Application Potentials for an Ontology-based Integration of Intelligent Maintenance Systems and Spare Parts Supply Chain Planning

Saalmann, Philipp; Zuccolotto, Marcos; Silva, Thiago Regal da; Wagner, Carolin; Giacomolli, Anderson Antônio; Hellingrath, Bernd; Pereira, Carlos Eduardo

doi:10.1016/j.procir.2015.12.135

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…The SPSC arguably presents such a challenge, its environment beset with unpredictable lumpy demand, in terms of quantity, frequency, and type. Attempts have been made to develop dynamic capabilities for this purpose, for example, through improved forecasting (Saalmann et al , 2016). However, a firm’s ability to capitalise on dynamic capabilities is also dependent on effective communication with supply chain partners and making sense of and utilising pertinent insights gleaned (Saenz et al , 2014).…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain

Muir

Haddud

2017

JMTM

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Purpose The purpose of this paper is to approximate the impact that additive manufacturing (AM) will have on firm inventory performance (IP) and customer satisfaction (CS) when it is applied within the spare parts (SP) supply chain of manufacturing organisations. This research also explores the influence of customer sensitivity (CSy) to price and delivery lead time and supply risk (SR) within those approximations. Design/methodology/approach An online survey was used to collect the primary data for this research. Data were collected from 69 respondents working for organisations in two industrial segments within the UK manufacturing sector: “Industrial and Commercial Machinery and Computer Equipment” and “Measuring, Analysing and Controlling Instruments, Photographic, Medical and Optical Instruments”. The respondents worked for entities that were categorised in three groups: customers, suppliers, and entities that were both customers and suppliers. The groups that were self-identified as “customers” or “suppliers” answered 20 survey items each and the group that was identified as both “customers” and “suppliers” answered 40 survey items. Findings The results revealed that AM was considered a suitable vehicle for the fulfilment of SP demand. However, AM appeared to make no material difference to CS; the scenario used improved delivery time of SP but increased price. Also, AM was thought to improve IP through less reliance on buffer stock to manage SR and spikes in demand and less carrying of SP at risk of obsolescence. Research limitations/implications The respondents worked for entities within two manufacturing industry segments within the UK and the insights garnered may not be indicative of similar organisations competing in other manufacturing industry segments within the UK or in other countries. In addition, approximately 82 per cent of the surveyed respondents worked for small organisations with fewer than 100 employees and the results may differ for larger organisations. Further limitations were the relatively small sample size and lack of open-ended questions used in the survey. Larger sample size and the usage of open-ended survey questions may lead to more reliable and valuable responses and feedback. Practical implications The findings from this research are considered to be of interest to practitioners contemplating adoption of AM and to developers of AM wishing to increase market share due to the positive reaction of entities within the industrial and commercial machinery and computer equipment, and measuring, analysing and controlling instrumentation industrial segments. This research raises awareness to the possible risks and rewards – from a range of perspectives, of AM to practitioners considering its adoption in the spare parts supply chain (SPSC). Originality/value The paper takes a novel perspective on AM in SPSCs by illuminating the supplier and buyer perspective based on empirical data. This research provides new insights about the appreciation of the use of AM in SPSCs of mostly small sized manufacturing companies located in the UK. This paper also gives new insights about the willingness/conditions of manufacturing companies in the UK to adopt AM for the provision of SP. The originality of this research is twofold: it broached the applicability of AM in the supply chains of the two targeted industrial segments, and as far as the authors are aware, the influence of CSy (e.g. to price or lead time) and SR on SPSC players’ attitude to AM is yet to be considered. Finally, this research adopted a systems theory lens and considered system-wide impact of AM introduction.

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Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain

Muir

Haddud

2017

JMTM

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“…This ontology is suitable for reliability analysis and might be reused, but no wide use or links to maintenance scheduling or any other services is considered in the actual version of the ontology. Saalmann et al proposed an integrated approach combining existing domain ontologies of various interacting systems for manufacturing systems [9,10]. Although the domain is different, the idea is similar, to some extent, to some of this paper's aims.…”

Section: Related Workmentioning

confidence: 91%

Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot

Louadah,

Papadakis,

McCluskey

et al. 2024

Applied Sciences

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The railway industry forecasts growth in passenger and freight traffic over the next 30 years. This places additional demands on rolling stock depot facilities, many of which were designed and built before the modern age of information technology. This paper explores the potential of improving the efficiency and effectiveness of rolling stock maintenance management to meet the challenges of the near future, by utilising advanced computing techniques. The objective of the work is to create optimised maintenance plans for a fleet of trains, considering optimal use of resources. As a “glue” for joining up functions and operations, a generic Depot and Vehicle ontology (called the Virtual Depot) is introduced. The ontology captures the structures, relationships, and attributes of objects in the Depot (rolling stock, sensors, depot assets, tools, resources, and staff). The ontology is populated with example company and fleet-specific knowledge using an automated knowledge acquisition method. This paper describes the systematic method for the creation of a Virtual Depot. Two particular aspects are discussed in detail—knowledge acquisition of fleet-specific information obtained from a manufacturer’s Vehicle Maintenance Instruction manuals and the construction of a short-term scheduling process within the Virtual Depot. Our evaluation considers the integrative aspects of the method, demonstrating how the ontological structure and its acquired specific information informs and benefits the scheduling process, in particular with respect to schedule optimisation. Results from an initial case study show there is significant potential to optimise short-term maintenance schedules, and the ability to automatically consider resource availability in short-term scheduling is demonstrated.

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“…In addition da Silva et al (2014a) also investigate the integration of parts supply chain and planning with an intelligent maintenance system touching on the use of ontology to describe communication within the combined architecture; along with Saalmann et al 2016who propose a multi-layer ontology incorporating existing semantic approaches to supply chain and intelligent systems. A particular use of ontology is in the potential integration of spare parts supply chains and the field of CBM, where the two entities possess distinct knowledge sets and express their data and parameters in different levels of granularity and importance (Saalmann et al, 2016). Saalmann et al (2016) make the case for a common terminology and utilise DPWS (Device Profile for Web Services) in order to obtain metadata from physical devices (DPWS is a standardised middleware for exposing parameters about and data from physical hardware devices).…”

Section: E-maintenancementioning

confidence: 99%

“…A particular use of ontology is in the potential integration of spare parts supply chains and the field of CBM, where the two entities possess distinct knowledge sets and express their data and parameters in different levels of granularity and importance (Saalmann et al, 2016). Saalmann et al (2016) make the case for a common terminology and utilise DPWS (Device Profile for Web Services) in order to obtain metadata from physical devices (DPWS is a standardised middleware for exposing parameters about and data from physical hardware devices).…”

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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Application Potentials for an Ontology-based Integration of Intelligent Maintenance Systems and Spare Parts Supply Chain Planning

Cited by 10 publications

References 17 publications

Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain

Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain

Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot

Intelligent decision support for maintenance: an overview and future trends

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