Big Data - Supply Chain Management Framework for Forecasting - Data Preprocessing and Machine Learning Techniques

Aug 4, 2026

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A circular big-data and supply-chain (SC) framework is proposed to improve forecasting performance through a full pipeline: data preprocessing for machine-learning (ML) models, model training and optimization, and post-process evaluation using SC KPIs. The framework links KPIs to error-measurement systems, guiding hyperparameter tuning and performance parameters while clarifying how phantom inventory can distort forecasting. The study also outlines how data collection strategies, different forecasting periods/objectives, and operational planning metrics support transparent, efficient control of inventory, workforce, production, and capacity decisions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Research gaps

Research objectives

Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000 . Digital Object Identiﬁer 10.1109/ACCESS.2017.DOI Big Data – Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques 24 Jul 2023 MD ABRAR JAHIN1 , MD SAKIB HOSSAIN SHOVON2 , JUNGPIL SHIN3 ,(Senior Member, IEEE), ISTIYAQUE AHMED RIDOY4 , YOICHI TOMIOKA3 ,(Member, IEEE), AND M. F. MRIDHA2 ,(Senior Member, IEEE) 1Department of Industrial Engineering and Management, Khulna University of Engineering and Technology (KUET), Khulna 9203, Bangladesh 2Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh 3Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan 4Institute of Business Administration, University of Dhaka, Dhaka, Bangladesh Corresponding author: Jungpil Shin (e-mail: [jpshin@u-aizu.ac.jp](jpshin@u-aizu.ac.jp)). arXiv :2307 . 12971v1 data analysis, machine-learning model training, hyperparameter tuning, performance evaluation, and optimization), forecasting effects on human-workforce, inventory, and overall SC. Initially, the need to collect data according to SC strategy and how to collect them has been discussed. The article discusses the need for different types of forecasting according to the period or SC objective. The SC KPIs and the error-measurement systems have been recommended to optimize the top-performing model. The adverse effects of phantom inventory on forecasting and the dependence of managerial decisions on the SC KPIs for determining model performance parameters and improving operations management, transparency, and planning efﬁciency have been illustrated. The cyclic connection within the framework introduces preprocessing optimization based on the post-process KPIs, optimizing the overall control process (inventory management, workforce determination, cost, production and capacity planning) . The contribution of this research lies in the standard SC process framework proposal, recommended forecasting data analysis, forecasting effects on SC performance, machine learning algorithms optimization followed, and in shedding light on future research. INDEX TERMS Data analysis; Decision making; Demand forecasting; Hyperparameter tuning; Literature review; Supply chain performance I. INTRODUCTION THE supply chain (SC) has evolved sufﬁciently over the past years to discover new methods and techniques for solving SCM problems. The SC can develop its conﬁguration based on its control, coordination, and management [16] . The advent of Big Data (BD) brings one such change. Like other ﬁelds, BD can be utilized to improve decision-making reprocesses and alter business models through multiple resources, tools, and applications [83] . Therefore, SC and BD usages are connected to help one another. Although the concepts for SCM are already well-developed, it is possible to improve further; recent researches on enhancing efﬁciency through collaboration [78], usage of RFID and intelligent goods [89] are two examples of such innovations that improved SCM processes. Newer technologies are further enabling the discovery of innovative strategies for solving SC problems. Big Data Analytics (BDA) is one such disruptive innovation. Although BD has been present for a long time, the approaches to making sense of BD are comparatively new, and such systems have not been wholly integrated into other branches of knowledge [143] . We identiﬁed the absence of data usage and relevant processes in SC directly as a major problem that needs to be addressed. BD has similarly grown popular over the years. After academic and technical publications ﬁrst mentioned such technological developments, it has drawn the attention of various people, including literary scholars, corporate leaders, and government ofﬁcials [17] . The most recognizable feature of BD is probably its size or the amount of data stored. Beyo

FAQs

What problem does the research identify in supply chain forecasting with big data?

The study identifies the absence of data usage and relevant processes in supply chains as a major issue that needs to be addressed using big data analytics.

How does the proposed framework connect preprocessing, forecasting, and evaluation?

It introduces a cyclic connection where preprocessing optimization is driven by post-process KPIs, linking model evaluation to upstream control and operational planning decisions.

Which factors are used to optimize the forecasting model in the supply chain setting?

Supply chain KPIs and error-measurement systems guide model performance parameters, while hyperparameter tuning and ML algorithms optimization are used to improve forecasting outcomes.

Aug 4, 2026

Upload by Patrick