Revitalising and Validating the Novel Approach of xAOSF Framework Under Industry 4.0 in Comparison with Linear SC

Din, Fareed Ud; Paul, David; Ryan, Joseph F.; Henskens, Frans; Wallis, Mark

doi:10.1007/978-981-15-5764-4_1

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Artificial Intelligence (AI) is applicable to a vast variety of interdisciplinary business systems, with the main objective of improving standards, reducing the dependable product quality control methods and creating new possibilities to meet customer satisfaction while maintaining low operational costs and robust business management [ 10 ]. For example, AI has been implemented to improve supply chain management [ 11 ], smart factory-based operational efficiency and warehouse management [ 12 ]. AI has helped improve a range of other domains as well such as health and medicine, cancer care [ 13 ] among other avenues such as pandemic management [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence (AI) to predict organizational agility

Shafiabady,

Hadjinicolaou,

Din

et al. 2023

PLoS ONE

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Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.

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Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence (AI) to predict organizational agility

Shafiabady,

Hadjinicolaou,

Din

et al. 2023

PLoS ONE

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Demystifying xAOSF/AOSR Framework in the Context of Digital Twin and Industry 4.0

Din

Paul

2022

Lecture Notes in Networks and Systems

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Conceptualised Visualisation of Extended Agent Oriented Smart Factory (xAOSF) Framework with Associated AOSR-WMS System

Din¹,

Paul²,

Henskens³

et al. 2021

JSW

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The emergence of the fourth industrial revolution (Industry 4.0) has sparked proliferation in thedomain of Cyber-Physical Systems (CPS), and extensive research has been conductedin this area since its beginning. However, recent literature claims that Smallto Medium Size Enterprises (SMEs) are not getting the benefits of Industry 4.0 (I4.0) in a full potential because of unresolved compatibility-mismatch issues and involvement of high infrastructuralcost. In order to help bridge this gap, the Extended Agent-Oriented Smart Factory (xAOSF) framework provides a high-level guideline solution, integrating the whole supply chain (SC), from supplier-end to customer-end with an objective to expose SMEs towards the benefits of I4.0. This paper, as part of a publication series, provides a conceptualised visualisation of the xAOSF framework as a customised CPS,which presents an elegant mediation mechanism between multiple xAOSF agents to uptake negotiation and coordination schemes at different enterprise levels. This paper also includes detail on howthe I4.0 based xAOSF framework caters to three-dimensional enterprise integration, in order to provide seamless connectivity and robustness in enterprise-wide operations. Furthermore, for the purpose of validation and to justify the claim, the experimentation is performed by applying a comprehensive test scenario onxAOSF's recommended AOSR WMS strategy in comparison with linear SC-based standard WMS system, which yields a substantial performance improvement in certain key-performance areas.

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Revitalising and Validating the Novel Approach of xAOSF Framework Under Industry 4.0 in Comparison with Linear SC

Cited by 4 publications

References 12 publications

Using Artificial Intelligence (AI) to predict organizational agility

Using Artificial Intelligence (AI) to predict organizational agility

Demystifying xAOSF/AOSR Framework in the Context of Digital Twin and Industry 4.0

Conceptualised Visualisation of Extended Agent Oriented Smart Factory (xAOSF) Framework with Associated AOSR-WMS System

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