Case Study – Big data technologies-based application is used to process, analyze and store the supply chain industry data. The ever-increasing importance of supply chain industry has brought together numerous challenges. Specifically, challenges are in collecting, processing, analyzing, and storing of data. These are due to the digitalization and automation devices have generate huge amount of data in the supply chain's applications. The supply chain industry applications collect data from multiple participants such as manufacturers, retailers, vendors, etc. The data is useful for planning, sourcing and development, execution, delivery and return of products. Currently, most of the supply chain applications still utilize traditional data processing systems to monitor and manage the real-time logistics tracking such as for sales numbers, inventory levels, delivery time, vendor, and shipping details, to name a few. In addition, most of the applications do not employ machine learning algorithms for prediction in the supply chain applications. Based on the above case study, answer the following questions. 1. Discuss any FIVE (5) data processing and analysis problems associated with traditional supply chain applications. 2. Based on the above case study, propose a big data technologies and cloud-based application, detailing each component of the application in an appropriate diagram. The proposed application must include machine learning algorithm for analyzing supply chain application' data. 3. Provide FIVE (5) justifications for selecting the big data and cloud technologies in reference to the answers given in (2). 4. Evaluate FIVE (5) disadvantages of proposing the big data and cloud technologies in reference to the answers given in (2). 5. Recommend the relevant solutions to mitigate the disadvantages as evaluated in (4).
Case Study – Big data technologies-based application is used to process, analyze and store the supply chain industry data. The ever-increasing importance of supply chain industry has brought together numerous challenges. Specifically, challenges are in collecting, processing, analyzing, and storing of data. These are due to the digitalization and automation devices have generate huge amount of data in the supply chain's applications. The supply chain industry applications collect data from multiple participants such as manufacturers, retailers, vendors, etc. The data is useful for planning, sourcing and development, execution, delivery and return of products. Currently, most of the supply chain applications still utilize traditional data processing systems to monitor and manage the real-time logistics tracking such as for sales numbers, inventory levels, delivery time, vendor, and shipping details, to name a few. In addition, most of the applications do not employ machine learning algorithms for prediction in the supply chain applications. Based on the above case study, answer the following questions. 1. Discuss any FIVE (5) data processing and analysis problems associated with traditional supply chain applications. 2. Based on the above case study, propose a big data technologies and cloud-based application, detailing each component of the application in an appropriate diagram. The proposed application must include machine learning algorithm for analyzing supply chain application' data. 3. Provide FIVE (5) justifications for selecting the big data and cloud technologies in reference to the answers given in (2). 4. Evaluate FIVE (5) disadvantages of proposing the big data and cloud technologies in reference to the answers given in (2). 5. Recommend the relevant solutions to mitigate the disadvantages as evaluated in (4).
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Transcribed Image Text:Case Study – Big data technologies-based application is used to process, analyze
and store the supply chain industry data.
The ever-increasing importance of supply chain industry has brought together numerous
challenges. Specifically, challenges are in collecting, processing, analyzing, and storing
of data. These are due to the digitalization and automation devices have generate huge
amount of data in the supply chain's applications.
The supply chain industry applications collect data from multiple participants such as
manufacturers, retailers, vendors, etc. The data is useful for planning, sourcing and
development, execution, delivery and return of products.
Currently, most of the supply chain applications still utilize traditional data processing
systems to monitor and manage the real-time logistics tracking such as for sales numbers,
inventory levels, delivery time, vendor, and shipping details, to name a few. In addition,
most of the applications do not employ machine learning algorithms for prediction in the
supply chain applications.
Based on the above case study, answer the following questions.
1.
Discuss any FIVE (5) data processing and analysis problems associated with
traditional supply chain applications.
2.
Based on the above case study, propose a big data technologies and cloud-based
application, detailing each component of the application in an appropriate diagram.
The proposed application must include machine learning algorithm for analyzing
supply chain application' data.
3.
Provide FIVE (5) justifications for selecting the big data and cloud technologies in
reference to the answers given in (2).
4.
Evaluate FIVE (5) disadvantages of proposing the big data and cloud technologies in
reference to the answers given in (2).
5.
Recommend the relevant solutions to mitigate the disadvantages as evaluated in (4).
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