Routing Risk Mitigation via Composite Risk Score Algorithm
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Solution Overview
Problem
Current transportation methods fail to effectively mitigate damage to goods during transit, despite precautions like cushioning and securing, leading to significant losses for retailers, carriers, and vendors, impacting customer costs and demand fulfillment.
Innovation Solution
A method and system using a Risk Assessment Algorithm (RAA) that analyzes historical data to determine route risk by assigning weights to road segments based on damage claims, penalizing high-risk segments and rewarding low-risk ones, influencing routing decisions to minimize damage risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cushioning and securing methods are used during transportation, then basic protection is provided, but damage to goods still occurs during transit
Solution Approach 1:
The system performs preliminary risk assessment by analyzing historical claims data and route information before transportation begins. It calculates risk scores for different routes and proactively selects optimal low-risk routes, preventing damage before it occurs rather than just protecting during transit
Solution Approach 2:
The system continuously monitors and updates route risk assessments by incorporating new claims data and route information. It uses feedback from historical damage patterns to refine risk calculations and improve route selection over time, creating a learning system that becomes more effective with use
2Loss of energy
If existing transportation precautions are implemented, then some damage prevention is achieved, but significant write-offs still occur due to transit damage
Solution Approach 1:
The system calculates route risk scores and identifies optimal transportation paths before goods are shipped. By proactively selecting low-risk routes based on historical data analysis, it prevents damage occurrences rather than merely responding to losses after they happen
Solution Approach 2:
The system replaces physical protection mechanisms (cushioning, securing) with an intelligent routing system that uses data analysis and algorithms to prevent damage. Instead of relying solely on mechanical protection, it substitutes a computational approach that identifies and avoids high-risk routes
3Device complexity
If route selection is based on traditional criteria only, then routing is simple, but high-risk routes may be selected leading to damage
Solution Approach 1:
The system integrates multiple functions into a single routing platform: it combines historical claims analysis, route risk calculation, optimal path selection, and continuous monitoring. This multi-functional approach consolidates what would otherwise require separate systems into one unified solution
Solution Approach 2:
The system transforms the routing problem by introducing risk scores as a new parameter alongside traditional routing criteria. It calculates and weights multiple parameters (historical claims, route characteristics, geographic factors) to create a comprehensive risk assessment that guides route selection
Data Source
AI summary
A method for mitigating routing risks during transport of goods is provided. In some embodiments, the method includes obtaining historical records for transported goods, where the records include routes with associated road segments. The method also includes establishing risk metrics for each road segment on each route based on the historical records and calculating, from the risk metrics, road risk and road probability metrics for each road segment on each route. The method further includes applying a weight to each of the road risk and road probability metrics, combining, for each road segment, the weighted road risk and road probability metrics for such road segment into a composite risk score (CRS) that defines whether such road segment is low-risk or high-risk for transporting goods, and selectively adjusting a length of each road segment defined as low-risk and/or each road segment defined as high-risk based on their corresponding CRS.