IoT Storage Optimization for Manufacturing Parts Priority
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Solution Overview
Problem
The re-configuration process in customer-configurable products manufacturing is inefficient due to lack of visibility in parts availability, time-consuming manual searches, and disorder in storage, leading to delays and inefficiencies in production.
Innovation Solution
Implementing a connected storage optimization system using IoT sensors for real-time monitoring and digital twin mapping, which calculates a priority index for parts based on shelf time and quantity, assigning categories for efficient storage and retrieval, and utilizing automated guided vehicles for physical storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual searching and storage management is used, then device complexity is reduced, but productivity decreases due to time-consuming operations and lack of visibility
Solution Approach 1:
The system enables automated self-service through IoT sensors that automatically monitor parts availability, calculate priority indices, and update digital twin mappings without human intervention. The automated guided vehicles autonomously retrieve and transport parts based on calculated priorities, eliminating manual searching and storage management while maintaining system efficiency.
Solution Approach 2:
The patent replaces manual mechanical searching and storage management with an automated information system. IoT sensors, digital twin technology, and automated calculation algorithms substitute human operators, while automated guided vehicles replace manual part retrieval. This mechanical-to-automated substitution resolves the contradiction by significantly improving productivity despite increased system complexity.
2Loss of time
If real-time monitoring and automated retrieval is implemented, then waiting time is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring parts availability and pre-calculating priority indices before parts are actually needed. The digital twin maintains real-time mappings of parts locations and availability, so when a re-configuration request occurs, the system already has the information needed for immediate retrieval, minimizing waiting time despite the complexity of continuous monitoring infrastructure.
Solution Approach 2:
IoT sensors provide continuous feedback on parts availability and location, which feeds into the digital twin model. This feedback loop enables real-time updates of priority indices and automatic adjustment of retrieval strategies. The feedback mechanism reduces retrieval time by ensuring the system always has current information, while the automated nature of the feedback collection minimizes the operational complexity burden.
3Productivity
If priority-based categorization is used, then storage efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The system changes parameters by calculating priority indices based on multiple factors including shelf time, parts availability, and re-configuration frequency. These parameter changes enable dynamic categorization of parts into priority levels, optimizing storage space utilization by placing high-priority parts in easily accessible locations. The automated calculation of these parameters reduces the burden of precise manual measurement while maintaining accurate prioritization.
Solution Approach 2:
The patent segments parts into different priority categories based on calculated indices, creating distinct storage zones for different priority levels. This segmentation improves storage efficiency by organizing parts according to their urgency and frequency of use. The automated calculation and dynamic re-segmentation based on changing parameters reduce the need for precise manual measurement and classification, as the system automatically adjusts categories based on real-time data.
Data Source
AI summary
In an approach for storage optimization for products manufacturing, a processor pulls parts data for a set of part numbers. A processor calculates an average shelf time for each part number of the set of part numbers. A processor calculates a priority index for each part number of the set of part numbers based on the average shelf time for each part number of the set of part numbers. A processor determines a category for each part number of the set of part numbers based on the priority index for each part number of the set of part numbers and a quantity of parts cumulative percentage for each part number of the set of part numbers.