Improving warehouse responsiveness by job priority management: A European distribution centre field study

https://doi.org/10.1016/j.cie.2018.12.011Get rights and content

Highlights

  • Slack for warehouse order fulfilment decreases due to competitive responsiveness.

  • Warehouses can efficiently improve responsiveness by prioritising each job.

  • Stochastic simulation helps warehouse practitioners to adopt the flow shop theories.

  • A priority control using real-time queue status can highly improve the performance.

Abstract

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.

Introduction

Intense competition for speedy order fulfilment characterises current retail markets. Responsiveness (Barclay, Poolton, & Dann, 1996) includes the ability to react purposefully within appropriate time to external environments for securing competitive advantage. Improving order fulfilment responsiveness is a significant challenge for boosting customer satisfaction (Doerr & Gue, 2013) and many firms, such as Amazon Prime, invest substantial capital to propel responsiveness. Though responsiveness hones competitiveness, it often leads to resource misallocation (Vincent, 2011), and improved responsiveness leads for two-thirds of all firms to increased labour cost (Pearcy & Kerr, 2013). Web retailers show responsiveness by advertising ‘Place an order before midnight for next-day delivery.’ Customers are nowadays accustomed to fast demand satisfaction in online markets and expect commensurate off-line service. Off-line retailers, therefore, attract customers with promises such as: ‘Buy online now and pick up in store tomorrow’, forcing off-line retail distributors to improve their responsiveness (Denman, 2017).

The overall speed of order fulfilment in off-line markets depends on processing and transportation speeds from manufacturers through warehouses and retail shops to end-users. This paper focuses on speedy order fulfilment in warehouses, in particular, original equipment manufacturer (OEM) warehouses delivering to retailer warehouses. Their order fulfilment process includes the inbound processes of receiving products and putting them away and the outbound processes of picking, packing, staging and shipping. As OEM warehouses receive products from their manufacturer, the inbound process is easily controlled compared to the somewhat unpredictable consumer demand leading to fast fluctuations of retailer orders. Another characteristic of OEM warehouse is that retailers order relatively large quantities of relatively few products (Bartholdi & Hackman, 2016). This distinguishes such warehouses from those delivering directly to consumers, where order sizes are small and range over a much broader product assortment. Whereas picking is usually the crucial stage for the latter type, in OEM warehouses the packing stage is often the most demanding one. As the receiving retailer warehouses differ in capacity and layout and trucks should be loaded efficiently, re-palletising is a significant task for OEM warehouses. Because of the large order volumes, the re-palletising activities of unpacking, repacking and stacking are relatively labour intensive.

Responsiveness of OEM warehouses is measured by their flexibility to dispatch products ordered by retailers as fast as possible. To mitigate the effect of demand spikes, most OEM warehouses limit their fulfilment liability by daily order cut-off time agreements with their clients to ensure sufficient slack for order fulfilment by the earliest dispatch day (Van den Berg, 2007). To improve responsiveness, these warehouses try to postpone the cut-off time and to handle the same order volume with less slack. Since orders typically have different fulfilment deadlines and processing times, priority-based job scheduling offers the key to efficient solutions. Flow shop scheduling (Johnson, 1954) has notably reduced waste from over-production and waiting times in the manufacturing field. It is genuinely new to apply flow shop scheduling for improving responsiveness in warehouse order fulfilment under cut-off time challenges. Job scheduling timely manages to allocate prioritised tasks to labour resources for chosen goals (Vincent & Billaut, 2006) as the first decision-making in the OEM warehouse. The second decision-making here is how OEM warehouses should strategically choose the proper type of job scheduling to allow later cut-off times for enhancing responsiveness. The goal of this paper is to provide a data-driven method that improves decision-making capability to practitioners in the warehouses.

Warehouse operations are faced with various uncertainties, including dynamic arrival, service and departure times. In particular, unexpected order arrivals with different processing times can yield long delays. There is usually no priority rule that is universally optimal (Lee, Piramuthu, & Tsai, 1997) in case of these uncertainties. Although there is much research on job-scheduling, it is still tricky for warehouse managers to select the most suitable scheduling rule for their circumstances. This paper presents an integrated decision system for cost-effective job scheduling using flow-shop priority methods to aid warehouses facing postponed order cut-off times. This framework integrates the multiple objectives of low earliness, low tardiness, low labour idleness, and low stocks through processing lanes into a single cost criterion, with weights derived from the cost structure and performance priorities of the warehouse. The methodology supports data-driven decision making by simulating stochastic models (Gong & De Koster, 2011) based on real-life operational data for order arrivals, due times, and service times. The framework assists warehouse practitioners in deciding which scheduling methods perform best under which circumstances. The simulation results presented here advance extant literature for the priority rules by applying a computer-aided, real-time, look-ahead parameter (Kemppainen, 2005) into a well-known priority rule. Warehouse practitioners can incorporate these real-time task-scheduling methods in their warehouse management system (WMS) to create and execute a string of order fulfilment jobs (Ramaa et al., 2012, Van den Berg, 1999).

The main contributions of this paper are as follows:

  • A flow shop scheduling problem subject to the responsiveness in OEM warehouses is studied inspired by flow shop research in factory production schedule.

  • A priority control with the real-time queue status is devised to deal with prevalent uncertainty in OEM warehouse.

  • A decision-making framework for the job priority control is demonstrated by customising the warehouse’s business requirements and cost perspectives.

The rest of this paper is structured as follows. Section 2 reviews literature related to responsiveness, warehousing and flow-shop methods. Section 3 describes the operational challenge of responsive order fulfilment for postponed cut-off times. Section 4 presents the priority rules and performance indicators. Section 5 shows simulation results for the case study, and Section 6 discusses some operational implications and conclusions.

Section snippets

Literature review

A brief review is given of literature related to the main aspects of the study, i.e., responsiveness, warehouses job schedule, priority-based job scheduling, and selecting criteria in warehouses.

Consumers can nowadays easily use the Internet to compare quality and prices of products across different suppliers. The offered service level remains the primary competitive quality, and warehouse clients perceive responsiveness mainly by the speed of delivery. Shaw, McFarlane, Chang, and Noury (2003)

Model and case study

The research question of central interest is how job priority scheduling can help OEM warehouses to improve their responsiveness to meet current trends of postponed daily order cut-off times for next-day delivery. As customers adapt their ordering policy by spiking demand briefly before the cut-off time, warehouses are confronted with order peaks that have to be processed faster when response times become shorter. OEM warehouses usually dispatch retailer orders by trucks on agreed pick-up times

Priority rules and performance criteria

The literature review mentioned some well-known priority rules for job scheduling from flow-shop production theory, which will now be described in more detail. The most straightforward rule is first-come-first-served (FCFS), where jobs that arrive earlier get higher priority. The so-called earliest due date (EDD) rule gives higher priority to jobs with earlier due time. Jackson (1955) proposed this priority rule and showed that it minimises the maximum of job tardiness. In this thesis OEM

Simulation results

The cost performance of alternative job priority rules is investigated by a simulation study, with parameters derived from a case study OEM retail distribution centre of a multinational consumer electronics manufacturer. Fig. 3 summarises the interactions of this distribution centre with its manufacturer, sales department, retail warehouses and shops, carriers, and labour provider. The order arrival process is determined by the sales department, and due times for order fulfilment are agreed

Some operational implications and conclusions

In this analysis, performance is distinguished along four dimensions by preventing earliness (staging costs), tardiness (demurrage costs), idleness (labour costs), and work-in-process inventories (stock costs). It depends on the business environment which of these dimensions is relevant. Preventing tardiness, for example, is imperative if delayed delivery spoils all product virtues, whereas it is less relevant if delays can be solved by the penalty-free rescheduling of pick-up times. The latter

Acknowledgement

The author wish to acknowledge the anonymous reviewers, Rommert Dekker, and Christiaan Heij for their constructive comments and suggestions.

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