Sensor-Based Resupply System for Irregular Consumption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated systems for resupplying consumable goods are inefficient due to irregular consumption patterns and inability to account for usage outside of designated appliances or preferred brands, leading to chronic oversupply or undersupply.
Innovation Solution
A sensor-based system using WiFi-connected scales to track consumption levels and predict resupply needs, integrating with a predictive system to order consumables before depletion, allowing for customization and optimal freshness, and accommodating various product sizes and brands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subscription service delivers goods on a specific schedule, then resupply timing is simplified, but goods are chronically oversupplied or undersupplied when consumption rates are irregular
Solution Approach 1:
The system performs preliminary actions by predicting future consumption rates based on historical data and sensor inputs, then proactively places resupply orders before the consumable is depleted. This advance planning allows the system to account for irregular consumption patterns and external factors, ensuring optimal resupply timing rather than relying on fixed schedules or simple threshold triggers.
2Extent of automation
If sensor data triggers resupply at a predetermined level, then resupply automation is achieved, but the system fails to account for variable consumption patterns such as heavy weekend use
Solution Approach 1:
The system continuously monitors consumption through sensor data and user interactions, then uses this feedback to dynamically adjust consumption rate predictions. The machine learning model processes ongoing sensor readings, weather data, holiday information, and user behavior patterns to refine its predictions in real-time, enabling accurate resupply timing that adapts to variable consumption patterns including weekend-heavy usage.
Solution Approach 2:
The system changes parameters by dynamically adjusting the predicted consumption rate based on multiple variable factors including weather conditions, holidays, user behavior patterns, and sensor data trends. Rather than using a fixed threshold, the system modifies its resupply trigger parameters continuously based on current conditions, allowing it to accurately predict depletion times even when consumption patterns vary significantly throughout the week.
3Measurement precision
If smart appliances measure consumable use, then automated tracking is enabled, but the system cannot account for consumable use outside the appliance or preferred brand loyalty
Solution Approach 1:
The system achieves universality by integrating multiple data collection methods beyond single-appliance sensors. It combines sensor data from various sources, manual user inputs through mobile applications, and external data sources to track consumable usage across different contexts including outside-the-appliance consumption. This multi-functional approach allows the system to accommodate brand loyalty and varied usage patterns while maintaining accurate tracking.
Solution Approach 2:
The system uses an intermediary mobile application as a mediator between the user and the tracking system. This intermediary allows users to manually input consumption data, photograph consumable items, and provide context information that supplements sensor data. The mobile app bridges the gap between limited appliance sensors and comprehensive consumption tracking, enabling the system to account for consumables used outside monitored appliances while maintaining user flexibility and brand preferences.
Data Source
AI summary
System for resupplying a consumable good based on sensors placed to measure the level or flow of a good, and using the data produced in predictive systems to determine the likelihood of running out of a good before resupply is likely to arrive, triggering an order which arrives before supply is exhausted.