Yr Athro Aris Syntetos

BSc, MSc, DPhil, PGCert, FIMA, FAcadMathSci

2026

• Wang, S. et al., 2026. Maintenance schedules by conditional inference trees. European Journal of Operational Research (10.1016/j.ejor.2026.08.022)
• Rekik, Y. et al., 2026. Inventory record inaccuracy in grocery retailing: impact of promotions and product perishability, and targeted effect of audits. Journal of Business Logistics 47 (3) e70079. (10.1111/jbl.70079)
• Teunter, R. H. , Babai, M. and Syntetos, A. A. 2026. Deriving near-optimality conditions using approximation procedures for an inventory control system with lost sales. International Journal of Production Economics 297 110015. (10.1016/j.ijpe.2026.110015)
• Balugani, E. et al., 2026. Multi-step newsvendors revisited: targeted marketing investment and informativeness. International Journal of Production Research , pp.1-24. (10.1080/00207543.2026.2683882)
• Chaldoupis, K. et al., 2026. Additive manufacturing for standard parts in the healthcare supply chain: What are the available performance improvements?. International Journal of Production Research 64 (10), pp.3919-3937. (10.1080/00207543.2024.2434591)
• Babai, M. Z. , Syntetos, A. A. and Teunter, R. H. 2026. Fifty years of inventory research from a forecasting perspective. European Journal of Operational Research 331 (1)(10.1016/j.ejor.2025.07.003)
• Ponte, B. et al., 2026. On the dynamics of pure remanufacturing systems. IMA Journal of Management Mathematics 37 (2), pp.489-531. dpag001. (10.1093/imaman/dpag001)
• Tziora, E. et al., 2026. On the impact of supply uncertainty on inventory decisions: EOQ-based models. International Journal of Production Research 64 (5), pp.1885-1903. (10.1080/00207543.2025.2606914)

2025

• Glock, C. H. , Syntetos, A. A. and Rekik, Y. 2025. On the measurement of inventory record inaccuracies. The International Review of Retail, Distribution and Consumer Research (10.1080/09593969.2025.2608729)
• Teunter, R. H. , Zied Babai, M. and Syntetos, A. A. 2025. Simple Economic Order Quantity heuristics for stochastic inventory control. IMA Journal of Management Mathematics 36 (4), pp.649-667. (10.1093/imaman/dpaf035)
• Halagmuwe Hewage, H. et al. 2025. A novel hybrid approach to contraceptive demand forecasting: integrating point predictions with probabilistic distributions. International Journal of Production Research (10.1080/00207543.2025.2526169)
• Jin, Z. et al. 2025. Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP. Autonomous Intelligent Systems 5 (1) 2. (10.1007/s43684-024-00088-4)

2024

• Johns, J. et al., 2024. Additive Manufacturing and circular economies. In: Emmit, S. and Charef, R. eds. Circular Economy for the Built Environment: Research and Practice. Routledge. , pp.250-268.
• Jin, Z. et al. 2024. Compact convolutional transformer for bearing remaining useful life prediction. Presented at: IWAR 2023- VII International Workshop on Autonomous Remanufacturing Caserta, Italy 18-19 October 2023. Published in: Fera, M. et al., Advances in Remanufacturing. IWAR 2023. Lecture Notes in Mechanical Engineering Cham, Switzerland: Springer. , pp.227-238. (10.1007/978-3-031-52649-7_18)
• Scarf, P. , Syntetos, A. and Teunter, R. 2024. Joint maintenance and spare-parts inventory models: a review and discussion of practical stock-keeping rules. IMA Journal of Management Mathematics 35 (1), pp.83-109. (10.1093/imaman/dpad020)
• Ford, G. et al. 2024. Simplifying complexity? On quality decision-making and non-conformance outcomes of megaprojects. IEEE Transactions on Engineering Management 71 , pp.5443-5454. (10.1109/TEM.2024.3359821)

2023

• Rostami-Tabar, B. , Babai, Z. and Syntetos, A. 2023. To aggregate or not to aggregate: Forecasting of finite autocorrelated demand. Journal of the Operational Research Society 74 (8), pp.1840-1859. (10.1080/01605682.2022.2118631)
• Wang, S. et al., 2023. Improving automotive garage operations by categorical forecasts using a large number of variables. European Journal of Operational Research 306 (2), pp.893-908. (10.1016/j.ejor.2022.06.062)
• Abolghasemi, M. , Rostami-Tabar, B. and Syntetos, A. 2023. The value of point of sales information in upstream supply chain forecasting: an empirical investigation. International Journal of Production Research 61 (7), pp.2162-2177. (10.1080/00207543.2022.2063086)

2022

• Demir, E. , Syntetos, A. and van Woensel, T. 2022. Last mile logistics: Research trends and needs. IMA Journal of Management Mathematics 33 (4), pp.549-561. (10.1093/imaman/dpac006)
• Petropoulos, F. et al., 2022. Forecasting: theory and practice. International Journal of Forecasting 38 (8), pp.705-871. (10.1016/j.ijforecast.2021.11.001)
• Rostami-Tabar, B. et al. 2022. Forecasting for social good. International Journal of Forecasting 38 (3), pp.1245-1257. (10.1016/j.ijforecast.2021.02.010)
• Lin, J. et al. 2022. Push or Pull? The impact of ordering policy choice on the dynamics of a hybrid closed-loop supply chain. European Journal of Operational Research 300 (1), pp.282-295. (10.1016/j.ejor.2021.10.031)
• Goltsos, T. E. et al. 2022. Inventory - forecasting: mind the gap. European Journal of Operational Research 299 (2), pp.397-419. (10.1016/j.ejor.2021.07.040)
• Babai, Z. et al., 2022. Forecasting of lead-time demand variance: implications for safety stock calculations. European Journal of Operational Research 296 (3), pp.846-861. (10.1016/j.ejor.2021.04.017)
• Best, J. et al., 2022. On the causes of positive inventory discrepancies in retail stores. International Journal of Physical Distribution & Logistics Management 52 (5/6), pp.414-430. (10.1108/IJPDLM-07-2021-0291)

2021

• Prak, D. et al., 2021. Robust compound Poisson parameter estimation for inventory control. Omega 104 102481. (10.1016/j.omega.2021.102481)
• Boylan, J. E. and Syntetos, A. 2021. Intermittent demand forcasting: Context, methods and applications. Wiley.
• Ponte, B. et al., 2021. Quality grading of returns and the dynamics of remanufacturing. International Journal of Production Economics 236 108129. (10.1016/j.ijpe.2021.108129)
• Babai, M. Z. et al., 2021. A compound-Poisson Bayesian approach for spare parts inventory forecasting. International Journal of Production Economics 232 107954. (10.1016/j.ijpe.2020.107954)
• Ren, X. et al., 2021. Global or local? Insights from 10 companies into the long-term impact of COVID-19 on global supply chains.

2020

• Chen, C. et al. 2020. Predictive maintenance using cox proportional hazard deep learning. Advanced Engineering Informatics 44 101054. (10.1016/j.aei.2020.101054)
• Ponte, B. , Naim, M. and Syntetos, A. 2020. The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains. Journal of Remanufacturing 10 , pp.1-14. (10.1007/s13243-019-00070-x)

2019

• Goltsos, T. E. et al. 2019. The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems. International Journal of Production Research 57 (23), pp.7361-7394. (10.1080/00207543.2018.1510191)
• Hedenstierna, C. P. T. et al. 2019. Economies of collaboration in build-to-model operations. Journal of Operations Management 65 (8), pp.753-773. (10.1002/joom.1014)
• Rekik, Y. , Syntetos, A. and Glock, C. 2019. Modelling (and learning from) inventory inaccuracies in e-retailing/B2B contexts. Decision Sciences 50 (6), pp.1184-1223. (10.1111/deci.12367)
• Rekik, Y. , Syntetos, A. A. and Glock, C. H. 2019. Measuring the sales impact of improving inventory records: How improving the accuracy of inventory records can grow sales by 4-8%.
• Ponte, B. , Naim, M. M. and Syntetos, A. A. 2019. The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems. European Journal of Operational Research 278 (2), pp.629-645. (10.1016/j.ejor.2019.04.019)
• Goltsos, T. E. , Syntetos, A. A. and van der Laan, E. 2019. Forecasting for remanufacturing: the effects of serialization. Journal of Operations Management 65 (5), pp.447-467. (10.1002/joom.1031)
• Demir, E. et al. 2019. Vehicle routing problem: Past and future. In: Wells, P. ed. Contemporary Operations and Logistics: Achieving Excellence in Turbulent Times. Cham: Palgrave Macmillan. , pp.97-117. (10.1007/978-3-030-14493-7_7)
• Van der Auweraer, S. , Boute, R. N. and Syntetos, A. 2019. Forecasting spare part demand with installed base information: A review. International Journal of Forecasting 35 (1), pp.181-196. (10.1016/j.ijforecast.2018.09.002)

2018

• Alamri, A. A. and Syntetos, A. A. 2018. Beyond LIFO and FIFO: Exploring an Allocation-In-Fraction-Out (AIFO) policy in a two-warehouse inventory model. International Journal of Production Economics 206 , pp.33-45. (10.1016/j.ijpe.2018.09.025)
• Van Foreest, N. D. , Teunter, R. H. and Syntetos, A. 2018. Base-stock policies with reservations. Omega 81 , pp.48-56. (10.1016/j.omega.2017.09.008)
• Teunter, R. H. et al., 2018. Revisiting the value of information sharing in two-stage supply chains. European Journal of Operational Research 270 (3), pp.1044-1052. (10.1016/j.ejor.2018.04.040)
• Wang, S. et al. 2018. Reliability analysis for automobile engines: conditional inference trees. Procedia CIRP 72 , pp.1392-1397. (10.1016/j.procir.2018.03.065)
• Lahy, A. et al., 2018. Developing a product-service system through a productisation strategy: a case from the 3PL industry. International Journal of Production Research 56 (6), pp.2233-2249. (10.1080/00207543.2017.1367861)
• Chen, C. et al. 2018. Reliability analysis using deep learning. Presented at: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Quebec City, Canada 26-29 August 2018. ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 1B: 38th Computers and Information in Engineering Conference Quebec City, Quebec, Canada, August 26–29, 2018. ASME. , pp.V01BT02A040. (10.1115/DETC2018-86172)
• Demir, E. , Abouarghoub, W. and Syntetos, A. 2018. Modelling the delivery slots for the last mile delivery: Costs and emissions. Presented at: The 23rd International Symposium on Logistics (ISL 2018) Bali, Indonesia 8-11 July 2018. Published in: Pawar, K. S. et al., Proceedings of the 23rd International Symposium on Logistics. Nottingham University Business School,. , pp.422-430.

2017

• Zahedi-Hosseini, F. , Scarf, P. and Syntetos, A. 2017. Joint optimisation of inspection maintenance and spare parts provisioning: a comparative study of inventory policies using simulation and survey data. Reliability Engineering & System Safety 168 , pp.306-316. (10.1016/j.ress.2017.03.007)
• Prak, D. et al., 2017. The Repair Kit Problem with positive replenishment lead times and fixed ordering costs. European Journal of Operational Research 261 (3), pp.893-902. (10.1016/j.ejor.2017.02.019)
• Rekik, Y. , Glock, C. H. and Syntetos, A. A. 2017. Enriching demand forecasts with managerial information to improve inventory replenishment decisions: exploiting judgment and fostering learning. European Journal of Operational Research 261 (1), pp.182-194. (10.1016/j.ejor.2017.02.001)
• Ali, M. M. et al., 2017. Supply chain forecasting when information is not shared. European Journal of Operational Research 260 (3), pp.984-994. (10.1016/j.ejor.2016.11.046)
• Teunter, R. , Syntetos, A. A. and Babai, M. 2017. Stock keeping unit fill rate specification. European Journal of Operational Research 259 (3), pp.917-925. (10.1016/j.ejor.2016.11.017)
• Prak, D. , Teunter, R. and Syntetos, A. 2017. On the calculation of safety stocks when demand is forecasted. European Journal of Operational Research 256 (2), pp.454-461. (10.1016/j.ejor.2016.06.035)

2016

• Alamri, A. A. , Harris, I. and Syntetos, A. A. 2016. Efficient inventory control for imperfect quality items. European Journal of Operational Research 254 (1), pp.92-104. (10.1016/j.ejor.2016.03.058)
• Syntetos, A. et al. 2016. Supply chain forecasting: theory, practice, their gap and the future. European Journal of Operational Research 252 (1), pp.1-26. (10.1016/j.ejor.2015.11.010)
• Syntetos, A. A. , Kholidasari, I. and Naim, M. M. 2016. The effects of integrating management judgement into OUT levels: in or out of context?. European Journal of Operational Research 249 (3), pp.853-863. (10.1016/j.ejor.2015.07.021)
• Syntetos, A. A. et al. 2016. On the benefits of delayed ordering. European Journal of Operational Research 248 (3), pp.963-970. (10.1016/j.ejor.2015.08.003)
• Babai, M. Z. et al., 2016. Reduction of the value of information sharing as demand becomes strongly auto-correlated. International Journal of Production Economics 181 (A), pp.130-135. (10.1016/j.ijpe.2015.05.005)
• Petropoulos, F. et al. 2016. The inventory performance of forecasting methods: a reanalysis of the M3-competition monthly industry data. Presented at: Logistics and Operations Management Section Annual Conference, Cardiff Business School, Cardiff University Cardiff, Wales, UK 15 January 2016.

2015

• Rostami-Tabar, B. et al. 2015. Non-stationary demand forecasting by cross-sectional aggregation. International Journal of Production Economics 170 (Part A), pp.297-309. (10.1016/j.ijpe.2015.10.001)
• Syntetos, A. , Babai, M. Z. and Luo, S. 2015. Forecasting of compound Erlang demand. Journal of the Operational Research Society 66 , pp.2061-2074. (10.1057/jors.2015.27)
• Boylan, J. E. et al., 2015. Reproducibility in forecasting research. International Journal of Forecasting 31 (1), pp.79-90. (10.1016/j.ijforecast.2014.05.008)
• Rekik, Y. , Syntetos, A. and Jemai, Z. 2015. An e-retailing supply chain subject to inventory inaccuracies. International Journal of Production Economics 167 , pp.139-155. (10.1016/j.ijpe.2015.04.011)
• Syntetos, A. , Zied Babai, M. and Gardner, E. S. 2015. Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping. Journal of Business Research 68 (8), pp.1746-1752. (10.1016/j.jbusres.2015.03.034)

2014

• Zied Babai, M. , Syntetos, A. and Teunter, R. 2014. Intermittent demand forecasting: An empirical study on accuracy and the risk of obsolescence. International Journal of Production Economics 157 , pp.212-219. (10.1016/j.ijpe.2014.08.019)
• Rostami-Tabar, B. et al. 2014. A note on the forecast performance of temporal aggregation. Naval Research Logistics 61 (7), pp.489-500. (10.1002/nav.21598)
• Lengu, D. , Syntetos, A. and Babai, M. Z. 2014. Spare parts management: Linking distributional assumptions to demand classification. European Journal of Operational Research 235 (3), pp.624-635. (10.1016/j.ejor.2013.12.043)
• Boylan, J. E. et al., 2014. Formation of seasonal groups and application of seasonal indices. Journal of the Operational Research Society 65 , pp.227-241. (10.1057/jors.2012.126)
• Harris, I. et al. 2014. Horizontal logistics collaboration in the retail sector: myth or reality?. Presented at: LRN Annual Conference and PhD Workshop 2014 Huddersfield, England 3-5 September 2014.
• Sanchez Rodrigues, V. A. et al. 2014. A horizontal logistics collaboration framework from a holistic supply chain perspective. Presented at: 19th International Working Seminar on Production Economics Innsbruck, Austria 24-28 February 2014.

2013

• Babai, M. Z. et al., 2013. Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis. International Journal of Production Economics 143 (2), pp.463-471. (10.1016/j.ijpe.2011.09.004)
• Bacchetti, A. et al., 2013. Empirically-driven hierarchical classification of stock keeping units. International Journal of Production Economics 143 (2), pp.263-274. (10.1016/j.ijpe.2012.06.010)
• Heinecke, G. , Syntetos, A. and Wang, W. 2013. Forecasting-based SKU classification. International Journal of Production Economics 143 (2), pp.455-462. (10.1016/j.ijpe.2011.11.020)
• Mason, R. J. et al. 2013. Evaluating the feasibility of horizontal coordination in horizontal collaboration in international logistics networks: a grocery retail perspective. Presented at: International Symposium on Logistics Vienna, Austria 7-10 July 2013.
• Rostami-Tabar, B. et al. 2013. Demand forecasting by temporal aggregation. Naval Research Logistics 60 (6), pp.479-498. (10.1002/nav.21546)

2012

• Ali, M. M. , Boylan, J. E. and Syntetos, A. 2012. Forecast errors and inventory performance under forecast information sharing. International Journal of Forecasting 28 (4), pp.830-841. (10.1016/j.ijforecast.2010.08.003)
• Syntetos, A. , Babai, M. Z. and Altay, N. 2012. On the demand distributions of spare parts. International Journal of Production Research 50 (8), pp.2101-2117. (10.1080/00207543.2011.562561)

2011

• Teunter, R. H. , Syntetos, A. and Zied Babai, M. 2011. Intermittent demand: Linking forecasting to inventory obsolescence. European Journal of Operational Research 214 (3), pp.606-615. (10.1016/j.ejor.2011.05.018)
• Strijbosch, L. W. et al., 2011. On the interaction between forecasting and stock control: The case of non-stationary demand. International Journal of Production Economics 133 (1), pp.470-480. (10.1016/j.ijpe.2009.10.032)
• Syntetos, A. et al. 2011. Judgement and supply chain dynamics. Journal of the Operational Research Society 62 , pp.1138-1158. (10.1057/jors.2010.56)
• Wang, W. and Syntetos, A. 2011. Spare parts demand: Linking forecasting to equipment maintenance. Transportation Research Part E: Logistics and Transportation Review 47 (6), pp.1194-1209. (10.1016/j.tre.2011.04.008)

2010

• Teunter, R. H. , Syntetos, A. and Babai, M. Z. 2010. Determining order-up-to levels under periodic review for compound binomial (intermittent) demand. European Journal of Operational Research 203 (3), pp.619-624. (10.1016/j.ejor.2009.09.013)
• Babai, M. Z. , Syntetos, A. and Teunter, R. 2010. On the empirical performance of (T,s,S) heuristics. European Journal of Operational Research 202 (2), pp.466-472. (10.1016/j.ejor.2009.05.030)
• Boylan, J. E. and Syntetos, A. 2010. Spare parts management: A review of forecasting research and extensions. IMA Journal of Management Mathematics 21 (3), pp.227-237. (10.1093/imaman/dpp016)
• Martin, H. et al., 2010. Integrating the spare parts supply chain: An inter-disciplinary account. Journal of Manufacturing Technology Management 21 (2), pp.226-245. (10.1108/17410381011014387)
• Nikolopoulos, K. et al. 2010. An aggregate-disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society 62 (3), pp.544-554. (10.1057/jors.2010.32)
• Syntetos, A. et al. 2010. Forecasting and stock control: A study in a wholesaling context. International Journal of Production Economics 127 (1), pp.103-111. (10.1016/j.ijpe.2010.05.001)
• Syntetos, A. and Boylan, J. E. 2010. On the variance of intermittent demand estimates. International Journal of Production Economics 128 (2), pp.546-555. (10.1016/j.ijpe.2010.07.005)
• Syntetos, A. , Nikolopoulos, K. and Boylan, J. E. 2010. Judging the judges through accuracy-implication metrics: the case of inventory forecasting. International Journal of Forecasting 26 (1), pp.134-143. (10.1016/j.ijforecast.2009.05.016)
• Teunter, R. H. , Babai, M. Z. and Syntetos, A. 2010. ABC classification: service levels and inventory costs. Production and Operations Management 19 (3), pp.343-352. (10.1111/j.1937-5956.2009.01098.x)

2009

• Babaï, M. Z. et al., 2009. Dynamic re-order point inventory control with lead-time uncertainty: Analysis and empirical investigation. International Journal of Production Research 47 (9), pp.2461-2483. (10.1080/00207540701666824)
• Syntetos, A. et al. 2009. Periodic control of intermittent demand items: theory and empirical analysis. Journal of the Operational Research Society 60 (5), pp.611-618. (10.1057/palgrave.jors.2602593)
• Syntetos, A. , Boylan, J. E. and Disney, S. M. 2009. Forecasting for inventory planning: a 50-year review. Journal of the Operational Research Society 60 , pp.S149-s160. (10.1057/jors.2008.173)
• Syntetos, A. , Keyes, M. and Babai, M. Z. 2009. Demand categorisation in a European spare parts logistics network. International Journal of Operations & Production Management 29 (3), pp.292-316. (10.1108/01443570910939005)
• Syntetos, A. et al. 2009. The effects of integrating management judgement into intermittent demand forecasts. International Journal of Production Economics 118 (1), pp.72-81. (10.1016/j.ijpe.2008.08.011)

2008

• Boylan, J. E. , Syntetos, A. and Karakostas, G. E. 2008. Classification for forecasting and stock control: a case study. Journal of the Operational Research Society 59 (4), pp.473-481. (10.1057/palgrave.jors.2602312)
• Fildes, R. et al., 2008. Forecasting and operational research: a review. Journal of the Operational Research Society 59 (9), pp.1150-1172. (10.1057/palgrave.jors.2602597)
• Syntetos, A. , Boylan, J. E. and Disney, S. M. 2008. Inventory, forecasting and planning: a 50 year review. Presented at: 50th Annual Conference of the Operational Research Society (OR 50) 2008 York, UK 9-11 September 2008.
• Syntetos, A. and Boylan, J. E. 2008. Demand forecasting adjustments for service-level achievement. IMA Journal of Management Mathematics 19 (2), pp.175-192. (10.1093/imaman/dpm034)

2007

• Boylan, J. and Syntetos, A. 2007. The accuracy of a Modified Croston procedure. International Journal of Production Economics 107 (2), pp.511-517. (10.1016/j.ijpe.2006.10.005)
• Polychronakis, Y. E. and Syntetos, A. 2007. 'Soft' supplier management related issues: An empirical investigation. International Journal of Production Economics 106 (2), pp.431-449. (10.1016/j.ijpe.2006.07.008)

2006

• Syntetos, A. and Boylan, J. E. 2006. On the stock control performance of intermittent demand estimators. International Journal of Production Economics 103 (1), pp.36-47. (10.1016/j.ijpe.2005.04.004)

2005

• Syntetos, A. and Boylan, J. E. 2005. The accuracy of intermittent demand estimates. International Journal of Forecasting 21 (2), pp.303-314. (10.1016/j.ijforecast.2004.10.001)

2004

• Syntetos, A. , Boylan, J. E. and Croston, J. D. 2004. On the categorization of demand patterns. Journal of the Operational Research Society 56 (5), pp.495-503. (10.1057/palgrave.jors.2601841)

2001

• Syntetos, A. and Boylan, J. E. 2001. On the bias of intermittent demand estimates. International Journal of Production Economics 71 (1-3), pp.457-466. (10.1016/S0925-5273(00)00143-2)