The Role of Artificial Intelligence and Machine Learning in Supply Chain Management and its Task Model

2020 ◽  
Author(s):  
Kajal Singh ◽  
S B Goyal ◽  
Pradeep Bedi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
Task Model

Use of Information Technology in the Supply Chain Management of the Pharmaceutical Industry

2022 ◽  
pp. 137-168
Author(s):  
Saibal Kumar Saha ◽  
Sangita Saha ◽  
Ajeya Jha
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Cloud Computing ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Pharmaceutical Industry ◽  
Successful Implementation ◽  
Chain Management ◽  
Role Of Information

An efficient supply chain management helps to increase the productivity of a business. Use of information technology and concepts like artificial intelligence, blockchain, and cloud computing have integrated the different aspects of supply chain with its stakeholders. Published literature in the field of SCM, IT, and the pharmaceutical industry has been reviewed, and different aspects of innovation, technique, risks, advancements, factors, and models have been taken into consideration to form a comprehensive chapter focusing on the role of information technology in the supply chain management of the pharmaceutical industry. The chapter finds that IT has made a significant impact in improving the efficiency of SCM. But its successful implementation and collaboration with other firms is the key to success for an efficient SCM. Within each category, gaps have been identified.

Role of machine learning in green supply chain management and operations management

2021 ◽  
Author(s):  
Vipin Kumar ◽  
Harikumar Pallathadka ◽  
Sanjay Kumar Sharma ◽  
Chetan M. Thakar ◽  
Manisha Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Operations Management ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Chain Management

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

The role of APS systems in Supply Chain Management: a theoretical and industrial analysis

2009 ◽  
Vol 5 (3/4) ◽  
pp. 356 ◽  
Author(s):  
France Anne Gruat La Forme ◽  
Valerie Botta Genoulaz ◽  
Jean Pierre Campagne
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management

The role of healt-hcare supply chain management in the wake of COVID-19 pandemic: hot off the press

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sriyanto Sriyanto ◽  
Muhammad Saeed Lodhi ◽  
Hailan Salamun ◽  
Sardin Sardin ◽  
Chairil Faif Pasani ◽  
...  
Keyword(s):  
Health Care ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Parameter Estimates ◽  
Chain Management ◽  
Content Type ◽  
Health Care Supply ◽  
Health Care Supply Chain ◽  
Ssa Countries

Purpose The study aims to examine the role of health-care supply chain management during the COVID-19 pandemic in a cross-section of 42 selected sub-Saharan African (SSA) countries. Design/methodology/approach The study used cross-sectional robust least square regression for parameter estimates. Findings The results confirmed the N-shaped relationship between the health-care logistics performance index (HLPI) and COVID-19 cases. It implies that initially HLPI increases along with an increase in COVID-19 cases. Later down, it decreases COVID-19 cases by providing continued access to medical devices and personal protective equipment. Again, it increases due to resuming economic activities across countries. Practical implications The continuing health-care supply chain is crucial to minimize COVID-19 cases. The international support from the developed world in providing health-care equipment, debt resettlement and resolving regional conflicts is deemed desirable to escape the SSA countries from the COVID-19 pandemic. Originality/value The importance of the health-care supply chain during the COVID-19 pandemic is evident in the forecasting estimates, which shows that from August 2021 to April 2022, increasing the health-care supply chain at their third-degree level would reduce coronavirus registered cases. The results conclude that SSA countries required more efforts to contain coronavirus cases by thrice increasing their health-care logistics supply chain.

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