[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125202-en":3,"doc-seo-125202-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125202,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Inventory Decisions under Stochastic Demand Scenario with High Inflation Rate - Machine Learning Approach","This study examines how hyperinflation affects inventory decisions using a real dataset from a Sri Lankan retail company collected during the 2022 hyperinflation period. A machine learning framework is developed to forecast optimal order quantities, aiming to reduce inventory holding costs. Six ML techniques are compared using Root Mean Squared Error and R-squared for rigorous evaluation. Results indicate Random Forest performs best for forecasting order quantities in high inflation conditions.","Aalborg Universitet  \nInventory Decisions under Stochastic Demand Scenario with High Inflation RateMachine Learning Approach  \nSiriwardena, Vinu; Kosgoda, Dilina; Perera, H. Niles; Nielsen, Izabela  \nPublished in:  \nIFAC-PapersOnLine  \nDOI (link to publication from Publisher):  \n10.1016/j.ifacol.2024.09.113  \nCreative Commons License  \nCC BY-NC-ND 4.0  \nPublication date: 2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nSiriwardena, V. , Kosgoda, D. , Perera, H. N. , & Nielsen, I. (2024) . Inventory Decisions under Stochastic Demand Scenario with High Inflation Rate-Machine Learning Approach. IFAC-PapersOnLine, 58(19), 133-138. [https://doi.org/10.1016/j.ifacol.2024.09.113](https://doi.org/10.1016/j.ifacol.2024.09.113)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \n[Available online at](Available online at www.sciencedirect.com)[ www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nIFAC PapersOnLine 58-19 (2024) 133–138  \nInventory Decisions Under Stochastic Demand Scenario with High Inflation  \nRate–Machine Learning Approach  \nVinu Siriwardena *, **, Dilina Kosgoda*, **, H. Niles Perera*, **, Izabela Nielsen***  \n*Center for Supply Chain, Operations & Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka (email: [vinusiriwardena7@gmail.com](vinusiriwardena7@gmail.com), [dilinak@uom.lk](dilinak@uom.lk), [hniles@uom.lk](hniles@uom.lk))  \n** Department of Transport Management and Logistics Engineering, University of Moratuwa,  \nKatubedda 10400, Sri Lanka  \n***Department of Materials and Production, Aalborg University, Denmark (email: [izabela@mp.aau.dk](izabela@mp.aau.dk))  \nAbstract: This study examined the effects of hyperinflation on inventory decisions using a real-world dataset obtained from a leading retail company in Sri Lanka during the hyperinflation period of 2022 and developed a Machine Learning model to forecast optimal order quantities with the intention of lowering inventory holding costs. Six distinct ML techniques were selected to identify the best ML model. Root Mean Squared Error and R Squared values were used to rigorously evaluate the performance of the six ML techniques. The results suggest Random Forest as the most appropriate ML model to forecast optimal order quantities during a high inflation situation.  \nCopyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/))  \nKeywords Inventory Control and Dynamic Pricing; Demand Forecasting; Supply Chain Management (SCM); High Inflation; Machine Learning Models  \n1. INTRODUCTION  \nInventory Management plays a significant role in maintaining an uninterrupted upstream and downstream flow by reducing supply chain disruptions that might impact the entire supply chain. The primary function of inventory is to ensure the smooth flow of products, and to achieve this, firms must consider both upstream supplier interactions and downstream customer demands. The main objective of inventor","cbCaieF3TP6DwSdy","https://ap.wps.com/l/cbCaieF3TP6DwSdy","pdf",601444,1,7,"English","en",105,"# 1. 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