Driven by increased diversification of customer needs, market fragmentation and fierce competition, manufacturers in many industries pursue a high product variety strategy to compete. A larger assortment enhances customer value due to better opportunity of fitting individual needs; meanwhile customers often find it a difficult and somewhat frustrating process to identify the ideal choice in a wide spectrum of choices. They exhibit choice indifference when faced by similar products. In the state of indifference, customers struggle to make a choice, and may end up with random selection or simply leaving without purchase due to unpleasant experience and poor decision quality.

Product customization, as an approach to managing product variety, promises the delivery of customized...[ Read more ]

Driven by increased diversification of customer needs, market fragmentation and fierce competition, manufacturers in many industries pursue a high product variety strategy to compete. A larger assortment enhances customer value due to better opportunity of fitting individual needs; meanwhile customers often find it a difficult and somewhat frustrating process to identify the ideal choice in a wide spectrum of choices. They exhibit choice indifference when faced by similar products. In the state of indifference, customers struggle to make a choice, and may end up with random selection or simply leaving without purchase due to unpleasant experience and poor decision quality.

Product customization, as an approach to managing product variety, promises the delivery of customized products that meet individual needs with high efficiency. Various online configurators have been developed to facilitate the process. Asking customers to select the preferred choice for each customizable attribute, configurators escalate the issue of choice indifference simply due to increased number of decisions. If not managed well, it leads to poor customer satisfaction and potentially loss of sales. Nevertheless, it indicates customers’ preference flexibility in product specifications, which can provide flexibility for manufacturers to fulfil demand according to their supply chain convenience. This is an untapped value to be unlocked, and has not been explicitly studied in prior research.

This research studies the problem of customer indifference in the context of product customization. To capture the value of customer indifference for the manufacturer, we introduce a mechanism termed flexible option. It is defined as a set of two or more specific alternatives that can be used to instantiate an attribute in product configuration. When a customer selects a flexible option, one alternative in the set will be assigned by the manufacturer to finalize the configuration after order placement. A flexible option could also be applied with price discount. By doing so, a win-win situation can be achieved: customers with choice indifference are relieved of decision burden while others still enjoy the variety of choices, and the manufacturer may induce more demand and gain operational flexibility for more efficient supply demand matching.

The economic value of customer indifference can only be captured when flexible options are appropriately designed. This research aims to develop a theoretical foundation and methodology so that manufacturers can optimally design flexible options for their customized products. In a nutshell, flexible option design needs to identify the best combination of two parameters, namely the set of alternatives and price discount, with the incorporation of customer indifference.

To do so, customer indifference is quantitatively characterized with the introduction of utility difference function and minimum perceived difference in Chapter 3. These are integrated into a holistic modeling structure that captures the characteristics of customer indifference and derives choice probabilities in presence of indifference behaviors.

Flexible option is essentially an input-switching real option, but conventional valuation methods fall short for this particular scenario. A new method to assess the economic value of flexible options is developed in Chapter 4 to identify and quantify the value and cost drivers of flexible options. This method enables to derive the objective function for the design optimization problem, which is detailed in the mathematical formulation part of Chapter 5. An analytical base model captures the most important features of the design problems before introducing excessive complexity. It serves a theoretical foundation for the following model extension. Given the intrinsic complexity of the problem, we develop a generic solution framework that separates the optimization into upper and lower level optimization in order to optimize the discrete variable (set of alternative) and the continuous variable (price discount) simultaneously.

Chapter 6 provides a case study and numerically investigates the application of flexible options. The findings suggest the impact of the degree of customer indifference and initial inventory position for the optimal design of flexible option and its corresponding performance. It can also be concluded that the pair of alternatives with larger indifference index and more unbalanced inventory has greater likelihood to be the optimal alternative set based on the simulation results.

This research contributes to the existing product customization literature by introducing flexible option to improve customization experience for customers and capture the value of customer indifference for manufacturers. It also enriches the understanding of flexible product concept, which is largely being applied within the scope of service industries to date. It is hoped that the research results can serve as a foundation for developing more sophisticated approaches that can be applied in practice, particularly the booming e-commerce business.