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Synthetic Procurement Item Classification
Synthetic Data Generation
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About
Simulating procurement and supply chain data, this resource is designed for strategic decision-making based on the Kraljic Matrix. The content allows for the classification of products and services according to supply risk and profit impact. It features realistic biases and patterns that mirror genuine procurement environments, accounting for supplier dependencies, geopolitical risks, and sustainability considerations. The data structure includes specific profiles for different procurement types, such as Strategic Items (high cost and high single-source risk), Bottleneck Items (supply risk concentrated in limited regions), Leverage Items (high order volumes), and Non-Critical Items (low costs).
Columns
The resource features 11 columns, consisting of a mixture of categorical, numerical, and ordinal data:
- Product_ID: A unique identifier assigned to each procurement item.
- Product_Name: The name of the specific product (e.g., lithium batteries or office supplies).
- Supplier_Region: The geographic location of the supplier, such as Asia or Global.
- Lead_Time_Days: The time required for product delivery, measured in days.
- Order_Volume_Units: The annual volume of units purchased.
- Cost_per_Unit: The price of a single unit, denominated in USD.
- Supply_Risk_Score: An ordinal score reflecting the risk of supply disruption (rated 1=Low to 5=High).
- Profit_Impact_Score: An ordinal score indicating the item's impact on profitability (rated 1=Low to 5=High).
- Environmental_Impact: An ordinal score assessing the sustainability risk (rated 1=Low to 5=High).
- Single_Source_Risk: A Boolean field indicating dependency on a single supplier (True/False).
Distribution
The resource contains 1,000 synthetic procurement instances. It is provided in a CSV file format titled
realistic_kraljic_dataset.csv, with a file size of 61.56 kB. It has 11 features in total. This is a static dataset and the expected update frequency is never.Usage
Ideal applications for this resource include:
- Segmentation Analysis: Classifying procurement items accurately into the four Kraljic quadrants.
- Risk Assessment: Identifying items subject to high supply risk, particularly those with single-source dependencies.
- Sustainability Analysis: Analysing the correlation between environmental impact scores and overall supply risk.
- Predictive Modeling: Training machine learning models to automatically classify new procurement items based on their attributes.
Coverage
The scope covers synthetic supply chain data relating to various product types, ranging from common office supplies to specialised materials like lithium batteries and rare earth metals. Risk scores (supply, profit, and environmental) are calibrated between 1 and 5. Geographically, supplier regions include concentrated sourcing from areas identified as geopolitically volatile, such as Asia and Africa, particularly for strategic items.
License
CC0: Public Domain
Who Can Use It
The resource is beneficial for professionals engaged in strategic procurement and supply chain management. Intended users include Data Scientists developing classification models, Supply Chain Analysts performing segmentation and risk identification, and Business Intelligence professionals examining real-world procurement biases.
Dataset Name Suggestions
- Kraljic Matrix Procurement Simulation
- Supply Chain Risk Strategy Dataset
- Synthetic Procurement Item Classification
- Kraljic Quadrant Decision Data
Attributes
Original Data Source: Synthetic Procurement Item Classification
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