A decision support framework to improve the business performance of e-commerce retailers and logistics providers through reverse logistics strategies selection
Nayely Grullón, Eloy Hontoria, Pilar Jiménez, Mónica Arroyo-VázquezPurpose
This study evaluates, from a business process management (BPM) perspective, four reverse logistics (RL) strategies (Lockers, Click & Collect, Microhubs and 3PL) once product returns are authorized. It addresses the critical first-stage decision – home collection vs designated drop-off – while explicitly analyzing the detrimental impact of failed collection attempts due to customer absence on urban logistics efficiency, aiming to enhance the business performance of e-commerce retailers and logistics providers.
Design/methodology/approach
A decision support framework is developed, applying the technique for order preference by similarity to ideal solution to assess each strategy and a simulation of 1,000 product return scenarios is conducted. The methodology incorporates a robust sensitivity analysis modeling varying rates of failed first-time collections, quantifying their effect on cost, cycle time, CO2 emissions and expected customer utility.
Findings
Baseline results identify Urban Microhubs as the most balanced strategy. However, sensitivity analysis reveals that as failed collections increase, the efficiency of attended strategies (3PL and Urban Microhubs) degrades significantly due to re-attempt costs. This shifts the strategic preference toward unattended strategies like lockers.
Originality/value
The study offers original value by integrating operational, environmental and customer-focused metrics into a unified framework for evaluating RL scenarios. Its multidimensional comparison of the four strategies provides novel insights that advance theory and guide the design of more efficient and sustainable return strategies, enabling e-commerce and logistics operators to adopt differentiated policy profiles (cost-driven or green-driven). Ultimately, the primary value of this research resides in providing a structured methodology to analyze complex operational scenarios. Additionally, the explicit modeling of failed collections provides novel insights that advance BPM theory, empowering context-specific managerial decision-making.