Supply chain management is an important part of the fashion and apparel industry. A manufacturing company must purchase raw materials, manage suppliers, control procurement costs, monitor delivery schedules and maintain product quality. Even a small delay in receiving fabric or accessories can affect production schedules, inventory availability and customer deliveries.
To help students and professionals understand these challenges, I have conceptualised a practical Supply Chain Data Analysis dataset based on a fictional German fashion apparel manufacturing company. The company sources raw materials from three countries: Brazil, India and China.
This dataset contains 300 purchase order records covering 2024, 2025 and 2026. It combines categorical, numerical and date-based fields, making it suitable for learning data cleaning, exploratory data analysis, business intelligence, statistical analysis and performance reporting.
The objective is not simply to create charts. It is to help learners understand how business data can be transformed into meaningful insights that support procurement decisions, supplier evaluation, cost management and operational improvement.
The dataset can be used with Power BI, Tableau, Python, Microsoft Excel and other data analysis tools.
1. Dataset Structure
The dataset contains 10 columns representing different aspects of the procurement process. Each record represents a purchase order placed by the fictional manufacturing company.
| Column | Data Type | Description |
|---|---|---|
| Purchase_Order_ID | Categorical/Identifier | Unique purchase order reference |
| Order_Date | Date | Date the purchase order was placed |
| Delivery_Date | Date | Simulated delivery date |
| Supplier_Country | Categorical | Brazil, India or China |
| Raw_Material | Categorical | Material purchased from a supplier |
| Quantity_Kg | Numerical | Quantity of material purchased |
| Unit_Cost_EUR | Numerical | Cost per kilogram or material unit in euros |
| Lead_Time_Days | Numerical | Procurement lead time in days |
| Defect_Rate_Pct | Numerical | Percentage of defective material |
| Shipment_Status | Categorical | Recorded shipment status: On Time or Delayed |
Get the Dataset Click Here
These fields introduce learners to different data types and their applications in business analysis.
Categorical columns allow students to group records by supplier country, raw material and shipment status. Numerical columns support calculations involving quantity, cost, lead time and defects. Date columns allow students to analyse monthly trends, yearly performance and seasonal purchasing patterns.
For example, learners can investigate whether procurement costs differ across Brazil, India and China. They can also examine which raw materials have higher defect rates or whether purchasing patterns changed between 2024 and 2026.
One important consideration is that the unit cost is expressed in euros, while the quantity is recorded in kilograms. Students should confirm the assumed pricing basis before interpreting the resulting expenditure. For this exercise, total procurement spend can be estimated by multiplying quantity by unit cost, assuming the cost is per kilogram.
2. Data Coverage
The dataset contains 300 records distributed across three calendar years.
| Year | Number of Records | Coverage |
|---|---|---|
| 2024 | 100 | Full-year data |
| 2025 | 100 | Full-year data |
| 2026 | 100 | Year-to-date data through October 10 |
The dataset includes suppliers from Brazil, India and China, with materials such as cotton, organic cotton, wool, polyester, zippers, buttons, lining fabric and natural dyes.
This variety allows students to study sourcing decisions involving different materials and supplier markets.
The three-year structure is particularly useful for time-based analysis. Students can compare purchasing expenditure, average lead times, material quality and shipment status across different years. They can also explore whether procurement performance appears to improve or deteriorate over time.
However, 2026 is a partial year. A direct comparison between the full year of 2024 or 2025 and the available 2026 records could produce misleading conclusions. Students should compare equivalent periods or clearly label the latest year as year-to-date.
The dataset is synthetic and designed for educational purposes. It does not represent the actual operations of a German company. Its purpose is to provide a realistic business scenario in which learners can practise analytical methods without requiring confidential corporate data.
3. What Students Can Build Using Different Data Analysis Tools
The dataset is not restricted to a single platform. Different tools provide different ways to analyse the same business problem, allowing students to develop transferable data analysis skills.
A. Power BI
Power BI is suitable for building interactive dashboards and business intelligence reports.
Students can import the CSV file, verify data types, create calculated measures using DAX and design report pages for procurement, supplier performance and quality control.
A procurement dashboard could display total procurement spend, total material quantity, average lead time and shipment delay rate. Bar charts could compare costs across supplier countries, while line charts could show monthly purchasing trends.
Students can also create slicers for year, supplier country and raw material. These filters allow managers to investigate specific categories without creating separate reports.
A useful exercise is to build a supplier performance dashboard that compares procurement cost, lead time and defect rates. Learners can then identify which suppliers may require further investigation.
B. Tableau
Tableau provides another approach to visual analytics, particularly for exploring relationships through interactive visualisations.
Students can connect the CSV dataset, create calculated fields and develop dashboards using charts, filters and drill-down functionality.
For example, a country-wise procurement dashboard could use a bar chart for purchasing expenditure, a line chart for monthly trends and a scatter plot to compare lead time with defect rates.
A scatter plot can help students investigate whether longer procurement lead times are associated with higher defect rates. This relationship should be treated as an analytical question rather than an established fact.
Tableau’s interactive dashboards can also help users compare raw materials, examine individual supplier markets and identify unusual observations.
C. Python
Python is useful for data cleaning, exploratory data analysis, statistical calculations and automation.
Using pandas, students can load the CSV file into a DataFrame, convert date columns into datetime format, inspect missing values and calculate derived fields.
NumPy can support numerical calculations, while Matplotlib and Seaborn can be used to create visualisations. Students can also explore statistical relationships between lead time, unit cost and defect rate.
For example, learners can group records by supplier country and calculate average lead time, total purchased quantity and average defect rate. They can use correlation analysis to explore relationships between numerical variables.
Python also provides an opportunity to automate repetitive reports. Instead of manually preparing monthly summaries, students can write a script that generates updated analytical tables and charts whenever new records are added.
D. Microsoft Excel
Excel is an accessible starting point for learners who are new to data analysis.
Students can import the CSV file, format date columns, inspect data quality and use formulas to calculate procurement expenditure.
PivotTables can summarise purchasing quantities and costs by country, material and year. PivotCharts can present the results visually, while slicers allow users to filter the analysis interactively.
For example, students can create a PivotTable showing total procurement spend by supplier country and raw material. Conditional formatting can highlight unusually high defect rates or longer lead times.
Power Query is particularly useful for teaching data transformation. Learners can practise changing data types, removing duplicates, renaming columns and combining future datasets with the existing records.
These exercises demonstrate that meaningful business analysis does not always require advanced programming.
E. Other Data Analysis Tools
The same dataset can also be used with SQL, R and other analytical platforms.
SQL learners can import the records into a relational database and practise grouping, filtering, aggregation, date functions and conditional calculations. R users can perform statistical analysis and create visualisations using packages such as dplyr and ggplot2.
Using multiple tools helps students understand that analytical thinking, data quality and business interpretation are more important than mastering a single software package.
4. Recommended KPIs for Students
Key Performance Indicators (KPIs) convert raw data into measurable indicators that managers can use to evaluate business performance.
The following KPIs are suitable for this dataset.
1. Total Procurement Spend
Estimated spend is calculated by multiplying quantity purchased by unit cost and summing the results.
It helps managers understand purchasing expenditure and compare costs across countries, materials and periods.
2. Total Quantity Purchased
This KPI measures the total volume of materials purchased. It can be used to compare sourcing activity across supplier countries and material categories.
3. Average Lead Time
Average lead time is calculated as the average number of days recorded in the Lead_Time_Days column. Lower lead times may support faster procurement, although cost and quality must also be considered.
4. Shipment Delay Rate
The delay rate is calculated by dividing the number of purchase orders marked Delayed by the total number of purchase orders and multiplying by 100.
It provides an overview of recorded shipment performance. Because shipment status is simulated independently of delivery dates, it should not be treated as a date-validated delay measure.
5. Average Defect Rate
This KPI measures the average recorded defect percentage. Students can compare defect rates across materials, countries and years to identify categories requiring further quality investigation.
6. Year-over-Year Procurement Spend Growth
This KPI measures the percentage change in procurement expenditure compared with the previous equivalent period. For 2026, students should use a comparable year-to-date period rather than comparing partial-year figures with full-year totals.
Together, these KPIs provide a foundation for more advanced analysis, including supplier scorecards, sourcing comparisons and operational risk assessments.
Conclusion
The Supply Chain Data Analysis dataset provides a practical starting point for students who want to understand how businesses use data to improve operational decisions.
With 300 records, 10 columns and three years of purchasing activity, learners can practise data preparation, exploratory analysis, visualisation, KPI development and business reporting.
Power BI and Tableau support interactive dashboards, Python enables flexible analytical workflows, and Excel provides accessible tools for calculations and reporting. SQL and R can extend the same project into database management and statistical analysis.
At SlideScope, my objective is to help learners move beyond software tutorials and develop the ability to solve practical business problems. This dataset encourages students to ask meaningful questions, validate their calculations, interpret trends carefully and communicate findings clearly.
The ultimate goal of supply chain analytics is not simply to report what happened. It is to understand why performance changed, identify where improvements may be possible and support better decisions about procurement, suppliers, costs and material quality.
Conceptualised by Ankit Srivastava, SlideScope, for educational use in data analytics training.
Note: Thumbnail Image in this Article was generated with Google Flow AI.