Printer Friendly

Mass customization: some trends and research.

1. Introduction

This paper presents an overview of some aspects of the current research and trends in the field of mass customization.

Technology, consumer sophistication and business globalization have led to strong global competition, which is forcing companies to change their activities from a seller point of view towards a buyer point of view, what results in faster new product introduction, more complex and value-added, customized products that lead to drastic increase in the number of product variants and costs what is one of the main characteristic trends of modern economic system. To maintain their competitiveness, companies are standardizing and modularizing their products and introducing platform concepts, and this transfer from no customizable products to modular products that involve individual customer variants is one of the most important industrial strategies nowadays (Chen et al., 2009; Lee & Tang, 1997; Forza & Salvador, 2007; Franke et al., 2001). The constant involvement of the customer (Chong et al., 2009) includes a need to focus on human-centric concerns, such as understanding the customers' behaviour needs and requirements of different social and cultural segments (Chen et al., 2009), as well as customers' knowledge about the needed product (Fuerstner & Anisic, 2009-a; Fuerstner & Anisic, 2009-b).

The development of IT technology enabled the software based product configuration systems that support the process of customized product development. They compose customer specific solutions using the modules based on the customers' requirements. However, increasing complexity from technological advances such as networking and embedded technologies require multidisciplinary information management, that leads toward the development of formal mechanisms for incorporating these factors consistently into the product design and development process (Chen et al., 2009), as well as toward the development of open framework for configuration systems (Abramovici et al., 2007).

Based on previous observation, the rest of the paper deals with the following problems:

* Development of an open framework for product (material product or service) configuration systems;

* Definition creation of the role of customers in mass customization;

* Determination of approaches considering the information retrieval for customer profile and the customers' needs definition.

2. Open Framework for Configuration Systems

Many companies develop their own configuration systems, so the required rules for combining product components or modules, as well as the design of the entire product are usually statically implemented in the configuration system. As a result of this approach, any change concerning the product or the system itself, requires comprehensive changes in the configuration system's source code. This direct conversion of the rules into source code also leads to more dependency on software engineers, because other users cannot edit or change the configuration rules. This can be a large psychological barrier for many companies due to the necessity to share know-how with external persons, knowledge engineers and computer scientists. The other problem is the unclear structure of the rules, which leads to hard serviceability. These problems lead towards a development of an open framework that would allow the creation and maintenance of different individual product configuration systems by the engineers themselves without high dependency on computer scientists (Abramovici at al., 2007).

The developed configuration solution should have the following main characteristics:

* Standardized services for the support of the system introduction to a company;

* Virtual model of the product (3D model or a structured set of services) as a result of the configuration process, which is applicable directly in the further developing process of the final product;

* Accurate assessment of costs and profit already before the start of a project with the goal of minimizing the risk;

* Unitized software system, which can be tailored to customers;

* open communication structure between the involved systems and concentration at the relevant object and attributes of the configuration, which facilitates less dependency on computer scientists;

* Standardized services for taking into account the human-centric concerns, such as understanding the customers' behaviour needs and requirements of different social and cultural segments, as well as customers' knowledge about the needed product.

The structure of the developed configuration software should have the following integral elements:

* The administration interface;

* The configuration database;

* The configuration software or user interface.

The administration interface should serve for knowledge acquisition and representation and for designing the configuration software's interface.

The product structure and the entire configuration knowledge should be filled centrally in the configuration database, which is serviced using the information from the administration side.

Customers should only use the configuration software's interface, which has read access to the configuration database on the server, and therefore always the current set of rules as well as the product data.

3. The Role of the Customer in Mass Customization

As the practice and research of mass customization develops, there is increasing understanding of how it can be implemented in terms of manufacturing capability and expertise, data transfer and management, the implementation of business systems, and the development of product architectures. The so called 'solution space' is defined, as a conceptual 'container' for the matrix of product possibilities that are made available for any given mass customized product to a customer co-designer, established through the assessment of product architecture, range, overall company strategy and manufacturing capability (Berger & piller, 2003). Beside this, the added importance of connection with customers is likely to have an equal, if not greater impact on future working methods and technologies (Tseng & piller, 2003). Mass customization by its very nature consists not only of the customizable product offering but of the co-design experience. This experience differs from purchasing a mass produced product as it requires engagement and participation in the creation process. There is a need to develop a conceptual model for mass customized product offerings that encompasses not only the current understanding of 'solution space', but also the wider aspects relating to the co-design experience ranging to the customer co-designer's emotional connection with the product and purchasing process (Herd et al., 2007). Therefore mass customization alters the traditional product development and moves towards a two-stage model, the first, the realm of company/designer establishing the solution space and the second, that of customer as co-designer. This second stage fundamentally changes the role of the customer from consumer of a product, to a partner in a process of adding value (Reichwald et al., 2004).

This alteration of traditional product development through the involvement of the customer into the configuration of the final product faces some obvious problems. The fundamental challenge is to avoid the abortion of the configuration process by the customer. In many cases, the customer aborts the configuration process by himself. Major problem areas include the lack of a customer desired option value regarding a specific attribute within the system as well as the inability of the customer to create definite preferences between certain option values. As a result, the customer aborts the configuration process and does not come up to the sales phase (Hansen et al., 2003). Also if customers are overwhelmed by the configuration task, they may as well abort the configuration process. Customers usually only want the product alternatives that exactly fulfil their requirements; if too much of a choice is offered, customers can feel frustrated or confused and therefore incapable of making proper decisions. This overload of information is sometimes called external complexity. This external complexity is caused by limited information processing capacity of humans, lack of customer knowledge about the product, and customer ignorance about his or her real individual needs (Blecker & Friedrich, 2006).

Based on problem analysis regarding customers involved in the configuration process, the main areas of investigation that should be considered can be stated as:

* Minimizing the potential complexity experienced by the customer, keeping their expenditure in the buying process as low as possible, whilst providing clearly perceptible benefits (Berger & piller, 2003; Kumiawan et al., 2003);

* Reducing cognitive overhead, which lies not only in extent of choice, but also in areas such as lack of understanding about which solution meets their needs, uncertainty about the behavior of the supplier, and uncertainty regarding the purchasing process, ordering and paying in advance for something that's only been seen virtually (Franke & piller, 2003).

If the co-design experience is an intrinsic element of a mass customized product, there is a need to develop a conceptual model for mass customized product offerings that encompasses this wider context within which the solution space resides, the entire co-design and product purchasing experience.

It is easy to assume that increased product performance heightens levels of customer satisfaction, but trends indicate that users are expecting increasing levels of 'connection' with everyday products (Demirbilek & Sener, 2003). As markets have segmented, product development has begun to move beyond traditional considerations of usability and functionality. Consumers now look for more from the products that they buy; "they are looking for pleasure and the fulfillment of their emotional needs" (Porter et al., 2005). There is a growing acceptance of the need to design for consumer experience.

To further understand this phenomenon, it is necessary to look not only at consumer behavior, but to delve deeper and understand the meaning that consumers attach to possessions, which drives the act of purchase.

Mass customization is an approach that is fundamentally driven by an individual customer's emotional connection with the product. To design for a co-design experience an understanding of the customers, their behavior, expectations and the interface between the customers and the configurator is needed.

One of the works (Jordan, 2000) that explicitly address the creation of positive feelings in product use is particularly useful as a hypothesis generating tool since it provides not a theory of pleasure, but rather a tool that helps those involved in the design process to take a structured approach to understanding the entire spectrum of pleasures a product can bring. It identifies four types of pleasure associated with products:

* Physio-pleasure--relates to the body and is concerned with positive feedback from the sensory organs;

* Socio-pleasure--refers to relationships with others--individuals, groups and society as a whole;

* Psycho-pleasure--refers to a user's cognitive interaction with a product and their subsequent emotional reaction;

* Ideo-pleasure--relates to peoples' values.

The previously discussed framework resulted in the development of the product envelope model (Bardill & Herd, 2006; Bardill et al., 2007) that appears to be the first user-centric mass customized product offering. The product envelope is generated by the producer of the mass customized product. As a co-designer, customer penetrates the envelope and engages with a number of experiential layers before reaching the solution space where the mass customized product resides; those layers are interconnected and the co-design experience will not necessarily provide a linear route through the envelope.

Brand is important in differentiating between products as brands generate choice, simplify purchase decisions, offer quality assurance, and reduce risks involved in purchase (Karjalainen, 2003). Brand has an ability to trigger emotional responses that will often provide it with a winning edge over less familiar products and services (Lewis & Bridger, 2004). Every brand consists of an essence consisting not only of the product or service, but the mental constructs associated with it.

A key aspect to designing the experience is to understand and design the product 'touch points'; the tangible aspects which make up the experience of using a product or service, "instances of direct contact either with the product or service itself, or with representations of it by the company or third party" (Meyer & Schwager, 2007).

4. Information Retrieval for Customer Profile Definition and Extraction of the Customers' Needs

The use of IT technology gives the customer a possibility to participate in a number of activities such as product development, feedback, support for other customers, recommendations etc. This participation can be active or simply a sharing of preferences (Hansen et al., 2003; Koch & Schubert, 2002).

There is a broad range of methods that can be used for data collection for defining customer profile and customers' needs. There are two ways of information retrieval about customer:

* Customer active data collection;

* Customer passive data collection.

Active data collection can be achieved by registration process that is used to query the customer about his preferences and goals so that the collected information can afterwards be used as basis for individual support and configuration suggestions. The provided information is explicitly issued from the customer, assumed to be correct, and therefore does not require any steps of reasoning. Drawbacks include the necessity of the customer to successfully complete the registration process in order to be able to begin with the configuration task. This can lead to an abortion of the registration process and therefore of the configuration as well. Another way of active data collection is to query the customer about his preferences indirectly, by asking questions that can be answered vaguely. The information issued from the customer is also assumed to be correct, but there is a need for reasoning that can be done based on experience, trial and error, etc. This approach also includes the necessity of the customer to complete the registration process but the information does not have to be detailed, so the probability of the abortion is lower.

Passive data collection can be achieved by monitoring the customer's behaviour. The behaviour of the customer is assessed and his previous choices are used to describe his configuration goal. By analyzing the customer's behaviour, the uncertainty about the correctness of the provided information is avoided; additionally the user can start configuring right away. Problems with passive data collection occur at the very beginning of the configuration process, since little information about user's preferences and goals are available.

This leads to the conclusion that a combination of active and passive data collection should be introduced. Additionally there is a possibility to import user profiles from other sources by setting up a user profile information management architecture independently from the services using it (Koch & Woerndl, 2001), or to iteratively request information from the user (Riecken, 2000). After data collection, there is a need to analyze the data collected from or generated by the customer (Arora et al., 2008) and to transform it to needs or to use it to form a customer profile. Lot of approaches from the area of data mining and data analysis can be used for information analysis and structuring. Some of them are (Al Salim, 2007; Bojadziev & Bojadziev, 2007; Hansen et al., 2003; Koch & Schubert, 2002; Post, 2005; Sreekumar & Mahapatra, 2009; Zimmermann, 1988):

* Content-based filtering;

* Rule-based filtering;

* Collaborative filtering;

* Association rules;

* Bayesian networks;

* Fuzzy decision making.

Content based filtering is based on annotating content objects with metainformation or deriving the meta information automatically form the content of the objects themselves. It describes similarities between the data of an object and the corresponding customer profile. Content-based filtering can produce problems in the case of product configurators, where the product consist not only from fully indexable data, but from physical components, or from data that can have a lot of synonyms and homonyms, what can easily occur in the specification of products by customers or sellers.

Rule-based filtering uses rules to connect a situation and a conclusion. It requires a large set of rules that can create a problem of defining a lot of rules for possible options.

Collaborative filtering tries to match customers with a similar taste. The similarity can be based on demographic data, on the sequence of configuration, on chosen values, etc.

Association rules is a technique where rules are extracted out of the database and then ordered based on the percentage of times they are correct and how often they apply. It captures the relation among events in a database and it takes the form of: if event A occurred, then event B will happen with a certain probability. Symbolically, A [right arrow] B and it is read A implies B. The two main concepts that determine the outline of the association rules are accuracy and coverage. Accuracy refers to the probability that if A is true then B will be true. High accuracy means that this is a rule that is highly dependable. Coverage refers to the number of records in the database that the rule applies to. High coverage means that the rule can be used very often.

Bayesian networks are tree structures, where objects are each represented as nodes within the tree. Each node has a table of probability attached, which can be used to calculate the likelihood of the occurrence of the succeeding object. The dependency between objects is defined. The drawback of this technique is the lack of flexibility.

Fuzzy system is introduced to deal with the issue of uncertainty and unsharpness of collected data. It is useful when data that is analyzed involves human judgement. Crisp data are not adequate to model these judgements as it involves human preferences.

5. Conclusions and Future Research Possibilities

It is clear that customer has become a central figure in today's business. Beside this, business globalization, the development of technology and the consumer sophistication resulted in strong global competition, what resulted in faster new product introduction, more complex and value-added, customized products. This change in today's business led to drastic increase in the number of product variants and costs what is one of the main characteristic trends of modern economic system. To maintain their competitiveness, companies are standardizing and modularizing their products and introducing platform concepts. The constant involvement of the customer includes a need to focus on human-centric concerns, such as understanding the customers' behaviour needs and requirements of different social and cultural segments, as well as customers' knowledge about the needed product. The development of IT technology enabled the software based product configuration systems that support the process of customized product development. However, increasing complexity from technological advances such as networking and embedded technologies require multidisciplinary information management, that leads toward the development of formal mechanisms for incorporating these factors consistently into the product design and development process, as well as toward the development of open framework for configuration systems.

Based on previous discussion, one can conclude that some of the most important research areas in the field of mass customization deal with the development of an open framework for product configuration systems, with modelling a definition of the role of customers in mass customization, as well as with the development of methods considering the information retrieval for customer profile and customers' needs definition.

The conclusions point towards several future research possibilities:

* Further research in the field of product configuration systems towards a development of a general framework system that would include all the aspects that are relevant in developing the final product (support of the system introduction to a company, virtual model of the product, assessment of costs and profit, software system that can be tailored to customers, open communication structure between the involved systems, etc.);

* Definition of the role and the position of the customer in the mass customization system and development of the model and structured methods for its incorporation in the customized offering by a company;

* Development of an intelligent decision making system that takes into consideration the input parameters and constraints and all the collected and analyzed data (the customer profile, customers' needs, previously accepted solutions, social networks, abortion ratios, etc.), and can automatically adjust the suggested solutions, which correspond to a greater extent to finally accepted results.

DOI: 10.2507/daaam.scibook.2009.52

6. References

Abramovici, M.; Ghoffrani, M.; Neubach, M. & Bertram, S. (2007). KOVIP - Product configuration Software and Services for Virtual Products, pp 1-10, GITo Verlag, Berlin

Al-Salim, B. (2007). Mass customization of travel packages: data mining approach, International Journal of Flexible Manufacturing Systems, Vol. 19, No. 4., pp 612-624, Springer Science + Business Media, ISSN: 0920-6299

Arora, N.; Dreze, X.; Ghose, A.; Hess, J. D.; Iyengar, R.; Jing, V.; Joshi, Y.; Kumar, V.; Lurie, N.; Neslin, S.; Sajeesh, S.; Su, M.; Syam, N.; Thomas, J. & Zhang, Z. J. (2008). Putting one-to-one marketing to work: Personalization, customization, and choice, Published online: 16.09.2008, Springer Science + Business Media, Available from, Accessed: 2009-06-30

Bardill, A.; Herd, K. & Karamanoglu, M. (2007). Product Envelopes: Designing Positive Interplay between Brand DNA and Customer Co-Designers, International Journal of Mass Customisation, Vol. 2, No. 1/2, pp 57-75, ISSN: 1742-4208

Bardill, A. & Herd, K. (2006). Maintaining Positive Interplay between Brand DNA and Customer Co-designers in Mass Customised Products, International Conference on Strategic Innovation and Creativity in Brand & Design Management, Seoul, Korea, November 2006

Berger, C. & Piller, F. (2003). Customers as Co-Designers, IEE Manufacturing Engineer, Vol. 82, No. 4, pp 42-46, ISSN: 0956-9944

Blecker, T. & Friedrich, G. (2006). Mass customization: challenges and solutions, Birkhauser, ISBN: 0-387-32222-1

Bojadziev, G. & Bojadziev, M. (2007). Fuzzy logic for business, finance, and management, World Scientific Publishing, Singapore, ISBN: 981-270-649-6

Chen, C. (2009). Editorial: Human-centered product design and development. Advanced Engineering Informatics, Vol. 23, No. 2, April 2009, pp 140-141, ISSN: 1474-0346

Demirbilek, o & Sener, B. (2003). Product Design, Semantics and Emotional Response, Ergonomics. 46, (13/14), pp 1346-1360, DTI International Service Mission Report, Affective Design (Kansei Engineering) in Japan, Sponsored by the Faraday Packaging Partnership

Forza, C. & Salvador, F. (2007). Product Information Management for Mas Customization, Palgrave Macmillan, ISBN: 0-230-0068205, Hampshire

Franke, H. J. & Firchau, N. L. (2001). Variantenvielfalt in Produkten und Prozessen--Erfarungen, Methoden und Instrumente zur erfolgreichen Beherrschung, Variety in products and processes--experiences, methods and tools for successful mastery, VDI-Berichte 1645, VDI-Verlag, Duesseldorf

Franke, N. & Piller F. (2003). Key Research Issues in User Interaction with Configuration toolkits in a Mass Customization System, International Journal of Technology Management, Vol. 26, No. 5/6, pp 578-599, ISSN: 0267-5730

Fuerstner, I. & Anisic, Z. (2009-a). Masterplast Intelligent Product Configurator - The New Approach in Thermo Insulation of Buildings, Proc. of the Int. Sci. Conf. MOTSP, pp 256-261, ISBN: 978-953-6313-09-9, Sibenik, June 2009, FSB, Zagreb

Fuerstner, I. & Anisic, Z. (2009-b). Intelligent product configurator--the new approach in thermo insulation of building, Journal of Engineering, Vol. 7, No. 2, pp 165-170, ISSN: 1584-2665

Hansen, T.; Scheer, C. & Loos, P. (2003). Product Configurators in Electronic Commerce--Extension of the Configurator Concept towards Customer Recommendation, Available from scheer03_configurator_ec_paper.pdf, Accessed: 2009-06-26

Herd, K.; Bardill, A. & Karamanoglu, M. (2007). Designing for co-design: using the product envelope model as a framework for reflection, World Conference on Mass Customization & Personalization, Cambrige, USA

Jordan, P. (2000). Designing Pleasurable Products. An Introduction to the New Human Factors, Taylor & Francis, ISBN: 978-0748408443, London

Karjalainen, T. (2003). Semantic knowledge in the creation of brand-specific product design, Available from:, Accessed: 2008-05-19

Koch, M. & Schubert, P. (2002). Personalization and Community Communication for Customer Support, Proceedings of 6th International Conference on Work With Display Units--World Wide Work, pp 530-532, Berchtesgaden, Germany, May 2002

Koch, M. & Woerndl, W. (2001). Community-Support and Identity Management. Proc. of European Conf. on Comp.-Supported Cooperative Work, pp 319-338, Bonn

Kumiawan, S.; Tseng, M. & So, R. (2003). Consumer Decision-Making Process in Mass Customization, Proceedings of the 2nd Interdisciplinary World Congress on Mass Customization and Personalization, Munich, Germany

Lee, H. L. & Tang, C. (1997). Modeling the costs and benefit of delayed product differentiation. Management Science, Vol. 43, No. 1, pp 40-53, ISSN: 00251909

Lewis, D. & Bridger, D. (2004). The Soul of the New Consumer, Nicholas Brealey Publishing, London

Meyer, C. & Schwager, A. (2007). Understanding Customer Experience, Harvard Business Review, Vol. 85, No. 2, pp 116-126, ISSN: 0017-8012

Porter, S.; Chhibber, S.; Porter, M. & Healey L. (2005). RealPeople: making users' pleasure needs accessible to designers, Accessible Design in the Digital World Conference, Scotland, August 2005

Reichwald, R.; Seifert, S.; Walcher, D. & Piller, F. (2004). Customers as part of value webs: Towards a framework for webbed customer innovation tools, Proceedings of the 37th Annual Hawaii International Conference on System Sciences, Hawaii 5-8 January

Riecken, D. (2000). Personalized Views of Personalization, Communication of the Association for Computing Machinery, Vol. 43, No. 8, ISSN: 0001-0782 Sreekumar & Mahapatra, S. S., (2009). A fuzzy multi-criteria decision making approach for supplier selection in supply chain management, Available from: 2009-05-19

Tseng, M & Piller, F. (2003). The Customer Centric Enterprise, Advances in Mass Customization and Personalization, Springer, ISBN: 3-540-02492-1, Berlin

Zimmermann, H. J. (1988). Fuzzy set theory--and its applications, Kluwer-Nijhoff Publishing, ISBN: 0-89838-150-9, Boston

This Publication has to be referred as: Anisic, Z[oran]; Fuerstner, I[gor] & Cosic, I[lija] (2009). Mass Customization: Some Trends and Research, Chapter 52 in DAAAM International Scientific Book 2009, pp. 521-530, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-901509-69-8, ISSN 1726-9687, Vienna, Austria

Authors' data: Dr. Sc. Anisic, Z[oran] *; Mr. Sc. Fuerstner, I[gor] **; Dr. Sc. Cosic, I[lija] *, * Faculty of Technical Sciences, Trg D.Obradovica 6, 21000, Novi Sad, Serbia, * *Subotica Tech, Marka Oreskovica 16, 24000, Subotica, Serbia,,,
COPYRIGHT 2009 DAAAM International Vienna
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2009 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Chapter 52
Author:Anisic, Z.; Fuerstner, I.; Cosic, I.
Publication:DAAAM International Scientific Book
Article Type:Report
Date:Jan 1, 2009
Previous Article:Analyzing STEP implementations for automated process planning.
Next Article:Optimized CFRAL composite for advanced aerospace applications.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |