Forecasting sales in industrial services: Modeling business potential with installed base information
ISSN: 1757-5818
Article publication date: 27 November 2017
Issue publication date: 16 March 2018
Abstract
Purpose
The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs).
Design/methodology/approach
This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base.
Findings
The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base.
Research limitations/implications
The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability.
Practical implications
OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches.
Originality/value
The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.
Keywords
Acknowledgements
The authors wish to thank the Finnish Funding Agency for Innovation (Tekes) for funding the research program, which resulted in this paper as well as Digital, Internet, Materials & Engineering Co-Creation (DIMECC) for the S4FLEET research program coordination. The authors also gratefully acknowledge the case company's valuable input, both in terms of research cooperation, and offering access to data collection. Finally, the authors would like to gratefully acknowledge the insightful comments provided by Jay Kandampully (Editor), Christian Kowalkowski (Associate Editor), and the two anonymous reviewers.
Citation
Stormi, K., Laine, T., Suomala, P. and Elomaa, T. (2018), "Forecasting sales in industrial services: Modeling business potential with installed base information", Journal of Service Management, Vol. 29 No. 2, pp. 277-300. https://doi.org/10.1108/JOSM-09-2016-0250
Publisher
:Emerald Publishing Limited
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