**3.1. Knowledge representation**

QFD consists of a series of matrices, in which the first row of one matrix becomes the first column of another one [25–27]. The matrices sequence (**Figure 2**) regarding product character-

• Identification and prioritization of customer requirements. Several information sources can be used for this purpose, such as [29] potential customers, the firm for which the product is being made, similar products and any authorities that can impose restrictions on the product (standards, safety, etc.) The customers' requirements are prioritized based on its relative importance, using a 1–5 rating scale, with 1 having the minimum priority and 5 having the maximum priority. The requirements are placed on the left side on the QFD matrix. Analyzing customer requirements needs a certain product function and a certain

• Technical requirements related to a product should be specified, and product features should be identified. Each product has its own attributes, and these attributes should be

• A relationship matrix between "what's" and "how's" should be established. Relations between customer expectations and product characteristics constitute the core part of the matrix. Typical relations between "what's" and "how's" are no relation, weak, strong and

• A trade-off matrix should be established, which is often named a roof matrix and shows the relationship between various technical requirements. A trade-off is positive when an increase of a feature value causes an increase of another one, and a trade-off is negative

• Customer competitive assessment is focused on comparing competitive products and product being developed, taking into consideration customer requirements. The right part of the matrix should include an importance coefficient of customer requirements. Customer expectations are rated, and product features importance for the customer is established. The next task in this step is product competitive comparison, which should be made with the use of a scale from 1 to 5, where 1 means the least satisfying and 5 stands

• The next step is technical competitive assessment of products. Each product feature pointed in the first row of the QFD matrix should be rated taking into consideration product comparison situated in the bottom part of the QFD matrix. Product technical feature analysis includes assessment of the degree of technical difficulty which represents the capability of an organization to make a given feature of the product. Technical competitive benchmark is a study that compares specification of different products, so, in this stage product alternatives are characterized and compared. Finally, target values of product parameters are set

very strong [23]. Symbols or numbers can be used as correlation marks.

when an increase of a feature value causes a decrease of another one.

istic, configuration items, manufacturing planning and operation planning matrix.

The main steps in QFD include [28]:

54 Product Lifecycle Management - Terminology and Applications

definition of dimension parameters [30].

for excellent performance [29].

in the bottom part of the matrix.

described [30] in the first row of a QFD matrix.

Product adaptation needs knowledge in the field of product and production process redesign. Product adaptations consist in changing technical documentation of products from the enterprise product portfolio. To support the redesign of product configuration, it is necessary to know the answers to the following questions [31]:


QFD-based knowledge base (QFD-KB) for product configuration needs proper methods of knowledge representation. There is plenty of research work focused on gap analysis between knowledge area, knowledge type, and methods of data analysis [32, 33].

Knowledge comes from different sources and could have a different form. Knowledge could be tacit, which means preverbal—understood as unvoiced—unspoken, intuitive and emotional. On the other hand, explicit knowledge is expressed clearly, verbally or in mathematical models [34].

Knowledge should be codified and stored in a way that enables other people to understand and reuse it easily [34].

Formal description of knowledge is called knowledge representation. According to the level of formalism used for knowledge representation, we can distinguish procedural knowledge, which defines algorithms that help to achieve given goals, and declarative knowledge, which gives the solution without analyzing the problem structure.

There are different methods and tools which could be used for knowledge representation. Knowledge representation methods include, among others [34]:

• Decision rules—which contain expressions such as IF *x1* is *F1* **and/or** *x2* is *F2* **and/or** … *xn* is *Fn*, THEN *y* is *P* where *x1, x2, ….. xn, y* denotes objects or attributes and *F1, F2, ……. Fn*, *P* denote values. Decision rules describe both information elements (expressions) and relations between them, and therefore, a set of such rules (*r*) defines a knowledge base: *KB* = {*r*1*, r*2*,…*}.

**3.2. Algorithm of QFD-based knowledge base for product configuration**

three levels can be developed with the use of QFD series of matrices.

product and process failure data.

manufacturing process characteristics.

networks were applied.

system was described by Gibson et al. [36].

**Figure 3.** A decision model for product configuration.

**Figure 4.** Algorithm of QFD-KB for product configuration.

Enterprises develop data bases to store different types of data, e.g., data orders, codes of products, technical documentation related to products and the manufacturing processes, and

Configuration of a Customized Product http://dx.doi.org/10.5772/intechopen.79523 57

Taking into consideration categories mentioned above, product configuration needs information related to customer requirements, product use circumstances, needed product characteristic analyzed from the functional point of view, product portfolio, parts characteristics, and

The problem of determining product configuration can be structured according to the decision method presented in **Figure 3**. The presented approach developing web-based selection

Product configuration is divided into three levels including product-level configuration, component-level configuration, and manufacturing parameter-level configuration [37]. These

In the algorithm of QFD-KB for product configuration presented in **Figure 4** [32], the methods of knowledge representation such as rules from an expert, case-based reasoning and neural

Product offer preparation requires information regarding product portfolio offered by the enterprise and an evaluation of differences between customer requirements and the offered


Knowledge can take many forms, and it is necessary to identify the kind of knowledge representation method which is the most suitable for solving a particular problem.

In the presented customized product configuration QFD-KB, the following methods of knowledge representation were used [32]:


The data and knowledge generated and used during manufacturing may be related to products, machines, processes, materials, inventories, maintenance, planning and control, assembly, logistics, performances, etc. [33].
