and managed throughout the Product Realization Process (PRP). While the PRP is a widely defined concept, this thesis focuses on its two distinct parts, namely production and product development. There are essentially two strategies for dealing with variation. One is to detect and eliminate sources of variation. This is usually done using Statistical Process Control (SPC) procedures. The second is to minimize the impact that the sources of variation have on important product characteristics. This is usually done using Robust Design Methodology (RDM) tools. The main objective of this thesis has been to develop statistical methods that support the execution of both strategies. More specifically, in order to identify and assess the risks related to variation from early phases of product development, a new statistically based engineering method, Variation Mode and Effect Analysis (VMEA), has been developed. Furthermore, to effectively address variation-related problems in production, especially when traditional control charts cannot detect assignable causes of variation, a number of modifications to Shewhart’s criteria and Exponentially Weighted Moving Average (EWMA) control charts have been proposed and applied in practice. Finally, some contributions have been made to the understanding and definition of predictability as an important notion in statistical quality control.

Robust Design Methodology

Variation Mode and Effect Analysis (VMEA).

Key Product Characteristics

Statistical Process Control (SPC)

sources of variation

10.00 Vasa A
Opponent: Professor Murat Kulahci, Arizona State University, USA


Alexander Chakhunashvili

Chalmers, Teknikens ekonomi och organisation, Industriell kvalitetsutveckling

Variation Mode and Effect Analysis: a Practical Tool for Quality Improvement

Quality and Reliability Engineering International,; Vol. 23(2006)

Artikel i vetenskaplig tidskrift

An EWMA Solution to Detect Shifts in a Bernoulli Process in an Out-of-Control Environment

Quality and Reliability Engineering International,; Vol. 22(2006)p. 419-428

Artikel i vetenskaplig tidskrift


Produktionsteknik, arbetsvetenskap och ergonomi



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 2433

PhD thesis - Department of Quality Sciences: 4

10.00 Vasa A

Opponent: Professor Murat Kulahci, Arizona State University, USA

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