Artificial intelligence, robotics, and logistics employment: The human factor in digital logistics

Klumpp, Matthias; Ruiner, Caroline

doi:10.1111/jbl.12314

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…The list of central aspects of applying artificial intelligence in logistics is still being determined. For instance, Klumpp & Ruiner (2022) highlight the importance of the human factor, especially in digital logistics; Pawlicka & Bal (2022) investigate possibilities of implementing AI and sustainable supply chain finances for innovative omnichannel logistics, and Emam et al (2021) suggest algorithms to solve complex and multimodal problems that face transportation logistics sector.…”

Section: Delivery Flexibilitymentioning

confidence: 99%

Applying artificial intelligence in the logistics sector of Lithuania: prospects and opportunities

Ambrusevič,

Gomienė

2024

JESI

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Logistics has been one of the most important sectors of Lithuania's economy. However, recent economic, social and political challenges significantly impacted sector development, making its representatives search for new ways of business development by changing conservative models with more advanced ones. For instance, changes in the logistics sector affected by the implementation of remote work opportunities have created possibilities for providing logistics-related services across the globe. Most of the attention is being paid to solutions based on the application of artificial intelligence, and the future of logistics sector development is closely dependent on it. The paper aims to discover new prospects in applying artificial intelligence in the logistics sector by bringing forward an overview of the main sector's activities, performing historical economic data analysis and conducting a survey with representatives of leading companies from the logistics sector in Lithuania. The multicriteria method is used in data analysis, helping establish the main prospects in the sector's development.

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Section: Delivery Flexibilitymentioning

confidence: 99%

Applying artificial intelligence in the logistics sector of Lithuania: prospects and opportunities

Ambrusevič,

Gomienė

2024

JESI

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“…The infiltration of AI applications within the L&SCM sector embodies a potent force capable of comprehensively reshaping the industry's employment dynamics. This signals a transformative shift that promises to redefine the contours of work dynamics, employment landscapes, and the nature of jobs within the sector, which has been widely discussed in the existing literature (Klumpp & Ruiner, 2022).…”

Section: Challenges: Containing Mr Hydementioning

confidence: 99%

Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

Richey,

Chowdhury,

Davis‐Sramek

et al. 2023

J of Business Logistics

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The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.

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“…Autonomizing such processes will have direct implications for the workforce and raise various questions that have not yet been adequately investigated. Several authors already highlighted the significance of the human factor in the introduction of AI systems (Dora et al, 2022;Dwivedi et al, 2021;Hoberg et al, 2020;Klumpp and Ruiner, 2022). However, most current research focuses on the technical implementation of specific UCs, as demonstrated in the literature reviews by Pournader et al (2021), Riahi et al (2021) and Toorajipour et al (2021).…”

Section: Managerial and Societal Implicationsmentioning

confidence: 99%

“…Autonomizing such processes will have direct implications for the workforce and raise various questions that have not yet been adequately investigated. Several authors already highlighted the significance of the human factor in the introduction of AI systems (Dora et al, 2022;Dwivedi et al, 2021;Hoberg et al, 2020;Klumpp and Ruiner, 2022) In highly digitalized and partially autonomous supply chains, the competence profiles of logistics managers will need to change. It is currently unclear to what extent systems can run autonomously or when employees will need to intervene in systems whose decision-making processes can only be understood to a limited extent.…”

Section: Managerial and Societal Implicationsmentioning

confidence: 99%

The impact of multiagent systems on autonomous production and supply chain networks: use cases, barriers and contributions to logistics network resilience

Nitsche

Brands

Treiblmaier

et al. 2023

SCM

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Purpose Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers. Design/methodology/approach A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants. Findings This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management. Practical implications The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome. Originality/value This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

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Artificial intelligence, robotics, and logistics employment: The human factor in digital logistics

Cited by 9 publications

References 37 publications

Applying artificial intelligence in the logistics sector of Lithuania: prospects and opportunities

Applying artificial intelligence in the logistics sector of Lithuania: prospects and opportunities

Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

The impact of multiagent systems on autonomous production and supply chain networks: use cases, barriers and contributions to logistics network resilience

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