scholarly journals A Prognostic Three-Axis Coordination Model for Supply Chain Regulation Using Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hariprasath Manoharan ◽  
Yuvaraja Teekaraman ◽  
Ramya Kuppusamy ◽  
Arun Radhakrishnan ◽  
Mohamed Yaseen Jabarulla
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Chain Management ◽  
Coordination Model ◽  
Effective Manner ◽  
Supply Chain Management System

In this article, data processing of a supply chain management system has been monitored using the Internet of Space (IoS) which can be able to create possessions for managing the business process. In modern circumstances, many business inventiveness are trading and exporting products on their possession, but in many cases, information on such manufactured products is not monitored in an effective manner. To overcome the abovementioned issue, a precise model of monitoring several distributed products in supply chain management has been introduced with high sustainability error reduction. The framed model in the management process has been integrated with the boosting algorithm, a type of machine learning algorithm where training dataset has been introduced appropriately. This variation in the incorporation of the boosting optimization process not only increases the efficiency of the proposed model but also attempts to prove the success strategy under five different scenarios, where after sequential tests IoS model delivers high improvement in the distribution process for an average percentile of 67% than the existing methods.

From Supply Chain Management to Blockchain-Based Management

2019 ◽  
pp. 59-63
Author(s):  
G. V. Zubakov ◽  
O D. Protsenko ◽  
I. O. Protsenko
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Digital Economy ◽  
Practical Implementation ◽  
Chain Management ◽  
Mutual Trade ◽  
Blockchain Technology ◽  
Distributed Ledger ◽  
Supply Chain Management System ◽  
The Government

The presented study addresses the current problems in the implementation of the distributed ledger (blockchain) technology in supply chain management mechanisms in the context of the digital economy. Aim. The study aims to analyze the application of the blockchain technology in modern economic processes from the perspective of logistics.Tasks. The authors consider the possibility of using the blockchain technology in the supply chain management system and explore ways to use the findings of the Eurasian Economic Commission (EEC) in the fieldof digital economy to organize information standardization processes within the supply chains of foreign and mutual trade.Methods. This study uses general scientific methods of cognition to examine approaches to the implementation of the blockchain technology in transport and logistics processes and to find opportunities for the implementation of smart contracts to ensure the traceability of the entire chain of commodity and information fl ws.Results. Implementation of the distributed ledger (blockchain) technology in the logistics processes of foreign and mutual trade increases the transparency of information fl ws and the speed of decisionmaking. This technology would allow the parties to negotiate directly, minimizing potential risks and the time required to approve a supply deal.Conclusions. The authors consider the possibility of using a systematic approach to the digitalization of transport and logistics processes and the subsequent standardization of information interaction at the B2B, B2G, and G2G levels, segmented by separate fields of transport and foreign trade and individual economic sectors. As a conclusion, the study assesses the prospects of the practical implementation of blockchain mechanisms in the creation of industrial platforms — digital platforms that provide integrated services for businesses and the government using a single window system.

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.

scholarly journals Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

2021 ◽  
Vol 11 (9) ◽  
pp. 3866
Author(s):  
Jun-Ryeol Park ◽  
Hye-Jin Lee ◽  
Keun-Hyeok Yang ◽  
Jung-Keun Kook ◽  
Sanghee Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Linear Regression ◽  
Coarse Aggregate ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Aggregate Size ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Concrete Compressive Strength

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

Supply Chain Management System Design in Vendor Selection Process

2021 ◽  
Author(s):  
Oryza Putri Suriana ◽  
Alfan Reynaldo ◽  
Muhammad Dwi Ferdian Suwardi ◽  
Indra Kusumadi Hartono
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
System Design ◽  
Management System ◽  
Selection Process ◽  
Vendor Selection ◽  
Chain Management ◽  
Supply Chain Management System

Blockchain and 6G-Based Supply Chain Management Framework for Oil and Gas Shipment

2021 ◽  
Author(s):  
Md Abdur Rahman ◽  
Syed M. Belal
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Oil And Gas ◽  
Current System ◽  
Oil And Gas Industry ◽  
Chain Process ◽  
Chain Management ◽  
It Systems ◽  
Supply Chain Management System ◽  
Gas Supply

Abstract Keeping track of the oil and gas supply chain is challenging task as the route and transportation requires sophisticated security environment - both physical systems’ and IT systems’ security. Thanks to the recent advancement in IoT, specialized sensors can keep track of the required supply chain environment. With the help of blockchain, the supply chain data can be immutably saved for further sharing with stakeholders. Due to the introduction of AI as an embedded element within 6G networks, the end-to-end supply chain process can now be automated for safety, security, and efficiency purposes. By leveraging 6G, AI, blockchain, and IoT, the supply chain data during the transportation or at rest can be monitored for any changed environment during the movement of the ship through national or international routes. In this paper, we study the requirements of such intelligent and secure supply chain management system conducive to the oil and gas industry. We also show our proof-of-concept implementation and initial test results. Our obtained results show promising prospect of the current system to be deployed to safeguard the oil and gas supply chain.

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