Super Store Sales Analysis and Forecasting
Pragyan Dhakal
SuperStore-Sales-Analysis-in-Power-BI
🎯 Objective
To contribute to the success of the business by utilizing data analysis techniques—specifically time-series analytics—to provide valuable insights and accurate 15-day sales forecasting.
đź“‹ Overview
This repository contains:
The Power BI dashboard (
.pbix) visualizing key sales metrics (Jan 2019–Oct 2020)A 15-day ARIMA-based forecast of daily sales
This README outlining methodology, findings, and actionable recommendations
🚀 Key Findings
Total Sales: $1.57 M
Total Profit: $175.3 K
Top Category: Office Supplies ($644 K; 41 % of revenue)
Leading Segment: Consumer (48 % of revenue)
Primary Ship Mode: Standard Class (58 % of orders)
Forecasted Daily Sales: $2.2 K–$5.3 K over next 15 days
đź› Methodology
Data Preparation
Source: Super Store transactional dataset
Period: January 2019–October 2020
Cleaned for returns, missing shipping info, and date consistency
Dashboard Design
KPIs selected for executive visibility
Visual types: bar charts, pie/donut charts, line trends, geographic maps
Time-Series Forecasting
Model: ARIMA with parameters tuned via AIC/BIC
Validation: Back-test on October 2020 hold-out
Output: 15-day point forecasts + 95 % prediction intervals
đź“Š KPI Summary
KPI Value Total Sales $1,570,000 Total Profit $175,260 Units Sold 22,000 Units Returned 287 Avg. Shipping Time (days) 3.93
🔍 Detailed Analysis
1. Sales by Category
Office Supplies: $644 K (41 %)
Technology: $471 K (30 %)
Furniture: $452 K (29 %)
2. Top 3 Sub-Categories
Phones: $197 K
Chairs: $182 K
Binders: $175 K
3. Sales by Ship Mode
Standard Class: $912 K (58 %)
Second Class: $315 K (20 %)
First Class: $243 K (15 %)
Same Day: $96 K (6 %)
4. Sales by Segment
Consumer: 48 %
Corporate: 33 %
Home Office: 19 %
5. Sales by Payment Mode
COD: 42.6 %
Online: 35.3 %
Cards: 22.0 %
6. Sales by Region
South: 33.4 %
West: 28.8 %
East: 21.8 %
Central: 16.0 %
7. Monthly Sales & Profit Trends
Seasonality: Peaks in December; troughs in Feb & Apr
YoY Growth: ~10 % increase in 2020 vs. 2019
đź”® 15-Day Sales Forecast
Date Forecast ($) Lower 95 % Upper 95 % 2021-01-01 5,263 1,940 8,586 2021-01-02 4,026 703 7,349 2021-01-03 2,914 –409 6,237 … … … … 2021-01-15 3,699 374 7,025
Figure B-1: Point forecasts with 95 % confidence intervals, more in report on sales analysis.
đź’ˇ Actionable Recommendations
Inventory & Logistics
Prioritize restocking Office Supplies & Phones
Scale Standard Class shipping capacity
Pricing & Promotions
Bundle high-margin sub-categories (e.g., phone accessories)
Incentivize pre-paid orders to reduce COD share
Regional Marketing
Target South & West with regional campaigns
Offer free-shipping thresholds in Central to boost penetration
Seasonal Planning
Staff up & increase inventory in Q4 (Nov–Dec)
Launch a mid-year clearance to smooth off-peak dips
Forecast-Driven Operations
Align procurement and staffing to 15-day forecasts
Monthly model retraining with fresh data
đź“ž Contact
For questions or freelance engagements, please reach out via my Linked profile or email at pragyan036@gmail.com.
Happy analyzing! 🚀
Like this project
Posted Apr 29, 2025
Conducted sales analysis and forecasting for Super Store using Power BI and ARIMA.
Likes
0
Views
0
Timeline
Jan 1, 2019 - Oct 31, 2020
