Monte Carlo Simulation of the Effect of Heterogeneous Too-Cheap Prices on the Average Price Preference for Remanufactured Products

A prevailing assumption in research on remanufactured products is “the cheaper, the better”. Customers prefer prices that are as low as possible. Customer price preference is modeled as a linear function with the minimal price at customers’ willingness to pay (WTP), which is assumed to be homogeneous and constant in the market. However, this linearity assumption is being challenged, as recent empirical studies have testified to customer heterogeneity in price perception and demonstrated the existence of too-cheap prices (TC). This study is the first attempt to investigate the validity of the linearity assumption for remanufactured products. A Monte Carlo simulation was conducted to estimate how the average market preference changes with the price of the remanufactured product when TC and WTP are heterogeneous across individual customers. Survey data from a previous study were used to fit and model the distributions of TC and WTP. Results show that a linear or monotonically decreasing relationship between price and customer preference may not hold for remanufactured products. With heterogeneous TC and WTP, the average price preference revealed an inverted U shape with a peak between the TC and WTP, independent of product type and individual customers’ preference function form. This implies that a bell-shaped or triangular function may serve as a better alternative than a linear function can when modeling market-price preference in remanufacturing research.

Inferring customer heterogeneity for rural tourism: A latent class approach based on a best-worst choice modelling

This research aims to analyse customers' preference heterogeneity for rural tourism in Spain and explore their preferences' personal and socio-demographic factors. To achieve this purpose, latent class analysis with the best-worst choice modelling has been applied through conducting a survey on 452 customers in Madrid and Barcelona. The results show that there are five classes in the Spanish rural tourism market: 'all-around seeker', 'leisure activist', 'culture explorer', 'comfort-driven user', and 'basic value pursuer'. The contribution of this investigation is that it is the first study that applied the latent class analysis with best-worst choice modelling to explore customers' preference heterogeneity for rural tourism.

A BWM-based approach for customer-oriented product development with insufficient information and its application to 5 G smartphone design

Quality function deployment (QFD) is a customer-oriented tool for developing products. Based on the idea of the best-worst method (BWM), a novel model is developed to determine the relative importance ratings (RIRs) of customer requirements (CRs) with interval grey linguistic (IGL) information, which plays a significant role in QFD. CRs are rated with IGL variables, and the degree of greyness degree function that can be used to handle the IGL variables is defined based on the power utility function. Then, considering customer heterogeneity, a model is constructed to derive the RIRs of CRs by following the logic of the BWM. Finally, a case study of 5 G smartphone development is provided to verify the validity and the feasibility of the proposed method.

Efficacy of retailer rebates and delayed incentives under customer heterogeneity

In automobile and household appliances, the manufacturer's decision to offer retailer rebates to retailers or delayed incentives directly to end customers is very important. Under game theoretic environment, We show that delayed incentive is an important tool not only for price discrimination but it also provides more flexibility to the manufacturer. We differentiate the impact of delayed incentives and retailer rebates. We draw implications for optimal rebate strategies for both retailer rebates and delayed incentives. We also discuss delayed incentives in the form coupons that give more control to the manufacturer, whereas retailer rebates require retailer's participation. Analysis and numerical insights show that the manufacturer prefers better observance of retail price. We provide a decision rule to the manufacturer on how to select the rebate type based on customer heterogeneity.

Strategic Timing and Dynamic Pricing for Online Resource Allocation

This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

Design and Pricing of Discretionary Service Lines

For discretionary services, longer service duration implies higher service quality. We study the optimal design and pricing of discretionary service lines, a range of services that are vertically differentiated through their duration (quality), offered to a market with customers who are heterogeneous in terms of their sensitivity to service duration. In a resource-constrained environment with stochastic arrival of demand, longer service duration also implies longer wait, which all customers dislike. Hence, service line design takes into consideration both customer heterogeneity in quality sensitivity and customer disutility in waiting. We first study the case when the firm serves customers in the order they arrive (first in first out (FIFO)). Then we investigate a processing-time-based sequencing rule with two priority classes (two classes shortest processing time (2-SPT)) as a practical operational improvement of FIFO. We identify service variety reduction as an instrument to mitigate congestion, in addition to service time reduction and throughput control used for discretionary service design. Although FIFO results in a range of closely related service durations and an increasing price schedule, we show that 2-SPT gives rise to a clustered service line with a gap between the ranges of service durations offered for different priority classes, along with a decrease of demand volume and a possible price drop from the lower-quality to the higher-quality cluster. Finally, even without customer heterogeneity, it is still optimal for the firm to offer a variety of services under 2-SPT, driven by operational efficiency improvement, whereas under FIFO the service line collapses to a single service. This paper was accepted by Terry Taylor, operations management.