Knowledge-based Configuration: From Research to Business Cases, FIRST EDITION (2014)
Part II. Basics
Chapter 9. Core Capabilities of Sustainable Mass Customization
Frank T. Pillera and Paul Blazekb, aRWTH Aachen, Aachen, Germany, bcyLEDGE, Vienna, Austria
The goal of mass customization is to efficiently provide customers with what they want, when they want it, at an affordable price. Realizing this promise demands from the perspective of process design the integration of customers into the value chain of the manufacturer. Here, configuration processes play a crucial role to manage this task by providing customers support and navigation in co-designing their individual product or service. But this capability of “choice navigation,” enabled by modern configuration systems, is just one of three strategic capabilities of mass customization. This chapter explains why configuration systems are playing a crucial role in meeting the particular demands of each individual customer and addresses the fundamental capabilities of sustainable mass customization: solution space development, the design of robust processes, and choice navigation.
Knowledge-based Configuration; Mass Customization; Strategic Capabilities; Resource-based View; Configuration Strategies
The objective of mass customization (MC)1 is to deliver goods and services that meet individual customers’ needs with near mass production efficiency (Tseng and Jiao, 2001). Product configuration systems that are also referred to as “co-design toolkits” (Franke and Piller, 2003, 2004) are the traditional tools in mass customization that enable the individual customer to specify product features (Sabin and Weigel, 1998). Configuration systems are also known as configurators, choice boards, design systems, toolkits, or co-design platforms (Hvam et al., 2008; Salvador and Forza, 2007). They are responsible for guiding the user through the preference elicitation process. Whenever the term configurator or configuration system is quoted in literature, for the most part, it is used in a technical sense, usually addressing a software tool. The success of such a tool, however, is by no means defined solely by its technological capabilities, but also by its integration into the sales environment, its ability to allow for learning, its ability to provide experience and process satisfaction, and its integration into the brand concept.
Configuration systems for mass customization have to be differentiated from configuration systems for complex technical requirements (Stumptner, 1997). The latter are expert tools that often need usage trainings and are mainly used by technical experts, whereas MC product configurators deal with company-to-customer interaction and cooperation (Khalid and Helander, 2003; Tseng et al., 2003). Already in 1991, Udwadia and Kumar were envisioning customers and manufacturers becoming co-constructors (i.e., co-designers) of those products intended for each customer’s individual use. In their view, co-construction would occur when customers have only a nebulous sense of what they want. Without a deep involvement of the customers, the manufacturer would be unable to cater to each individual product demand adequately.
After this seminal publication (Udwadia and Kumar, 1991), computer technology, particularly the capacity to simulate potential product designs before a purchase, has strongly enabled co-design technologies (Haug and Hvam, 2007; Ulrich et al., 2003). This type of collaboration is also known as collaborative customization (Gilmore and Pine, 1997). In collaborative customization, the manufacturer and customer work together to identify and satisfy the customer’s needs via a system that allows easy articulation of exact requirements (i.e., design, fit, and function of a product). Anderson-Connell et al. (2002) used the term co-design to describe a collaborative relationship between consumers and manufacturers in which, via a process of interaction between a design manager and a consumer, a product is designed according to the consumer’s specification and based on the existing manufacturing components.
Sophisticated configurators have been the enabling force in various domains; in mass customization they have been one of the efficiency drivers for the concept of bringing together custom manufacturing and mass production through shifting the time-consuming demands identification process to the customers themselves. Moving the responsibility of creating individualized products to the customer comes along with a set of requirements that have to be fulfilled in order to avoid confusion. To understand the nature and complexity of required configuration functionalities, it is necessary to take a closer look at the capabilities that play a role when dealing with product customization.
Companies that successfully implemented a mass customization strategy have built competences around a set of three core capabilities that are driving a sustainable mass customization business. The key to profiting from mass customization is to see it as a set of organizational capabilities that can supplement and enrich an existing system. While specific answers on the nature and characteristics of these capabilities are clearly dependent on industry context or product characteristics, three fundamental groups of capabilities determine the ability of a company to mass customize. These capabilities, Solution Space Development, Robust Process Design, and Choice Navigation, are derived from work by Salvador et al. (2009, 2008). The methods behind these capabilities are often not new. Some of them have been around for years. But successful mass customization demands that these methods be combined into capabilities in a meaningful and integrated way, to design a value chain that creates value from serving individual customers differently.
9.2 Solution Space Development
A mass customizer must first identify the idiosyncratic needs of its customers, specifically, those product attributes along which customer needs diverge the most. This is in contrast to a mass producer, who generally tries to focus on serving universal needs, ideally shared by all the target customers. Once that information is known and understood, a business can define its “solution space” clearly delineating what it will offer and what it will not. This space determines what universe of benefits an offer is intended to provide to customers and then what specific permutations of functionality can be provided within that universe (Pine, 1995).
9.2.1 Options for Customization
From the perspective of product development, value by customization can be achieved via three design features of a product (or service), any of which can become the starting point for customization: (1) the fit (measurements), (2) the functionality, and (3) the form(style and aesthetic design) of an offering (Piller, 2005). These are generic dimensions that match the demand of a customer with regard to an offering. Along these three dimensions, heterogeneities of demand from a customer perspective can be derived. The solution space should represent choice options for those dimensions where customer heterogeneities matter in a particular case.
Fit and comfort (measurements). The traditional starting point for customization in consumer good markets is to fit a product according to the measurements provided by the client; for example, body measurements or the dimensions of a room or other physical objects. Market research identifies better fit as one of the strongest arguments in favor of mass customization. Especially with products that are directly related to the human body it is one of the most difficult dimensions to achieve, demanding complex systems to gather the customers’ proportions exactly and to transfer them into a product that has to be based on a parametric design. This often calls for a total product redesign and the costly development of flexible product architectures with enough slack to accommodate all possible fitting demands of the customer community. In sales, for example, expensive 3D scanners are needed, which in turn demands highly qualified sales staff to operate them (Berger et al., 2005). Achieving fit with non-human-centered products (e.g., made-to-measure entrance doors) might not be as demanding in the initial data gathering, however, the needed flexibility in the product architecture with all relevant rules requires an accurate knowledge of all product parameters.
Functionality. Functionality addresses issues such as speed selection, precision, power, cushioning, output devices, interfaces, connectivity, upgradeability, and similar technical attributes of an offering according to the requirements of the client. This is the traditional starting point for customization in industrial markets, where machines, for example, are adjusted to fit in with an existing manufacturing system, or components are produced according to the exact specifications of their buyers. In manufacturing, however, the growing share of software control in many products enables the customizability of functional components more easily.
Form (style and aesthetic design). This dimension relates to modifications aiming at the sensual or the visual senses; for example, selecting colors, styles, applications, cuts, or flavors. Many mass customization offerings in business-to-consumer e-commerce are based on the possibility of co-designing the outer appearance of a product. This kind of customization is often relatively easy to implement in manufacturing, especially if digital printing technology can be applied. The desire for a particular outer appearance is often inspired by fashions, peers, role models, and such as the individual’s desire is to copy and to adopt these trends. Along this line, the construct of consumers’ need for uniqueness has been discussed in the psychological marketing literature (Tepper et al., 2001). Consumers acquire and display their unique material possessions for the purpose of feeling differently from other people. Mass customization can be a further means to express their uniqueness, where consumers can design products according to their own personal specifications in order to vary from the rest.
To illustrate these three dimensions (fit, functionality, and form), consider the example of shoes. Fit is mostly defined by the last on which the shoe is formed, but also by the design of the uppers, insole, and outsole. Style is an option for influencing the aesthetic design of the product, for example, the colors of the leathers, or patterns. A shoe’s functionality can be defined by its cushioning, heel form, or cleat structure.
To understand the large variety of configurable products that are available online, we recommend a closer look at the benchmark study “The Customization 500” (Walcher and Piller, 2012) and at the “Configurator Database Report 2013” (Blazek et al., 2013).
9.2.2 Methods for Solution Space Definition
To define the solution space, the company has to identify those needs where customers are different, and where they care about these differences. Matching the options represented by the solution space with the needs of the targeted market segment is a major success factor of mass customization (Hvam et al., 2008). The core requirement at this stage is to access “customer need information”; that is information about preferences, needs, desires, satisfaction, and motives of customers and users of the product or service offering. Need information builds on an in-depth understanding and appreciation of the customers’ requirements, operations, and systems. Spotting untapped differences across customers is not an easy task, because information about unfulfilled customer needs is “sticky”—that is, difficult to access and codify for the solutions provider (von Hippel, 1998). Understanding heterogeneous customer needs in terms of identifying differentiating attributes, validating product concepts, and collecting customer feedback can be a costly and complex endeavor, but several approaches can help.
The first is to engage in conventional market research techniques; that is to meticulously gather data from representative customers on a chosen market sector. To reduce the risk of failure, need-related information from customers is integrated iteratively at many points in the new product development process (Dahan and Hauser, 2002; Griffin and Hauser, 1993). The manufacturer selects and surveys a group of customers to obtain information on needs for new products, analyzes the data, develops a responsive product idea, and screens this idea against customer preferences (needs) and purchasing decisions. This model is dominating especially in the world of consumer goods, where market research methodology such as focus groups, conjoint analysis, customer surveys, and analyses of customer complaints is used regularly to identify and evaluate customer needs and desires.
A second approach companies can use to define their solution space is to provide customers with toolkits for user co-creation (Franke and Piller, 2004; von Hippel and Katz, 2002). These are software design tools similar to CAD systems, but with an easy-to-use interface and a library of basic modules and functionalities. With these toolkits, customers can by themselves translate their preferences directly into a product design, highlighting unsatisfied needs during the process. The resulting information can then be evaluated and potentially incorporated by the company into its solution space.
Third, in developing their solution space (see Table 9.1) companies can employ some form of “customer experience intelligence”; that is to apply methods for continuously collecting data on customer transactions, behaviors, or experiences and analyzing that information to determine customer preferences. This also includes incorporating data not just from customers, but also from people who might have taken their business elsewhere. Consider, for example, information about products that someone has evaluated, but did not order. Such data can be obtained from log files generated by the browsing behavior of people using online configurators (Piller et al., 2004; Rangaswamy and Pal, 2003; Squire et al., 2004). By systematically analyzing that information, managers can learn much about customer preferences, ultimately leading to a refined solution space. A company could, for instance, eliminate options that are rarely explored or selected, and it could add more choices for the popular components. In addition, customer feedback can even be used to improve the very algorithms that a particular application deploys.
Solution space definition approaches.
Market research techniques
Selecting and surveying a group of customers to obtain information on needs for new products with the help of conventional market research approaches.
Dahan and Hauser (2002), Griffin and Hauser (1993)
Toolkits for user co-creation
Offering software tools that enable customers to translate their preferences directly into a product design and identifying unsatisfied needs during the process.
Franke and Piller (2004), von Hippel and Katz (2002)
Customer experience intelligence
Continuously collecting data on all transactions, behaviors, or experiences not only from customers but from all users and analyzing that information to determine preferences.
Piller et al. (2004), Rangaswamy and Pal (2003), Squire et al. (2004)
9.2.3 Modular Product Architectures
Once the relevant options to be represented in a solution space have been identified, these have to be transferred into a product architecture that includes the configuration model (Felfernig, 2007). It is important to note that mass customization does not mean to offer limitless choice, but to offer choice that is restricted to options that are already represented in the production system. In the case of digital goods (or components), customization possibilities may be infinite. In the case of physical goods, however, they are limited and may be represented by a modular product architecture. Modularity is an important part of many mass customization strategies (Duray, 2002; Gilmore and Pine, 1997; Kumar, 2005; Piller, 2005; Salvador, 2007). Each module provides one or more well-defined functions of the product and is available in several options that deliver a different performance level for the function(s) the product is intended to serve.
In order to manage the additional costs of variety, product families of similar or identical units can be formed. The product family approach has been recognized as an effective means to accommodate an increasing product variety across diverse market niches while still being able to achieve economies of scale (Tseng and Jiao, 2001; Zhang and Tseng, 2007). In addition to leveraging the costs of delivering variety, product family design can reduce development risks by reusing proven elements in a firm’s activities and offerings. Setting the modular product family structure of a mass customization system, and thus its solution space, becomes one of the foremost competitive capabilities of a mass customization company.
9.3 Robust Process Design
A core idea of mass customization is to ensure that an increased variability in customers’ requirements will not significantly impair the firm’s operations and supply chain (Pine et al., 1993). This can be achieved through robust process design, which is the capability to reuse or recombine existing organizational and value-chain resources to deliver customized solutions with high efficiency and reliability. A successful mass customization system hence is characterized by stable, but still flexible, responsive processes that provide a dynamic flow of products (Badurdeen and Masel, 2007; Pine, 1995; Salvador et al., 2004; Tu et al., 2001). Value creation within robust processes is the major differentiation of mass customization versus conventional (craft) customization. Traditional (craft) customizers often reinvent not only their products, but also their processes for each individual customer. Mass customizers use stable processes to deliver high-variety goods (Pine et al., 1993), which allows them to achieve near mass production efficiency, but it also implies that the customization options are somehow limited. Customers are being served within a list of predefined options or components, the company’s solution space.
The core objective of robust process design is to prevent or counterbalance the additional cost resulting from the flexibility a company needs to build in order to serve its customers individually. We can differentiate two sources of additional cost of flexibility (Su et al., 2005): (1) increased complexity and (2) increased uncertainty in business operations, which by implication results in higher operational cost.
Methods to establish robust processes. A primary mechanism to create robust processes in mass customization is the application of delayed product differentiation (postponement). Delayed product differentiation refers to partitioning the supply chain into two stages (Yang and Burns, 2003; Yang et al., 2004). A standardized portion of the product is produced during the first stage, while the differentiated portion of the product is produced in the second stage, based on customer preferences that were expressed in an order. The success of delayed product differentiation is a direct manifestation of the fact that most companies offer a portfolio of products that consists of families of closely related products that differ from each other in a limited number of differentiated features. An example of delayed product differentiation in the automotive industry would be to send a standard version of the car (a stripped or partially equipped version) to dealers and then allow the dealer to install, on the basis of customer-specific requests, options such as a CD/DVD player, the interior leather or fabric, and the cruise control system. Prior to the point of differentiation, product parts are reengineered in such a way that as many parts or components as possible are common to each configuration. Cost savings result from the risk-pooling effect and reduction in inventory stocking costs (Yang et al., 2004).
While postponement starts with the design of the offerings, another possibility to achieve robust processes is through flexible automation (Koste et al., 2004; Tu et al., 2001; Zhang et al., 2003). Although the words “flexible” and “automation” might have been contradictory in the past, that’s no longer the case. In the automotive industry, robots and automation are compatible with high levels of versatility and customization. Even process industries (e.g., pharmaceuticals, food, and so on), once synonymous with rigid automation and large batches, nowadays enjoy levels of flexibility once considered unattainable. Similarly, many intangible goods and services also lend themselves to flexible automated solutions, oftentimes based on the Internet.
A complementary approach to flexible automation is process modularity, which can be achieved by thinking of operational and value-chain processes as segments, each one linked to a specific source of variability in the customers’ needs (Pine et al., 1993). As such, the company can serve different customer requirements by appropriately recombining the process segments, without the need to create costly ad-hoc modules (Zhang et al., 2003). BMW’s Mini factory, for instance, relies on individual mobile production cells with standardized robotic units. BMW can integrate the cells into an existing system in the plant within a few days, thus enabling the company to quickly adapt to unexpected swings in customer preferences without extensive modifications of its production areas.
9.4 Choice Navigation
9.4.1 Paradox of Choice
A mass customizer must support customers in identifying their own needs and creating solutions while minimizing complexity and the burden of choice. When a customer is exposed to a myriad of choices, the cost of evaluating those options can easily outweigh the additional benefit from having so many alternatives. The resulting syndrome has been called the “paradox of choice” (Schwartz, 2004), in which too many options can actually reduce customer value instead of increasing it (Desmeules, 2002; Huffman and Kahn, 1998). In such situations, customers might postpone their buying decisions and, worse, classify the vendor as difficult and undesirable. Research in marketing has addressed this issue in more detail and has found that the perceived cognitive cost is one of the largest hurdles toward a larger adaption of mass customization from the consumer perspective (Dellaert and Stremersch, 2005). To avoid that, companies have to provide means of choice navigation to simplify the ways in which people explore their offerings.
9.4.2 Effective Support of Interaction
When looking at the interaction patterns offered by configurators it becomes obvious that the different types with their different goals and solution spaces must have specific characteristics in defining the way the interface to the customer has to look like (see alsoLeitner et al., 20142). The range of product configurators spans from simple Select-to-order (STO) and Pick-to-order (PTO) configurators, to currently massive growing Assemble-to-order (ATO) and Configure-to-order (CTO) configuration systems, up to sophisticated Make-to-order (MTO) and high-end Engineer-to-order (ETO) solutions that bridge the field of product configurators with the field of user innovation configurators (see Table 9.2).
Types of product configurators.
The customer selects all needed components of a product. There are no component dependencies.
The customer picks the components of a product and takes care himself of the dependencies without support of the configurator.
The configurator matches prefabricated components considering component dependencies.
The configurator supports the customer in selecting the components that fit to each other based on a modular system.
The configurator allows the customer to define specific parameters based on product rules. Manufacturing takes place after order.
Very high level of configuration freedom. New components and new rules might be required to satisfy the configuration needs of the customer.
The more complex the purpose of a configuration system is, the lower is the similarity between individual systems. Nevertheless a number of findings shed light on recurring patterns, which are the basis for more general interaction rules. It seems to be clear that the success of a configuration system is not only defined by its technological capabilities but by many more factors such as its ability to allow learning by doing, to add experience effects, and to initiate process satisfaction (Franke and Piller, 2003).
As almost any interaction throughout the customer buying cycle might take place via online channels, it is important to see the information delivery aspects that play an enabling and an accompanying role to the configuration process itself. According to Totz and Riemer (2001), information has to be communicated in a clear and sufficient way to eliminate uncertainties when configuring products. Information on available products and services and the configuration possibilities has to be provided. The user has to be informed about prices, delivery, and after-sales conditions. The producer has to provide information on the company’s capabilities as well as a brand and shopping experience. Furthermore the proposition of the most suitable products and services can be helpful to reduce uncertainties.
What makes information delivery tricky is that there are different ways to represent a product to the customer. Salvador and Forza (2007) point out that descriptions of the same product may focus on different degrees of abstraction, like focusing on the product performance, on the product functions, or on the physical product components. Increasing the ability of the customer to understand the offerings by using an approach, a language, and tone that is fitting his/her cognitive expectations results in the reduction of the configuration complexity; also diminishing the overall information load for the customer should be part of this simplification strategy. Rogoll and Piller (2004) state that usability is directly responsible for the success or failure of a configuration system. Important usability aspects are operability and self explanation (i.e., intuitive handling and navigation), orientation (i.e., transparency and traceability of the application structure), individual access on information (i.e., information has to be accessible in different ways such as textual, visual, alphabetical, etc.), loading time (i.e., the application must not take too long for loading or actualizing each configuration step), and support (i.e., the support functionality has to be able to deal with potential problems occurring in the configuration process).
Besides these usability aspects there are additional parameters that strongly influence the perceived quality of a product configuration system: visual realism (i.e., how realistic is the visualization of the product configuration process), creativity (i.e., the degree and limitations of creational freedom while configuring the product), enjoyment (i.e., how much fun, delight, pleasure, entertaining and interesting the configuration process is), choice options (i.e., the perceived degree of given choice options in the solution space; Walcher and Piller, 2012). Convincing the customer to purchase under uncertainty and meeting the specific needs of each customer well enough remains the quest to be mastered.
9.4.3 Attainable Benefits and Process Satisfaction
Choice navigation not only avoids complexity of choice and the negative effects of variety from the customers’ perspective; offering choice to customers in a meaningful way, on the contrary, can provide new profit opportunities (Franke and Schreier, 2010). Research has shown that up to 50% of the additional willingness to pay for customized (consumer) products can be explained by the positive perception of the co-design process itself (Franke and Piller, 2004; Franke and Schreier, 2010; Merle et al., 2010; Schreier, 2006). Products co-designed by customers may also provide symbolic (intrinsic and social) benefits for them, resulting from the actual process of co-design rather than its outcome. Schreier (2006) quotes, for example, a pride-of-authorship effect. This effect relates to the desire for uniqueness, as discussed before, but here it is based on a unique task and not the outcome. In addition to enjoyment, task accomplishment has a sense of creativity. Participating in a co-design process may be considered a highly creative problem-solving process by the individuals engaged in this task, thus becoming a motivator to purchase a mass-customized product. Communicating this created product individuality is supported by a growing number of social media applications (e.g., Facebook, Twitter, Pinterest, YouTube, Google Plus). The integration of customers in social networks is consequently another field, which recent papers (Blazek et al., 2012; Piller et al., 2012) focus on.
An important precondition for customer satisfaction through co-design is that the process itself should be successful. The customer has to be capable of performing the task. This competency issue involves flow, a construct often used by researchers to explain how customer participation in a process increases satisfaction (Csíkszentmihályi, 1990). Flow is the process of optimal experience achieved when motivated users perceive a balance between their skills and the challenge at hand during an interaction process (Novak et al., 2000). Interacting with a co-design toolkit may lead to this state, as recent research in marketing indicates (Dellaert and Stremersch, 2005; Franke et al., 2008; Fuchs et al., 2010). Accordingly, recent research has provided several design guidelines that should facilitate this effect of process satisfaction (Dellaert and Dabholkar, 2009; Franke et al., 2009; Randall et al., 2005).
9.4.4 Learning Relationship
The interaction between the manufacturer and the customer that is underlying a co-design process offers further possibilities for building loyalty and lasting customer relationships. Once a customer has successfully purchased an individual item, the knowledge acquired by the manufacturer represents a possible barrier against switching to other suppliers. Reordering becomes much easier for the customers. Consider, as an example, the case of Adidas, the large manufacturer of sporting goods (Berger et al., 2005). In 2001, the company introduced its mass customization program “mi adidas,” offering custom sport shoes with regard to fit, functionality, and aesthetic design. The process starts with a customer who wants to buy personalized running shoes for around $150. The more customers tell the vendor about their likes and dislikes during the integration process, the better the chance of a product being created that meets the customers’ exact needs at the first try. The manufacturer can draw on detailed information about the customer for the next sale, ensuring that the service provided becomes quicker, simpler, and more focused. The information status is increased and more finely tuned with each additional sale.
When Adidas entered into a learning relationship with its customers, it increased the revenues from each customer because, in addition to the actual product benefits, it simplified the purchasing decision, so that the customer keeps coming back. By aggregating information from a segment of individual customers, Adidas also gains valuable market research knowledge. As a result, new products for the mass market segment can be planned more efficiently, and market research is more effective because of unfiltered access to data on market trends and customers’ needs. This is of special benefit to those companies that unite large-scale make-to-stock production with tailored services. Mass customization can thus become an enabling strategy for higher efficiency of a mass production system.
This learning relationship may lead to new cost-saving potential (Piller et al., 2004), based on better access to knowledge about the needs and demands of the customer base (Kotha, 1995; Squire et al., 2004). This includes (1) the reduced need for forecasting product demand (or at least the possibility to focus forecasting), (2) reduced or eliminated inventory levels of finished goods, (3) reduced product returns, (4) reduced obsolescence or antiquated fashion risks, and (5) the prevention of lost sales if customers cannot find the product in a store that fits their requirements and, thus, allocate the purchasing budget to another item. The savings from these effects can be huge. Forrester Research estimated that the US automotive industry could save up to $3,500 per vehicle by moving from its current build-to-stock model to a build-to-order system (Reichwald and Piller, 2009).
9.4.5 Advanced Methods of Choice Navigation
The application of toolkits for customer co-design may be the most used approach to help customers navigate choice in a mass customization system. But a number of other approaches exist, too. One effective approach is what we labeled assortment matching (Salvador et al., 2009), in which software automatically builds configurations for customers by matching models of their needs with characteristics of existing sets of options. Then customers only have to evaluate the predefined configurations, which saves considerable effort and time in the search process. Using special software, for example, customers at Sears.com can build avatars of themselves by selecting different body types, hair styles, facial characteristics and so on. From that information, the system can then recommend items out of the vast assortment of an online merchant.
But customers might not always be ready to make a decision after they’ve received recommendations (see also Tiihonen et al., 20143). They might not be sure about their real preferences, or the recommendations may not appear to fit their needs. In such cases, combining a recommendation system with a co-design toolkit is a perfect solution. Consider online shoppers at 121Time.com, a leading provider of mass-customized Swiss watches. Customers in the watch market might have a general idea of what they want, but while using an online configurator to play around with various options, combining colors and styles, they can actually see how one choice influences another and affects the entire look of a watch. Through that iterative process, they learn about their own preferences—important information that is then represented in subsequent configurations.
A number of companies are engaging in even more innovative and drastic approaches to choice navigation. Choice navigation has been completely automated in recent products that understand how they should adapt to the user and then reconfigure themselves accordingly. Equipped with so-called embedded configuration capability, the products paradoxically become standard items for the manufacturer while the user experiences a customized solution. This can be regarded as a postponement configuration strategy where the user can be enabled to modify the product after it has been manufactured and has reached the user domain, offering an embedded product “smartness.” Imagine products with a high feature load (e.g., cars) where the customer uses ICT interfaces and sensors to create a feature configuration that better suits his/her preferences and needs (Piller et al., 2010).
Mass customization can be regarded as a response to today’s opportunities of heterogeneous demands and the need for companies to become truly customer-centric. While traditionally mass customization has been associated with flexible manufacturing technology and the development of modular product architectures, we have shown in this chapter that mass customization cannot be realized without conceptualizing, designing, and implementing a configurator. Besides product modeling and the integration of the configuration solution into the IT systems of the firm, the task in implementing such a configuration system is to offer an appropriate user interface that is easy to understand without training, intuitive to operate, providing support in navigating complex choice sets, guiding the user to find a fitting solution, and that at the same time is also fun to operate, leading to a sustaining process satisfaction of the customer. The latter has been shown to be a core factor of success, especially in BtoC mass customization markets. But as argued in this chapter, in the end mass customization requires a business to develop three fundamental capabilities; configuration system design is not enough. Only when the solution space is defined correctly and robust processes are in place, choice navigation via configuration toolkits will lead to sustainable success.
Overall, mass customization should be considered a journey rather than a destination. There is not a perfect state of mass customization (Salvador et al., 2009). What matters to most companies instead is to continuously increase their overall capabilities to define the solution space, to design robust processes, and to help customers navigate through available choices. A company offering standard goods may already profit tremendously from just implementing better, say, choice navigation capabilities to match diverse requests of customers not familiar with the product category with options from an existing assortment of standard products. We have called this understanding mass customization thinking (Piller, 2005). It provides a way to profit from heterogeneities of a firm’s customers. Mass customization thinking means to build the three capabilities and to apply them for designing a value chain that creates value from serving customers individually. Here we call for future research to investigate these relationships more closely, and also to study more closely how configurators have to be embedded within other capabilities.
1. Anderson-Connell LJ, Ulrich PV, Brannon EL. A consumer-driven model for mass customization in the apparel market. Journal of Fashion Marketing and Management. 2002;6(3):240–258.
2. Badurdeen F, Masel D. A modular minicell configuration for mass customization manufacturing. International Journal of Mass Customization. 2007;2(1–2):39–56.
3. Berger C, Möslein K, Piller FT, Reichwald R. Co-designing modes of cooperation at the customer interface: learning from exploratory research. European Management Review. 2005;2(1):70–87.
4. Blazek P, Kolb M, Partl M, Streichsbier C. The usage of social media applications in product configurators. International Journal of Industrial Engineering and Management. 2012;3(4):179–183.
5. Blazek, P., Partl, M., Streichsbier, C., 2013. Configurator Database Report 2013. Tech. rep., Raleigh, NC. <www.configurator-database.com/report2013>.
6. Csíkszentmihályi M. Flow: The Psychology of Optimal Experience. New York: Harper & Row; 1990.
7. Dahan E, Hauser J. The virtual customer. Journal of Product Innovation Management. 2002;19(5):332–353.
8. Dellaert BG, Dabholkar P. Increasing the attractiveness of mass customization: the role of complementary online services and range of options. International Journal of Electronic Commerce. 2009;13(3):43–70.
9. Dellaert BGC, Stremersch S. Marketing mass customized products: striking the balance between utility and complexity. Journal of Marketing Research. 2005;42(2):219–227.
10. Desmeules R. The impact of variety on consumer happiness: marketing and the tyranny of freedom. Academy of Marketing Science Review. 2002;12:1–18.
11. Duray R. Mass customization origins: mass or custom manufacturing? International Journal of Operations & Production Management. 2002;22(3):314–328.
12. Felfernig A. Standardized configuration knowledge representations as technological foundation for mass customization. IEEE Transactions on Engineering Management. 2007;54(1):41–56.
13. Franke N, Piller FT. Key research issues in user interaction with configuration toolkits in a mass customization system. International Journal of Technology Management. 2003;26:587–599.
14. Franke N, Piller FT. Value creation by toolkits for user innovation and design: the case of the watch market. Journal of Product Innovation Management. 2004;21(6):401–415.
15. Franke N, Schreier M. Why customers value self-designed products: the importance of process effort and enjoyment. Journal of Product Innovation Management. 2010;27(7):1020–1031.
16. Franke N, Keinz P, Schreier M. Complementing mass customization toolkits with user communities: how peer input improves customer self-design. Journal of Product Innovation Management. 2008;25(6):546–559.
17. Franke N, Keinz P, Steger C. Testing the value of customization: when do customers really prefer products tailored to their preferences? Journal of Marketing. 2009;73(5):103–121.
18. Fuchs C, Schreier M, Prandelli E. The psychological effects of empowerment strategies on consumers’ product demand. Journal of Marketing. 2010;74(1):65–79.
19. Gilmore JH, Pine BJ. The four faces of mass customization. Harvard Business Review. 1997;75(1):91–101.
20. Griffin A, Hauser J. The voice of the customer. Marketing Science. 1993;12(1):1–27.
21. Haug A, Hvam L. The modeling techniques of a documentation system that supports the development and maintenance of product configuration systems. International Journal of Mass Customization. 2007;2(1–2):1–18.
22. Huffman C, Kahn B. Variety for sale: mass customization or mass confusion. Journal of Retailing. 1998;74(4):491–513.
23. Hvam L, Mortensen N, Riis H. Product Customization. Heidelberg, Berlin: Springer; 2008.
24. Khalid H, Helander M. Web-based do-it-yourself product design. In: Tseng M, Piller FT, eds. The Customer Centric Enterprise: Advances in Mass Customization and Personalization. New York: Springer; 2003:247–266.
25. Koste L, Malhotra MK, Sharma S. Measuring dimensions of manufacturing flexibility. Journal of Operations Management. 2004;22(2):171–196.
26. Kotha S. Mass customization: implementing the emerging paradigm for competitive advantage. Strategic Management Journal. 1995;16(S1):21–42.
27. Kumar A. Mass customization: metrics and modularity. International Journal of Flexible Manufacturing Systems. 2005;16(4):287–312.
28. Leitner G, Felfernig A, Blazek P, Reinfrank F, Ninaus G. User interfaces for configuration environments. In: Felfernig A, Hotz L, Bagley C, Tiihonen J, eds. Knowledge-based Configuration – From Research to Business Cases. Waltham, MA: Morgan Kaufmann Publishers; 2014:89–106. (Chapter 8).
29. Merle A, Chandon J, Roux E, Alizon F. Perceived value of the mass–customized product and mass customization experience for individual consumers. Production & Operations Management. 2010;19(5):503–514.
30. Novak T, Hoffmann D, Yung Y. Measuring the customer experience in online environments: a structural modeling approach. Marketing Science. 2000;19(1):22–42.
31. Piller FT. Mass customization: reflections on the state of the concept. International Journal of Flexible Manufacturing Systems. 2005;16(4):313–334.
32. Piller FT. Mass customization. In: Sage Publications 2008:420–430. Wankel C, ed. 21st Century Management: A Reference Handbook. vol. 1.
33. Piller FT, Möslein K, Stotko C. Does mass customization pay? An economic approach to evaluate customer integration. Production Planning & Control. 2004;15(4):435–444.
34. Piller FT, Ihl C, Steiner F. Embedded toolkits for user co-design: a technology acceptance study of product adaptability in the usage stage. In: 43th Hawaii International Conference on System Science (HICSS), Honolulu, HI, CD–ROM. 2010; p. 10.
35. Piller FT, Vossen A, Ihl C. From social media to social product development: the impact of social media on co-creation of innovation. Die Unternehmung. 2012;66(1):7–27.
36. Pine BJ. Challenges to total quality management in manufacturing. In: Cortada JW, Woods JA, eds. The Quality Yearbook. New York: McGraw-Hill; 1995:69–75.
37. Pine BJ, Victor B, Boynton A. Making mass customization work. Harvard Business Review. 1993;71(5):108–119.
38. Randall T, Terwiesch C, Ulrich K. Principles for user design of customized products. California Management Review. 2005;47(4):1–18.
39. Rangaswamy A, Pal N. Introduction: gaining business value from personalization technologies. In: Pal N, Rangaswamy A, eds. The Power of One: Gaining Business Value from Personalization Technologies. Victoria, BC, Canada: Trafford Publishing; 2003:1–9.
40. Reichwald R, Piller F. Interactive value creation in the production: individualization and mass customization Interactive Value Creation: Open Innovation, Individualization and New Forms of Division of Labour. second ed Gabler Verlag 2009; pp. 263–267 (in German: Interaktive Wertschöpfung in der Produktion: Individualisierung und Mass Customization).
41. Rogoll T, Piller FT. Product configuration from the customer’s perspective: a comparison of configuration systems in the apparel industry. In: International Conference on Economic, Technical and Organisational Aspects of Product Configuration Systems (PETO 2004), Lyngby, Kopenhagen, Denmark. 2004;179–199.
42. Sabin D, Weigel R. Product configuration frameworks - a survey. IEEE Intelligent Systems. 1998;13(4):42–49.
43. Salvador F. Towards a product modularity construct: literature review and reconceptualization. IEEE Transactions on Engineering Management. 2007;54(2):219–240.
44. Salvador F, Forza C. Principles for efficient and effective sales configuration design. International Journal of Mass Customization. 2007;2(1–2):114–127.
45. Salvador F, Rungtusanatham M, Forza C. Supply-chain configurations for mass customization. Production Planning & Control. 2004;15(4):381–397.
46. Salvador F, Rungtusanatham M, Akpinar S, Forza C. Strategic capabilities for mass customization: theoretical synthesis and empirical evidence. In: Academy of Management Annual Meeting. 2008:1–6.
47. Salvador F, de Holan M, Piller FT. Cracking the code of mass customization. MIT Sloan Management Review. 2009;50(3):70–79.
48. Schreier M. The value increment of mass-customized products: an empirical assessment. Journal of Consumer Behavior. 2006;5(4):317–327.
49. Schwartz B. The Paradox of Choice: Why More is Less. New York: HarperCollins; 2004.
50. Squire B, Readman J, Brown S, Bessant J. Mass customization: the key to customer value? Production Planning & Control. 2004;15(4):459–471.
51. Stumptner M. An overview of knowledge-based configuration. AI Communications. 1997;10(2):111–126.
52. Su JCP, Chang Y-L, Ferguson M. Evaluation of postponement structures to accommodate mass customization. Journal of Operations Management. 2005;23(3–4):305–318.
53. Tepper K, Bearden WO, Hunter GL. Consumers’ need for uniqueness: scale development and validation. Journal of Consumer Research. 2001;28(1):50–66.
54. Tiihonen J, Felfernig A, Mandl M. Personalized configuration. In: Felfernig A, Hotz L, Bagley C, Tiihonen J, eds. Knowledge-based Configuration – From Research to Business Cases. Waltham, MA: Morgan Kaufmann Publishers; 2014:167–179. (Chapter 13).
55. Totz C, Riemer K. The effect of interface quality on success - an integrative approach on mass customization design. In: Tseng M, Piller FT, eds. Proceedings of the 1st World Congress on Mass Customization and Personalization, Hong Kong, China. 2001; CD ROM.
56. Tseng M, Jiao J. Mass customization. In: Salvendy G, ed. Handbook of Industrial Engineering. third ed New York: Wiley; 2001:684–709. (Chapter 25).
57. Tseng M, Kjellberg T, Lu SC-Y. Design in the new e-commerce era. Annals of the CIRP. 2003;52(2):509–519.
58. Tu Q, Vonderembse M, Ragu-Nathan T. The impact of time-based manufacturing practices on mass customization and value to customer. Journal of Operations Management. 2001;19(2):201–217.
59. Udwadia F, Kumar R. Impact of customer co-construction in product/service markets. International Journal of Technological Forecasting and Social Change. 1991;40(3):261–272.
60. Ulrich P, Anderson-Connell L, Wu W. Consumer co-design of apparel for mass customization. Journal of Fashion Marketing and Management. 2003;7(4):398–412.
61. von Hippel E. Economics of product development by users: the impact of “sticky” local information. Management Science. 1998;44(5):629–644.
62. von Hippel E, Katz R. Shifting innovation to users via toolkits. Management Science. 2002;48(7):821–833.
63. Walcher, D., Piller, F.T., 2012. The Customization 500 – An International Benchmark Study on Mass Customization and Personalization in Consumer E–Commerce. Tech. rep., Lulu Inc., Raleigh, NC. www.mc-500.com.
64. Yang B, Burns ND. Implications of postponement for the supply chain. International Journal of Production Research. 2003;41(9):2075–2090.
65. Yang B, Burns ND, Backhouse CJ. Postponement: a review and an integrated framework. International Journal of Operations & Production Management. 2004;24(5):468–487.
66. Zhang M, Tseng M. A product and process modeling based approach to study cost implications of product variety in mass customization. IEEE Transactions on Engineering Management. 2007;54(1):130–144.
67. Zhang Q, Vonderembse M, Lim J-S. Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction. Journal of Operations Management. 2003;21(2):173–191.
1This chapter combines the argumentation developed in two earlier publications: Piller (2008) and Salvador et al. (2009).