INTRODUCTION
The objective of this report is to offer a preliminary analysis of the supply sales dataset. Through an examination of the dataset's structure and contents, we aim to identify important variables, spot obvious trends, and suggest areas for further analysis.
The dataset comprises sales data with various variables, including ordernumber, quantityordered, price, orderlinenumber, sales, orderdate, status, qtr_id, month_id, and year_id, among others.
OBSERVATIONS
Dataset Structure and Key Variables:
The dataset contains 25 columns and 2824 rows with different data types. For example, the column QUANTITYORDERED, PRICEEACH and ORDERLINENUMBER are numerical. STATUS and PRODUCTLINE columns are categorical; and CUSTOMERNAME and CITY column are string datatypes. The ORDERDATE column is in “string” format and should be converted to a datetime format for time-series analysis.
Geographical Insights: The dataset includes geographical data such as CITY, STATE, and COUNTRY which can be useful for identifying top market regions with the highest sales volumes.
Sales Distribution: The SALES column shows a wide range of sales amounts. This indicates varying order sizes, which might be as a result of different DEALSIZE categories (Small, Medium, Large).
Order Status: The STATUS column provides insight into the state of each order (e.g., Shipped, On Hold, Cancelled). This column is important for understanding the fulfillment process and identifying any potential delays or cancellations.
Insights
Upon visual inspection, sales tend to vary significantly across different months. A monthly trend analysis can help identify peak sales periods.
Different PRODUCTLINEs (Classic Cars, Motorcycles, Trucks and Buses) might have varying sales performance. Analyzing sales by product line could highlight the most and least popular product categories.
The majority of orders have a status of "Shipped," indicating a high fulfillment rate. However, further investigation is needed to understand the reasons behind cancelled orders.
Conclusion
This review of the sales dataset highlights several areas for further analysis, including monthly sales trends, product line performance, and order fulfillment status. By exploring these areas in more depth, we can gain valuable insights to inform business decisions.
Next Steps
Convert ORDERDATE to datetime format for time-series analysis.
Perform detailed trend analysis on sales data across different time periods.
Analyze product line performance to identify the most popular products.
Investigate order cancellations to improve the fulfillment process.
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