scholarly journals Smart Logistics and Supply Chain with Machine Learning and IoT

2021 ◽  
Vol 9 (VI) ◽  
pp. 2180-2184
Author(s):  
Pranav R.M
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Internet Of Things ◽  
Customer Satisfaction ◽  
Small Businesses ◽  
Smart Logistics ◽  
Rural Businesses ◽  
Complex Process ◽  
The Right ◽  
The Military

Logistics is an important part of the Supply Chain. The management of logistics and supply chain is a complex process of planning and management of services, goods from the origin to the point of consumption. Logistics is defined as the process of managing movement of goods in and out of an organization. Supply Chain is defined as the process of managing movement and coordination of goods in between multiple organizations. Together Logistics and Supply Chain includes planning the transport, warehousing, inventory and sales an example of logistics could be the military stockpiling ammo whereas an example of Supply Chain management is making sure the right amount of goods reaches the destination, supplying more could lead to increased storage costs and supplying less could lead to inefficiencies. So, the main objective of Logistics is customer satisfaction and the main objective of Supply Chain is to have a competitive advantage by being more efficient. This project aims to have both customer satisfaction and efficiency and will achieved by implementing Internet of Things and Machine Learning to Logistics and Supply Chain. Large Corporations invest a lot of money to improve supply chains. This paper aims to help small businesses and rural businesses to improve their Supply Chain. This project aims at providing customer satisfaction and efficiency. Implementation using IoT and machine learning provides better efficiency. Keywords— Smart Logistics, Smart Supply Chain, Industry 4.0, Internet of Things, Machine Learning.

scholarly journals Artificial Intelligence Applications in the New Model of Logistics Development Based on Wireless Communication Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Shuaiqi Wang
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Internet Of Things ◽  
Cost Reduction ◽  
Sharing Economy ◽  
Smart Logistics ◽  
Supply Chain Logistics ◽  
Service Efficiency ◽  
Artificial Intelligence Technology ◽  
The Internet Of Things

With the continuous development of artificial intelligence technology, the supply chain logistics industry has shown new changes. The products of the intelligent era such as smart devices, big data computing, and Internet of Things technology have gradually become the transformation and innovation of logistics and supply chain enterprises. The important driving force of the logistics and supply chain industry will greatly improve the operational efficiency of the logistics and supply chain industry. Methods. This article studies the technical framework of artificial intelligence and explores the upgrading and transformation of supply chain logistics enterprises in logistics infrastructure, production tools, and logistics operation processes under the promotion of artificial intelligence technology, from warehouse location, inventory management, warehousing operations, transportation, and distribution. The data analysis and prediction analyze the impact of artificial intelligence on the supply chain logistics field and finally point out the problems in the intelligent development of the supply chain logistics field and put forward targeted suggestions to promote the modern supply chain logistics to become more intelligent. developThe new development trend of smart logistics is towards sharing economy, automation, service efficiency, and cost reduction. Results. In this context, if logistics companies want to achieve higher-quality development, they cannot do without business model innovation and larger-scale collaboration, transparency of logistics information, and more comprehensive information sharing. The new trend of the development of smart logistics is to develop in the direction of sharing economy, automation, service efficiency, and cost reduction. Conclusion. Intelligence and the Internet of Things are the inevitable trend of the development of smart logistics, which is mainly realized through the Internet of Things path in terms of visual information technology, intelligent robot operation, vehicle scheduling, and cargo traceability.

Mobile Applications for Adaptive Supply Chains

2003 ◽  
pp. 298-311 ◽  
Author(s):  
Ravi Kalakota ◽  
Marcia Robinson ◽  
Pavan Gundepudi
Keyword(s):  
Supply Chain ◽  
Customer Satisfaction ◽  
Mobile Computing ◽  
Supply Chains ◽  
Market Share ◽  
Mobile Technology ◽  
Service Management ◽  
Revenue Growth ◽  
The Right ◽  
Computing Models

Streamlining supply chains is a high priority for corporations. In a volatile economy, customer satisfaction, market share and revenue growth become dependent on getting the right product to the right place at the right time. As a result, the notion of adaptive supply chains is emerging as the next competitive battlefield. Fulfillment velocity, inventory visibility, and supplier coordination versatility form the three pillars of adaptive supply chains. To support these business objectives, traditional tethered computing models are inadequate. Untethered models, enabled by mobile computing, facilitate the improvement, management and re-design of next generation supply chains. In this chapter, we examine the different ways mobility is morphing supply chain applications. Specifically, we show how mobile technology and infrastructure is transforming the key areas of procurement, supply execution, supply chain visibility and after-sales service management.

Logistik-Digitalisierung: Politik der kleinen Schritte

2019 ◽  
Vol 33 (07-08) ◽  
pp. 22-24
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Internet Of Things ◽  
Künstliche Intelligenz ◽  
Neue Technologien

Interview | Der globale SAP-Logistikpartner leogistics unterstützt Unternehmen bei der Transition aus der alten in die neue IT-Welt, in der Digital Supply Chain, beim Transportmanagement und der Werks- und Standortlogistik. Im Gespräch mit dieser Zeitschrift analysiert leogistics-CEO André Käber die Hürden der Digitalisierung und spricht über konkrete Chancen, die sich durch neue Technologien rund um das Internet of Things (IoT), Künstliche Intelligenz (KI) und Machine Learning für die Optimierung der Werks- und Transportlogistik nutzen lassen.

Analysis of risk factors in financial supply chain based on machine learning and IoT technology

2020 ◽  
pp. 1-11
Author(s):  
Sun Hongjin
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Supply Chain ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
The Internet ◽  
Supply Chain Risk ◽  
Financing Model ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The financial supply chain is affected by many factors, so an artificial intelligence model is needed to identify supply chain risk factors. This article combines the actual situation of the financial supply chain, improves the traditional machine learning algorithm, and takes the actual company as an example to build a corresponding risk factor recognition model. From the perspective of optimizing the supply chain financial model, this paper combines the functions of the Internet of Things technology and the characteristics of the supply chain financial inventory pledge financing model to design a new type of inventory pledge financing model. The new model makes up for the defects of the original model through the functions of intelligent identification, visual tracking and cloud computing big data processing of the Internet of Things technology. In addition, this study verifies the performance of the system, uses a large amount of data in Internet finance as an object, and obtains the corresponding results through mathematical statistical analysis. The research results show that the model proposed in this paper has a certain effect on the identification and analysis of financial supply chain risk factors.

scholarly journals An Unsupervised Machine Learning-Based Framework for Transferring Local Factories into Supply Chain Networks

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3114
Author(s):  
Mohd Fahmi Bin Mad Ali ◽  
Mohd Khairol Anuar Bin Mohd Ariffin ◽  
Faizal Bin Mustapha ◽  
Eris Elianddy Bin Supeni
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Manufacturing Systems ◽  
Supply Chain Network ◽  
Kuala Lumpur ◽  
Manufacturing Firm ◽  
Supply Chain Networks ◽  
The North ◽  
Local Manufacturing ◽  
The Right

Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler’s center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812).

scholarly journals Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints

2021 ◽  
Vol 7 (2) ◽  
pp. 138
Author(s):  
Ika Nurkasanah
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Reinforcement Learning ◽  
Supply Chain Management ◽  
Machine Learning Algorithms ◽  
Inventory Policy ◽  
Chain Management ◽  
Q Learning ◽  
Lead Time Uncertainty ◽  
The Right

Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning

Identifying Business-to-Business Unfair Trading Practices in the Food Supply Chain: The case of Cyprus

New Medit ◽  
2020 ◽  
Vol 19 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Marinos Markou ◽  
Andreas Stylianou ◽  
Marianthi Giannakopoulou ◽  
Georgios Adamides
Keyword(s):  
Supply Chain ◽  
Food Supply ◽  
Small Businesses ◽  
State Level ◽  
Strategic Partnerships ◽  
Food Supply Chain ◽  
National Legislation ◽  
Food Supply Chains ◽  
Enforcement Policy ◽  
Monitoring And Enforcement

Unfair Trading Practices (UTPs) between businesses in the food supply chain have a significant impact on the various stakeholders involved, and on the environment. So far, no attempt has been made at the Member State level for the identification of UTPs in the food supply chain and their impact on the relevant stakeholders. This study drew on this gap and attempted to identify the UTPs that exist in the Cypriot food supply chain, assess their impact on the involved stakeholders and provide guidelines that will assist the transposition of EU relevant Directive to the national law. To achieve this goal, the study was based on a quantitative survey of a representative sample of businesses using a specific questionnaire. The results showed that particular UTPs do appear in the food supply chain with a different frequency, while the majority of businesses have been victims of UTPs in the last five years. Notably, the estimated cost of UTPs as a percentage of the business annual turnover is considered important ranging from 5.7% for retailers to 31.9% for farmers. Thus, most participants agree that UTPs in the agricultural food sector should be regulated by national legislation. We argue that the national legislation for UTPs should be a mix of policies that integrate private, administrative and judicial methods of monitoring and enforcement. Policy and decision makers should seek to reinforce the role and the bargaining power of small businesses in the food supply chain. This might be accomplished through the development of efficient producers’ organizations, short food supply chains, interbranch organizations and strategic partnerships.

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

PENGARUH KUALITAS LAYANAN DAN CITRA PERUSAHAAN TERHADAP KEPUASAN NASABAH PT. BANK PERKREDITAN RAKYAT GRACIA MANDIRI BEKASI TIMUR

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Dr. Iwan Kurniawan Subagja, SE., MM.
Keyword(s):  
Quality Of Service ◽  
Regression Analysis ◽  
Customer Satisfaction ◽  
Small Businesses ◽  
Sampling Technique ◽  
Corporate Image ◽  
Business World ◽  
Long Period ◽  
Number Of Customers

In the current era of globalization the level of competition in the business world becomes increasingly tight. Many are doing small businesses that require capital, or small businesses that are developing to increase the ability of an increasing economy. This relates to one of the most important objectives and should be undertaken by all types of business: maintaining the viability of the company over a long period of time (going concern), business activities sometimes, visiting some things with competition to gain additional capital. This also makes it a challenge for bank companies to showcase their brand and quality of service that is superior and satisfactory to the purpose and number of customers. This study aims to describe the quality of service and corporate image to customer satisfaction PT. Bank Perkreditan Rakyat Gracia Mandiri Bekasi Timur. Samples and this research lied 100 respondents with sampling technique purposive sampling. The method of analysis used are description and regression analysis. The results showed that the quality of service and corporate image include customer satisfaction.

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