Learn Predictive Analytics Techniques for Retail and Fashion in 6 Weeks
Summer 2021 Dates (Remote Online)
WOF 020: June 14 - July 26, 2021 (skips July 5)
Time: Every Monday and Wednesday, 6:10pm to 8:40pm EDT
Register before 4pm on June 10.
Tuition: $1,630 - $1,810
Online Platform: Google Meet (via FIT email account)
FIT’s Predictive Analytics for Retail and Fashion Certificate Program is an interactive 6-week course that covers analytics techniques applied to retail and fashion-related industries. The course contains lectures, discussions, and case studies involving analytical techniques that can be applied to retail and fashion business scenarios. Both the analytics and business side of all calculations and techniques will be discussed. How these techniques are used and why they make sense for a business are woven together throughout the course.
You will learn how to analyze demand and retail data and how components such as seasonality, trend, and weather affect the data. You will apply statistical techniques to predict forecasts and measure their accuracy. In an extended case study for weather analytics, you will analyze the effect of weather on business data. For this you will work directly with visiting industry experts from Planalytics.
You will develop the ability to analyze inventory management decisions using the tools of statistics and probability and gain facility in decision making under uncertainty. This course will involve hands-on use of common spreadsheet tools with many specific applications involving demand, seasonality, trend, forecasting, tracking, lost sales, lead time, safety stock, promotions, advertising, pricing, and markdowns. Emphasis will be on common analytical techniques that can be leveraged for any business situation.
Some knowledge of elementary statistics and spreadsheet software is recommended, but there will also be references and a review for anyone needing to brush up on this background. This is a course for the practitioner.
High-speed internet connection, standard laptop or desktop capable of running the latest browser and streaming media, webcam, and speakers.
What You Will Learn
- How to use predictive analytics techniques to improve profits and optimize inventory
- How to analyze demand and retail time series data using forecasting methods
- To understand the effects of seasonality and trend on forecasting
- How to apply simple and multiple regression to analyze data using statistics
- How to analyze the effects of weather on business data
- How to use weather analytics as a planning tool
- How to make inventory decisions for your business in the context of risk and uncertainty
- How to model order quantity, frequency, lead time and safety stock using quantitative techniques
- To use probability and optimization to balance trade-offs in inventory involving the cost of ordering too much and the cost of ordering too little
- This program will provide you with the background and skills to use and incorporate common predictive analytics techniques into your business.
- You will have a firm foundation in retail analytics and decision making based on data.
- Earn a certificate from FIT/SUNY, a world renowned college of art and design, business and technology.
Meet Our Faculty
Professor, Science and Math, FIT
Calvin Williamson is a Professor in the Science and Mathematics Department at FIT. He has a Ph.D. in Mathematics from the University of Michigan and teaches mathematics, statistics, machine learning, and programming. He has also worked as a software engineer specializing in computer graphics and film production software for special effects companies like Rhythm and Hues Studios in Los Angeles.
Assistant Professor, Fashion Business Management, FIT
Gary Wolf is currently a Tenured Assistant Professor in the Fashion Merchandise Department and previously Acting Assistant Chairperson at Fashion Institute of Technology in New York. He has an MBA from Georgia State University. Gary has also been an Adjunct Professor not only at FIT, but at NYU, St John’s University, Hofstra University, Baruch University, Fordham University, and Parsons School of Design. During the summers of 2016 through 2018, he instructed a merchandise math and inventory valuation class at ZSTU in China. He has extensive experience in strategic decision making in ecommerce, product development, and fashion merchandising companies.
Senior Director, Value Engineering and Strategic Analytics, Planalytics
Mohan is the Senior Director of Value Engineering. In this role, he works to measure performance and quantify the value clients receive from Planalytics. More specifically, he sheds light on the benefits from addressing the impact of weather in various retail and supply chain settings. Value is often relayed through client-specific KPIs and top/bottom line impacts. Mohan enjoys working with data and numbers, especially as a means toward understanding and solving interesting problems. He takes pride in employing rigorous methodology but communicating results in a simple yet tractable form. His background in economics and research has bolstered his quantitative skills and ability to draw insight from data. Prior to joining Planalytics, Mohan worked in EdTech as well as at the Federal Reserve Bank of Philadelphia. Mohan has BA and MA degrees in Economics from The University of Pennsylvania.
EVP, Global Partnerships and Alliances, Planalytics
Evan is the EVP of Global Partnership & Alliances at Planalytics. In this role, he helps to connect weather analytics with retail technologies and related data solutions. He has spent his entire career spanning over 25 years in the retail and wholesale industries. Prior to joining Planalytics, Evan's executive experience was at Macy's. He has also engaged with retailers as a management consultant. His expertise has focused on Business Weather Intelligence, information technology, merchandising, pricing, store operations, and logistics. Evan has a B.S. from Drexel University and a M.B.A. from the University of Pittsburgh.
Demand and Time Series Data
Analyze demand and time series data to understand the forecasting situation. Understand how non-repeating components like promotions or weather affect your ability to forecast accurately in the future. Also learn about the effect of lost sales on forecasting demand.
Forecasting Methods, Accuracy (MAPE)
Learn to forecast demand using standard approaches including the naive (last year), moving averages and exponential smoothing forecasting techniques. Use MAPE (mean absolute percentage error) to describe how accurate forecasts are.
Seasonality, Deseasonalized Data
Identify seasonality and understand how data with seasonality is described by seasonal indices and analysis of a baseline. Learn to compute seasonal indices and how to work with deseasonalized data.
Simple and Multiple Regression Modeling
Work with prediction for linear models and how to use simple and multiple regression in a spreadsheet. Learn how to recognize when predictors are significant in a model and approaches for selecting the best set of predictors when analyzing more than one. Learn about using p-values for testing models and coefficients.
Weather Analytics Case Study
Work with industry experts from Planalytics on a case study to understand the effect weather has on business data. Use statistical techniques to understand how weather analytics works and how it can help optimize decisions about sales, inventory, and marketing in your business.
Identify important concepts in inventory management and learn how analytics techniques can be used on the supply side of your business.
Depletion Graphs, Periodic, Continuous Review Inventory
Learn the fundamentals of describing inventory balances using depletion graphs. Investigate the differences between periodic and continuous review inventory policies and understand the uncertainties in each situation
Service Level, Safety Stock
Use statistical modeling to understand lost sales, service level and safety stock. Understand how to calculate safety stock for common situations using these models.
Reorder Point, Lead Time
Understand how analytics can help inform decisions about re-ordering products and planning based on lead time uncertainty.
Economic Order Quantity (EOQ)
Understand the ideal Economic Order Quantity (EOQ) for inventory situations. Model the trade-offs between acquisition and holding costs using spreadsheets.
Newsvendor Inventory Model
Use probability to understand and model the tradeoffs between ordering too little and ordering too much for order once situations when additional replenishment is not an option.
Center for Continuing and Professional Studies (CCPS)
FIT's CCPS offers credit and noncredit courses in fashion, business, design, computer technology, and marketing, as well as a range of certificate programs to help you enter and advance in the fashion and related industries. With short seminars, multi-session courses, and flexible schedules, you can learn at a time and pace you can manage and afford.