Using Machine Learning Methods for Automation of Size Grid Building and
  Management
Salim Yunus Dries Benoit Filipa Peleja
Fashion apparel companies require planning for the next season, a year in
advance for supply chain management. This study focuses on size selection
decision making for Levi Strauss. Currently, the region and planning group
level size grids are built and managed manually. The company suffers from the
workload it creates for sizing, merchant and planning teams. This research is
aiming to answer two research questions: "Which sizes should be available to
the planners under each size grid name for the next season(s)?" and "Which
sizes should be adopted for each planning group for the next season(s)?". We
approach to the problem with a classification model, which is one of the
popular models used in machine learning. With this research, a more automated
process was created by using machine learning techniques. A decrease in
workload of the teams in the company is expected after it is put into practice.
Unlike many studies in the state of art for fashion and apparel industry, this
study focuses on sizes where the stock keeping unit represents a product with a
certain size.