Last-mile supply chain efficiency: an analysis of learning curves in online ordering

Purpose ‐ As companies extend supply chains via direct delivery to consumers, supply chain efficiency depends upon the usability of the online ordering system. The purpose of this paper is to focus on customer order cycle efficiency gains through the "learnability" of web sites. Design/methodology/approach ‐ The paper analyzes empirical data using nonlinear regression from seven firms and over 4,000 customers to examine how order time ‐ an important performance metric ‐ changes within an online grocery ordering environment. Findings ‐ The evidence supports various forms of power-law learning for web-based ordering (i.e. the first few orders involve substantial learning). However, significant differences exist between web sites, and a portion of the ordering time may be irreducible. Research limitations/implications ‐ The research lends insight into how web sites influence last-mile supply chain efficiency via differing learning rates in the order cycle. Perceptual measures were used in order to assess customer beliefs. Practical implications ‐ Online order entry serves as the starting point for many supply chain actions. Managers can use this research to benchmark their web site performance and subsequently take action to improve the efficiency and service of their supply chain. Originality/value ‐ The empirically validated model allows researchers and web-based businesses to utilize the provided learning rate measure as an ease of use performance metric.