I can help you manage inventory using sales and demand forecasting
- 4.3
- (5)
Project Details
Why Hire Me?
I bring 7+ years of cross-industry experience in data-driven inventory management, with a deep understanding of forecasting methods tailored for e-commerce. My services focus on preventing overstock and stockouts, improving turnover, and aligning inventory with dynamic customer demand.
Why Clients Choose Me:
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Expertise in sales forecasting, demand prediction, and inventory optimization
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Proficient in analyzing historical sales data, seasonality, and promotions
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Skilled in applying models like moving average, regression, exponential smoothing, and ML-based forecasts
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Capable of developing category-specific inventory strategies, including JIT and EOQ models
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Delivery includes structured reports, charts, and dashboard-ready outputs
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Experience across product types: FMCG, fashion, electronics, and niche D2C items
What I Need to Start Your Work
Please provide the following to initiate your inventory forecasting and optimization project:
1) Project Scope and Inventory Management Goals
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Objective of the project (e.g., reduce holding costs, improve fulfillment rates)
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Product categories or SKUs to focus on
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Any known inventory issues or goals (e.g., high stockouts, dead stock)
2) Sales and Demand Data Specifications
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Historical sales data (format, frequency – daily, weekly, monthly)
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Inventory logs (stock on hand, stockouts, restock dates)
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Sales trends during campaigns or seasonal periods
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Any preprocessing already applied to the dataset
3) Forecasting Methodology
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Preferred forecasting method (if any): time series, regression, ML-based
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Software/tool to be used: Excel, R, Python, etc.
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Any relevant business rules (e.g., buffer stock during sales periods)
4) Inventory Strategy Development
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Strategy expectations: JIT, EOQ, safety stock levels
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Input on vendor lead times, MOQ, reorder frequency
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Approach to handle irregular demand or supply chain disruptions
5) Reporting and Analysis Requirements
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Format of reports: Excel sheets, PowerPoint decks, PDFs
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Charts and dashboards: stock projection graphs, reorder schedules
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Level of detail expected (executive summary vs technical model description)
6) Data Privacy and Ethical Considerations
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Any NDA or legal requirements
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Compliance with internal data governance or external regulations
7) Project Timeline and Milestones
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Final delivery date
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Review points or intermediate deliveries (e.g., model validation)
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Urgency or time-sensitive targets (e.g., festive season planning)
8) Communication and Collaboration
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Preferred channels (Zoom, WhatsApp, Email)
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Review meeting frequency
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File sharing protocols (Google Drive, Dropbox, etc.)
9) Additional Requirements or Preferences
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Any competitive benchmarks to match
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Past reports, templates, or examples (if any)
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Other special instructions relevant to your e-commerce vertical
Portfolio

Seasonal Sales Forecasting for Apparel Inventory Optimization
Learn how an online fashion store used time series forecasting and EOQ models to reduce stockouts and overstock. A case study in inventory optimization using Python and Excel for seasonal apparel management.

Machine Learning Forecasting for Grocery Inventory Optimization
See how a grocery chain used Random Forest and XGBoost to improve sales forecasting and reduce waste. A case study in machine learning–driven inventory management for perishables and fast-moving SKUs.

Festive Demand Forecasting for Home Appliance Retail
Discover how a home appliance retailer used ARIMAX and regression models to forecast festive demand and reduce stockouts. A data-driven case study in inventory planning for seasonal sales campaigns.
Process

Customer Reviews
5 reviews for this Gig ★★★★☆ 4.3
I run a boutique with lots of SKUs and tracking demand was a headache. He simplified the data and showed me which items were consistently underperforming and what to restock. Now my cash isn’t stuck in the wrong products anymore.
My online store used to run out of bestsellers randomly. His model helped us see the patterns hidden in the past orders and seasonality. Since implementing his suggestions, our sales fulfillment rate has improved a lot.
He delivered a decent forecast but the format of the report was a bit plain for my team to understand. Still, the reorder suggestions based on demand trends were accurate and saved us a lot. Would definitely recommend but ask for more visual dashboards.
We had no idea how much we were overbuying until we saw his demand prediction charts. The weekly forecasting model helped us reduce wastage by almost 20 percent. Wish I had taken this service earlier during Q4 rush.
I was always struggling with dead stock and last-minute stockouts during sales. After working with him, my inventory turnover improved drastically. His forecast was so close to our actual demand that we didn’t over-order for the first time in 2 years.