Designing Smart Services: A System Dynamics-Based Business Modeling Method  for IoT-Enabled Maintenance Services

Akkermans, Hans; Zhu, Quan Yin; Fang, Feng; Lamper, Laurens; Kerkhof, Roland van de

doi:10.24251/hicss.2019.714

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…As noted previously, we worked with multiple OEMs in a research consortium for 4 years. Our specific focus here was on a dynamic modeling approach to assess business models for IoT-enabled smart maintenance services (Akkermans et al, 2019). In Appendix A, we provide case vignettes of the four OEMs from which we draw our data.…”

Section: Empirical Findings: Data-driven Growth Inhibitorsmentioning

confidence: 99%

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Transition paths for condition‐based maintenance‐driven smart services

Akkermans,

Basten,

Zhu

et al. 2024

J of Ops Management

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This research investigates growth inhibitors for smart services driven by condition‐based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under‐ or over‐maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.

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Section: Empirical Findings: Data-driven Growth Inhibitorsmentioning

confidence: 99%

“…These digitization trends are leading to “smart maintenance” practices (e.g., Akkermans et al, 2019; Bokrantz et al, 2020) such as CBM. Digitization has made CBM technically more feasible.…”

Section: Cbm and Smart Servicesmentioning

confidence: 99%

Transition paths for condition‐based maintenance‐driven smart services

Akkermans,

Basten,

Zhu

et al. 2024

J of Ops Management

Self Cite

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“…The CS department lacks the tools and experience to understand the logistics costs involved for CSCM, and CSCM has little experience with the impact of failures on CS. The usefulness of tools to provide insights between internal stakeholders of an organization is recognized by the literature (Akkermans et al, 2019). Therefore, the models developed in this project could help with creating insights and support decision-making in these control tower meetings.…”

Section: Contributionsmentioning

confidence: 99%

Component Redesigns and The Impact of Their Implementation Policy

Neefs,

Lamghari-Idrissi,

Basten

et al. 2023

2023 Winter Simulation Conference (WSC)

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The semiconductor industry is under a lot of pressure due to major chip shortages. Downtime of lithography systems can cost semiconductor companies up to ten thousands of euros per hour (ASML Holding N.V., 2014). To improve their systems' reliability, ASML replaces old or frequently failing components with new and more reliable redesigns. In this research, we develop a tool that is able to determine the best policy to implement these redesigned components in the field. Seven distinct policies are considered that differ in (1) roll-out strategy, (2) whether or not to use old parts that are still on stock, and (3) whether or not to rework failed old parts that are built out of the machines. The roll-out strategy indicates whether parts are preventively replaced through a Field Change Order (FCO) or correctively through a Replace on Failure (ROF). Using a Markov Chain approach and a simulation approach, we test two models: one in which old parts can be reworked into the redesigned version, and one where this is not possible. Generally, the policies where the old parts can be reworked are preferred as they result in lowest costs and failures. ROF is preferred when the aim is to reduce costs, and FCO is preferred when the aim is to reduce the number of failures. When the focus is on reducing waste (circular economy) we recommend to rework parts, but not in combination with an FCO roll-out strategy as the parts on stock are then at high risk of becoming excess or obsolete. i Probability that an old part can be repaired s Base stock level w n Salvage value of new parts w o Salvage value of old parts z n Costs to repair a new part (including repair and normal shipment) z o Costs to rework an old part to the new version x

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“…This paradigm addressed existing practices that were much more focussed on the improved management of maintenance, from reactive to proactive activities, through prognostics based on largely disconnected datasets with potential for data quality and timeliness penalties. The increasing importance of maintenance as a service in industry is highlighted by Akkermans et al (2019); these authors highlight that the product service methodology has evolved into the provision of smart maintenance services to complement products, made possible by more accurate quantifications of service needs and costs based on real-time analytics.…”

Section: E-maintenancementioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

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The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

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Designing Smart Services: A System Dynamics-Based Business Modeling Method for IoT-Enabled Maintenance Services

Cited by 5 publications

References 20 publications

Transition paths for condition‐based maintenance‐driven smart services

Transition paths for condition‐based maintenance‐driven smart services

Component Redesigns and The Impact of Their Implementation Policy

Intelligent decision support for maintenance: an overview and future trends

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