ITT PPT Piyush Singla final - Piyush Singla.pptx

Here’s a ~3000-character description on ERP (Enterprise Resource Planning) — written in a clear, professional, and detailed manner suitable for CA, MBA, or business presentations:


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Enterprise Resource Planning (ERP)

Enterprise Resource Planning, commonly known as ERP, refers to an integrated software system that manages and automates core business processes within an organization. It serves as a centralized platform that brings together various functions such as finance, accounting, human resources, supply chain, inventory, procurement, manufacturing, customer relationship management, and more. By unifying these operations into a single system, ERP helps organizations achieve efficiency, consistency, and transparency in their day-to-day business activities.

At its core, ERP eliminates data duplication and provides a “single source of truth.” Instead of maintaining separate systems for accounting, sales, and production, an ERP system consolidates all data into one database accessible across departments. This integration allows real-time data sharing, improved coordination, and better decision-making. For example, when a sales order is entered, the system automatically updates inventory levels, production schedules, and financial records without manual intervention.

Historically, ERP systems evolved from material requirements planning (MRP) and manufacturing resource planning (MRP II) systems used in the 1960s and 1980s. Over time, as businesses became more complex, ERP expanded beyond manufacturing to include functions like finance, HR, logistics, and customer service. Modern ERP systems are cloud-based, mobile-friendly, and powered by advanced analytics, artificial intelligence (AI), and machine learning (ML), enabling predictive insights and data-driven strategies.

The key features of ERP include:

1. Integration: Seamless connectivity between departments ensures consistent data flow.


2. Automation: Reduces manual work, errors, and duplication of effort.


3. Real-Time Operations: Enables instant updates and insights for faster decisions.


4. Scalability: Grows with the business by adding modules or users as needed.


5. Customization: Allows companies to tailor workflows and reports to their unique needs.


6. Data Security: Centralized control helps safeguard sensitive business information.



Advantages of ERP:

Enhanced productivity through process standardization.

Better financial control and accurate reporting.

Improved inventory and supply chain management.

Greater customer satisfaction through faster and more reliable service.

Cost efficiency by reducing redundant systems and manual errors.


However, ERP implementation also comes with challenges such as high initial cost, complex installation, need for employee training, and potential resistance to change. A poorly executed ERP rollout can disrupt operations and lead to financial losses. Therefore, careful planning, change management, and post-implementation support are

1. TOPIC – MACHINE LEARNING SUBMITTED BY – PIYUSH SINGLA NRO NO . – NRO0565404 CENTRE – LUDHIANA BATCH-NAME - ICITSSITT__LUDHIANA_21 SUBMITTED TO – LUDHIANA BRANCH OF NIRC OF ICAI 1 ICITSSITT PROJECT REPORT

2. ACKNowelegement We would like to express our heartfelt gratitude to our ICITSS faculty as well as our virtuous & honourable Institute of Chartered Accountants of India who gave us the golden opportunity to do the project on the topic of “Machine Learning” which helped us doing a lot of research which open our mind. I am also thankful to my parents and beloved members of the team for their constant encouragement and cooperation throughout this project. 2

3. “Learning is any process by which a system improves performance from experience.” - Herbert Simon Definition by Tom Mitchell (1998): Machine Learning is the study of algorithms that • improve their performance P • at some task T • with experience E. A well-defined learning task is given by . Machine learning

4. History of Machine Learning 1950s: Turing Test 1957: Perceptron 1997: IBM's Deep Blue defeats chess champion 2012: Deep learning breakthrough 4

5. Why is Machine Learning Important? 5 Machine learning and other AI and analytics techniques help accelerate research, improve diagnostics and personalize treatments for the life sciences industry.

6. How Does It Work?

7. Types of Machine Learning • Supervised Learning – Learns with labeled data • Unsupervised Learning – Finds patterns in unlabeled data • Reinforcement Learning – Learns by trial and error Click to edit Master text styles Click to edit Master text styles 7

8. Supervised Learning Example Use Case: Email Spam Filter • Input: Labeled emails • Output: Spam or Not Spam Fact: Gmail blocks 99.9% of spam using ML

9. Unsupervised Learning - Learns from unlabeled data - Example: Customer segmentation in marketing 9 Reinforcement Learning - Agent learns by interacting with an environment - Example: Self-driving cars, game playing (Chess, Go)

10. Application of Machine Learning - Healthcare (disease prediction) - Finance (fraud detection) - E-commerce (recommendations) - Agriculture (crop monitoring) 10

11. ML in COMMERCE AND Industries - Healthcare: Predictive diagnosis - Finance: Stock prediction - Retail: Customer insights - Agriculture: Crop monitoring 11

12. bibliography 12

13. THANK YOU