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Forecasting sales in industrial services: modeling business potential with installed base information

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Forecasting sales in industrial services: modeling business potential with installed base information. / Stormi, Kati; Laine, Teemu; Suomala, Petri; Elomaa, Tapio.

In: JOURNAL OF SERVICE MANAGEMENT, Vol. 29, No. 2, 2018.

Research output: Contribution to journalArticleScientificpeer-review

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Stormi, Kati ; Laine, Teemu ; Suomala, Petri ; Elomaa, Tapio. / Forecasting sales in industrial services: modeling business potential with installed base information. In: JOURNAL OF SERVICE MANAGEMENT. 2018 ; Vol. 29, No. 2.

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@article{79b2b9a16b4d4b97a293b245daf5d6c1,
title = "Forecasting sales in industrial services: modeling business potential with installed base information",
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.",
author = "Kati Stormi and Teemu Laine and Petri Suomala and Tapio Elomaa",
year = "2018",
doi = "10.1108/JOSM-09-2016-0250",
language = "English",
volume = "29",
journal = "JOURNAL OF SERVICE MANAGEMENT",
issn = "1757-5818",
publisher = "Emerald",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Forecasting sales in industrial services: modeling business potential with installed base information

AU - Stormi, Kati

AU - Laine, Teemu

AU - Suomala, Petri

AU - Elomaa, Tapio

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1108/JOSM-09-2016-0250

DO - 10.1108/JOSM-09-2016-0250

M3 - Article

VL - 29

JO - JOURNAL OF SERVICE MANAGEMENT

JF - JOURNAL OF SERVICE MANAGEMENT

SN - 1757-5818

IS - 2

ER -