ANALYTICS & PROJECTS
BUSINESS PROCESS IMPROVEMENT
Long Wait Time in the ER
Long wait times in the Emergency Room (ER) are a critical issue, affecting both patient care and hospital efficiency. This project focuses on addressing key problems such as overcrowding, insufficient staffing, and delays in the triage process, which are primary contributors to extended wait times.
The first step will be to improve the triage process, which is often the bottleneck in patient flow. Issues like staff burnout, inefficient resource use, and poor communication slow down triage and delay patient care. By focusing on these areas, we aim to make an immediate impact on reducing wait times.
Proposed solutions include increasing staffing during peak hours, integrating automated triage technology, and improving communication protocols among medical teams. Optimizing resource allocation and patient flow will further enhance efficiency. These changes will help reduce wait times, improve patient care, and create a more efficient ER operation.
RECRUITMENT OPTIMIZATION
Similarity Score Evaluation Model
This project proposes a new, data-driven approach for hiring by focusing on the alignment between candidate profiles and ideal role requirements. By utilizing a similarity-based evaluation method, the approach seeks to objectively assess candidates' resumes, cover letters, interview responses, and pre-screen question answers. Key components of the method include:
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Profile Matching: Resumes and other candidate materials are mapped to an ideal candidate profile, with a similarity score assigned to help identify the best matches for the role.
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Cover Letter Analysis: An assessment of effort and AI usage within cover letters is conducted to gauge genuine interest and individual input.
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Keyword Matching: Interview responses and pre-screen questions are analyzed through keyword matching against the job description to evaluate candidate relevance and suitability.
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Red-Flag Identification: The method includes a red-flag screening mechanism to ensure candidates' suitability based on predefined criteria.
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Referral and Tenure Data: Data on candidate referrals and expected tenure are incorporated to further enrich the hiring process.
This innovative approach aims to streamline the recruitment process, ensuring a more accurate and efficient selection while reducing bias and improving hiring outcomes. By focusing on data-driven insights, the method ensures that candidates are evaluated on objective criteria directly aligned with job requirements.
BUSINESS OPERATIONS OPTIMIZATION
Basket Analysis & Floor Plan Optimization
This project applies basket analysis to uncover customer purchasing patterns, aiming to optimize pricing strategies and store floor plans in retail environments. Using SQL, R, and Tableau, the project processes, analyzes, and visualizes data to drive decisions on product placement, pricing, and store layout.
Transactional data is first queried and cleaned using SQL, then analyzed in R using basket analysis algorithms, such as association rule mining, to identify frequently purchased product combinations. These insights help inform product placement strategies, encouraging cross-selling and boosting sales.
R is also used to analyze demand elasticity, segmenting customers by age and gender to optimize pricing and tailor marketing efforts. By examining how price changes impact purchasing patterns, retailers can refine their pricing strategies to maximize revenue.
Tableau is used to visualize the analysis, creating interactive dashboards that help decision-makers explore product associations and pricing strategies. Additionally, Tableau aids in designing an optimized store layout by visually mapping product groupings, improving store navigation and enhancing the shopping experience.
Ultimately, this project demonstrates the value of using SQL for data management, R for analysis, and Tableau for visualization, enabling data-driven decisions that enhance retail profitability through improved pricing, product placement, and store design.