Research of RFID Data mining based on supply chain management

2014 ◽  
Author(s):  
Chen Lei ◽  
Tian Zhiyong
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
Rfid Data

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 A Split-Path Schema-Based RFID Data Storage Model in Supply Chain Management

Sensors ◽  
2013 ◽  
Vol 13 (5) ◽  
pp. 5757-5776 ◽  
Author(s):  
Hua Fan ◽  
Quanyuan Wu ◽  
Yisong Lin ◽  
Jianfeng Zhang
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Data Storage ◽  
Chain Management ◽  
Storage Model ◽  
Rfid Data

scholarly journals The impact of supply chain management on business intelligence

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Audrey Langlois ◽  
Benjamin Chauvel
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Private Companies ◽  
Chain Management ◽  
Conceptual Paper ◽  
The Impact

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

Data Mining for Supply Chain Management in Complex Networks

2005 ◽  
pp. 708-713
Author(s):  
Mahesh S. Raisinghani ◽  
Manoj K. Singh
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Processes ◽  
Supply And Demand ◽  
Organizational Level ◽  
Order Fulfillment ◽  
Chain Management ◽  
Planning And Control ◽  
And Control

Supply chain comprises the flow of products, information, and money. In traditional supply chain management, business processes are disconnected from stock control and, as a result, inventory is the direct output of incomplete information. The focus of contemporary supply chain management is to organize, plan, and implement these flows. First, at the organizational level, products are manufactured, transported, and stored based on the customers’ needs. Second, planning and control of component production, storage, and transport are managed using central supply management and replenished through centralized procurement. Third, the implementation of the supply chain involves the entire cycle from the order-entry process to order fulfillment and delivery. Data mining can create a better match between supply and demand, reducing or sometimes even eliminating the stocks.

Data Mining for Supply Chain Management in Complex Networks

2001 ◽  
pp. 173-193 ◽  
Author(s):  
Manoj K. Singh ◽  
Mahesh S. Raisinghani
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Complex Networks ◽  
Business Processes ◽  
Operating Efficiency ◽  
Chain Management ◽  
Making Sense ◽  
Large Databases ◽  
Very Large Databases

The concept and philosophy behind supply chain management is to integrate and optimize business processes across all partners in the entire production chain. Since these are not simple supply chains but rather complex networks, tuning these complex networks comprising supply chain/s to the needs of the market can be facilitated by data mining. Data mining is a set of techniques used to uncover previously obscure or unknown patterns and relationships in very large databases. It provides better information for achieving competitive advantage, increases operating efficiency, reduces operating costs and provides flexibility in using the data by allowing the users to pull the data they need instead of letting the system push the data. However, making sense of all this data is an enormous technological and logistical challenge. This chapter helps you understand the key concepts of data mining, its methodology and application in the context of supply chain management of complex networks.

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