I can help you build inventory management model using R
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Project Details
Why Hire Me?
I provide customized, data-driven inventory solutions using R, tailored to your operational needs. From EOQ to forecasting and simulation, I ensure every model is accurate, scalable, and actionable.
Why My R-Based Models Stand Out:
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Strong command of R packages:
forecast,shiny,tidyverse,lpSolve,ggplot2 -
End-to-end support: from data preprocessing to dashboard deployment
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Models tested using historical and simulated data
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Domain experience across retail, manufacturing, pharma, and e-commerce
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Clear reporting and ongoing support included
What I Need From You to Start the Project
To create a model that fits your inventory context precisely, please share the following:
1) Project Scope and Business Goals
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Description of your inventory challenges
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Specific objectives: stockout reduction, holding cost minimization, lead-time alignment, etc.
2) Data Details
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Inventory data: sales history, stock levels, lead times
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Format of data: CSV, Excel, SQL extract, etc.
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Preprocessing: indicate if raw or cleaned
3) Modeling Preferences
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Forecasting needed? (Yes/No)
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Preference for EOQ, JIT, safety stock, or ABC analysis
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R packages you prefer (if any)
4) Dashboard and UI (Optional)
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Need for R Shiny dashboard?
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Visual KPIs like turnover, reorder alerts, aging stock?
5) Validation Strategy
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Availability of historical outcomes for model testing
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Any specific KPIs to assess model performance
6) Reporting and Deliverables
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Desired report structure: methodology, model code, summary tables, graphs
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Export format: PDF, HTML, Word, or Markdown
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Screenshots or R Shiny embedded report (if needed)
7) Implementation Plan
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Will the model integrate with your system, or run standalone?
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Deployment format: script, RMarkdown report, Shiny app
8) Timeline and Communication
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Key dates and deadlines
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Preferred mode of communication (email, WhatsApp, Google Meet)
9) Documentation and Support
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Need for user manual or training video?
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Do you require ongoing support?
Portfolio
Optimizing Reorder Points and EOQ for a U.S. Specialty Food Distributor Using R
Built an EOQ and reorder point model using R for a perishable goods distributor. The model reduced stockouts and waste while maintaining order frequency.
Building a Real-Time Inventory Forecasting Dashboard in R for a U.S. Auto Parts Retail Chain
Developed a Shiny dashboard in R for real-time forecasting and inventory monitoring across 20+ retail locations. Enabled accurate replenishment and reduced stock imbalances.
Inventory Simulation and Safety Stock Optimization Using R for a U.S. E-commerce Apparel Brand
Built a stochastic inventory simulation model in R to estimate safety stock levels during seasonal sales. Enabled better purchase planning and improved fulfillment rates.
Process
Customer Reviews
5 reviews for this Gig ★★★★☆ 4.4
Great value for the price, though setting up the Shiny app required some back-and-forth to get the inputs right.
Fast delivery, clean code, and helpful guidance on how to maintain the model for future use.
The R-based dashboard made it easier to visualize our inventory flow and optimize reorder points.
Excellent technical modeling, but I needed a bit more explanation to understand how the simulation worked.
The demand forecasting in R was accurate and helped us prepare better for seasonal sales cycles.
EOQ Model in R
Starts at $55
Simple EOQ model using base R or tidyverse, with defined cost functions
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3
Days Delivery
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1
Revision
Forecasting-Driven Inventory Model
Starts at $95
R-based demand forecasting and reorder point model with visualization
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5
Days Delivery
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3
Revisions
Interactive Inventory Dashboard in R
Starts at $150
R Shiny dashboard with live controls for EOQ, forecast inputs, and simulations
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7
Days Delivery
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Unlimited
Revisions
