DSS approach for sustainable system design of shuttle-based storage and retrieval systems

Wang, Yanyan; Qin, Jinning; Mou, Shandong; Huang, Ke; Zhao, Xiaofeng

doi:10.1007/s10696-022-09447-w

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The recent reviews of Kosanić et al (2018) and Li and Li (2022) highlighted that the most debated research stream in SBS/RS literature regards the design, evaluation and optimization of the picking process (Boysen et al 2019a, b;Cano et al 2023), i.e., studying the efficiency of automated warehouses in terms of local performance such as cycle time and/or energy consumption (Wang et al 2022;Lanza et al 2023). As an example, the combined evaluation of both cycle time and energy performance was proposed by Ekren et al (2018), which provided a tool for briefly computing such performance by giving input parameters such as number of tiers, shuttle and lifts velocity.…”

Section: Sbs/rs Literaturementioning

confidence: 99%

Balancing picking and outbound loading efficiency in an SBS/RS through a digital twin

Guerrazzi,

Mininno,

Aloini

2024

Flex Serv Manuf J

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Warehouses are essential elements of almost every Supply Chain and have a significant impact on its performance. However, existing research on warehouse operations mainly aims at maximizing operational performance, neglecting their effect on downstream nodes. In this paper, we propose the use of a digital twin (DT) to support warehouse managers to identify the picking policy that most effectively balances picking and outbound loading efficiencies in an SBS/RS, with the aim of providing both a cost-effective and timely delivery to the subsequent nodes. The problem is set referring to a real case study of the logistics hub of a tire distributor company. The DT was built and validated based on real data from plant sensors and information systems. Afterwards, the DT was used to define three picking strategies that differently impact on both picking and outbound loading efficiency. The DT was then employed on a daily basis and fed with real orders, machine and rack availability to replicate stocking and picking operations and to directly communicate the recommended picking strategy to the warehouse PLC. Several demand scenarios have been considered to extend managerial inferences. Results show that the DT is a valuable tool to support the balancing of picking and outbound loading performance.

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Section: Sbs/rs Literaturementioning

confidence: 99%

Balancing picking and outbound loading efficiency in an SBS/RS through a digital twin

Guerrazzi,

Mininno,

Aloini

2024

Flex Serv Manuf J

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“…Sarkis 2014, Chardine-Baumann and Botta-Genoulaz 2014, Chen and Kitsis 2017,Choudhary et al 2021, Guo and Wu 2022, Hassini et al 2012, Khan et al 2017, Kuwornu et al 2023, Lee and Wu 2014, Narimissa et al 2020, Ngan et al 2018, Pinto 2017, Piotrowicz and Cuthbertson 2015, Rajesh 2022, Resat and Unsal 2019, Sadeghi et al 2022, Saeed and Kersten 2017, Varsei et al 2014 Economic(Bai and Sarkis 2014, Chardine-Baumann and Botta-Genoulaz 2014, Chen and Kitsis 2017, Chiarini 2017, Choudhary et al 2021, De Sousa Jabbour et al 2015, Feitó-Cespón et al 2017, Guo and Wu 2022, Hassini et al 2012, Ivascu 2020, Khan et al 2017, Kuwornu et al 2023, Lee and Wu 2014, Narimissa et al 2020, Ngan et al 2018, Pinto 2017, Piotrowicz and Cuthbertson 2015, Sadeghi et al 2022, Saeed and Kersten 2017, Sivarethinamohan et al 2021, Tsolakis et al 2018, Varsei et al 2014, Wang et al 2022 Innovation(Bai and Sarkis 2014, Chardine-Baumann and Botta-Genoulaz 2014…”

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confidence: 99%

Towards Data-Driven, Sustainable Supply Chain Quality Management 5.0 Indicators

Lazzaris,

M. Carvalho,

Sameiro Carvalho

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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There is a growing interest in the concept of Industry 5.0 (I5.0), which supports a transition towards a more sustainable, resilient and human-oriented industrial paradigm. This paper focuses on the I5.0 sustainability pillar, which is crucial to optimize and support I5.0's data-driven and circular supply chains. In order to pursue a Sustainable Supply Chain (SSC) and improve data quality management across it, it is first imperative to understand to what extent the existing SSC quality indicators are aligned with the opportunities of the digitalization era, and if they support the path to I5.0 sustainable practices. This paper aims to do a comprehensive study and analysis of relevant groups of SSC metrics and indicators. Through a broad literature review, SSC metrics and indicators are identified. Then, they are assessed to determine if they are suitable and relevant to assist decision makers in the challenges towards SSC 5.0. Results reveal that despite growing interest in the progress of data-driven SSC in recent years, there are still research gaps, mainly concerning the standardization of indicators, the simplification of models, and the use of consistent models for the treatment and conversion of proper data into relevant information.

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Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions

Qu,

Kim

2024

Sustainability

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In the post-pandemic era, the uncertain global market and rising social-environmental issues drive organizations to adapt their supply chain strategies to more dynamic, flexible models, leveraging advanced technologies like AI, big data analytics, and decision support systems. This review paper aims to examine the current research on AI-integrated technologies in sustainable supply chain management (SSCM) to inform future research directions. We adopted bibliometric and text analysis, targeting 170 articles published between 2004 and 2023 from the Scopus database following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. We confirm that AI-integrated technologies have demonstrated the capability to enable SSCM across various sectors. We generated ten future research topics using the Latent Dirichlet Allocation (LDA) method and proposed 20 propositions. The results show that AI-integrated technologies in supply chain processes primarily address sustainability, focusing on environmental and economic issues. However, there is still a technological gap in tackling social issues like working conditions and fair dealing. Thus, we proposed a dynamic framework of AI in SSCM to help researchers and practitioners synthesize AI-integrated technologies in SSCM and optimize their supply chain models in future directions.

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DSS approach for sustainable system design of shuttle-based storage and retrieval systems

Cited by 3 publications

References 21 publications

Balancing picking and outbound loading efficiency in an SBS/RS through a digital twin

Balancing picking and outbound loading efficiency in an SBS/RS through a digital twin

Towards Data-Driven, Sustainable Supply Chain Quality Management 5.0 Indicators

Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions

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