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Designing Causal Inference Systems for Value-Based Spare Parts Pricing

An ADR Study at MAN Energy Solutions

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Perspectives in Business Informatics Research (BIR 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 398))

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Abstract

In the wake of servitization and increased aftersales competition, original equipment manufacturers (OEMs) begin to change their pricing strategies from traditional cost-based to value-based pricing. As value-based pricing is much more individualized and data-driven, it becomes increasingly important to validate one’s pricing hypotheses by estimating the causal effects of pricing interventions. Randomized controlled trials (RCTs) are conceptually the best method for making such causal inferences. However, RCTs are complicated, expensive, and often not feasible. MAN Energy Solutions was facing a similar challenge. In reaction to his, we conducted an action design research study (ADR) in which we designed and implemented a novel causal inference system for value-based spare parts pricing. Based on this, we formalize design principles for the broader class of such systems that emphasize the need for pre-aggregation when dealing with lumpy aftersales data, scalability when having to run numerous analyses on heterogenous spare parts portfolios, and incorporating unaffectedness conditions that help to avoid spillover effects caused by often interdependent spare parts purchases. Also, they encourage analysts to take pre-intervention predictability into account when interpreting causal effects, to incorporate a manipulated treatment variable into the causal inference model, and to present the system output in interactive user interfaces to aid understanding and acceptance.

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Author information

Authors and Affiliations

  1. IT University of Copenhagen, Rued Langgards vej 7, 2300, Copenhagen, Denmark

    Tiemo Thiess

  2. Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany

    Oliver Müller

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  1. Tiemo Thiess
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  2. Oliver Müller
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Correspondence to Tiemo Thiess .

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Editors and Affiliations

  1. Babeș-Bolyai University, Cluj Napoca, Romania

    Robert Andrei Buchmann

  2. University of Camerino, Camerino, Italy

    Andrea Polini

  3. Linköping University, Linköping, Sweden

    Björn Johansson

  4. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

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Thiess, T., Müller, O. (2020). Designing Causal Inference Systems for Value-Based Spare Parts Pricing. In: Buchmann, R.A., Polini, A., Johansson, B., Karagiannis, D. (eds) Perspectives in Business Informatics Research. BIR 2020. Lecture Notes in Business Information Processing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-61140-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-61140-8_13

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  • Publisher Name: Springer, Cham

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