Automated Pallet Cost Modeling for Warehouse Labor and Energy
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Solution Overview
Problem
Storage facilities face inefficiencies in managing labor and energy costs due to varying pallet storage conditions, routing complexities, and temperature fluctuations, leading to increased operational expenses and waste.
Innovation Solution
A computer-based system that models and projects pallet-level costs using machine learning and data analytics, integrating data from disparate sources to optimize energy and labor usage, and provides interactive GUI dashboards for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pallets are stored in different locations throughout the facility with varying routing methods, then storage capacity and flexibility are improved, but routing complexity and energy consumption increase
Solution Approach 1:
The facility is divided into multiple storage zones with different temperature requirements, and pallets are routed through specific segments of the facility based on their destination and temperature needs. This segmentation allows flexible storage while managing routing complexity through zone-based organization.
Solution Approach 2:
The routing system dynamically adjusts paths and methods based on real-time conditions such as pallet temperature requirements, facility congestion, and available resources. This dynamic routing optimizes energy consumption while maintaining storage flexibility.
2Measurement precision
If cooling systems are activated to maintain temperature fluctuations, then temperature control precision is improved, but energy consumption increases
Solution Approach 1:
The system pre-cools or pre-heats pallets before they enter the main storage area, reducing the energy needed for temperature control later. Temperature adjustments are made in advance based on predicted pallet temperatures and facility requirements, minimizing subsequent cooling energy consumption.
Solution Approach 2:
The system adjusts temperature setpoints and cooling system operation parameters dynamically based on incoming pallet temperatures, ambient conditions, and storage requirements. This parameter optimization maintains temperature precision while reducing overall energy consumption.
3Ease of operation
If manual routing methods are used for pallets, then operational flexibility is improved, but labor costs and time consumption increase
Solution Approach 1:
The system automatically routes pallets based on their destination and temperature requirements without requiring manual intervention. Autonomous vehicles or automated conveyors transport pallets, while the control system dynamically determines optimal paths, eliminating manual routing time while maintaining operational flexibility.
Solution Approach 2:
Manual mechanical routing operations are replaced with automated systems such as autonomous vehicles, robotic conveyors, or automated guidance systems. This substitution reduces routing time and labor requirements while maintaining or improving operational flexibility through programmable control.
4Measurement precision
If data from disparate sources is integrated for cost modeling, then cost projection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system uses a unified data platform that integrates multiple data sources (warehouse management systems, refrigeration control systems, external APIs) through a common data model and processing framework. This universal approach handles diverse data types consistently, improving cost projection accuracy while managing processing complexity through standardized procedures.
Solution Approach 2:
The system introduces intermediate data layers and normalization processes that bridge disparate data sources. Data from different sources is transformed into a common format through intermediary processing steps, reducing the complexity of direct integration while maintaining comprehensive data coverage for accurate cost projections.
Data Source
AI summary
Disclosed are techniques for modeling costs per pallet in a facility. A computer system can: receive, from a warehouse management system (WMS) and/or a refrigeration control system (RCS), time series data for a period of time for pallets in a facility, retrieve, from a data store, pallet movement data, changes in temperature data, labor usage data, and energy consumption data for each pallet amongst the pallets, determine costs per pallet over the period of time based on correlating the time series data with the retrieved data for each pallet amongst the pallets, the costs per pallet including at least one of energy costs per pallet or labor costs per pallet, determine projected costs per pallet based on modeling the costs per pallet over the period of time, the projected costs being determined for future time periods, and generate output indicating the projected costs per pallet for the future time periods.