Advanced search
Publication Cover
Journal of the Operational Research Society Volume 56, 2005 - Issue 5
Submit an article Journal homepage
2,818
Views
233
CrossRef citations to date
0
Altmetric
Case-Oriented Paper

On the categorization of demand patterns

A A SyntetosUniversity of SalfordGreater ManchesterUKCorrespondence[email protected]
,
J E BoylanBuckinghamshire Chilterns University CollegeBucksUK
&
J D CrostonIndependent Systems and Statistical ConsultantHertsUK
Pages 495-503 | Received 01 Nov 2003, Accepted 01 Jun 2004, Published online: 21 Dec 2017

Citations (233)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (26)

Rodrigue Fokouop, Evren Sahin, Zied Jemai & Yves Dallery. (2024) A heuristic approach for multi-echelon inventory optimisation in a closed-loop supply chain. International Journal of Production Research 62:10, pages 3435-3459.
Read now
Chen-Fu Chien, Hsuan-An Kuo & Yun-Siang Lin. (2024) Smart semiconductor manufacturing for pricing, demand planning, capacity portfolio and cost for sustainable supply chain management. International Journal of Logistics Research and Applications 27:1, pages 193-216.
Read now
Li Li, Yanfei Kang, Fotios Petropoulos & Feng Li. (2023) Feature-based intermittent demand forecast combinations: accuracy and inventory implications. International Journal of Production Research 61:22, pages 7557-7572.
Read now
Ahlam Azzamouri, Pierre Baptiste, Robert Pellerin & Guillaume Dessevre. (2023) Impact of the continuous and periodic assessment of a buffer replenishment on the DDMRP method. International Journal of Production Research 61:16, pages 5637-5658.
Read now
M. Zied Babai, John E. Boylan & Bahman Rostami-Tabar. (2022) Demand forecasting in supply chains: a review of aggregation and hierarchical approaches. International Journal of Production Research 60:1, pages 324-348.
Read now
Peng Jiang, Yibin Huang & Xiao Liu. (2021) Intermittent demand forecasting for spare parts in the heavy-duty vehicle industry: a support vector machine model. International Journal of Production Research 59:24, pages 7423-7440.
Read now
Chen-Fu Chien, Yun-Siang Lin & Sheng-Kai Lin. (2020) Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor. International Journal of Production Research 58:9, pages 2784-2804.
Read now
F. Lolli, E. Balugani, A. Ishizaka, R. Gamberini, B. Rimini & A. Regattieri. (2019) Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control 30:1, pages 76-89.
Read now
Ville Sillanpää & Juuso Liesiö. (2018) Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions. International Journal of Production Research 56:12, pages 4168-4185.
Read now
Manuel Cardós, Ester Guijarro & Eugenia Babiloni. (2017) On the estimation of on-hand stocks for base-stock policies and lost sales systems and its impact on service measures. International Journal of Production Research 55:16, pages 4680-4694.
Read now
Qamar Iqbal, Don Malzahn & Lawrence Whitman. (2017) Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides. Production & Manufacturing Research 5:1, pages 15-39.
Read now
Xun Wang & Fotios Petropoulos. (2016) To select or to combine? The inventory performance of model and expert forecasts. International Journal of Production Research 54:17, pages 5271-5282.
Read now
Aris A Syntetos, Mohamed Zied Babai & Shuxin Luo. (2015) Forecasting of compound Erlang demand. Journal of the Operational Research Society 66:12, pages 2061-2074.
Read now
Fotios Petropoulos & Nikolaos Kourentzes. (2015) Forecast combinations for intermittent demand. Journal of the Operational Research Society 66:6, pages 914-924.
Read now
M.Z. Babai, T. Ladhari & I. Lajili. (2015) On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research 53:1, pages 279-290.
Read now
A.A. Syntetos, M.Z. Babai & N. Altay. (2012) On the demand distributions of spare parts. International Journal of Production Research 50:8, pages 2101-2117.
Read now
K Nikolopoulos, A A Syntetos, J E Boylan, F Petropoulos & V Assimakopoulos. (2011) An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society 62:3, pages 544-554.
Read now
A A Syntetos, M Z Babai, Y Dallery & R Teunter. (2009) Periodic control of intermittent demand items: theory and empirical analysis. Journal of the Operational Research Society 60:5, pages 611-618.
Read now
R Fildes, K Nikolopoulos, S F Crone & A A Syntetos. (2008) Forecasting and operational research: a review. Journal of the Operational Research Society 59:9, pages 1150-1172.
Read now
Alexandre Dolgui & Maksim Pashkevich. (2008) Extended beta-binomial model for demand forecasting of multiple slow-moving inventory items. International Journal of Systems Science 39:7, pages 713-726.
Read now
J E Boylan, A A Syntetos & G C Karakostas. (2008) Classification for forecasting and stock control: a case study. Journal of the Operational Research Society 59:4, pages 473-481.
Read now
A V Kostenko & R J Hyndman. (2006) A note on the categorization of demand patterns. Journal of the Operational Research Society 57:10, pages 1256-1257.
Read now
A A Syntetos, J E Boylan & J D Croston. (2006) Reply to Kostenko and Hyndman. Journal of the Operational Research Society 57:10, pages 1257-1258.
Read now
Josef Schosser. Beyond point forecasts: Uncertainty quantification in tensor extrapolation for relational time series data. Communications in Statistics: Case Studies, Data Analysis and Applications 0:0, pages 1-18.
Read now
Emna Turki, Oualid Jouini, Zied Jemai, Yazid Traiy, Adnane Lazrak, Patrick Valot & Robert Heidseick. Forecasting spare part extractions from returned systems in a closed-loop supply chain. International Journal of Production Research 0:0, pages 1-17.
Read now
Marwa Hasni, M. Zied Babai & Bahman Rostami-Tabar. A hybrid LSTM method for forecasting demands of medical items in humanitarian operations. International Journal of Production Research 0:0, pages 1-18.
Read now

Articles from other publishers (207)

Shengjie Wang, Yanfei Kang & Fotios Petropoulos. (2024) Combining probabilistic forecasts of intermittent demand. European Journal of Operational Research 315:3, pages 1038-1048.
Crossref
Arnoud P. Wellens, Robert N. Boute & Maximiliano Udenio. (2024) Simplifying tree-based methods for retail sales forecasting with explanatory variables. European Journal of Operational Research 314:2, pages 523-539.
Crossref
Ashish K Singh, J B Simha & Rashmi Agarwal. (2024) Prediction of Intermittent Demand Occurrence using Machine Learning. EAI Endorsed Transactions on Internet of Things 10.
Crossref
Tássia Bolotari Affonso, Samuel Vieira Conceição, Leandro Reis Muniz, João Flávio de Freitas Almeida & Juliana Cássia de Lima. (2024) A new hybrid forecasting method for spare part inventory management using heuristics and bootstrapping. Decision Analytics Journal 10, pages 100415.
Crossref
Corey Ducharme, Bruno Agard & Martin Trépanier. (2024) Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering. Journal of Forecasting.
Crossref
Ewelina Chołodowicz & Przemysław Orłowski. (2024) Neural Network Control of Perishable Inventory with Fixed Shelf Life Products and Fuzzy Order Refinement under Time-Varying Uncertain Demand. Energies 17:4, pages 849.
Crossref
Eduardo E. Romanus, Eugênio Silva & Ronaldo R. Goldschmidt. (2024) Empirical probabilistic forecasting: An approach solely based on deterministic explanatory variables for the selection of past forecast errors. International Journal of Forecasting 40:1, pages 184-201.
Crossref
Lilli Lagoda & Matthias Klumpp. 2024. Dynamics in Logistics. Dynamics in Logistics 390 405 .
Diksha Shrivastava, Sarthak Pujari, Yatin Katyal, Siddhartha Asthana, Chandrudu K & Aakashdeep Singh. (2023) INFRANET: Forecasting intermittent time series using DeepNet with parameterized conditional demand and size distribution. INFRANET: Forecasting intermittent time series using DeepNet with parameterized conditional demand and size distribution.
Pierre DodinJingyi XiaoYossiri Adulyasak, Neda Etebari AlamdariLea GauthierPhilippe GrangierPaul LemaitreWilliam L. Hamilton. (2023) Bombardier Aftermarket Demand Forecast with Machine Learning. INFORMS Journal on Applied Analytics 53:6, pages 425-445.
Crossref
Chen-Fu Chien, Chien-Chun Ku & Yi-Yun Lu. (2023) Ensemble learning for demand forecast of After-Market spare parts to empower data-driven value chain and an empirical study. Computers & Industrial Engineering 185, pages 109670.
Crossref
Katerina Huskova & Jakub Dyntar. (2023) Inventory Control of Products at the End of their Lifecycle Based on Nonparametric Methods. Inventory Control of Products at the End of their Lifecycle Based on Nonparametric Methods.
Reinard C. Ganzevoort & Jan H. van Vuuren. (2023) A two-phased cluster-based approach towards ranked forecast-model selection. Machine Learning with Applications 13, pages 100482.
Crossref
Lars M. Heijenrath & Wim J. C. Verhagen. (2023) The Influence of Repair Quality on Aircraft Spare Part Demand Variability. Aerospace 10:8, pages 731.
Crossref
Phattaraporn Kalaya, Preecha Termsuksawad & Thananya Wasusri. (2023) Forecasting and Inventory Planning for Irregular Demand Patterns. International Journal of Knowledge and Systems Science 14:1, pages 1-21.
Crossref
Ernesto Armando Pacheco-Velázquez, Manuel Robles-Cárdenas, Saúl Juárez Ordóñez, Abelardo Ernesto Damy Solís & Leopoldo Eduardo Cárdenas-Barrón. (2023) A Heuristic Model for Spare Parts Stocking Based on Markov Chains. Mathematics 11:16, pages 3550.
Crossref
Emre Cakmak & Eda Guney. (2023) Spare parts inventory classification using Neutrosophic Fuzzy EDAS method in the aviation industry. Expert Systems with Applications 224, pages 120008.
Crossref
Xin Song, Daofang Chang & Tian Luo. (2023) A Single-Product Multi-Period Inventory Routing Problem under Intermittent Demand. Information 14:6, pages 331.
Crossref
Ewelina Cholodowicz & Przemyslaw Orlowski. (2023) Switching robust neural network control of perishable inventory with fixed shelf life products under time-varying uncertain demand. Journal of Computational Science 70, pages 102035.
Crossref
Rodrigo Sarlo, Cristiano Fernandes & Denis Borenstein. (2023) Lumpy and intermittent retail demand forecasts with score-driven models. European Journal of Operational Research 307:3, pages 1146-1160.
Crossref
Aura Jalal, Eli Angela Vitor Toso & Reinaldo Morabito. (2023) A location–transportation problem under demand uncertainty for a pharmaceutical network in Brazil. Computers & Chemical Engineering 174, pages 108233.
Crossref
Lilin Fan, Xia Liu, Wentao Mao, Kai Yang & Zhaoyu Song. (2023) Spare Parts Demand Forecasting Method Based on Intermittent Feature Adaptation. Entropy 25:5, pages 764.
Crossref
Kamal Sanguri, Sabyasachi Patra & Sushil Punia. (2023) Forecast reconciliation in the temporal hierarchy: Special case of intermittent demand with obsolescence. Expert Systems with Applications 218, pages 119566.
Crossref
Ferhat Yuna, Burak Erkayman & Mustafa Yılmaz. (2023) Inventory control model for intermittent demand: a comparison of metaheuristics. Soft Computing 27:10, pages 6487-6505.
Crossref
Wenhan Fu, Sheng Jing, Qinming Liu & Hao Zhang. (2023) Resilient Supply Chain Framework for Semiconductor Distribution and an Empirical Study of Demand Risk Inference. Sustainability 15:9, pages 7382.
Crossref
Anastasios Kaltsounis, Evangelos Spiliotis & Vassilios Assimakopoulos. (2023) Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning. Algorithms 16:4, pages 206.
Crossref
Hsuan-An Kuo & Chen-Fu Chien. (2023) Semiconductor capacity expansion based on forecast evolution and mini-max regret strategy for smart production under demand uncertainty. Computers & Industrial Engineering 177, pages 109077.
Crossref
Eugenia Babiloni, Ester Guijarro & Juan R. Trapero. (2023) Stock control analytics: a data-driven approach to compute the fill rate considering undershoots. Operational Research 23:1.
Crossref
Felix Schmidt & Richard Pibernik. (2023) Data-Driven Inventory Control For Large Product Portfolios: A Practical Application of Prescriptive Analytics. SSRN Electronic Journal.
Crossref
Joost van der Haar, Arnoud P. Wellens, Robert N. Boute & Rob J.I. Basten. (2023) Supervised Learning for Integrated Forecasting and Inventory Control. SSRN Electronic Journal.
Crossref
Marta Rinaldi, Marcello Fera, Roberto Macchiaroli & Eleonora Bottani. (2023) A new procedure for spare parts inventory management in ETO production: a case study. Procedia Computer Science 217, pages 376-385.
Crossref
Evangelos Theodorou, Evangelos Spiliotis & Vassilios Assimakopoulos. (2023) Optimizing inventory control through a data-driven and model-independent framework. EURO Journal on Transportation and Logistics 12, pages 100103.
Crossref
Harsora Karn & Ajay Gupta. 2023. Applications of Emerging Technologies and AI/ML Algorithms. Applications of Emerging Technologies and AI/ML Algorithms 257 266 .
Yang Zhang, Hongyi Zhu, Yujing Wang & Tianchen Li. (2022) Demand Forecasting: From Machine Learning to Ensemble Learning. Demand Forecasting: From Machine Learning to Ensemble Learning.
Yuan Ye, Yonggang Lu, Powell Robinson & Arunachalam Narayanan. (2022) An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control. European Journal of Operational Research 303:1, pages 255-272.
Crossref
Mouna Derbel, Awad M. Aljuaid & Wafik Hachicha. (2022) Empirical Safety Stock Estimation Using GARCH Model, Historical Simulation, and Extreme Value Theory: A Comparative Study. Applied Sciences 12:19, pages 10023.
Crossref
Spyros Makridakis, Evangelos Spiliotis & Vassilios Assimakopoulos. (2022) M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting 38:4, pages 1346-1364.
Crossref
Spyros Makridakis, Evangelos Spiliotis & Vassilios Assimakopoulos. (2022) Predicting/hypothesizing the findings of the M5 competition. International Journal of Forecasting 38:4, pages 1337-1345.
Crossref
Spyros Makridakis, Evangelos Spiliotis & Vassilios Assimakopoulos. (2022) The M5 competition: Background, organization, and implementation. International Journal of Forecasting 38:4, pages 1325-1336.
Crossref
Robert Fildes, Shaohui Ma & Stephan Kolassa. (2022) Retail forecasting: Research and practice. International Journal of Forecasting 38:4, pages 1283-1318.
Crossref
Erjiang E, Ming Yu, Xin Tian & Ye Tao. (2022) Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting. Mathematics 10:17, pages 3179.
Crossref
Martim Sousa, Ana Maria Tomé & José Moreira. (2022) Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality. Data Science and Management 5:3, pages 137-148.
Crossref
Emre EKİN. (2022) An Application on Demand Forecasting and Stock Control for Aircraft ComponentsUçak Komponentleri İçin Talep Tahmini Ve Stok Kontrolüne İlişkin Bir Uygulama. Havacılık ve Uzay Çalışmaları Dergisi 3:1, pages 1-40.
Crossref
Jože Martin Rožanec, Blaž Fortuna & Dunja Mladenić. (2022) Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand. Sustainability 14:15, pages 9295.
Crossref
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova & Florian Ziel. (2022) Forecasting: theory and practice. International Journal of Forecasting 38:3, pages 705-871.
Crossref
Evangelos Spiliotis, Spyros Makridakis, Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos. (2020) Comparison of statistical and machine learning methods for daily SKU demand forecasting. Operational Research 22:3, pages 3037-3061.
Crossref
Xiaotian Zhuang, Ying Yu & Aihui Chen. (2022) A combined forecasting method for intermittent demand using the automotive aftermarket data. Data Science and Management 5:2, pages 43-56.
Crossref
Jože M. Rožanec, Blaž Fortuna & Dunja Mladenić. (2022) Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI). Information Fusion 81, pages 91-102.
Crossref
Jože Rožanec, Elena Trajkova, Inna Novalija, Patrik Zajec, Klemen Kenda, Blaž Fortuna & Dunja Mladenić. (2022) Enriching Artificial Intelligence Explanations with Knowledge Fragments. Future Internet 14:5, pages 134.
Crossref
Yi Yang, Jiaying Gu, Siyu Huang, Meilin Wen, Yong Qin, Wei Liu & Linhan Guo. (2022) Spare Parts Transportation Optimization Considering Supportability Based on Uncertainty Theory. Symmetry 14:5, pages 891.
Crossref
Karzan Mahdi Ghafour. (2022) Establishing inventory measurements using intermittent demand forecasting. Journal of Intelligent & Fuzzy Systems 42:4, pages 3465-3475.
Crossref
Alessandro Tufano, Riccardo Accorsi & Riccardo Manzini. (2021) A machine learning approach for predictive warehouse design. The International Journal of Advanced Manufacturing Technology 119:3-4, pages 2369-2392.
Crossref
Ester Guijarro, Eugenia Babiloni & Manuel Cardós. (2022) On the estimation of the fill rate for the continuous (s, S) inventory system for the lost sales context. PLOS ONE 17:2, pages e0263655.
Crossref
Jianqiang Gong, Yaping Luo, Zhaowen Qiu & Xiangdong Wang. (2022) Determination of key components in automobile braking systems based on ABC classification and FMECA. Journal of Traffic and Transportation Engineering (English Edition) 9:1, pages 69-77.
Crossref
R. Fokouop, Z. Jemai, E. Sahin & Y. Dallery. (2022) Multi-echelon inventory optimization in closed-loop supply chain. IFAC-PapersOnLine 55:10, pages 2992-2997.
Crossref
Albert LiaoAlbert Liao. 2022. Warranty Chain Management. Warranty Chain Management 115 140 .
Guodong Yi, Lifan Yi & Lu Wang. 2022. Advances in Mechanical Design. Advances in Mechanical Design 1823 1832 .
Hadid Wahedi, Kacper Wrona, Mads Heltoft, Sarkaft Saleh, Thomas Roum Knudsen, Ulrik Bendixen, Izabela Nielsen, Subrata Saha & Gregers Sandager Borup. 2022. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action 3 10 .
Kyle C. McDermott, Ryan D. Winz, Thom J. Hodgson, Michael G. Kay, Russell E. King & Brandon M. McConnell. (2021) Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand. Journal of Defense Analytics and Logistics 5:2, pages 179-213.
Crossref
Wenjia Chen & Jinlin Li. (2021) Teleconsultation demand classification and service analysis. BMC Medical Informatics and Decision Making 21:1.
Crossref
Çerağ Pinçe, Laura Turrini & Joern Meissner. (2021) Intermittent demand forecasting for spare parts: A Critical review. Omega 105, pages 102513.
Crossref
Ali Caner Türkmen, Tim Januschowski, Yuyang Wang & Ali Taylan Cemgil. (2021) Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. PLOS ONE 16:11, pages e0259764.
Crossref
Patrik Zajec, Jože M. Rožanec, Elena Trajkova, Inna Novalija, Klemen Kenda, Blaž Fortuna & Dunja Mladenić. (2021) Help Me Learn! Architecture and Strategies to Combine Recommendations and Active Learning in Manufacturing. Information 12:11, pages 473.
Crossref
Agaraoli Aravazhi. (2021) Hybrid Machine Learning Models for Forecasting Surgical Case Volumes at a Hospital. AI 2:4, pages 512-526.
Crossref
Howard Hao‐Chun Chuang, Yen‐Chun Chou & Rogelio Oliva. (2021) Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics . Journal of Operations Management 67:7, pages 828-852.
Crossref
Xin Tian, Haoqing Wang & Erjiang E. (2021) Forecasting intermittent demand for inventory management by retailers: A new approach. Journal of Retailing and Consumer Services 62, pages 102662.
Crossref
Jože M. Rožanec, Blaž Kažič, Maja Škrjanc, Blaž Fortuna & Dunja Mladenić. (2021) Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies. Applied Sciences 11:15, pages 6787.
Crossref
Karthikeswaren R, Kanishka Kayathwal, Gaurav Dhama & Ankur Arora. (2021) A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods. A Survey on Classical and Deep Learning based Intermittent Time Series Forecasting Methods.
John Boylan & Aris Syntetos. 2021. Intermittent Demand Forecasting. Intermittent Demand Forecasting 347 364 .
John Boylan & Aris Syntetos. 2021. Intermittent Demand Forecasting. Intermittent Demand Forecasting 305 327 .
John Boylan & Aris Syntetos. 2021. Intermittent Demand Forecasting. Intermittent Demand Forecasting 243 262 .
Andrew Johnson, Steven Carnovale, Ju Myung Song & Yao Zhao. (2021) Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis. International Journal of Production Economics 237, pages 108138.
Crossref
Thi-Nha Ngo, Dan Jeric Arcega Rustia, En-Cheng Yang & Ta-Te Lin. (2021) Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks. Sensors 21:11, pages 3900.
Crossref
Konstantinos Nikolopoulos. (2021) We need to talk about intermittent demand forecasting. European Journal of Operational Research 291:2, pages 549-559.
Crossref
Xiaolei Hu, Xue Zhang & Dai Zhang. (2021) A Novel Approach of Intermittent Demand Prediction in Industrial Domain. Journal of Physics: Conference Series 1756:1, pages 012009.
Crossref
M.Z. Babai, H. Chen, A.A. Syntetos & D. Lengu. (2021) A compound-Poisson Bayesian approach for spare parts inventory forecasting. International Journal of Production Economics 232, pages 107954.
Crossref
Amri Muhaimin, Dedy Dwi Prastyo & Henry Horng-Shing Lu. (2021) Forecasting with Recurrent Neural Network in Intermittent Demand Data. Forecasting with Recurrent Neural Network in Intermittent Demand Data.
Merve Şahin, Fahrettin Eldemir & Ali Turkyilmaz. (2021) Inventory Cost Minimization of Spare Parts in Aviation Industry. Transportation Research Procedia 59, pages 29-37.
Crossref
Z. Kenzhevayeva, A. Katayeva, K. Kaikenova, A. Sarsembayeva, M.Z. Babai, M. Tsakalerou & C.T. Papadopoulos. (2021) Inventory control models for spare parts in aviation logistics. Procedia Manufacturing 55, pages 507-512.
Crossref
M. Baisariyev, A. Bakytzhanuly, Y. Serik, B. Mukhanova, M.Z. Babai, M. Tsakalerou & C.T. Papadopoulos. (2021) Demand forecasting methods for spare parts logistics for aviation: a real-world implementation of the Bootstrap method. Procedia Manufacturing 55, pages 500-506.
Crossref
Mariusz Doszyń. (2021) Expert Systems in Intermittent Demand Forecasting. Procedia Computer Science 192, pages 3598-3606.
Crossref
Nikolaos Kourentzes & George Athanasopoulos. (2021) Elucidate structure in intermittent demand series. European Journal of Operational Research 288:1, pages 141-152.
Crossref
Daniel Kiefer, Markus Bauer & Florian Grimm. 2021. Human Centred Intelligent Systems. Human Centred Intelligent Systems 152 162 .
German Wehinger & Josh Beal. 2021. Data Science – Analytics and Applications. Data Science – Analytics and Applications 24 30 .
Peipei Liu. (2020) Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic. Health Informatics Journal 26:4, pages 3106-3122.
Crossref
Peipei Liu, Wei Ming & Chuanchao Huang. (2020) Intelligent modeling of abnormal demand forecasting for medical consumables in smart city. Environmental Technology & Innovation 20, pages 101069.
Crossref
Ana Luísa Ferreira Andrade Ramos, José António Vasconcelos Ferreira & Sara Rego da Costa. (2020) The Impact of the Handling Unit on Logistics Costs: The Case of a Portuguese Food Retail Supply Chain. International Journal of Mathematical, Engineering and Management Sciences 5:5, pages 835-850.
Crossref
M Z Babai, A Tsadiras & C Papadopoulos. (2020) On the empirical performance of some new neural network methods for forecasting intermittent demand. IMA Journal of Management Mathematics 31:3, pages 281-305.
Crossref
Mahdi Abolghasemi, Eric Beh, Garth Tarr & Richard Gerlach. (2020) Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. Computers & Industrial Engineering 142, pages 106380.
Crossref
Asnat Greenstein-Messica & Lior Rokach. (2020) Machine learning and operation research based method for promotion optimization of products with no price elasticity history. Electronic Commerce Research and Applications 40, pages 100914.
Crossref
Jie Liu, Lin Lin, Zhen Li, Hao Guo & Yancheng Lv. (2020) Spare Aeroengine Demand Prediction Model Based on Deep Croston Method. Journal of Aerospace Information Systems 17:2, pages 125-133.
Crossref
Saymon Galvão Bandeira, Symone Gomes Soares Alcalá, Roberto Oliveira Vita & Talles Marcelo Gonçalves de Andrade Barbosa. (2020) Comparison of selection and combination strategies for demand forecasting methods. Production 30.
Crossref
Daniel M. Baquero, Galo Mosquera-Recalde & Sonia Valeria Avilés-Sacoto. 2020. Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems. Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems 157 190 .
Duarte Dinis, Ana Barbosa-Póvoa & Ângelo Palos Teixeira. (2019) A supporting framework for maintenance capacity planning and scheduling: Development and application in the aircraft MRO industry. International Journal of Production Economics 218, pages 1-15.
Crossref
Lei, Li & Yu. (2019) Demand Forecasting Approaches Based on Associated Relationships for Multiple Products. Entropy 21:10, pages 974.
Crossref
Mariassunta Viggiano, Lorenzo Busetto, Domenico Cimini, Francesco Di Paola, Edoardo Geraldi, Luigi Ranghetti, Elisabetta Ricciardelli & Filomena Romano. (2019) A new spatial modeling and interpolation approach for high-resolution temperature maps combining reanalysis data and ground measurements. Agricultural and Forest Meteorology 276-277, pages 107590.
Crossref
Riccardo Patriarca, Tianya Hu, Francesco Costantino, Giulio Di Gravio & Massimo Tronci. (2019) A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution. Sustainability 11:19, pages 5180.
Crossref
Wenhan Fu & Chen-Fu Chien. (2019) UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution. Computers & Industrial Engineering 135, pages 940-949.
Crossref
Mariusz Doszyń. (2019) Intermittent demand forecasting in the Enterprise: Empirical verification. Journal of Forecasting 38:5, pages 459-469.
Crossref
Thanos E. Goltsos, Aris A. Syntetos & Erwin van der Laan. (2019) Forecasting for remanufacturing: The effects of serialization. Journal of Operations Management 65:5, pages 447-467.
Crossref
Aslı Boru, Ayşe Tuğba Dosdoğru, Mustafa Göçken & Rızvan Erol. (2019) A Novel Hybrid Artificial Intelligence Based Methodology for the Inventory Routing Problem. Symmetry 11:5, pages 717.
Crossref
Yu-Chung Tsao, Nani Kurniati, I. Nyoman Pujawan & Alvin Muhammad 'Ainul Yaqin. (2019) Spare Parts Demand Forecasting in Energy Industry. Spare Parts Demand Forecasting in Energy Industry.
Hendra, Humala L Napitupulu, Harmien Nasution & Juliza Hidayati. (2019) Portfolio Effect on End User Spare parts based on Demand Patterns. IOP Conference Series: Materials Science and Engineering 505:1, pages 012075.
Crossref
M. Hasni, M.Z. Babai, M.S. Aguir & Z. Jemai. (2019) An investigation on bootstrapping forecasting methods for intermittent demands. International Journal of Production Economics 209, pages 20-29.
Crossref
Nikolaos Kourentzes, Dong Li & Arne K. Strauss. (2017) Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management 18:1, pages 27-41.
Crossref
Laura Turrini & Joern Meissner. (2019) Spare parts inventory management: New evidence from distribution fitting. European Journal of Operational Research 273:1, pages 118-130.
Crossref
Ignacio Aranís Mahuzier, Pablo A. Viveros Gunckel, Rodrigo Mena Bustos, Christopher Nikulin Chandía & Vicente González-Prida Díaz. 2019. Handbook of Research on Industrial Advancement in Scientific Knowledge. Handbook of Research on Industrial Advancement in Scientific Knowledge 247 263 .
Tonya Boone, John E. Boylan, Robert Fildes, Ram Ganeshan & Nada Sanders. (2019) Perspectives on supply chain forecasting. International Journal of Forecasting 35:1, pages 121-127.
Crossref
Fotios Petropoulos, Xun Wang & Stephen M. Disney. (2019) The inventory performance of forecasting methods: Evidence from the M3 competition data. International Journal of Forecasting 35:1, pages 251-265.
Crossref
F. Wang, H. Y. Ng & T. E. Ng. (2018) Novel SKU Classification Approach for Autonomous Inventory Planning. Novel SKU Classification Approach for Autonomous Inventory Planning.
R. Babajanivalashedi, A. Baboli, M. K. Shahzad & R. Tonadre. (2018) A Predictive Approach to Define the Best Forecasting Method for Spare Parts: A Case Study in Business Aircrafts’ Industry. A Predictive Approach to Define the Best Forecasting Method for Spare Parts: A Case Study in Business Aircrafts’ Industry.
Francesco Costantino, Giulio Di Gravio, Riccardo Patriarca & Lea Petrella. (2018) Spare parts management for irregular demand items. Omega 81, pages 57-66.
Crossref
Konstantinos V. Katsikopoulos, Ian N. Durbach & Theodor J. Stewart. (2018) When should we use simple decision models? A synthesis of various research strands. Omega 81, pages 17-25.
Crossref
Janet Godsell, Donato Masi, Antonios Karatzas & Timothy Mark Brady. (2018) Using project demand profiling to improve the effectiveness and efficiency of infrastructure projects. International Journal of Operations & Production Management 38:6, pages 1422-1442.
Crossref
Roseline Ezekwesili, M. K. Shahzad, A. Baboli & R. Tonadre. (2018) Optimizing Bootstrap method to improve forecasting accuracy in business jet spare parts supply chains. Optimizing Bootstrap method to improve forecasting accuracy in business jet spare parts supply chains.
Qiwei Hu, John E. Boylan, Huijing Chen & Ashraf Labib. (2018) OR in spare parts management: A review. European Journal of Operational Research 266:2, pages 395-414.
Crossref
Chunghun Ha, Hyesung Seok & Changsoo Ok. (2018) Evaluation of forecasting methods in aggregate production planning: A Cumulative Absolute Forecast Error (CAFE). Computers & Industrial Engineering 118, pages 329-339.
Crossref
E. Balugani, F. Lolli, R. Gamberini, B. Rimini & A. Regattieri. (2018) Clustering for inventory control systems. IFAC-PapersOnLine 51:11, pages 1174-1179.
Crossref
Wenhan Fu, Chen-Fu Chien & Zih-Hao Lin. 2018. Advances in Production Management Systems. Smart Manufacturing for Industry 4.0. Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 65 72 .
Luís Miguel D. F. Ferreira, Amílcar Arantes & Cristóvão Silva. 2018. Operations Research and Enterprise Systems. Operations Research and Enterprise Systems 210 223 .
Merve Sahin & Fahrettin Eldemir. 2018. Industrial Engineering in the Industry 4.0 Era. Industrial Engineering in the Industry 4.0 Era 159 171 .
E. Guijarro, E. Babiloni & M. Cardós. 2018. Closing the Gap Between Practice and Research in Industrial Engineering. Closing the Gap Between Practice and Research in Industrial Engineering 161 168 .
Matthew Lindsey & Robert Pavur. 2017. Advances in Business and Management Forecasting. Advances in Business and Management Forecasting 121 135 .
F. Lolli, A. Ishizaka, R. Gamberini & B. Rimini. (2017) A multicriteria framework for inventory classification and control with application to intermittent demand. Journal of Multi-Criteria Decision Analysis 24:5-6, pages 275-285.
Crossref
Ming Lei, Zihan Yin, Shalang Li & Qian Tan. (2017) Intermittent demand forecasting and inventory control with multiple temporal and cross-sectional aggregation and disaggregation methods. Intermittent demand forecasting and inventory control with multiple temporal and cross-sectional aggregation and disaggregation methods.
K. Nemati Amirkolaii, A. Baboli, M.K. Shahzad & R. Tonadre. (2017) Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI). IFAC-PapersOnLine 50:1, pages 15221-15226.
Crossref
P. Jiang, X. Liu, Y. Huang & Y. Yuan. (2017) An Adaptive Data-driven Model for Erratic Demand Forecasting. IFAC-PapersOnLine 50:1, pages 9020-9025.
Crossref
Shabnam Rezapour, Janet K. Allen & Farrokh Mistree. (2017) Reliable flow in stochastic supply networks servicing pre- and after-sales markets. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31:2, pages 120-142.
Crossref
Clint L.P. Pennings, Jan van Dalen & Erwin A. van der Laan. (2017) Exploiting elapsed time for managing intermittent demand for spare parts. European Journal of Operational Research 258:3, pages 958-969.
Crossref
Qamar Iqbal, Don Malzahn & Lawrence E. Whitman. (2017) Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate. Advances in Decision Sciences 2017, pages 1-11.
Crossref
A T Bon & T K Ng. (2017) An Optimization of Inventory Demand Forecasting in University Healthcare Centre. IOP Conference Series: Materials Science and Engineering 166, pages 012035.
Crossref
Francesco Lolli, Alessio Ishizaka, Rita Gamberini, Elia Balugani & Bianca Rimini. (2017) Decision Trees for Supervised Multi-criteria Inventory Classification. Procedia Manufacturing 11, pages 1871-1881.
Crossref
F. Lolli, R. Gamberini, A. Regattieri, E. Balugani, T. Gatos & S. Gucci. (2017) Single-hidden layer neural networks for forecasting intermittent demand. International Journal of Production Economics 183, pages 116-128.
Crossref
Dennis Prak, Ruud Teunter & Aris Syntetos. (2017) On the calculation of safety stocks when demand is forecasted. European Journal of Operational Research 256:2, pages 454-461.
Crossref
Eugenia Babiloni, Ester Guijarro & Manuel Cardós. 2017. Engineering Systems and Networks. Engineering Systems and Networks 109 116 .
Fotios Petropoulos, Nikolaos Kourentzes & Konstantinos Nikolopoulos. (2016) Another look at estimators for intermittent demand. International Journal of Production Economics 181, pages 154-161.
Crossref
John E. Boylan & M. Zied Babai. (2016) On the performance of overlapping and non-overlapping temporal demand aggregation approaches. International Journal of Production Economics 181, pages 136-144.
Crossref
Chen-Yang Cheng, Kuo-Liang Chiang & Meng-Yin Chen. (2016) Intermittent Demand Forecasting in a Tertiary Pediatric Intensive Care Unit. Journal of Medical Systems 40:10.
Crossref
Matthew Lindsey & Robert Pavur. 2016. Advances in Business and Management Forecasting. Advances in Business and Management Forecasting 39 47 .
Jie Chen & Ting Chen. (2016) Research on classification method of spare parts inventory based on warranty data. Research on classification method of spare parts inventory based on warranty data.
Stephan Kolassa. (2016) Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting 32:3, pages 788-803.
Crossref
Aris A. Syntetos, Zied Babai, John E. Boylan, Stephan Kolassa & Konstantinos Nikolopoulos. (2016) Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research 252:1, pages 1-26.
Crossref
Raghad Hemeimat, Lina Al-Qatawneh, Mazen Arafeh & Shadi Masoud. (2016) Forecasting Spare Parts Demand Using Statistical Analysis. American Journal of Operations Research 06:02, pages 113-120.
Crossref
Adriano O. Solis. 2016. Supply Management Research. Supply Management Research 79 95 .
Stewart Stevenson & Pauline Found. 2016. Understanding the Lean Enterprise. Understanding the Lean Enterprise 153 184 .
Fengyu Wang & Laura Xu Xiao Xia. (2015) Evaluation and selection of periodic inventory review policy for irregular demand: A case study. Evaluation and selection of periodic inventory review policy for irregular demand: A case study.
Ester Guijarro, Manuel Cardos & Eugenia Babiloni. (2015) On the selection of fill rate estimation method to determine the optimal base stock in periodic review systems. On the selection of fill rate estimation method to determine the optimal base stock in periodic review systems.
José Roberto do Rego & Marco Aurélio de Mesquita. (2015) Demand forecasting and inventory control: A simulation study on automotive spare parts. International Journal of Production Economics 161, pages 1-16.
Crossref
Peter Wanke & Víctor Leiva. (2015) Exploring the Potential Use of the Birnbaum-Saunders Distribution in Inventory Management. Mathematical Problems in Engineering 2015, pages 1-9.
Crossref
E. van Wingerden, R.J.I. Basten, R. Dekker & W.D. Rustenburg. (2014) More grip on inventory control through improved forecasting: A comparative study at three companies. International Journal of Production Economics 157, pages 220-237.
Crossref
Nikolaos Kourentzes. (2014) On intermittent demand model optimisation and selection. International Journal of Production Economics 156, pages 180-190.
Crossref
Fotios Petropoulos, Spyros Makridakis, Vassilios Assimakopoulos & Konstantinos Nikolopoulos. (2014) ‘Horses for Courses’ in demand forecasting. European Journal of Operational Research 237:1, pages 152-163.
Crossref
Adnane Lazrak, Bruno Castanier, David Lemoine, Robert Heidsieck & Cyrille Thenot. (2014) Integration approaches of forecasting methods selection with inventory management indicators in the case of spare parts supply chain. Integration approaches of forecasting methods selection with inventory management indicators in the case of spare parts supply chain.
D. Lengu, A.A. Syntetos & M.Z. Babai. (2014) Spare parts management: Linking distributional assumptions to demand classification. European Journal of Operational Research 235:3, pages 624-635.
Crossref
Hong Long Mao, Jun Wei Gao, Xi Juan Chen & Jin Dong Gao. (2014) Demand Prediction of the Rarely Used Spare Parts Based on the BP Neural Network. Applied Mechanics and Materials 519-520, pages 1513-1519.
Crossref
Rita Gamberini, Francesco Lolli, Alberto Regattieri & Bianca Rimini. (2014) Dynamic Re-order Policies for Irregular and Sporadic Demand Profiles. Procedia Engineering 69, pages 1420-1429.
Crossref
E. Babiloni, E. Guijarro, M. Cardós & S. Estellés. 2014. Annals of Industrial Engineering 2012. Annals of Industrial Engineering 2012 351 358 .
Adriaan Van Horenbeek, Philip A. Scarf, Cristiano A. V. Cavalcante & Liliane Pintelon. (2013) The Effect of Maintenance Quality on Spare Parts Inventory for a Fleet of Assets. IEEE Transactions on Reliability 62:3, pages 596-607.
Crossref
A. Bacchetti, F. Plebani, N. Saccani & A.A. Syntetos. (2013) Empirically-driven hierarchical classification of stock keeping units. International Journal of Production Economics 143:2, pages 263-274.
Crossref
Seongmin Moon, Andrew Simpson & Christian Hicks. (2013) The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand. International Journal of Production Economics 143:2, pages 449-454.
Crossref
G. Heinecke, A.A. Syntetos & W. Wang. (2013) Forecasting-based SKU classification. International Journal of Production Economics 143:2, pages 455-462.
Crossref
Fotios Petropoulos, Konstantinos Nikolopoulos, Georgios P. Spithourakis & Vassilios Assimakopoulos. (2013) Empirical heuristics for improving intermittent demand forecasting. Industrial Management & Data Systems 113:5, pages 683-696.
Crossref
Nikolaos Kourentzes. (2013) Intermittent demand forecasts with neural networks. International Journal of Production Economics 143:1, pages 198-206.
Crossref
Peter Wanke. (2012) Quadro conceitual para gestão de estoques: enfoque nos itens. Gestão & Produção 19:4, pages 677-687.
Crossref
Andrea Bacchetti & Nicola Saccani. (2012) Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega 40:6, pages 722-737.
Crossref
Somnath Mukhopadhyay, Adriano O. Solis & Rafael S. Gutierrez. (2011) The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand. Journal of Forecasting 31:8, pages 721-735.
Crossref
Cristian O. Zambrano & Juan D. Velasquez. (2012) Is it captures the cyclical and trend component in the neural networks models?. Is it captures the cyclical and trend component in the neural networks models?.
Ward Romeijnders, Ruud Teunter & Willem van Jaarsveld. (2012) A two-step method for forecasting spare parts demand using information on component repairs. European Journal of Operational Research 220:2, pages 386-393.
Crossref
Tim J. van Kampen, Renzo Akkerman & Dirk Pieter van Donk. (2012) SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management 32:7, pages 850-876.
Crossref
Jing Lin & Behzad Ghodrati. (2012) Information sharing in a spares demand system. Information sharing in a spares demand system.
Qingzheng Xu, Na Wang & Heping Shi. (2012) Review of Croston's method for intermittent demand forecasting. Review of Croston's method for intermittent demand forecasting.
Nezih Altay, Lewis A. Litteral & Frank Rudisill. (2012) Effects of correlation on intermittent demand forecasting and stock control. International Journal of Production Economics 135:1, pages 275-283.
Crossref
Ulrich Küsters & Jan Speckenbach. 2012. Prognoserechnung. Prognoserechnung 75 108 .
Manuel D. Rossetti, Nebil Buyurgan & Edward Pohl. 2012. Handbook of Healthcare System Scheduling. Handbook of Healthcare System Scheduling 245 280 .

Displaying 200 of 233 citing articles. Use the download link below to view the full list of citing articles.

Download full citations list

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.