Benefits of Configuration Systems - Introduction - Knowledge-based Configuration: From Research to Business Cases, FIRST EDITION (2014)

Knowledge-based Configuration: From Research to Business Cases, FIRST EDITION (2014)

Part I. Introduction

Chapter 4. Benefits of Configuration Systems

Alexander Felferniga, Claire Bagleyb, Juha Tiihonenc, Lois Wortleyb and Lothar Hotzd, aGraz University of Technology, Graz, Austria, bOracle Corporation, Burlington, MA, USA, cAalto University, Aalto, Finland, dHITeC e.V., University of Hamburg, Hamburg, Germany


In this chapter, we summarize challenges related to the implementation of mass customization. In this context, we discuss benefits that result from a successful deployment and application of configuration technologies.


Knowledge-based Configuration; Challenges; Benefits

4.1 Introduction

Configuration is a key technology for successfully implementing mass customization (see Piller and Blazek, 20141).In this chapter, we take a look at key challenges in the context of mass customization and consequent benefits when using configuration technologies to address these challenges. Configurators can act as key components for efficiently reducing the time to market of potentially large, complex, and customizable products. Table 4.1 summarizes the challenges and benefits detailed hereafter.

Table 4.1

Mass Customization (MC) challenges and benefits of configuration technologies.


4.2 Challenges and Benefits

Challenge 1: Avoiding Erroneous and Suboptimal Offers. The goal of offering highly variant (configurable) products requires sales representatives to invest more time to understand the product assortment. The result of being confronted with high variability are erroneous offers that are often not noticed before production (e.g., Heiskala et al., 2007). Even if an offer is correct it can be suboptimal, including additional services/costs that are not needed for fulfilling the wishes and needs of the customer. This situation can lead to a turnover of customers. Configuration technologies can tackle this challenge in several ways. They include constraint technologies that support automated feasibility checks of customer requirements. Such checks contribute to a significant reduction of errors in the recording of orders (Hvam et al., 2010). Fewer errors in the order recording phase result in less customer complaints and confusion, shorter lead times (Barker et al., 1989; Haug et al., 2011), and increased sales performance (Hvam et al., 2010). For the same amount of successfully acquired customer orders, we can expect lower (personnel) costs due to lower cycle times (Barker et al., 1989). A high degree of correct offers makes effort estimations stable. Furthermore, increased variety can be offered because sales representatives are enabled to handle the complexity (Heiskala et al., 2007).

Challenge 2: Avoiding Mass Confusion. Mass Confusion (Huffman and Kahn, 1998) is triggered by large and complex product assortments. It denotes the fact that customers (users) may be overwhelmed by the size and complexity of a product assortment and, as a consequence, are not able to make a decision. The related psychological effect is that the higher the amount of available options, the higher the cognitive effort to identify and compare potential alternatives, and the lower the probability of high-quality decisions (Felfernig et al., 2007, 2006, 2008; Piller et al., 2005). In this context, configurators can provide a search interface that helps to narrow down the number of relevant options. Personalization technologies support more efficient decision processes (Felfernig et al., 2006). Easier to use (personalized) interfaces can encourage customers to apply a configurator autonomously (customer self-service) and, as a consequence, customers are preinformed. Finally, configuration knowledge can act as a kind of corporate memory that can be exploited for the education of new sales representatives. In this context, there is a lower risk of losing sales knowledge and product knowledge becomes more standardized (Hvam et al., 2010).

Challenge 3: Complexity Handling of Needs Elicitation. As mentioned in Challenge 2, configuration and personalization technologies are needed to efficiently support users when searching for (configuring) a product. In this context, intelligent needs elicitation is an important issue. Without having a clear view of the preferences of a user, it is simply not possible to identify the most relevant configurations (Ardissono et al., 2003; Falkner et al., 2011; Tiihonen and Felfernig, 2010). When starting a configuration process, users typically do not know their preferences beforehand but construct them within the scope of the configuration session (see Mandl et al., 20142). This aspect has to be taken into account by intelligent configurator user interfaces. Interfaces that successfully support customers in finding a configuration that fits their wishes and needs are able to increase sales and also allow a deep understanding of customer preferences (see, e.g. Felfernig et al., 2006). If a customer enters a set of requirements for which no solution (configuration) can be identified, this information can be exploited as well: the requirements can be collected for open innovation processes; that is, processes that explicitly integrate customers into a company’s new product development (Felfernig et al., 2004b).

Challenge 4: Knowledge Integration. Successful companies are continually streamlining their core information technology systems in order to remain competitive. Businesses are strengthening the communication and information shared with customers through investments, for example, in Customer Relationship Management (CRM) systems and focusing on the data and knowledge that flows into their supply chain. The management of customers and suppliers relies heavily on the management of product knowledge across the enterprise. From the front-end to the back-end operations, knowledge must flow seamlessly and be accessible across every facet of the enterprise. A key issue when dealing with complex products and services is that configuration technologies are deeply integrated into supply chains (Ardissono et al., 2003). On the basis of such an integration, companies that are able to leverage timely information from these processes are better able to anticipate and predict demand, ultimately allowing them to better influence day-to-day operations and events (see, e.g., Hugos, 2011). State-of-the-art configurators can be fully and seamlessly integrated into a larger system to decrease the time to market of any large, complex, and customizable product; simplify the configuration of such products;reduce order and processing errors; and, therefore, increase profitability. As such, configurators provide the foundation for an integrated configuration, pricing, and quotation (CPQ) solution (Dunne and Alvarez, 2011).

Challenge 5: Efficient Knowledge Management. Central to the configurator implementation is the definition of the product (configuration) knowledge (Mittal and Frayman, 1989). This knowledge may exist in a variety of sources including product data management systems, spreadsheets, legacy enterprise and CRM systems, or even in the heads of soon-to-retire employees. Early in the implementation of a configuration model, a knowledge acquisition and data cleansing stage is required to centralize the product knowledge (including the corresponding product data), preferably into a single source of truth shared throughout the enterprise. Sharing of common data across the enterprise is critical to maintain order accuracy and reduce cycle times.

A successful configuration tool relies on its ability to create maintainable configuration models (Felfernig et al., 2013; Haug, 2010; Haug and Hvam, 2007) that enable users to efficiently enter needs (DeSisto, 1997). Top-of-the-line configurators enable businesses to create and maintain large configuration models (with a large set of constraints and large related product models) rapidly by giving product specialists the flexibility to model complex products without programming expertise in a time-dependent fashion (Alvarez and Dutra, 2009). Once a product structure has been created and/or imported from a central product definition repository, configuration modelers can state the necessary product business rules (constraints) and preferences that will ensure the validity of the product configurations while meeting the customer’s requirements. Effective configurators also support mechanisms for testing large and complex configuration knowledge bases, providing an environment that simulates user interactions with a configuration model, checking that the configurator produces the expected configurations, providing explanations in the case of inconsistencies (Felfernig et al., 2004a; 2006). Fulfilling these requirements, configuration environments are able to successfully tackle the knowledge acquisition bottleneck in terms of efficient development and maintenance processes.

4.3 Conclusion

In this chapter, we provided an overview of major challenges related to the implementation of mass customization. For each of these challenges, we discussed application scenarios for configuration technologies and benefits related to the application of these technologies. With this overview of business benefits related to the application of configuration technologies, this chapter provides insights that are of particular relevance for persons with limited experience in the application of configuration technologies. Further details on the impacts of configuration technologies in industrial environments can be found in overview publications such as Barker et al. (1989), Forza and Salvador (2002), Haug et al. (2011), Heiskala et al. (2005, 2007), and Hvam et al. (2010).


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1Chapter 9.

2Chapter 14.