https://github.com/lamiaaali/graduation-project-data-analytics-beh1_dat1_m1e--supply-chain-group-2

Data Analytics BEH1_DAT1_M1e group 2
https://github.com/lamiaaali/graduation-project-data-analytics-beh1_dat1_m1e--supply-chain-group-2

Last synced: 23 days ago
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Data Analytics BEH1_DAT1_M1e group 2

• Host: GitHub
• URL: https://github.com/lamiaaali/graduation-project-data-analytics-beh1_dat1_m1e--supply-chain-group-2
• Owner: lamiaaali
• Created: 2024-10-19T10:34:55.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-10-20T10:02:54.000Z (3 months ago)
• Last Synced: 2024-10-28T02:33:04.107Z (2 months ago)
• Language: Jupyter Notebook
• Size: 14.2 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Graduation-Project-Data-Analytics-BEH1_DAT1_M1e--Supply-Chain-group-2

Data Analytics BEH1_DAT1_M1e group 2

What is supply chain analysis?

Supply chain analysis is the process of evaluating every stage of a supply chain starting from the time the business acquires raw materials or supplies from its suppliers to the delivery of final products to the customers.

The purpose of the analysis is to determine which part of the supply chain can be improved or shortened to deliver the product more quickly and efficiently to the customers.

What are supply chain analytics and it's different types?

Each of these supply chain analytics can increase the overall efficiency of business operations, which can lead to sizable cost savings.

Descriptive Analytics focuses on understanding what happened in the past by analyzing historical data. It can provide insights on key performance metrics, such as inventory levels, lead times, and delivery performance. Descriptive analytics can help identify patterns and trends in past supply chain operations, allowing organizations to make informed decisions about future strategies.

Diagnostic Analytics goes beyond descriptive analytics by identifying the root causes of supply chain issues. By analyzing data from different sources, such as suppliers, logistics providers, and customers, organizations can identify the factors that contribute to delays, disruptions, or quality issues in their supply chain. This can help them take corrective actions to prevent similar problems from happening in the future.

Predictive Analytics uses statistical models and machine learning algorithms to forecast future supply chain events. By analyzing historical data, organizations can identify patterns and trends that can help predict demand, inventory levels, and delivery performance. This can help organizations optimize their supply chain operations, reduce costs, and improve customer satisfaction.

Prescriptive Analytics takes predictive analytics one step further by providing recommendations on how to optimize supply chain operations. By using optimization algorithms and simulations, prescriptive analytics can help organizations identify the best course of action to improve supply chain performance. This can help organizations make better decisions and improve their overall supply chain efficiency.