Optimization of bike manufacturing and distribution (use-case)

This is a use-case scenario of using Julia for planning and optimization of production in one of the largest bicycle manufacturing plants in Europe. The optimization model has been implemented utilizing JuMP and custom made heuristics. The Julia solution has increased profitability of the manufacturing plant over 10% (compared to the previous approach) and the optimal part allocation made it possible to increase the bike production volume by 25%.

Kross S.A. (https://kross.eu/) is one of the largest bicycle manufacturers in Europe with a production capacity of up to 1 million bikes a year. The company is also exporting their products to over 50 countries around the globe. The problem that currently the entire bicycle manufacturing industry is facing is the shortage of various key bike components due to the COVID-19 logistic chain disturbances. The goal of the company is to maximize customer (retailer) satisfaction by simultaneously meeting all business constraints with regard to production (part availability, assembly line capacity) and the observed demand for bikes (taking into consideration possible bike substitution, pricing and discount policies) In order to optimize the bicycle production and optimize the distribution plan we have built a mathematical model of the manufacturing plant. The basic model formaulation includes an NP-hard Mixed Linear Integer Programming optimization problem with 4,000,000 decision variables and over 100,000,000 business constraints. The mathematical model has been implemented in Julia programming language using the JuMP package along with Julia linear algebra features and several heuristics and algebra transformations. The model has been subsequently solved using a custom designed heuristics as well as solver packages. This data science project had an overall huge effect on the business of the customer. The computational model made it possible to manufacture 25% more bikes and yields a 10% higher total profitability of the bike factory compared to the best recommendations by a leading ERP solution that has been previously used by the company for production planning.