AOSR 2.0: A Novel Approach and Thorough Validation of an Agent-Oriented Storage and Retrieval WMS Planner for SMEs, under Industry 4.0

Din, Fareed Ud; Paul, David; Ryan, J. T.; Henskens, Frans; Wallis, Mark

doi:10.3390/fi13060155

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…e training set selected for this thesis is obtained by collation in the WMS intelligent warehouse management system. e software in the WMS intelligent warehouse management system refers to the software part that supports the operation of the whole system, including pick operation, shelf management, receive processing, replenishment management, matrix charging, platform management, warehouse operation, cycle counting, overstock operation, RF operation, and process management, [31]. e WMS process is shown in Figure 2.…”

Section: Datasetmentioning

confidence: 99%

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

Zou

Jin

2022

Journal of Advanced Transportation

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With advent of the postepidemic era, the development of digital logistics operations management is imminent. Among the various logistics delivery methods, same-city delivery is chosen by the vast majority of customers for its timeliness and safety. Online ordering and delivery methods for same-city delivery are also gaining increasing attention from enterprises which need to know the inventory balance of all same-city warehouses in time for early deployment and response. However, in practice, the inventory balance of each warehouse can be affected by other warehouses in the same city, and there is often a lack of data in the inventory management system due to equipment and other issues resulting in a poor response from the company to handle emergencies. To address these issues, an improved matrix decomposition model was designed to interpolate the missing data by taking into account the spatiotemporal correlation between warehouses. The L-curve criterion was used to select hyperparameter values, the spatiotemporal regularize was used to capture the time dependence of the time series, and the model performance was evaluated using root mean square error and mean absolute percentage error. Comparisons with classical interpolation techniques were made to validate the improved performance of the proposed method.

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Section: Datasetmentioning

confidence: 99%

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

Zou

Jin

2022

Journal of Advanced Transportation

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“…The most effective AI methodologies are Machine Learning (ML) methods e.g., Support Vector Machine (SVM), Decision Trees (DT), and K-Nearest Neighbours (KNN), as detailed in the later sections, which have been utilised in this research article to predict organizational agility. Applications of AI and ML methods include commercial use of SVM [ 16 – 19 ], energy load management [ 20 ], finance and portfolio management, inventory management [ 21 , 22 ], sales forecasting, profit maximisation, and sales growth [ 23 ], among other domains e.g., dynamic cognitive networking approach [ 18 ], modelling and identifying frailty [ 24 , 25 ]. In order to maximise the potential of revenue generation, predicting Organizational Agility (OA) is an important factor.…”

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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“…Pemanasaan global dan perubahan keragaman hayati membawa dampak akan bencana dimasa akan datang. Industry 4.0 membuat sistim produksi menjadi lebih efisien, efektif, dan berkelanjutan merupakan terobosan besar industri dengan menggabungkan objek fisik dan teknologi digital seperti the internet of people (IoP) (Ghobakhloo, 2018), internet of thing (IoT) (Feldmann et al 2010;Boyes et al 2018;Tran-Dang et al 2021), cyber physical system (CPS) (Gružauskas et al, 2018;Ud Din et al 2021;Mrugalska et al 2021;Lyu et al, 2021), big data analytic (BDA) (Ghobakhloo, 2018), block chain (BC) (Schwab. 2018;Saberi et al 2018), additive manufacturing (AM) (Mellor et al 2014;Tjahjono et al 2017;Verboeket and Krikke 2019), cloud computing (CC) (Hofmann et al 2017;Stergiou et al 2018), augmented reality (AR) (Yew et al 2016), automation (AU) (Lu et al 2017;Mastos et al 2020), simulation (SI) (Xu et al 2017), robotic (RO) (Schwab, 2018;Duong et al 2020), semantic technologies (STs) (Vujasinovic et al 2009;Janssen et al 2010) yang akan bedampak terhadap produktivitas dan fleksibilitas dimana teknologi industry 4.0 dapat mengurangi waktu set up, biaya tenaga kerja, material, produksi dan desain (Schroeder et al 2019;Koh et al 2019;Fatorachian et al 2021).…”

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VOSviewer: Bibliometric Analysis Tools for Industry 4.0 and Supply Chain

et al. 2022

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Introduction/Main Objectives: Application of industry 4.0 to support the supply chain in manufacturing by using information and communication technology, a company can optimize integration into the environment, increase production capacity and capabilities, and increase customer value in facing the world of competition in the current era of globalization. The primary purpose of this study is to determine the extent of research on the application of industry 4.0 in the supply chain. Background Problems: Business competition in the manufacturing sector is getting faster and more dynamic. An organization must adapt to environmental changes to innovate to produce a more efficient production system by combining industrial 4.0 technology in a sustainable supply chain by optimizing resources, set-up time, labour costs, materials, production, and product design. Novelty: There is a research gap on logistics and warehouse variables that are not directly connected to industry 4.0 and supply chain management. Research Methods: The method used is a literature review, using bibliometric analysis of the VOSviewer application with data sourced from Scopus. Finding/Results: Found 155 research articles from 2015 to 2022, each year showing significant growth, especially a spike in 2020 to 2021, with 34 research articles. Conclusion: Scientific research related to the application of industry 4.0 in the supply chain in the field of engineering from 2015 to 2022 is increasing, with the direction of research being concentrated on the topic of circular economy, sustainability, cyber-physical system, smart manufacturing, internet of things, block chain, big data, and digitalization.

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AOSR 2.0: A Novel Approach and Thorough Validation of an Agent-Oriented Storage and Retrieval WMS Planner for SMEs, under Industry 4.0

Cited by 7 publications

References 26 publications

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

A Study of Missing Collaborative Data Imputation Models based on Same-City Delivery

Using Artificial Intelligence (AI) to predict organizational agility

VOSviewer: Bibliometric Analysis Tools for Industry 4.0 and Supply Chain

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