Food Batch Allocation Using Estimated Weight Data
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Solution Overview
Problem
Food processing lines face challenges in adapting quickly to changing inputs and orders while minimizing giveaway of food products during batch sizing, particularly in real-time, due to variations in delivery times and order modifications.
Innovation Solution
A method that involves obtaining estimated weight data for food items, allocating weight batch orders based on this data, and scheduling fulfillment dynamically, utilizing IoT devices and artificial intelligence to optimize batch allocation and processing, even up to the point of cutting and packaging, to ensure accurate matching of orders with available food items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fixed number of items are packed in batches according to average weight, then batch weight targets are met, but giveaway increases due to overweight packaging
Solution Approach 1:
The system dynamically changes the allocation parameters by using estimated weight data to determine the best corresponding orders for each supply batch. Instead of fixed average weight calculations, the system adjusts order allocations based on real-time weight estimates, reducing overweight packaging while meeting batch targets.
Solution Approach 2:
The system performs preliminary allocation of orders to supply batches based on estimated weight data before actual processing. By determining the best corresponding orders in advance using weight estimates, the system optimizes batch composition to minimize giveaway while ensuring target accuracy.
2Adaptability or versatility
If traditional batching methods are used, then processing is straightforward, but the system cannot adapt quickly to changing deliveries and orders in real-time
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring estimated weight data of supply batches and dynamically reallocating orders based on current conditions. The allocation system receives feedback on weight estimates and adjusts order assignments to maintain optimal matching between supply and demand in real-time.
Solution Approach 2:
The batching system transitions from static fixed allocations to dynamic real-time allocations. The system continuously adapts order assignments based on changing weight estimates, delivery conditions, and order modifications, making the batching process flexible and responsive to real-time variations.
3Loss of substance
If detailed weight measurement and allocation optimization are implemented, then giveaway is minimized, but processing time and system complexity increase
Solution Approach 1:
The system performs weight estimation and order allocation in advance based on predicted weight data. By determining the best corresponding orders before actual processing using estimated weights, the system minimizes giveaway while avoiding time-consuming measurements during critical processing stages.
Solution Approach 2:
The system uses estimated weight data as a copy or proxy for actual weight measurements. Instead of requiring precise physical measurements of each item, the system relies on weight estimates and historical data to make allocation decisions, reducing processing time while maintaining allocation accuracy.
Data Source
AI summary
A method of fulfilling a plurality of weight batch orders in a food item processing line, including: obtaining an estimated weight data of a first supply batch of food items; receiving a plurality of weight batch orders; allocating a subset of the plurality of weight batch orders to the first supply batch of food items by determining which weight batch order best corresponds with the estimated weight data; and scheduling fulfilment of the determined best corresponding weight batch order.