Design and management of assembly systems 4.0: systematic literature review and research agenda

Dolgui, Alexandre; Sgarbossa, Fabio; Simonetto, Marco

doi:10.1080/00207543.2021.1990433

Cited by 68 publications

(19 citation statements)

References 219 publications

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“…Content analysis. Content analysis is defined by Krippendorff (2004) as "a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use" it helps researchers to describe and quantify specific phenomena [11]. In this section, we divide the papers based on problem type (RQ14), data-driven methodology (RQ15), and heuristic type (RQ16).…”

Section: 31mentioning

confidence: 99%

“…In addition to the descriptive and content analysis based traditional systematic literature review, bibliometric and social network analysis are used in this study. This review followed the guidelines proposed by [11] in order to ensure that it is systematic, replicable, thorough, and transparent in the traditional systematic review part. The steps suggested by [11] involve (i) the keywords, (ii) first refinement, (iii) inclusion and exclusion criteria, and (iv) second refinement.…”

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confidence: 99%

“…This review followed the guidelines proposed by [11] in order to ensure that it is systematic, replicable, thorough, and transparent in the traditional systematic review part. The steps suggested by [11] involve (i) the keywords, (ii) first refinement, (iii) inclusion and exclusion criteria, and (iv) second refinement.…”

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confidence: 99%

“…Figure 1 Defining the keywords through an iterative process is the first step in a successful systematic review [34]. Group-based keywords [11] were defined based on the literature. Three groups (Group A, Group B, and Group C) of keywords are shown in Table 1.…”

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confidence: 99%

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A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

Deniz

Özceylan

2023

JIMO

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<p style='text-indent:20px;'>Transport and logistics systems include a range of activities that deal with all sorts of decisions and operations from material handling to vehicle routing. One of the main challenges for transport and logistics processes is to deal with large-scale and complex problems. However, with increasingly diverse sets of operational real-world data becoming available, data-driven heuristic approaches are promising to pave the path for solving the problems in the field of transport and logistics. Thus, a comprehensive review is needed to observe the reflections of this path in literature. To bridge this gap, a total of 40 papers on the topic of "data-driven heuristic approaches to logistics and transportation problems" are determined. Before the categorization and content analysis; descriptive, bibliometric and social network analysis are carried out to identify the current state of the literature. All the papers are systemically reviewed based on different perspectives, namely data-driven methodology, heuristics, sub-problems and etc. Based on the review, suggestions for future research are likewise provided. Subsequently, machine learning and deep learning methods are considered to be among the most promising data-driven methodologies. The review may be useful for academicians, researchers, and practitioners for a better understanding of data-driven heuristic approaches to transportation and logistics problems.</p>

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

Deniz

Özceylan

2023

JIMO

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“…Companies have to efficiently adapt to the required changes in processing functions, production capacity, and dispatching of orders by rapidly adaptable resources ; Järvenpää et al (2019)). New Industry 4.0 (I4.0) enabled technologies can help companies to face these mass customisation challenges and offer the opportunity to create new, more dynamic and flexible assembly configurations (Dolgui, Sgarbossa, and Simonetto (2021)). However, it is recognised that data-driven models and related methods to design, configure, control and maintain I4.0 assembly systems are indispensable.…”

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confidence: 99%

A formal skill model to enable reconfigurable assembly systems

Ginste

Cock

Alboom

et al. 2022

International Journal of Production Research

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As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Hereto, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration.

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Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

Grover,

Ashraf

2024

Transp. j.

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Eyeing superior operational performance, transportation, and warehousing management, researchers have turned their much‐needed attention to autonomous and IoT‐driven intralogistics systems. Despite its potential, a systematic evaluation of its overall business needs and the criteria for its success at each stage of adoption is missing in the literature. Using the business analysis framework, augmented by the technology adoption model, this study seeks to provide the business context for adopting autonomous and IoT‐driven intralogistics by identifying business requirements and critical success factors for such systems. We thematically analyze 85 recent research articles on autonomous and IoT‐driven intralogistics systems to identify business requirements that are linked to the mission of maximizing operational profit for the warehousing and storage industry. Then, using the identified business requirements as a base, we thematically analyze those 85 research articles again to identify critical success factors at different stages of technology adoption, namely information, analysis, acquisition, and utilization. We use the findings to develop propositions for future researchers. These findings provide a foundation for developing empirical, descriptive, and normative research on adopting and managing these systems for the warehousing and storage industry.

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Design and management of assembly systems 4.0: systematic literature review and research agenda

Cited by 68 publications

References 219 publications

A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

A bibliometric and social network analysis of data-driven heuristic methods for logistics problems

A formal skill model to enable reconfigurable assembly systems

Autonomous and IoT‐driven intralogistics for Industry 4.0 warehouses: A thematic analysis of the literature

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