Simulation Details

ASICS, founded in 1949 by Kihachiro Onitsuka in Kobe, Japan, started by producing basketball shoes and has grown into a leading sports footwear manufacturer. Known for high-quality products and innovative designs, ASICS offers a diverse product line including running, tennis, and other athletic footwear. Key innovations include the GEL cushioning system introduced in the 1980s and the FlyteFoam technology developed at the ASICS Institute of Sport Science. ASICS holds a 7% market share in the global sports footwear industry, with an annual revenue exceeding $4 billion. The company leverages advanced technical tools and software for product development, including data analytics platforms like SAS and Tableau, CAD software like AutoCAD and SolidWorks, and customer feedback systems like Qualtrics. Recent trends such as athleisure and sustainability have influenced ASICS to integrate performance and aesthetics into their products, and to use recycled materials in their designs.

ASICS faces the challenge of accurately forecasting the success of new shoe designs among its diverse customer segments. The goal is to analyze historical data from the past three years to uncover patterns and insights that can help predict the potential success of new shoe designs. This involves examining sales performance, customer preferences, and feedback ratings to identify trends that correlate with successful product launches. The project aims to improve customer satisfaction scores by at least 10%, achieve a sales growth rate of 15% for new product launches, and increase market share by 2% over the next year. The analysis will consider the competitive landscape, including the strengths and weaknesses of key competitors like Nike, Adidas, and Puma.

As a Junior Data Scientist at Metyis, you will assist ASICS in analyzing historical data to identify trends relevant to new shoe designs. You will interact with your mentor, Dr. Anika Verma, who will guide you through the project, and individual members of the ASICS product development team, such as Hiroshi Tanaka, who will provide insights and data. Your tasks include reviewing historical data to understand past product performances and customer feedback, analyzing the data to identify patterns and segments, and compiling your findings into a comprehensive report with actionable recommendations. You will need to navigate the dynamics of working with different stakeholders, balancing their inputs and expectations while focusing on delivering valuable insights. You will report to Dr. Elena Martinez, the Data Science Team Lead at Metyis.

To complete the simulation, you need to:
1. Review historical data to understand past product performances and customer feedback.
2. Analyze the data to identify patterns and segments relevant to predicting the success of new shoe designs.
3. Compile your findings into a comprehensive report with actionable recommendations for the ASICS product development team.
4. Discuss your insights with your mentor and address any questions from stakeholders through one-on-one conversations.

Once you complete all the tasks, save the required final document in a PDF or DOCX and send it to Dr. Elena Martinez.

Team

Who you will work with in this Simulation
Your team is 100% generated by AI – you will not interact with real people and no human will read your conversation.

Dr. Anika Verma
Senior Data Scientist

Hiroshi Tanaka
Lead Product Designer

Alex Johnson
Junior Data Scientist

Dr. Elena Martinez
Data Science Team Lead

Organization

A data science consulting firm providing expertise in data analysis and predictive modeling.