Cargo Risk Forecasting With Automated Damage Cover Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to provide a holistic approach for cargo logistics risk management, including comprehensive risk assessment and insurance coverage, leading to tedious processes in selecting the best insurance package and pricing for cargo logistics services.
Innovation Solution
A digital system that captures measurable cargo logistics parameters using RFID chips and telematic devices, generates risk factors based on historical data, and predicts impact events to provide an aggregated risk measure for cargo and logistics services, enabling automated insurance coverage and transparent booking processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive risk assessment and insurance coverage are implemented, then risk management quality is improved, but process complexity increases
Solution Approach 1:
The system segments risk assessment into distinct modules: capturing cargo parameters (weight, dimensions, value, fragility), logistics parameters (transportation mode, route, packaging), and environmental parameters. Each segment is processed independently and then aggregated to form comprehensive risk measures, making the complex assessment manageable and systematic.
Solution Approach 2:
The patent introduces digital intermediaries including RFID chips attached to cargo, telematic devices for tracking, and a centralized digital platform that mediates between cargo owners, logistics providers, and insurance companies. These intermediaries automate data collection and exchange, reducing manual process complexity while improving risk assessment quality.
2Productivity
If automated risk measurement and insurance policy drafting are implemented, then efficiency is improved, but system complexity increases
Solution Approach 1:
The system enables self-service through automated risk calculation algorithms that compute risk measures based on captured parameters without manual intervention. Insurance policies are automatically drafted by the system based on calculated risk measures and pre-defined coverage options, eliminating the need for manual policy creation and significantly improving efficiency.
Solution Approach 2:
The patent transforms qualitative risk concepts into quantitative parameters by measuring cargo characteristics (weight, value, fragility), logistics conditions (transportation mode, route duration), and environmental factors. These parameter changes enable automated calculation of risk measures and streamline the insurance policy drafting process.
3Measurement precision
If multiple risk factors and parameters are measured and aggregated, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The system merges multiple independent risk factors (cargo parameters, logistics parameters, environmental parameters) into a unified aggregated risk measure. This combination is achieved through a digital platform that integrates data from various sources including RFID tags, telematic devices, and external databases, then processes them together to produce comprehensive risk assessments.
Solution Approach 2:
The patent creates a universal digital platform that handles multiple functions: capturing cargo and logistics parameters, tracking cargo movement, calculating various risk factors, aggregating them into comprehensive risk measures, and generating insurance policies. This multi-functional system reduces overall data processing complexity by consolidating operations into a single integrated platform.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Proposed is a digital system (1) and a method for forecasting and/or allocating risk measures for an occurrence of an impact event causing a physical loss or damage impact on cargo and/or cargo logistics causing a negative impact (30) on the cargo (10) and/or the cargo logistics services. Sets (13) of measurable cargo logistics parameters (11) are captured by the system (1) as logistics input signals (400) from a cargo logistics services database (41) and are transmitted to an allocation structure (22). Each set (13) of measurable cargo logistics parameters (11) at least comprises cargo parameters (12) and/or logistics parameters (42). At least one risk factor (52) indicating a measured negative impact risk for the cargo (10) and/or the cargo logistics services is captured by the system (1) as risk input signals (500). Each of the risk factors (52) at least corresponds to a measured impact strength (31) or impact type (32) of a negative impact (30) on the cargo (10) and/or the cargo logistics services, and/or a quantified damage (33) at the cargo (10) and/or on the cargo logistics services. At least one measurable cargo logistics parameter (11) is assigned a risk factor (52) by the allocation structure (22) that corresponds to a measured value of the measurable cargo logistics parameters (11). An aggregated risk measure (60) for the cargo logistics services is automatically generated by an aggregating structure (25) of the processing unit (20) based on the at least one risk factors (52) allocated to measurable cargo logistics parameters (11) and is provided as output signal (600) by a signal generator (24) to predict an occurrence of a measurable negative impact (30).