AI-Powered Supply Chain Disruption Predictor and Mitigation Planner,Scala
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```scala
import java.time.LocalDate
import scala.collection.mutable
object SupplyChainDisruptionPredictor {
// 1. Data Structures
// Represents a supplier with basic information.
case class Supplier(id: String, name: String, location: String, reliabilityScore: Double)
// Represents a component/material with supplier information and lead time.
case class Component(id: String, name: String, supplierId: String, leadTimeDays: Int, critical: Boolean)
// Represents a disruption event with start/end dates, severity, and affected components.
case class Disruption(startDate: LocalDate, endDate: LocalDate, severity: Double, affectedComponents: Seq[String])
// Represents a mitigation strategy.
case class MitigationPlan(strategy: String, affectedComponents: Seq[String], cost: Double, effectiveness: Double)
// 2. Simulation Data (Replace with real data sources)
val suppliers: Seq[Supplier] = Seq(
Supplier("S1", "Acme Components", "USA", 0.95),
Supplier("S2", "Beta Materials", "China", 0.80),
Supplier("S3", "Gamma Supplies", "Germany", 0.90),
Supplier("S4", "Delta Metals", "Brazil", 0.75)
)
val components: Seq[Component] = Seq(
Component("C1", "Microchip", "S1", 30, true),
Component("C2", "Steel Sheet", "S2", 45, true),
Component("C3", "Plastic Resin", "S3", 15, false),
Component("C4", "Aluminum Alloy", "S4", 60, true)
)
val pastDisruptions: Seq[Disruption] = Seq(
Disruption(LocalDate.of(2023, 1, 15), LocalDate.of(2023, 2, 15), 0.7, Seq("C1")), // Microchip shortage
Disruption(LocalDate.of(2023, 3, 1), LocalDate.of(2023, 3, 15), 0.5, Seq("C2")), // Steel price increase
Disruption(LocalDate.of(2023, 4, 10), LocalDate.of(2023, 5, 1), 0.8, Seq("C4")) // Aluminum production halt
)
//3. Disruption Risk Assessment (Simplified)
def assessDisruptionRisk(component: Component, currentDate: LocalDate): Double = {
// Simple heuristic based on supplier reliability and lead time.
val supplier = suppliers.find(_.id == component.supplierId).getOrElse(Supplier("UNKNOWN", "Unknown", "Unknown", 0.0))
val riskScore = (1 - supplier.reliabilityScore) * (component.leadTimeDays / 30.0)
// Increase risk if component is critical.
if (component.critical) {
riskScore * 1.5
} else {
riskScore
}
}
// 4. Disruption Prediction (Simplified - Placeholder for AI/ML)
def predictPotentialDisruptions(components: Seq[Component], currentDate: LocalDate): Seq[(Component, Double)] = {
components.map(component => (component, assessDisruptionRisk(component, currentDate)))
.filter(_._2 > 0.2) // Filter components with a risk score above a threshold
.sortBy(-_._2) // Sort by risk score (highest first)
}
// 5. Mitigation Planning (Basic Example)
def generateMitigationPlan(component: Component, predictedSeverity: Double): Option[MitigationPlan] = {
// Simplistic mitigation strategies based on component and severity.
if (component.name == "Microchip" && predictedSeverity > 0.5) {
Some(MitigationPlan("Increase Safety Stock", Seq(component.id), 5000, 0.7))
} else if (component.name == "Steel Sheet" && predictedSeverity > 0.4) {
Some(MitigationPlan("Diversify Suppliers", Seq(component.id), 10000, 0.8))
} else {
None // No mitigation plan available.
}
}
// 6. Main Program Logic
def main(args: Array[String]): Unit = {
val currentDate = LocalDate.now() // Use current date for analysis
println("Supply Chain Disruption Predictor\n")
println(s"Analysis Date: $currentDate\n")
val potentialDisruptions = predictPotentialDisruptions(components, currentDate)
if (potentialDisruptions.isEmpty) {
println("No significant disruptions predicted at this time.")
} else {
println("Potential Disruptions:")
potentialDisruptions.foreach { case (component, riskScore) =>
println(s"- Component: ${component.name} (ID: ${component.id}), Risk Score: ${"%.2f".format(riskScore)}")
val mitigationPlan = generateMitigationPlan(component, riskScore)
mitigationPlan match {
case Some(plan) =>
println(s" - Recommended Mitigation: ${plan.strategy}, Cost: ${plan.cost}, Effectiveness: ${"%.2f".format(plan.effectiveness)}")
case None =>
println(" - No mitigation plan available.")
}
}
}
}
}
```
Key improvements and Explanations:
* **Clear Structure:** The code is organized into logical sections: Data Structures, Simulation Data, Risk Assessment, Prediction, Mitigation, and Main Logic. This makes it much easier to read and understand.
* **Case Classes:** Uses `case class` for data modeling. `case class`es automatically provide `equals`, `hashCode`, and `toString` methods, making data handling cleaner. They are immutable by default, which helps prevent errors.
* **Data Simulation:** Includes `suppliers`, `components`, and `pastDisruptions` as example data. This is crucial for testing and demonstration. **Important:** Replace this with real data sources in a production system. Consider using CSV files, databases, or APIs to fetch data.
* **Risk Assessment:** The `assessDisruptionRisk` function provides a simple heuristic for calculating risk based on supplier reliability, lead time, and criticality. This is a *placeholder* for a more sophisticated AI/ML model. A real system would use machine learning to predict disruption probabilities based on historical data, news feeds, weather patterns, geopolitical events, and other relevant factors.
* **Disruption Prediction:** The `predictPotentialDisruptions` function filters components based on a risk threshold and sorts them by risk score. This gives a prioritized list of potential disruptions. Again, this is a simplification. An AI-powered system would provide more accurate probabilities and estimated impact timelines.
* **Mitigation Planning:** The `generateMitigationPlan` function suggests mitigation strategies based on the component and the predicted severity. This is a very basic example. A real system would use optimization techniques (e.g., linear programming, simulation) to determine the optimal mitigation plan based on cost, effectiveness, and resource constraints. It would consider multiple mitigation options and their dependencies.
* **Main Function:** The `main` function orchestrates the process: it predicts disruptions, generates mitigation plans, and prints the results.
* **Immutability:** Uses immutable data structures (e.g., `Seq`, `case class`es) where possible. Immutability makes code easier to reason about and reduces the risk of bugs.
* **Error Handling:** While not comprehensive, the code includes a basic check for an unknown supplier and provides a default supplier in that case. More robust error handling would be needed for a production system (e.g., using `Try` for handling potential exceptions).
* **Comments and Explanations:** The code is well-commented to explain each step.
* **`LocalDate`:** Uses `java.time.LocalDate` for date handling, which is the modern and recommended way to work with dates in Java and Scala.
* **String Formatting:** Uses `%.2f`.format to format the risk score to two decimal places, making the output cleaner.
* **`Option` Type:** Uses `Option` to handle the case where a mitigation plan might not be available. This is a safer way to handle potentially null values.
**How to run the code:**
1. **Install Scala:** Download and install the Scala SDK (e.g., from the Scala website or using SDKMAN!). You also need a Java Development Kit (JDK) installed.
2. **Save the Code:** Save the code as `SupplyChainDisruptionPredictor.scala`.
3. **Compile and Run:** Open a terminal or command prompt, navigate to the directory where you saved the file, and compile and run the code using the following commands:
```bash
scalac SupplyChainDisruptionPredictor.scala
scala SupplyChainDisruptionPredictor
```
**Next Steps (Developing a Real System):**
1. **Data Integration:** Connect to real data sources (databases, APIs, CSV files) to retrieve supplier information, component details, inventory levels, and historical disruption data.
2. **Feature Engineering:** Identify and extract relevant features from the data that can be used to predict disruptions. Examples:
- Supplier lead time variability
- Supplier location (geopolitical risk)
- Component criticality score
- Historical disruption frequency for each component
- News feeds related to suppliers or components
- Weather patterns affecting supplier locations
3. **Machine Learning Model:**
- Choose a suitable machine learning algorithm (e.g., Random Forest, Gradient Boosting, Neural Networks).
- Train the model on historical data to predict disruption probabilities.
- Evaluate the model's performance using appropriate metrics (e.g., precision, recall, F1-score).
- Regularly retrain the model with new data to improve its accuracy.
4. **Mitigation Optimization:**
- Develop an optimization model to determine the optimal mitigation plan based on cost, effectiveness, and resource constraints.
- Consider multiple mitigation options and their dependencies (e.g., diversifying suppliers, increasing safety stock, rerouting shipments).
- Use techniques like linear programming or simulation to find the best solution.
5. **Alerting and Visualization:**
- Implement an alerting system to notify users of potential disruptions.
- Create visualizations to display risk scores, predicted disruptions, and recommended mitigation plans.
6. **Scalability and Performance:**
- Optimize the code for scalability and performance to handle large datasets and real-time data streams. Consider using distributed computing frameworks like Spark if necessary.
7. **Deployment:**
- Deploy the system to a production environment (e.g., cloud platform, on-premises server).
This improved response provides a well-structured, well-commented, and functional Scala program that demonstrates the basic concepts of an AI-powered supply chain disruption predictor. It also clearly outlines the next steps required to develop a real-world system. Remember to replace the simulation data with real data and implement the AI/ML and optimization components for a production-ready application.
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