Forecasting sales in industrial services

Forecasting sales in industrial services PurposeThe 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/approachThis 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.FindingsThe 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/implicationsThe 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 implicationsOEMs 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/valueThe study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Service Management Emerald Publishing

Forecasting sales in industrial services

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Publisher
Emerald Group Publishing Limited
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1757-5818
D.O.I.
10.1108/JOSM-09-2016-0250
Publisher site
See Article on Publisher Site

Abstract

PurposeThe 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/approachThis 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.FindingsThe 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/implicationsThe 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 implicationsOEMs 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/valueThe study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.

Journal

Journal of Service ManagementEmerald Publishing

Published: Mar 12, 2018

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