Forecasting Product Life Cycle Curves: Practical Approach and Empirical Analysis

Manufacturing & Service Operations Management, Forthcoming

Vanderbilt Owen Graduate School of Management Research Paper No. 2867528

39 Pages Posted: 10 Nov 2016 Last revised: 7 Jun 2018

See all articles by Kejia Hu

Kejia Hu

Vanderbilt University - Operations Management

Jason Acimovic

Penn State University, Smeal College of Business

Francisco Erize

Dell Inc.

Douglas J. Thomas

University of Virginia - Darden School of Business

Jan A. Van Mieghem

Northwestern University - Kellogg School of Management

Date Written: July 19, 2017

Abstract

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. Fourth-order polynomial curves provide the best in-sample fit with piecewise-linear curves a close second. Using a trapezoidal fit, we find that the PLCs in our data have very short maturity stages; more than 20% have no maturity stage and are best fit by a triangle.

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. Our conservative out-of-sample forecast evaluation, using data-driven clustering of triangles and trapezoids, results in mean absolute errors approximately 2-3% below Dell’s forecasts. We also apply our method to a second data set of a smaller company and find consistent results.

Keywords: Forecasting, Product Life Cycle, Dell, clustering

JEL Classification: C80, C44, C20, C53, C52

Suggested Citation

Hu, Kejia and Acimovic, Jason and Erize, Francisco and Thomas, Douglas J. and Van Mieghem, Jan Albert, Forecasting Product Life Cycle Curves: Practical Approach and Empirical Analysis (July 19, 2017). Manufacturing & Service Operations Management, Forthcoming; Vanderbilt Owen Graduate School of Management Research Paper No. 2867528. Available at SSRN: https://ssrn.com/abstract=2867528 or http://dx.doi.org/10.2139/ssrn.2867528

Kejia Hu

Vanderbilt University - Operations Management ( email )

Nashville, TN 37203
United States

Jason Acimovic

Penn State University, Smeal College of Business ( email )

University Park
State College, PA 16802
United States

Francisco Erize

Dell Inc. ( email )

1 Dell Way
Round Rock, TX 78682
United States

Douglas J. Thomas

University of Virginia - Darden School of Business ( email )

P.O. Box 6550
Charlottesville, VA 22906-6550
United States

Jan Albert Van Mieghem (Contact Author)

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
753
Abstract Views
3,167
rank
32,201
PlumX Metrics