Distribution
Solutions
| Distribution Type | Use Cases in SCM | Examples |
|---|---|---|
| Empirical Data Distribution | Modeling real-world phenomena based on historical data | - Customer Demand Patterns - Lead Time Variability - Return Rates |
Poisson Distribution  | Events occurring independently at a constant rate | - Arrival of Orders - Supply Chain Failures - Warehouse Receiving Activities |
Uniform Distribution  | Simplifications when specific patterns are not evident | - Random Inspections - Lead Times for New Suppliers - Task Time Estimations |
Normal Distribution  | Data clustering around a central value with symmetric tails | - Demand Forecasting - Manufacturing Process Control - Transportation Time Analysis |
| Triangle Distribution | Limited data but known minimum, maximum, and most likely outcomes | - Project Completion Times - Cost Estimation for New Products - Supplier Performance Assessment |
Exponential Distribution  | Time between continuous, independent events at a constant rate | - Time Between Supply Chain Disruptions - Lead Time Distribution - Failure Rates of Equipment |
Log-Normal Distribution  | Positively skewed data where the log of the variable is normally distributed | - Product Life Cycle Times - Demand Distribution for High-value Items - Order Quantity Distribution |
Weibull Distribution  | Flexible modeling for various behaviors | - Equipment Lifespan and Reliability - Lead Time Variability - Product Failure Rates |
Beta Distribution  | Variables with a finite range, such as proportions and percentages | - Project Completion Progress - Quality Control Metrics - Inventory Levels as a Proportion of Capacity |
Bernoulli Distribution  | Binary outcomes | - Quality Inspection Outcomes - Demand Occurrence - Supplier On-time Delivery |
Binomial Distribution  | ||
Gamma Distribution  | ||
Student t-Distribution  |