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Purpose

This research proposes and illustrates a conditional view of lean supply chain management (LSCM) based upon the contextual contingent alignment between lean performance objectives (i.e. a contextual factor) and supply chain management challenges (i.e. a contingent condition) in the selection of lean approaches (i.e. a contingent event).

Design/methodology/approach

Drawing on the notions of contingency-based practices and strategic fit, the authors’ LSCM reconceptualization jointly considers contextual and contingency factors in specifying what lean approaches to adopt. The authors illustrate the practical relevance of LSCM reconceptualization for the Canadian agri-food industry using the Delphi method.

Findings

The authors highlight that LSCM is founded upon alignment associations between specific lean performance objectives and supply chain challenges as well as their influence on the selection of suitable lean approaches. The empirical illustration shows that those alignment associations do not occur at random, which supports the conditional view of LSCM.

Research limitations/implications

The contextual contingent view of LSCM can inform future scholarly inquiry and can reframe practically relevant middle-range theorization on LSCM.

Practical implications

The Delphi method-derived descriptive model of LSCM provides guidance to managers in the Canadian agri-food sector in identifying suitable lean approaches to adopt given the specific performance objective(s) pursued and supply chain management challenge(s) encountered.

Originality/value

The authors advance scholarly theorization and managerial understanding of LSCM by providing a conditional conceptualization that jointly considers relevant contextual and contingency factors that hitherto have not been examined. In ascribing what lean approach(es) to adopt to the alignment associations influence between lean performance objective(s) pursued and supply chain management challenge(s) encountered, the authors provide compelling conceptual and empirical support for the joint conditional view of LSCM.

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