A new proposal to improve the customer competitive benchmarking in QFD

ABSTRACT

Quality Function Deployment (QFD) is a structured tool that supports the design of new products/services, translating customer requirements into technical and process characteristics. The so-called Customer Competitive Benchmarking is a module of the QFD's House of Quality, in which a sample of (potential) customers express their perceptions on a set of competing products/services; this information is then elaborated by a cross-functional team of experts and used to define improvement and strategic goals. Despite the importance of this kind of benchmarking for the whole QFD process, the scientific literature reveals limited research. This article critically analyzes the traditional procedure of customer-competitive benchmarking, highlighting its major weaknesses and problematic aspects. Additionally, it proposes an alternative procedure to overcome (at least partly) those weaknesses, without undermining the simplicity in data collection and processing of the traditional procedure. The alternative procedure utilizes the Thurstone's Law of Comparative Judgment, which allows to transform subjective judgments by multiple respondents into a collective cardinal scaling. The description is supported by several pedagogical and real-life examples.

KEYWORDS:

• customer competitive benchmarking
• customer requirements
• indicator aggregation
• Law of Comparative Judgment
• preference ordering
• QFD
• ratio scale
• Thurstone's scaling

Notes

1 The adjective “our” denotes the existing product/service of the company implementing the QFD process. Similarly, the expression “our company” will be used to denote the company itself.

2 The substitution rate between two generic sub-indicators (e.g., I⁽¹⁾ and I⁽³⁾) is defined as the rate at which the value of one sub-indicator (e.g., I⁽¹⁾) can be increased/decreased in exchange for a decrease/increase in the value of the other sub-indicator (e.g., I⁽³⁾), maintaining the same value of the aggregated indicator (e.g., I).

3 In the original formulation, Thurstone (1927) uses the term “stimuli,” which is commonly used in the field of cognitive science.

4 This also applies to Phases 2, 3 and 4 of the HoQ, where aggregated judgments are treated as if they were defined on a (ratio) scale with an absolute-zero point (cf. Sect. “Critical Description of Customer Competitive Benchmarking”).

5 We have implicitly assumed that the (unknown) psychological continuum is included between an absolute-zero point, corresponding to the absence of the attribute, and a point corresponding to the maximum-imaginable degree of the attribute (Torgerson 1958). This assumption, which is quite common for psychometric studies on subjective perceptions (Lim 2011), will be discussed in more detail later on in the article.

6 The adjective “comparable” means that the resulting scales should have a common unit; e.g., let us assume that the LCJ is used to evaluate the courtesy of some call-center operators, according to the judgments of a sample of customers, and this evaluation is repeated annually: without proper normalization, comparing the results of two processes would not be correct.

7 According to this axiom, the preference between two objects O_i and O_j should depend only on the individual preferences between O_i and O_j exclusively: if one object is removed, the algorithm scaling should result into the same ordering of the remaining objects.

8 The S_A, S_B, and S_C values relating to a certain CR are obtained by considering the result of the Thurstone's scaling, rescaled into the range [0, 10], for that specific CR.

Additional information

Notes on contributors

Fiorenzo Franceschini

Fiorenzo Franceschini is Professor of Quality Engineering at Politecnico di Torino (Italy) – Department of Management and Production Engineering. He is author or coauthor of 7 books and many published papers in prestigious scientific journals, and international conference proceedings. His current research interests focus on Quality Engineering, Process Quality Indicators and QFD. He is a member of ASQ. Franceschini may be contacted at [email protected]

Domenico Maisano

Domenico Maisano is an Associate Professor of Quality Engineering at the Department of Management and Production Engineering (Politecnico di Torino – Italy). He is author or coauthor of many published papers in prestigious scientific journals, and international conference proceedings. His main scientific interests are in the areas of Quality Engineering, Industrial Metrology and QFD. He is a member of Quality Engineering Editorial Board. Maisano may be contacted at [email protected]