Business Case Study: Optimizing Packaging
Explore how Bayesian optimization is applied to solve packaging challenges for Tracky Consumer Goods. Understand data collection, surrogate modeling, acquisition function use, iterative sampling, and model updates to minimize material waste and packaging costs while maintaining product safety. Gain practical insight into deploying Bayesian methods for real-world optimization problems.
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Bayesian optimization is a very diverse and thorough optimization approach that works on a variety of problems and, therefore, is very widely used in the engineering industry. Here, one specific case study of Bayesian optimization is considered in the packaging industry.
Business case study
Tracky Consumer Goods is a global e-commerce retailer that specializes in selling a wide range of products. Because they have a significant volume of shipments, Tracky is focused on optimizing its packaging process to reduce material waste, lower packaging costs, and enhance sustainability while ensuring the safe delivery of products. To tackle this challenge, Tracky decides to utilize Bayesian optimization, a powerful optimization technique, to identify optimal packaging configurations that align with their objectives.
Problem statement
Tracky Consumer Goods faces two main challenges in its packaging process: excessive material usage and high packaging costs. The current packaging configurations are not fully optimized, leading to unnecessary waste and increased expenses. The objective is to find the best combination of packaging materials, including box sizes, padding materials, and wrapping techniques, to minimize waste, reduce packaging costs, and maintain product safety standards during transportation.
The work flow of Bayesian optimization
Let’s see how the Bayesian optimization strategy can be applied to solve this problem for Tracky Consumer Goods.
Data collection and objective definition
Tracky collects historical packaging data, including product dimensions, weight, fragility ratings, and the associated packaging materials used. They also gather information on packaging costs for different materials and sizes. With this data, the objective function for Bayesian optimization is defined as the simultaneous minimization of material ...