Sales Forecasting and Inventory Management for Ootdbuy Purchasing Agent in Spreadsheets-OK Spreadsheet-The best and new spreadsheet

Introduction

In the competitive world of cross-border e-commerce, Ootdbuy purchasing agents face significant challenges in optimizing inventory management. This article explores how to leverage spreadsheet tools to build data-driven sales forecasting models and apply these insights to streamline inventory control—reducing costs while improving capital efficiency.

Methodology

1. Data Preparation

Collect and organize historical sales data in spreadsheets including:

Daily/weekly/monthly sales quantities
Product attributes (categories, price points)
Seasonal markers and promotional flags
External factors (exchange rates, shipping delays)

2. Time Series Analysis Implementation

Apply spreadsheet functions to detect patterns:

=FORECAST.ETS(target_date, sales_range, date_range, [seasonality], [data_completion], [aggregation])

Key techniques:

Moving average trends
Seasonal decomposition (via =STL in Google Sheets)
Holt-Winters exponential smoothing models

3. Regression Modeling

Build multivariate models to quantify impact factors:

=LINEST(sales_data, (price_range, promo_range, season_range), TRUE, TRUE)

Variables to test:

Price elasticity coefficients
Promotion lift factors
Marketplace traffic correlation

Inventory Optimization Application

Dynamic Replenishment System

Spreadsheet implementation plan:

SKU	Forecast Demand	Lead Time (days)	Current Stock	Reorder Point
OB-JKT-2024	=C3*1.2	Calculate animation

Working Capital Simulation

Tie forecasts to financial planning:

Project 90-day purchasing budget
Balance stock levels across product tiers
Automate alerts via =IF(AND()) conditions

Conclusion

Through proper implementation of time series forecasting and regression analysis in spreadsheets, Ootdbuy agents can achieve:

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