Modularization is the key technique for modern manufacturing system, which resolves the conflict between flexibility and productivity. The challenge of deciding which modules should choose under resource limitation from a large amount of available alternative modules has been well recognized in academia and industry correspondingly in producing customized production. For this reason, this paper proposes a new module selection method to deal with the problem, which combines rough set theory into total quality development (QFD) framework. First of all, a decision table is build up and then be modified through examining the importance of each attribute. Afterwards, the basic importance rating vector is calculated and the modifying index of the importance will be determined to get the final result. Finally, the feasibility and efficiency of the proposed method is manifested by a case study.
With the intensive competition among manufacturing industry, manufacturers must have the abilities to differen- tiate their products according to individual customer needs as well as to keep and even better to improve the ef- ficiency, effectiveness, and the low cost that customers expect. Customized services are an important source of revenue for many companies, particularly those whose customer satisfaction is of supreme importance. This marketing approach has resulted in the attempting of increasingly diverse products in order to satisfy most seg- ments of the market [
The challenge of building customized production rapidly while at the same time maintaining the same profit as mass production has been well recognized in academia and industry correspondingly [
Modularization is a high effective method to provide customized products or services based on economical and flexible development by allowing highly differentiated products to be developed from a common platform while targeting individual products to distinct market segments. With the modular design approach, new prod- ucts can be quickly developed by using alternative modules. The relevant combinations of modules can form different final products which meet customers’ various demands. As common modules can be shared by differ- ent products, the time and cost of design and manufacturing can then be reduced significantly. In modular design and manufacture process, all possible functional modules will be first selected from the module base to meet specific customer requirements. Product modularization faces many challenges and the resource shortage is the most common difficulty that happens in almost every enterprises. The manufacturing enterprises usually cannot have enough resources to implement all functional modules that have been selected in the first step into the real manufacturing process. Thus, the manufacturers have to decide which of those modules should be kept for the next step under certain limitation in a short response time due to the rapid introduction of new products.
The objective in this article is to develop an acceptable resource-constrained module selection method for meeting maximum customer satisfaction by using Rough-QFD theory. This proposed model combines rough set theory into total quality development (QFD) framework. In this method, decision tables are built up and modi- fied through examining the importance of each attribute. Afterwards, the basic importance rating vector is cal- culated and the modifying index of the importance will be determined to get the final result. The remainder of this article is organized as follows. Section 2 reviews related literature and background information on modular design as well as QFD and Rough set theory applications. Section 3 describes customized product innovation process. Section 4 presents the module selection method based on the combination of QFD and Rough set theory. Section 5 conduct a case study by using the method proposed in Section 4. Closing remarks are presented in Section 6.
Several strategies about modular design and module selection were proposed in the literature by many literates. Fujita [
Quality function deployment (QFD), developed by Dr. Yoji Akao in Japan in 1966, is a strategic management technique typically used for identifying and translating customer wants into technical specifications for product (or service) planning, design, processes, and production [
In application of traditional QFD method, the accuracy of prioritization result is influenced by how specified the evaluation criterions are. However, Different from the material resources, the customer satisfaction is ab-
stract, dynamic and complex. The information is usually incomplete because it is quite impossible for the end users to express their entire wants clearly in technical terms. Hence, Rough set theory is considered to be inte- grated within a QFD framework to cope with this weakness. Rough set theory is an effective widely recognized mathematical tool for processing incomplete and uncertainty problems, proposed by Pawla in 1982 [
Rough set theory was proposed by Pawlak (1981). It is a new mathematical tool to deal with inconsistent, ambiguity, and incomplete information. Rough sets can be used to represent, vagueness and general uncertainty. Rough set theory is useful for rule induction from those incomplete data sets.
This paper proposes a systematic module selection method by combining Rough Set theory with QFD method. There are four hypotheses that will be used in this paper and be illustrated below [
Hypothesis 1:
Hypothesis 2: There is an equivalence relation R,
Hhypothesis 3: There is an equivalence relation in U: P and Q. The positive area P is the object set which contains all classification information of U/P that can be accurately classified into Q, symbolized as PosP(Q).
Hypothesis 4: set up a decision table B,
PosT(J) means that T is the positive area of J. Besides,
Within a customization environment in modern manufacturing industry, customer satisfaction is becoming su- preme important. The nuclear purpose of launching new product is to achieve the maximum customer happiness. Therefore, the module selection process should have special consideration of customer satisfaction. The enter- prises should take all possible functional modules into the comprehensive consideration, analyze the priority and decide which of them need to be launched into final product to achieve maximum customer satisfaction.
The design specification of customized products are transformed and ultimately determined by end user re- quirements. The customer’s requirements guide the direction of the innovation of customized products. Mean- while, customized product innovation can then improve the demand level of customers. Therefore, the innova- tion process and the customer requirements form a good dynamic circulation of each other. Form the point of transformation mechanism, the innovation chain trends waveform forward (as shown in
The whole conversion and the innovation process can be divided into the following four steps: identification and predictions of customer requirements; development selection of customer requirements; transformation of requirements to the product specification and the design; manufacture and production of final products. Among them, selecting the appropriate demand resources from the identified set of requirements is the crucial step of successful customized product innovation. Differed from material resources, the demand resources is abstract, dynamic and complex. It is no practical significance that converts all customer demands onto product functions under resource constraints. The risk of requirement selection is usually presented in two aspects: inaccurate de- mand choosing and developed more than expected. The developed new product based on inaccurate choices cannot achieve customer satisfaction. In addition, in all identified requirements, some invisible requirements of the product attributes are likely to show its actual application value after a long period of time. Therefore, if the requirements are developed excessively of inappropriately, the innovation process will increase the difficulty and the cost, extend the development cycle, and at the same time be criticized by the end customers due to pro- viding excessive additional functionality that is no practical value. Therefore, we need to find a way to compre- hensive consideration to a variety of demand, analyze the important degree of customer requirements, translate them into new products and finally achieve maximum customer satisfaction.
Based on above analysis, the module selection process can be described as
Stept 1. Build up a decision system and transform the initial data of customer satisfaction into a decision ta- ble.
Derived from customer investigation, the fuzzy customer needs are obtained. In accordance with the acquired result, the modules
initial data is defined as SL. TN is a condition attributes set,
Step 2: Examine the importance of each attribute and modify the decision table.
The original classification of the decision table built in Step 1 is utilized as a benchmark to examine how the decision table will change after deleting each attribute. If the final decision does not alter after removing an at- tribute, this attribute is less important and can be ignored. Otherwise, this attribute is enough important and need to be ranked in accordance with the importance in next step. The decision table should be modified and delete the relevant attributes of no use.
Step 3: Calculate the basic importance rating vector
The importance of every condition attribute can be correspondingly used to measure the importance of func- tional modules in QFD. According to the importance of the condition attributes
Step 4: Determine the modifying index of the importance to get the final result.
The importance obtained from Step 3 is directly accessed by the end user without consideration of other fac- tors that may affect the customer satisfaction. Therefore, the results need to be adjusted. The modifying index is defined as
Hence, the final importance, shown as
In order to make this method more limpid, above steps are collectively shown in
A manufacturing enterprise is planning to launch an improvement of a current complex product according to the customer’s requirements. In accordance with the customer needs, four functional modules M1, M2, M3 and M4 should be added and adjusted to achieve all requirements. With the resource limitation, the enterprise can only apply two modules into the actual production. Under this circumstance, the manufacturer has to choose the most proper ones from those four modules to get the achievable maximum customer satisfaction. A second study on customer satisfaction of each module is conducted through considering the possible functions that each module could achieve in final product and the relative data are collected. There are three levels of customer satisfaction (low, medium and high), corresponding the value of 1, 2, and 3. After eliminating duplicate and redundant data, a decision table is composed by the module and the satisfaction levels and, shown as
U | T1 | T2 | T3 | T4 | J | U | T1 | T2 | T3 | T4 | J | U | T1 | T2 | T3 | T4 | J | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 1 | 1 | 1 | 1 | 14 | 1 | 2 | 2 | 2 | 2 | 27 | 2 | 1 | 3 | 3 | 3 | |
2 | 1 | 1 | 1 | 2 | 1 | 15 | 1 | 2 | 2 | 3 | 3 | 28 | 2 | 2 | 1 | 1 | 1 | |
3 | 1 | 1 | 1 | 3 | 2 | 16 | 1 | 2 | 3 | 1 | 3 | 29 | 2 | 2 | 1 | 2 | 2 | |
4 | 1 | 1 | 2 | 1 | 1 | 17 | 1 | 2 | 3 | 2 | 3 | 30 | 2 | 2 | 1 | 3 | 3 | |
5 | 1 | 1 | 2 | 2 | 2 | 18 | 1 | 2 | 3 | 3 | 3 | 31 | 2 | 2 | 2 | 1 | 1 | |
6 | 1 | 1 | 2 | 3 | 3 | 19 | 2 | 1 | 1 | 1 | 1 | 32 | 2 | 2 | 2 | 2 | 2 | |
7 | 1 | 1 | 3 | 1 | 2 | 20 | 2 | 1 | 1 | 2 | 2 | 33 | 2 | 2 | 2 | 3 | 3 | |
8 | 1 | 1 | 3 | 2 | 3 | 21 | 2 | 1 | 1 | 3 | 3 | 34 | 2 | 2 | 3 | 1 | 3 | |
9 | 1 | 1 | 3 | 3 | 3 | 22 | 2 | 1 | 2 | 1 | 1 | 35 | 2 | 2 | 3 | 2 | 3 | |
10 | 1 | 2 | 1 | 1 | 1 | 23 | 2 | 1 | 2 | 2 | 3 | 36 | 2 | 2 | 3 | 3 | 3 | |
11 | 1 | 2 | 1 | 2 | 1 | 24 | 2 | 1 | 2 | 3 | 3 | 37 | 2 | 2 | 2 | 2 | 3 | |
12 | 1 | 2 | 1 | 3 | 3 | 25 | 2 | 1 | 3 | 1 | 3 | |||||||
13 | 1 | 2 | 2 | 1 | 2 | 26 | 2 | 1 | 3 | 2 | 3 |
condition attributes T1, T2, T3 and T4 respectively represent functional modules M1, M2, M3 and M4. Decision at- tribute J represents the customer satisfaction SL.
According to Equation (1) and Hypothesis 2, all conditional attributes T1, T2, T3 and T4 cannot be ignored to the decision attributes J. Therefore, no modification is needed and the decision table will stay the same. The importance of all four modules needs to be positioned.
By using the Equation (2) and the data of
(VJm refers to the value of decision attribute J)
Similarly,
Equation (3) is used to calculate the importance:
Repeating above steps, the dependency and importance of other attributes can be calculated and the results are shown in
According to Equation (4), the basic importance rating vector of the modules can be calculated.
Through market investigation and experts’ evaluation, other related information that could affect customer sa- tisfaction of the four modules can be obtained. In this case, they are embodied in four fields: the current competi- tive situation (CC), the objective of the competitiveness (OC), the possibility of improvement (PI), and the mar- ket competitive advantages (CA). The results are shown in
Equation (5) is used to calculate the modifying index based on above data.
Finally, according to Equation (6), the final importance ratings of modules in developing the complex product can be calculated.
The maximum value is that of Z3, which means M3 is the most important module and should have the highest priority. Similarly, the other modules should be chosen as the sequence of z4, z1 and z2.
Known from above analysis, the enterprise can make decision on which model to choose under resource limi- tation by following the final importance sequence. In this case, the enterprise can make the final decision on ap- plying M3 and M4 into the actual production.
T1 | T2 | T3 | T4 | |
---|---|---|---|---|
Dependency | 0.595 | 0.703 | 0.243 | 0.243 |
Importance | 0.351 | 0.243 | 0.703 | 0.703 |
CC | OC | PI | CA | |
---|---|---|---|---|
M1 | 4 | 2 | 0.80 | 1.2 |
M2 | 3 | 1 | 0.85 | 1.5 |
M3 | 5 | 1 | 0.75 | 1.2 |
M4 | 4 | 2 | 0.70 | 1.0 |
The module selection is the first and crucial step for using modular technology. The approach presented in this paper provides a new methodology for solving module selection problem under resource limitation. The pro- posed method has a wide range of application, which takes customer satisfaction into account and avoids the risk of subjective judgments. The direct and clear result can be comprehended by the decision maker easily, which greatly improves the practicability of this method. However, whether this method provides a completely satisfactory program for decision-makers will mainly depend on whether the satisfaction obtained reflects the objective function actual level of satisfaction. There is no single unified approach, which can be employed to obtain the value of satisfaction. The specific solutions need to be carried out for specific problems. In a word, this Rough-QFD module selection method is a simple, effective and practical decision-making technique, which sets up a communication bridge between customers and manufacturers.
As time and resource limitation, this approach this paper proposed hasn’t completed. There are several aspects can be further studied in the future, such as, the study of the transformation process of the customers’ require- ments to relative technological features.