Data-based decision-making in maintenance service delivery: the D3M framework

Sala, Roberto; Bertoni, Marco; Pirola, Fabiana; Pezzotta, Giuditta

doi:10.1108/jmtm-08-2020-0301

Cited by 18 publications

(8 citation statements)

References 44 publications

(70 reference statements)

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“…More in detail, the spread of IoT and AI technologies has led several studies to design data-driven methodologies based on ML and DL, exploiting techniques for time series analysis and mining (see Tortorella et al, 2022 andLundgren et al, 2021). In addition, a recent study proposed by Sala et al (2021) underlines the features of a framework for PdM capable of jointly analyzing historical and real-time data, to make a continuous improvement of its performances. Some of the most popular proposals are summarized in Table 1, where it is easy to note as the majority of the approaches is based on long-short term memory (LSTM) networks and convolutional neural networks (CNNs).…”

Section: Related Workmentioning

confidence: 99%

A deep attention based approach for predictive maintenance applications in IoT scenarios

Luca

Ferraro²,

Galli³

et al. 2023

JMTM

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PurposeThe recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approachIn this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.FindingsThe achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implicationsA comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implicationsThe proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/valueThe proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

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Section: Related Workmentioning

confidence: 99%

A deep attention based approach for predictive maintenance applications in IoT scenarios

Luca

Ferraro²,

Galli³

et al. 2023

JMTM

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“…From this literature background, it emerges that there is a necessity for guidance in terms of data collection and usage processes, as well as the availability of decision-making tools for the companies dealing with maintenance-based services and exploiting the Industry 4.0 technologies [8,19,38,42]. Other authors proposed data-driven approaches and case studies for maintenance delivery (e.g., [24,[43][44][45]), focusing on specific instruments aimed at supporting problem identification or resolution, but none adopt the comprehensive perspective proposed in [10] at a theoretical level. In [24,43,44], the use of FMECA is suggested to handle the definition of the criticality of the asset, and [43] uses BPMN2.0 to describe the maintenance-related process.…”

Section: Literature Backgroundmentioning

confidence: 99%

“…Maintenance is, among others, the service that can benefit the most from digitalization, since the acquisition and processing of data from the field can substantially improve the way maintenance service is delivered [9]. Leveraging the case of an Italian manufacturing company, this paper contributes to the understanding of how a maintenance service delivery process can be re-engineered in a data-driven fashion thanks to the guidance of the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M) framework proposed by [10]. Based on the D3M framework, this paper shows how the company has identified weaknesses in its original process and has coped with them by selecting and customizing activities and methods to support the newer-engineered, data-driven, maintenance service delivery process.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

et al. 2022

Self Cite

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Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

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“…In contrast, Tummers et al (2015) define service delivery as frontline personnel's interpersonal efforts when engaging with customers to manage, accommodate, or minimise external and internal demands and disputes they face regularly. Therefore, service delivery has been identified as a critical component of maintenance services in maintenance management to meet the needs of building users and increase organisational performance (Sala et al, 2021;Fang et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment on The Maintenance Service Delivery Characteristics (MSDCs): Case Study of The Public Schools in Perak, Malaysia

Radzuan¹,

Ali²,

Sudirman³

et al. 2022

IJARBSS

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This paper presents the results of a questionnaire survey conducted on the selected public schools at Perak in Malaysia. Thus, it involves the school's teachers as the appointed respondents to deal with non-technical issues about the Level of maintenance performance concerning service delivery. Questionnaires are distributed to the five primaries and four secondary schools, respectively. The questionnaire instrument was used for collecting primary data. Data were analysed using the Statistical Package of Social Science (SPSS 27.0) to generate Factor Analysis (FA). As a result, there are fourteen identified Maintenance Service Delivery Characteristics (MSDCs): Timeliness, Beyond the call of duty, Records and documents, Monitoring and inspection, Manpower management, Work order completion, maintenance-related accidents, Flexibility, Service Quality, Knowledge Sharing, Contractors' skill, Administration, Standard operating procedure and Level of a nuisance. The results generated from the survey are proposed to be considered in the school maintenance service delivery as a part of initiatives to improve the efficiency of school maintenance performance. The significant impact of the study findings will benefit the school building users by providing a conducive school environment (CSE).

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Data-based decision-making in maintenance service delivery: the D3M framework

Cited by 18 publications

References 44 publications

A deep attention based approach for predictive maintenance applications in IoT scenarios

A deep attention based approach for predictive maintenance applications in IoT scenarios

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Assessment on The Maintenance Service Delivery Characteristics (MSDCs): Case Study of The Public Schools in Perak, Malaysia

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