Design of smart connected manufacturing resources to enable changeability, reconfigurability and total-cost-of-ownership models in the factory-of-the-future

Brad, Stelian; Murar, Mircea; Brad, Emilia

doi:10.1080/00207543.2017.1400705

Cited by 43 publications

(15 citation statements)

References 58 publications

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“…Productivity. Industry 4.0 has a potentially positive impact on a series of activities, such as employee's optimization (Kim 2018;Ortíz et al 2018), unit cost reduction (Kumar et al 2018;Brad et al 2018), unit time reduction (Moeuf et al 2018;Jung et al 2017), and inventory and warehouse management (Buer et al 2018;Qu et al 2017).…”

Section: Goalsmentioning

confidence: 99%

“…Productivity; Decision-making. Brad et al 2018;Ortíz et al 2018;Putman et al 2017;Theorin et al 2017. Taking the first line of Table 2 as an example, productivity can be obtained if production line technologies are adopted, provided that some internal and external barriers (e.g.…”

Section: The Industry 40 Frameworkmentioning

confidence: 99%

“…Another problem linked to this barrier is optimizing and coordinating (i.e. managing) the operations of Industry's 4.0 autonomous systems, all requiring advanced scheduling techniques to be successful(Brad et al 2018;Lv and Lin 2017).Information Management. This barrier reflects the difficulties inherent in managing and organizing a large quantity of information (i.e.…”

mentioning

confidence: 99%

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Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

Calabrese

Dora

Ghiron

et al. 2020

Production Planning & Control

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Industry 4.0 has fused digitalisation with traditional industrial processes bridging the physical and virtual worlds and opening unimagined possibilities for 21st century business growth. Research is still evolving toward the development of frameworks linking Industry's 4.0 enabling technologies to specific goals and to their impact on the manufacturers' businesses. A systematic review of all peer-reviewed managerial research is performed to extract Industry's 4.0 enabling technologies, barriers and goals. A framework linking technologies, barriers, and goals is then developed together with a web application based on its contents. The use of the framework is demonstrated through its application to two specific empirical case studies. The review shows that there are 9 classes of Industry's 4.0 enabling technologies, whose different arrangements lead to the achievement of up to 15 business goals that can be thwarted by 21 barriers. The pursuit of these goals leads to three different destinations, i.e., three diverse digital transformations of the manufacturing firms. This paper provides a holistic framework that analyses the relationships among Industry's 4.0 enabling technologies, barriers and goals. It appeals to managers who can use its contents along with the web application to derive recommendations and suggestions tailored to their Industry's 4.0 journeys.

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Section: Goalsmentioning

confidence: 99%

Section: The Industry 40 Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

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Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

Calabrese

Dora

Ghiron

et al. 2020

Production Planning & Control

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“…There are some approaches for the computer-aided design and modelling of assembly systems [6,20,26,36,52] but they generally do not address system qualities such as self-adaptation, self-reconfiguration, and self-healing, more generally called the self-x qualities [28,29]. Those approaches that do address self-x qualities in an automated manner [10,25,46,50]-for example through agent-based systems-often focus primarily on one aspect, such as the software and configuration side, rather than a complete product-process-system approach that includes a formal description of the system from its function to the physical hardware.…”

Section: Literature Review-design Frameworkmentioning

confidence: 99%

A Function-Behaviour-Structure design methodology for adaptive production systems

Sanderson

Chaplin

Ratchev

2019

Int J Adv Manuf Technol

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Adaptive production systems are a key trend in modern advanced manufacturing. This stems from the requirement for the system to respond to disruption, either in the form of product changes or changes to other operational parameters. The design and reconfiguration of these systems are therefore a unique challenge for the community. One approach to systems design is based on functional and behavioural modelling, drawn from the field of design theory. Existing approaches suffer from lack of focus on the adaptive properties of the system. While traditional production systems design focusses on the physical system structure and associated processes, new approaches based on functional and behavioural models are particularly suited to addressing the challenges of disruptive production environments resulting from Industry 4.0 and similar trends. We therefore present a Function-Behaviour-Structure (FBS) methodology for Evolvable Assembly Systems (EAS), a class of self-adaptive reconfigurable production systems, comprising an ontology model and design process. The ontology model provides definitions for Function, Structure, and Behaviour of an adaptive production system. This model is used as the input to a functional modelling design process for EAS-like systems, where the design process must be integrated into the system control behaviour. The framework is illustrated with an example taken from a real EAS instantiation using industrial hardware.

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“…Already in 1989, Davis introduced this trend, referring to it as a production strategy focused on the broad provision of personalized products and services and naming it "mass customization" (MC) (Davis 1989). However, the fulfilment of such a paradigm requires the definition of new production concepts in order to achieve the requested increased level of flexibility, reconfigurability, and performance (Dashchenko 2006; Many scientists have identified the fourth industrial revolution, known as Industry 4.0 (I4.0), as a response to these new challenges (Ivanov et al 2020;Brad et al 2018;Prinz et al 2019). In the I4.0 context, information technology (IT) merges with operations technology (OT) to make possible the achievement of the expected performance targets in a highly customized market scenario (Grassi et al 2020a;Thames and Schaefer 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

et al. 2021

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Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors.

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Design of smart connected manufacturing resources to enable changeability, reconfigurability and total-cost-of-ownership models in the factory-of-the-future

Cited by 43 publications

References 58 publications

Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

A Function-Behaviour-Structure design methodology for adaptive production systems

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

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