Battery Replacement Scheduling for Construction Equipment Downtime
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Solution Overview
Problem
The existing battery supply chain management for heavy construction equipment is inefficient, leading to prolonged downtime due to battery replacement delays and inadequate inventory management.
Innovation Solution
A fully automated supply chain optimization system that uses a digital twin approach to predict battery degradation and demand, optimizing inventory levels and delivery schedules to minimize downtime and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery replacement is performed in a specialized shop, then battery replacement can be done professionally, but heavy equipment must be taken out of operation causing construction delays
Solution Approach 1:
The system predicts battery degradation and schedules battery delivery in advance before the battery actually fails. By performing preliminary actions (predicting failure, ordering battery, arranging delivery) before the replacement is needed, the system ensures the battery arrives at the equipment site ready for immediate installation, eliminating construction downtime while maintaining professional replacement quality
Solution Approach 2:
A specialized battery replacement service acts as an intermediary between the specialized shop and the construction site. This intermediary service handles the entire battery replacement process including professional assessment, battery ordering, delivery coordination, and installation scheduling, allowing the equipment to remain on-site while professional services are coordinated through the intermediary
2Loss of time
If new batteries are kept in inventory, then replacement can be done quickly, but inventory costs and shelf time increase
Solution Approach 1:
The system orders and arranges battery delivery in advance based on predicted failure dates, but does not maintain large inventory stocks. By performing the ordering action preliminarily while using just-in-time delivery, the system achieves quick replacement without incurring high inventory holding costs or extended shelf time
Solution Approach 2:
The system continuously monitors battery health status and uses this feedback to dynamically adjust delivery timing. By receiving real-time feedback on battery degradation, the system can optimize delivery schedules to arrive just before replacement is needed, minimizing both replacement time and inventory costs
3Reliability
If the healthiest battery is used for Economy category, then customer demand is met, but it is not cost effective
Solution Approach 1:
The system matches battery quality to specific customer needs by category. Premium category receives healthiest batteries (95%+ SOH), Standard category receives good condition batteries (90%+ SOH), and Economy category receives acceptable condition batteries (85%+ SOH). This local quality differentiation ensures each customer segment receives appropriate quality without paying for unnecessary battery health, improving cost effectiveness while still meeting demand
Solution Approach 2:
The system changes the SOH parameter threshold based on customer category requirements. Different SOH thresholds are applied for different service levels, allowing the system to optimize the balance between reliability and cost by adjusting the quality parameter to match customer expectations and payment willingness
Data Source
AI summary
The system obtains SOH of a first component, where the SOH indicates a difference between a current state of the first component and a state of a rated new component. The system obtains historical use of the first component. Based on the SOH and the historical use, the system determines a future SOH of the first component. The system obtains an indication of a requested SOH of the first component. Based on the indication of the requested SOH and the future SOH of the first component, the system determines a first time indicating when the first component will need to be replaced. The system determines a second time indicating when the second component is available to replace the first component, where the second time occurs before the first time. The system requests a delivery of the second component when the second time is substantially close to the first time.