Revenue management for multiple product recovery options : a triangulation approach

Chanintrakul, Piyawat

Abstract

In recent times large numbers of end-of-use/end-of-life returns have been the result of the increasing pressure from environmental legislations, particularly the directive on Waste Electrical and Electronic Equipment (WEEE) in the European Union. These returns incur acquisition costs and take-back operation costs regarded as a sunk cost by many industries. Thus, returned/recovered product valuation and marketing issues become crucial factors for survival and profitability of many firms in various sectors in today’s competitive world.

The research undertaken is relevant as pricing and revenue management for recovered products. Indeed, this theme is considered as a niche research and the fifth phase (prices and markets) of the evolution of closed loop supply chain research. Hence, it has been noted as one of the most critical research areas in quantitative modelling for reverse logistics and closed loop supply chain management studies. The research area is in its early stage because it can be seen that only a handful of articles have been published in peer reviewed international journals, exploring a pricing and marketing decision of recovered products. Hence, there are significant opportunities to conduct pricing and revenue management research in reverse logistics, particularly with regard to multiple recovery options.

The primary objective of this research work is to formulate three pricing models by using a non-linear programming approach to determine optimal profit-maximising acquisition prices and selling prices, together with UK-based case studies in the mobile phone and computer recycling businesses. Moreover, this research aims to formulate two simulation models based on these case companies by investigating the impact of the uncertainty element in terms of return quantity and reprocessing time on firm’s profit. The triangulation approach is employed, specifically the multilevel model comprising case studies, questionnaire survey, and empirical quantitative models in order to address the principal research questions i.e. “What are optimal acquisition prices of received mobile phones and optimal selling prices of reprocessed handsets?”, “What are optimal selling prices of reprocessed computers?”, and based on the total profit, “What if the model's parameters change?”

The contribution of this research covers the generation of pricing and simulation models that are suitable for the recycled mobile phone and computer sector. The literature review discovers that the research on this subject lacks considerations of multiple recovery options, return rate and demand rate as exponential functions, recovery capacity limitation, product substitution policy, the element of uncertainty in terms of return quantity and reprocessing time, and multiple time periods. Hence, this research fulfils six main research gaps in academic literature as follows. First, this study takes multiple recovery options into account. Second, return and demand rate are modelled as an exponential function. Third, pricing and simulation models cope with a limit to recovery capacity. Fourth, models with product substitution policy are investigated. Fifth, the element of uncertainty in terms of return quantity and reprocessing time is added into proposed models. Finally, this study proposes models with multiple time periods.

The results from this research work support current pricing and revenue management research and most importantly, the results generated from these proposed models can enhance managers’ decision making in recovery operations and reverse logistics.

Citation

Chanintrakul, P. (2011). Revenue management for multiple product recovery options : a triangulation approach. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4211743

• Thesis Type: Thesis
• Deposit Date: Jan 17, 2012
• Publicly Available Date: Feb 22, 2023
• Keywords: Business
• Public URL: https://hull-repository.worktribe.com/output/4211743
• Additional Information: Business School, The University of Hull
• Award Date: Sep 1, 2011