Meyer Intelligent Products for Enhancing the Utilization of Tracking Technology in Transportation Flashcards
What is the main challenge with utilizing tracking technology in transportation?
Many companies struggle to transform large amounts of real-time tracking data (from GPS, RFID, etc.) into actionable operational control decisions.
What are intelligent products in the context of transportation?
Intelligent products are physical objects (e.g., pallets, trucks) that use tracking data and embedded reasoning capabilities to make real-time operational decisions.
Example: A pallet equipped with RFID and AI can track its own location, detect delays, and adjust its routing automatically.
How do intelligent products improve operational control in logistics?
Enhancing visibility – They provide real-time status updates on shipments.
Problem detection – They identify and report unexpected events (e.g., delays, temperature changes).
Decision support – They suggest corrective actions to planners.
What are the three levels of intelligence in intelligent product systems?
Information Handling – Collects and processes tracking data.
Problem Detection – Identifies operational issues automatically.
Decision Support – Suggests solutions to planners or autonomously resolves minor issues.
What were the three main problems identified with using tracking technology?
Manual data analysis is too time-consuming – Planners struggle to analyze huge volumes of tracking data.
Delayed detection of unexpected events – Many disruptions go unnoticed until they cause severe delays.
Complex interactions between shipments are overlooked – Decisions don’t always consider how delays in one shipment affect others.
What advantages does intelligent product systems have?
Automatically processes large amounts of tracking data
Detects potential disruptions in real-time
Considers how different shipments affect each other when suggesting solutions
What technology supports intelligent products?
RFID & GPS – For tracking location and movement.
AI-based decision-making – To analyze data and predict disruptions.
Machine learning – To improve accuracy over time based on past disruptions.
What are some real-world benefits of intelligent products in logistics?
Reduces manual workload for planners.
Prevents costly delays by detecting problems early.
Optimizes transport operations, reducing fuel use and emissions.
Example: A logistics firm using intelligent pallets reduced late deliveries by 15% through real-time delay alerts.
