07-27, 19:10–19:20 (UTC), Green
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.
Przemysław Szufel is an Assistant Professor at SGH Warsaw School of Economics, Adjunct Professor at Ryerson University, Toronto, co-owner at StatXplorer.com - company offering custom made optimization and machine learning models and Founding Partner of Nunatak Capital - a VC fund that specializes in investing in startups that build their value on data analytics. His main research focus is applying advanced analytics methods, and in particular, machine learning, simulation and optimization in modelling in bringing new value to business processes. He is a co-author of several tools and algorithms for optimal and cost efficient collection and analysis of large data sets in the cloud. He is a co-author of over 40 publications, including handbooks and journal papers, in the area of applying advanced analytics, machine learning and simulation methods to making optimal business decisions. He is an active member of the Julia language community - maintains 3 official Julia packages and has 3rd place in the world answering Julia-related questions on StackOverflow. He is a co-author of book “Julia 1.0 Programming Cookbook: Over 100 numerical and distributed computing recipes for your daily data science workflow”. Przemyław is also co-managing SilverDecisions.pl project (that aims for representing and supporting business decisions), which has been elected by the European Commission to the Innovation Radar programe, grouping the best innovations financed by the EU funds. Przemyslaw has been awarded by the Polish Ministry of Science and Higher Education for implementing data science innovations to business environment. Recently, in a survey by SGH Student Council he has been selected the best teaching professor at SGH Warsaw School of Economics scoring the highest number of student votes among the entire faculty.