Using Machine Learning Methods for Automation of Size Grid Building and Management
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingManagementSimilar Papers 제목 키워드 기반
Sensor Attacks and Resilient Defense on HVAC Systems for Energy Market Signal Tracking
The power flexibility from smart buildings makes them suitable candidates for providing grid services. The building automation system (BAS) that employs model predictive control (MPC) for grid services relies heavily on …
Model Predictive ControlStochastic OptimizationData-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey
Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants' comfort. However, the building energy management system (BEMS) is now facing more challe…
energy managementManagementModel Predictive Controlreinforcement-learning+1Data-driven Operation of the Resilient Electric Grid: A Case of COVID-19
Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g., Ukraine cyber-attack, H…
Data-driven Identification of Occupant Thermostat-Behavior Dynamics
Building occupant behavior drives significant differences in building energy use, even in automated buildings. Users' distrust in the automation causes them to override settings. This results in responses that fail to sa…
Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart Cities
5G wireless systems will extend mobile communication services beyond mobile telephony, mobile broadband, and massive machine-type communication into new application domains, namely the so-called vertical domains includin…
Survey