Perishable Replenishment Planning for Trailer and Pallet Utilization
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Solution Overview
Problem
The current perishable item replenishment process is inefficient and resource-intensive, involving manual demand planning, leading to increased costs, excessive resource consumption, and inefficient transportation due to partial pallets and less-than-truckload shipments, especially for multi-supplier and multi-purpose items like meat, fresh produce, and bakery goods.
Innovation Solution
A system utilizing mixed integer linear programming (MILP) models to optimize perishable item replenishment by minimizing transportation resources, reducing partial pallets, and optimizing pallet build parameters, while considering supplier constraints and demand fluctuations, thereby generating dynamic replenishment orders with reduced transportation costs and improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual demand planning and replenishment planning is used, then human time and effort are consumed, but costs increase and productivity decreases
Solution Approach 1:
The system enables self-service through automated demand planning and replenishment planning algorithms that independently analyze data, generate plans, and execute replenishment decisions without human intervention, transforming the manual process into an autonomous system that improves productivity while reducing operational complexity
Solution Approach 2:
The patent replaces the mechanical manual planning process with computer-based algorithms and automated systems that process replenishment data, calculate optimal orders, and manage logistics electronically, thereby eliminating human time consumption while significantly improving operational efficiency and productivity
2Ease of operation
If manual replenishment tasks are performed, then human resources are utilized, but excessive resource consumption occurs
Solution Approach 1:
The automated system performs replenishment tasks autonomously by collecting data from multiple sources, analyzing demand patterns, and executing ordering decisions without human resource involvement, thereby eliminating excessive consumption of human time and effort while maintaining operational effectiveness
Solution Approach 2:
The patent extracts the replenishment decision-making function from human operators and isolates it into an automated computational system that processes information and generates plans independently, removing the burden of manual tasks and the associated excessive resource consumption
3Quantity of substance
If delivery trucks are over-utilized, then transportation capacity is increased, but delivery costs increase
Solution Approach 1:
The system dynamically optimizes transportation capacity utilization by continuously analyzing delivery requirements, consolidating orders, and adjusting truck allocation in real-time based on actual demand, thereby maintaining adequate transportation capacity while minimizing unnecessary truck usage and associated costs
Solution Approach 2:
The patent applies partial action by utilizing only the necessary portion of available transportation capacity rather than consistently deploying full truckloads, optimizing the balance between maintaining adequate delivery capacity and reducing costs by avoiding excessive truck utilization
4Productivity
If items are inefficiently shipped, then transportation is performed, but transportation issues and shrinkage increase
Solution Approach 1:
The system performs preliminary actions by pre-planning shipments, optimizing load configurations, and preparing accurate delivery schedules before items are shipped, thereby preventing transportation issues and shrinkage from occurring in the first place rather than addressing them after they arise
Solution Approach 2:
The patent implements feedback mechanisms that monitor shipping performance, track items during transit, and analyze delivery outcomes to continuously improve shipping efficiency, thereby reducing transportation issues and shrinkage through data-driven optimization of the shipping process
5Adaptability or versatility
If partial pallets and less than truckload shipments occur, then flexibility is increased, but trailer and pallet utilization efficiency decreases
Solution Approach 1:
The system merges multiple smaller orders into consolidated full-truckload shipments by aggregating demand data across different destinations and time periods, thereby maintaining the flexibility to handle varied order sizes while significantly improving trailer and pallet utilization efficiency through optimized load consolidation
Solution Approach 2:
The patent dynamically adjusts shipment consolidation strategies based on real-time data about order sizes, destinations, and available capacity, enabling the system to adaptively determine when to combine orders into full loads versus when to ship separately, thereby optimizing both flexibility and resource utilization
Data Source
AI summary
Examples provide for dynamically replenishing perishable items. Dynamic replenishment data including forecast daily demand data, current inventory data of DCs, and/or supplier constraint data. The replenishment data is used by one or more mixed integer linear programming (MILP) based model(s) to generate results, including supplier-to-recipient breakout orders dynamically adjusted in real-time based on total demand and supply imbalances with specific upper and lower bounds. The system generates results for weekly and/or daily perishable item replenishment results, including transport vehicle assignments and pallet build parameters for reducing shipping costs and minimizing the number of less than truckloads (LTLs) in shipments from suppliers. The pallet build parameters are customized at an item and/or supplier level to reduce the number of partial pallet occurrences in shipments from suppliers to further reduce transportation resource usage and determine the number of cases shipped from each supplier to each recipient for each item daily.