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

Introduction

Efficient sales forecasting and inventory management are critical for Ootdbuy, a purchasing agent service that sources and resells goods across international markets. By leveraging historical sales data and market variables in spreadsheets, Ootdbuy can build predictive models to optimize stock levels, minimize costs, and improve capital efficiency.

1. Data Preparation and Structuring

To begin, organize historical sales data in a structured spreadsheet format with columns such as:

Date:
Product ID:
Units Sold:
Market Variables:

A sample dataset might resemble:

Date Product ID Units Sold Promotion Flag Season

2024-01-01 OB123 85 No Winter

2024-01-08 OB123 112 Yes Winter

Date	Product ID	Units Sold	Promotion Flag	Season
2024-01-01	OB123	85	No	Winter
2024-01-08	OB123	112	Yes	Winter

2. Forecasting Methods Implementation

2.1 Time Series Analysis

In spreadsheets, apply formulas to calculate:

Moving Averages (MA): =AVERAGE(B2:B6)
Exponential Smoothing (ETS): Google Sheets built-in function:=FORECAST.ETS(B2:B24, A2:A24, A25)

2.2 Regression Analysis

Use multivariate regression to quantify relationships between sales and predictors (e.g., promotions, seasonality):
=LINEST(sales_data, marketing_factors, TRUE, TRUE)
Key outputs include R-squared (model fit) and p-values (variable significance).

3. Inventory Management Integration

Link forecasts to inventory control with these spreadsheet components:

Metric Spreadsheet Formula Purpose

Reorder Point =AVG_Sales_Lead_Time + Safety_Stock Trigger new orders

Optimal Order Quantity =SQRT((2*Annual_Demand*Order_Cost)/Holding_Cost) (EOQ model)

Stock-out Risk =NORMDIST(Reorder_Point, Forecasted_Demand, STDEV, TRUE) Service level evaluation

4. Practical Application Example

For a trending skincare product (OB456), Ootdbuy’s analysis revealed:

15% higher winter demand (regression coefficient)

Promotions increase sales by 22% (p < 0.05)

Optimal order quantity: 320 units bi-monthly

These insights reduced overstocking by 18% and improved cash flow efficiency (30% shorter inventory cycle).

5. Key Benefits

Cost Reduction

Minimize dead stock and storage expenses

Dynamic Adaptation

Real-time adjustments for unexpected demand shifts

Cross-functional Coordination

Shared spreadsheets align purchasing, logistics, and finance teams

Implementation Recommendations

1. Automate data collection via APIs or Google Sheets IMPORTXML
2. Validate models quarterly against actuals with =RSQ(actual, forecast)
3. Integrate with Ootdbuy's ERP system for end-to-end visibility

*Example files available upon request

Metric	Spreadsheet Formula	Purpose
Reorder Point	=AVG_Sales_Lead_Time + Safety_Stock	Trigger new orders
Optimal Order Quantity	=SQRT((2Annual_DemandOrder_Cost)/Holding_Cost)	(EOQ model)
Stock-out Risk	=NORMDIST(Reorder_Point, Forecasted_Demand, STDEV, TRUE)	Service level evaluation