Forecasting New Product Life Cycle Curves: Practical Approach and Empirical Analysis

Date: 2018-07-11 15:00:10
Time: 9:30-11:30
Venue: Zijingang Campus
Speaker: Kejia Hu
Category: Talk & Lecture

Venue: Room 1002, Library & Information Center Building C, Zijingang Campus


Dr. Kejia Hu is an Assistant Professor at Owen Graduate School of Management from Vanderbilt University. Her research interests are empirical research in service management, supply chain management and sustainability. She obtains her Ph.D. from Kellogg School of Management at Northwestern University. She has published her research in peer-reviewed journals such as Manufacturing & Service Operations Management, Energy Policy and others.


We present an approach to forecast customer orders of ready-to-launch new products that are similar to past products. The approach fits product life cycle (PLC) curves to historical customer order data, clusters the curves of similar products, and uses the representative curve of the new product's cluster to generate its forecast. We propose three families of curves to fit the PLC: Bass diffusion curves, polynomial curves and simple piecewise-linear curves (triangles and trapezoids).  Using a large data set of customer orders for 4,037,826 units of 170 Dell computer products sold over three and a half years, we compare goodness-of-fit and complexity for these families of curves. The fitted PLC curves of similar products are clustered either by known product characteristics or by data-driven clustering. Our key empirical finding is that, for our large data set, data-driven clustering of simple triangles and trapezoids, which are simple-to-estimate and explain, performs best for forecasting.