College Inventory Forecasting Reports
Budget: ₹1,500 – ₹12,500 INR
My proposal for a college-level inventory-management project has already been approved, but the mid-term report missed several rubric points—twice. I have the raw transactional data, the accepted proposal, and the official templates for both the mid-term and the final report. What I don’t have is the time to redo the mid-term properly and build out the remaining analysis for the final submission.
Here’s where I am:
• Data: raw CSVs covering sales, lead times, and current stock.
• Goal: create a basic time-series forecast and an EOQ-based inventory model, extract clear trends, patterns, and summary statistics, and weave everything into two polished reports that meet the exact college format I’ll share.
• Tools: Can use Python—Pandas, NumPy(Matplotlib or Seaborn for visuals are fine too) or Excel.
You’ll start by cleaning the data, documenting every step in commented notebooks or scripts. From there, generate descriptive statistics and visual trend analyses, then fit a straightforward forecasting model (Basic Time Series Forecasting methods like Simple moving average, exponential moving average and time adjusted moving average). The inventory section should calculate EOQ, reorder points, and any cost comparisons that strengthen the narrative.
Deliverables
1. Revised mid-term report that fully addresses the previously flagged rubric items (I’ll provide the rejection notes for guidance).
2. Final report, formatted exactly to the college template, including methodology, findings, charts, and appendices with well-commented Python code.
3. Cleaned dataset and executable notebooks/scripts.
Both reports must be submission-ready; wording, figures, and references must match the template I provide. If anything is unclear, ask early—I’d rather clarify than repeat another rejection.
Here’s where I am:
• Data: raw CSVs covering sales, lead times, and current stock.
• Goal: create a basic time-series forecast and an EOQ-based inventory model, extract clear trends, patterns, and summary statistics, and weave everything into two polished reports that meet the exact college format I’ll share.
• Tools: Can use Python—Pandas, NumPy(Matplotlib or Seaborn for visuals are fine too) or Excel.
You’ll start by cleaning the data, documenting every step in commented notebooks or scripts. From there, generate descriptive statistics and visual trend analyses, then fit a straightforward forecasting model (Basic Time Series Forecasting methods like Simple moving average, exponential moving average and time adjusted moving average). The inventory section should calculate EOQ, reorder points, and any cost comparisons that strengthen the narrative.
Deliverables
1. Revised mid-term report that fully addresses the previously flagged rubric items (I’ll provide the rejection notes for guidance).
2. Final report, formatted exactly to the college template, including methodology, findings, charts, and appendices with well-commented Python code.
3. Cleaned dataset and executable notebooks/scripts.
Both reports must be submission-ready; wording, figures, and references must match the template I provide. If anything is unclear, ask early—I’d rather clarify than repeat another rejection.