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International Journal of Production Research

For a Special Issue on

Supply Chain Forecasting: A Special Issue in Memory of Professor John E. Boylan, 1959-2023

Manuscript deadline
25 December 2024

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Special Issue Editor(s)

M. Zied Babai, Kedge Business School, France
[email protected]

Aris A. Syntetos, Cardiff University, UK
[email protected]

Anna-Lena Sachs , Lancaster University, UK
[email protected]

Huijing Chen, University of Portsmouth, UK
[email protected]

Ivan Svetunkov, Lancaster University, UK
[email protected]

Thanos Goltsos, Cardiff University, UK
[email protected]

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Supply Chain Forecasting: A Special Issue in Memory of Professor John E. Boylan, 1959-2023

Description of the topic

John E. Boylan was a celebrated academic known for his seminal contributions to the field of supply chain forecasting. We dedicate this special issue (SI) to his memory.

Demand forecasts form the basis of most decisions in supply chain management. This is true for most strategic decisions in supply chains, such as the location of new distribution or production centres where long-term, high-level forecasts are used. It also applies to most operational decisions, such as inventory replenishment or workshop jobs scheduling, where short term forecasts are required at the individual stock keeping unit level. Accurate forecasts at all levels are crucial for supply chain performance.

Furthermore, and due to the hierarchical nature of supply chains and to customers’ behaviour, demand can exhibit strange patterns making forecasting a very challenging task. Specific demand modelling approaches, such as state space, autoregressive integrated moving average (ARIMA), and integer autoregressive moving average (INARMA), amongst others, are being used to deal with both fast- and slow-moving items. Various strategies have been proposed in the literature to complement forecasting and improve forecast performance, including demand aggregation, forecast information sharing, and categorisation-based forecasts.

Given the challenges and the advances in this research field, the International Journal of Production Research is pleased to call for papers on various topics related to supply chain forecasting. The guest editors invite authors to submit their research to this special issue. Theoretical and/or empirical contributions that significantly advance the field of supply chain forecasting will be considered for publication. Authors are also reminded to consider the interests of the journal’s readers and to focus on the interaction between forecasting and supply chain management decisions. Submissions may address, but are not restricted to, the following topics, which were the main areas of John Boylan’s contributions over the years.

  • Demand categorisation
  • Intermittent demand forecasting
  • Spare parts demand forecasting
  • Forecast information sharing in supply chains
  • Supply chain trend and seasonal demand forecasting
  • Aggregation and hierarchical forecasting in supply chains
  • Supply chain demand modelling (State space formulations, ARIMA, INARMA)
  • Interface between forecasting and inventory management
  • Forecasting in reverse logistics and closed loop supply chains
  • (Interface between statistical and) judgemental forecasting
  • Forecasting within system approaches
  • Forecast performance measurement
  • Non-parametric forecasting approaches

Submission Instructions

Manuscripts should be prepared following the information on IJPR's instructions for authors, and submission made using Taylor & Francis submission portal, both of which can be accessed via the IJPR homepage. When you submit your paper, please indicate that this is intended for the SI on 'Supply Chain Forecasting: In memory of Professor John Boylan'. If you have any enquiries or if you need to discuss whether any work-in-progress of yours fits the scope of the SI please contact the guest editors. All papers will be refereed according to the standards of the International Journal of Production Research.

Publication schedule

Manuscript submission: December 25, 2024

Reviewers’ reports: April, 2025

Revised paper submission: June, 2025

Submission to publisher: October, 2025

Publication: January, 2026 (vol. 64)

Instructions for AuthorsSubmit an Article