Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities
M Seyedan, F Mafakheri - Journal of Big Data, 2020 - Springer
Big data analytics (BDA) in supply chain management (SCM) is receiving a growing
attention. This is due to the fact that BDA has a wide range of applications in SCM, including …
attention. This is due to the fact that BDA has a wide range of applications in SCM, including …
Data science and big data analytics: A systematic review of methodologies used in the supply chain and logistics research
Data science and big data analytics (DS &BDA) methodologies and tools are used
extensively in supply chains and logistics (SC &L). However, the existing insights are …
extensively in supply chains and logistics (SC &L). However, the existing insights are …
A hybrid deep learning framework with CNN and Bi-directional LSTM for store item demand forecasting
In the era of ever-changing market landscape, enterprises tend to make quick and informed
decisions to survive and prosper in the competition. Decision makers within an organization …
decisions to survive and prosper in the competition. Decision makers within an organization …
Explainability in supply chain operational risk management: A systematic literature review
It is important to manage operational disruptions to ensure the success of supply chain
operations. To achieve this aim, researchers have developed techniques that determine the …
operations. To achieve this aim, researchers have developed techniques that determine the …
Applying machine learning approach in recycling
Waste generation has been increasing drastically based on the world's population and
economic growth. This has significantly affected human health, natural life, and ecology. The …
economic growth. This has significantly affected human health, natural life, and ecology. The …
The role of deep learning in manufacturing applications: Challenges and opportunities
There is a growing interest in using deep learning technologies within the manufacturing
industry to improve quality, productivity, safety, and efficiency, while also reducing costs and …
industry to improve quality, productivity, safety, and efficiency, while also reducing costs and …
Applications of deep learning into supply chain management: a systematic literature review and a framework for future research
F Hosseinnia Shavaki… - Artificial Intelligence …, 2023 - Springer
In today's complex and ever-changing world, Supply Chain Management (SCM) is
increasingly becoming a cornerstone to any company to reckon with in this global era for all …
increasingly becoming a cornerstone to any company to reckon with in this global era for all …
[HTML][HTML] Approaching sales forecasting using recurrent neural networks and transformers
I Vallés-Pérez, E Soria-Olivas, M Martínez-Sober… - Expert Systems with …, 2022 - Elsevier
Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the
precise execution of the corresponding downstream processes (inbound and outbound …
precise execution of the corresponding downstream processes (inbound and outbound …
Understanding the influential and mediating role of cultural enablers of AI integration to supply chain
Artificial Intelligence (AI) has been claimed to offer transformational power across industries
and sectors. To date, research has largely focused on the technical characteristics of AI and …
and sectors. To date, research has largely focused on the technical characteristics of AI and …
Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects
MG Meharie, WJ Mengesha, ZA Gariy… - Engineering …, 2022 - emerald.com
Purpose The purpose of this study to apply stacking ensemble machine learning algorithm
for predicting the cost of highway construction projects. Design/methodology/approach The …
for predicting the cost of highway construction projects. Design/methodology/approach The …