Retail | Human Resources
Contract Business Agreement Measurement
Objective
The client faced significant challenges negotiating and ratifying Contract Business Agreements (CBAs) for blue-collar workers. These challenges included ensuring fair wages, managing labor disputes, and maintaining operational efficiency.
Challenges
- High complexity in negotiating fair wages and benefits.
- Prolonged negotiation periods lead to increased costs.
- Potential labor disputes causing operational disruptions.
- Lack of real-time data and insights to inform decision-making.
- Difficulty in ensuring compliance with industry standards and regulations.
Solution Proposed
- Azure Open AI’s capabilities were used to analyze and summarize large volumes of negotiation documents, reducing the time required for review.
- Implemented Business Intelligence (BI) tools like Tableau to provide real-time data and insights on labor trends, wage benchmarks, and compliance requirements.
- Leveraged engineering solutions to automate the CBA drafting and ratification process, ensuring accuracy and efficiency.
Outcome
- 30% reduction in negotiation time due to data-driven insights and automated processes.
- Decrease in labor disputes through fair and transparent negotiation strategies.
- Operational efficiency improved due to reduced disruptions and streamlined processes.
- Compliance with industry standards and regulations ensured, minimizing legal risks.
- Enhanced employee satisfaction and retention due to fair and timely CBAs.
Design & Thinking Wins
- Successful integration of Azure Open AI for predictive analytics in CBA negotiations.
- Effective use of BI tools for real-time data visualization and decision support.
- Automation of CBA drafting and ratification processes, ensuring accuracy and efficiency.
- Improved stakeholder collaboration communication through data-driven insights.
- Scalable solution adaptable to various retail organizations and labor agreements.
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Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
