Warehouses employ order cut-off times to ensure sufficient time for fulfilment. To satisfy increasing consumer’s expectations for higher order responsiveness, warehouses competitively postpone these cut-off times upholding the same pick-up time. This paper, therefore, aims to schedule jobs more efficiently to meet compressed response times. Secondly, this paper provides a data-driven decision-making methodology to guarantee the right implementation by the practitioners. Priority-based job scheduling using flow-shop models has been used mainly for manufacturing systems but can be ingeniously applied for warehouse job scheduling to accommodate tighter cut-off times. To assist warehouse managers in decision making for the practical value of these models, this study presents a computer simulation approach to decide which priority rule performs best under which circumstances. The application of stochastic simulation models for uncertain real-life operational environments contributes to the previous literature on deterministic models for theoretical environments. The performance of each rule is evaluated in terms of a joint cost criterion that integrates the objectives of low earliness, low tardiness, low labour idleness, and low work-in-process stocks. The simulation outcomes provide several findings about the strategic views for improving responsiveness. In particular, the critical ratio rule using the real-time queue status of jobs has the fastest flow-time and performs best for warehouse scenarios with expensive products and high labour costs. The case study limits the coverage of the findings, but it still closes the existent gap regarding data-driven decision-making methodology for practitioners of supply chains
