.png){kind=icon}
.png){kind=icon}
Return
Big Data-Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
.png){kind=icon}
PRE
AI
DOI：10.1007/s11831-024-10092-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article systematically identifies and comparatively analyzes state-of-the-art supply chain (SC) forecasting strategies and technologies within a specific timeframe, encompassing a comprehensive review of 152 papers spanning from 1969 to 2023. A novel framework has been proposed incorporating Big Data Analytics in SC Management (problem identification, data sources, exploratory 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.
Keywords:
INPUT FEATURE-SELECTION
MUTUAL INFORMATION
PERFORMANCE-MEASUREMENT
ROUGH SETS
SYSTEM
CLASSIFICATION
VARIABILITY
IMPUTATION
REGRESSION
DESIGN
Journal
IF:
12.1
Papers:
1.8K
Citations:
1.2W
View more organizations