Business logistics models in omni-channel: a classification framework and empirical analysis

Abstract

Purpose

Companies are currently moving from multi-channel strategies to offer their customers an omni-channel (OC) experience. So far, OC research has been mainly tackled from a sales-based view, with numerous operational challenges to be fully investigated yet. The purpose of this paper is to investigate how companies set the logistics variables in their OC management strategy and the business logistics models currently most adopted.

Design/methodology/approach

A two-step methodology was adopted. First, a systematic combining approach with scientific literature review and case studies allowed to derive a framework for classifying the key logistics variables and the related options. The framework was then used to conduct a qualitative survey targeting 92 Italian companies operating in food manufacturing, food retailing and non-food retailing. Collected data were analysed by means of cluster analysis.

Findings

Implementing an OC management strategy requires to set 11 logistics variables belonging to four strategic areas: delivery service, distribution setting, fulfilment strategy and returns management. A broad empirical investigation showed the choices made by companies when setting the logistics variables to implement an OC management strategy. Lastly, four business logistics models, differing in terms of both business sector and OC maturity, were discussed.

Originality/value

The proposed framework extends earlier studies by including additional significant logistics variables. The empirical analysis provides new insights on how to re-structure the business logistics model in OC, suggesting channel integration and the coexistence of multiple configurations as main enablers of an OC proposition.

Citation

Marchet, G., Melacini, M., Perotti, S., Rasini, M. and Tappia, E. (2018), "Business logistics models in omni-channel: a classification framework and empirical analysis", International Journal of Physical Distribution & Logistics Management, Vol. 48 No. 4, pp. 439-464. https://doi.org/10.1108/IJPDLM-09-2016-0273