Dele Fei¹ and Yu Sun², ¹USA, ²California State Polytechnic University, USA

This is a data science project for a manufacturing company in China [1]. The task was to forecast the likelihood that each product would need repair or service by a technician in order to forecast how often the products would need to be serviced after they were installed. That forecast could then be used to estimate the correct price for selling a product warranty [2]. The underlying forecast model in the R Programming language for all of the companies products is established. In addition, an interactive web app using R Shiny is developed so the business could see the forecast and recommended warranty price for each of their products and customer types [3]. The user can select a product and customer type and input the number of products and the web app displays charts and tables that show the probability of the product needing service over time, the forecasted costs of service, along with potential income and the recommended warranty price.

Operation Management, Machine Learning, Data Mining.