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.

Mobile Lösungen für die Supply Chain

VDI-Z ◽  
2019 ◽  
Vol 161 (10) ◽  
pp. 79-80
Author(s):  
Patrick Molemans
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Künstliche Intelligenz

Viele aktuelle Diskussionen rund um die Supply Chain beschäftigen sich mit wichtigen Trends und Innovationen, welche die Branche zukünftig noch deutlich beeinflussen werden – wie Künstliche Intelligenz, Blockchain, Drohnen und Roboter oder das Internet of Things (IoT). Doch was beschäftig den Markt aktuell ganz konkret in der Praxis? Vorherrschendes Thema ist die Umstellung der mobilen Endgeräte auf das „Android“-Betriebssystem.

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.

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.

ÖVKT-Tagung in Pörtschach vom 24. bis 27. september: Was kommt nach der künstlichen Intelligenz?

2019 ◽  
Vol 24 (07/08) ◽  
pp. 95-95
Author(s):  
Maria Thalmayr
Keyword(s):  
Künstliche Intelligenz ◽  
Neue Technologien

Robotik, BIM, 3-D-Druck, Drohnen, Künstliche Intelligenz, … innovative Technologien stehen in den Startlöchern, die Arbeitswelt, wie wir sie heute kennen, grundlegend zu verändern. Braucht das Gesundheitswesen dazu neue Strukturen, Ausbildungen und Berufsbilder und wie können wir neue Technologien schneller, aber dennoch sicher einsetzen?

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.

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.

Machine Learning Based Activity Learning for Behavioral Contexts in Internet of Things (IoT)

2020 ◽  
Vol 46 (8) ◽  
pp. 626-635
Author(s):  
Muhammad Safyan ◽  
Sohail Sarwar ◽  
Zia Ul Qayyum ◽  
Muddasar Iqbal ◽  
Shancang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things
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