InterNetWork

In today's dynamic business environment, companies often need to efficiently manage employee transfers between departments, teams, or locations to optimize their workforce, promote career growth, and address changing business needs. However, the process of employee transfers can be cumbersome, time-consuming, and error-prone when handled manually.

This repository hosts a solution that leverages Machine Learning (ML) to streamline and automate the employee transfer process, ensuring it is seamless, fair, and mutually beneficial for both the employees and the organization.

Problem Statement

Current manual employee transfer procedures, plagued by inefficiencies and complexities, impede workforce optimization and career progression within organizations.

Key Challenges

1. Optimizing Workforce Allocation: Ensuring that employees are transferred to roles or locations where their skills and expertise are most needed, maximizing overall organizational productivity.

2. Fairness and Equity: Ensuring that employee transfers are conducted in a transparent and fair manner, taking into account factors such as seniority, performance, and employee preferences.

3. Efficiency: Reducing the administrative burden of managing employee transfers by automating tasks such as communication, documentation, and approvals.

4. Employee Satisfaction: Enhancing the employee experience by providing visibility into the transfer process, offering support, and aligning transfers with career development goals.

5. Compliance: Ensuring that all transfers adhere to company policies, labor laws, and regulations.

How the Solution Works

The ML-powered solution aims to revolutionize the way employee transfers are managed within the organization, making the process more data-driven, efficient, and employee-centric. It includes the following components:

• Workforce Optimization: HuggingFace's Sentence-Transformers ML model is deployed to analyzes employee skills, projects done in the company, and departmental needs to recommend optimal transfers.

• Fair Transfer Algorithms: Made use of PyTorch's Cosine Similarity algorithm that derives the similarity score between the person to be transferred and potential personnel in the company.

• Employee Portal: A user-friendly employee portal to visualise essential Human Resource (HR) information within the company

To use all the features

Log in with:

Email: danielb@gmail.com.

Password: password.

Getting Started

To get started with this solution, follow these steps:

1. Clone this repository to your local machine.

2. Install the required dependencies and libraries as specified in the documentation.

3. Configure the solution to work with your organization's HR systems and policies.

4. Train the ML model on your employee data to make transfer recommendations.

5. Deploy the solution within your organization and provide access to employees.

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Note: This README is a high-level overview of InterNetWork project. Detailed documentation, setup instructions, and usage guidelines can be found in the project's documentation directory. Youtube Link https://youtu.be/CVPXzVC0L48?si=etv7c_Vsi2_U5auc