Forecasting sales in industrial services
| Autor: | Petri Suomala, Tapio Elomaa, Kati Stormi, Teemu Laine |
|---|---|
| Přispěvatelé: | Tampere University, Industrial and Information Management, Doctoral Programme in Business and Technology Management, Research group: Cost Management Center, Signal Processing, Research group: Computer Science and Applied Logics, Research group: Algorithmic Machine Learning -AML |
| Rok vydání: | 2017 |
| Předmět: |
Service (business)
Strategy and Management 05 social sciences 512 Business and management Customer lifetime value 113 Computer and information sciences Original equipment manufacturer Manufacturing engineering Constructive research Tourism Leisure and Hospitality Management 0502 economics and business Business Management and Accounting (miscellaneous) Production (economics) 050211 marketing Profitability index Business Sales management Installed base Marketing 050203 business & management |
| Zdroj: | Journal of Service Management. 29:277-300 |
| ISSN: | 1757-5818 |
| DOI: | 10.1108/josm-09-2016-0250 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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