Logistics Dynamics and Demographic Change

Klumpp, Matthias; Abidi, Hella; Bioly, Sascha; Buchkremer, Rüdiger; Ebener, Stefan; Sandhaus, Gregor

doi:10.1007/978-3-319-45117-6_31

Cited by 3 publications

(3 citation statements)

References 64 publications

(58 reference statements)

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“…Because supply chain management (SCM) and logistics have often been addressed alongside each other in a broad array of journals [86], we conducted a parallel in-depth analysis [87]. Again, we utilized the data platform ''Web of Science'' because the contents of that database are often characterized by high-quality measures and the retrieval algorithms that can be formulated offer a relatively high degree of flexibility.…”

Section: Detection Of Major Trends In Logistics and Trend Interactmentioning

confidence: 99%

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

et al. 2019

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To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a ''double funnel of artificial intelligence.'' Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a ''classical'' literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from ''regular'' review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed. INDEX TERMS Computational and artificial intelligence, document handling, fuzzy control, knowledge acquisition, pattern analysis, scientific publishing, text mining, text processing.

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Section: Detection Of Major Trends In Logistics and Trend Interactmentioning

confidence: 99%

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

et al. 2019

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“…The literature has shown that logistics competitiveness is strongly related to trade expansion, export diversification, direct foreign investment and economic growth. The description of competitiveness is difficult for researchers, because organizations have many and often overlapping targets [12][13][14][15][16][17]. Some of these may include: Quality [13], Vendor performance [14], Productivity [15], Efficiency [16], Competitive position [18], Operating performance [19], Distribution effectiveness [20,21], Raw financial statistics (e.g., net income, gross sales), Cost statistics (e.g., transport cost, standard labor costs), firm evaluation [22], waste management [23], and input/output measures or performance criteria (e.g., number of shipments/vehicle hour) [12], etc.…”

Section: Logistics Competitiveness Evaluationmentioning

confidence: 99%

Logistics competitiveness of OECD countries using an improved TODIM method

Özmen

2019

Sādhanā

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The Organization for Economic Cooperation and Development (OECD) provides a forum where governments can work together to increase the global welfare and to seek solutions to common problems through economic growth, where logistics plays an important role and contributes to financial stability. Evaluation of the logistics competitiveness of countries is a technical decision-making issue involving a variety of criteria. Most importantly, these criteria usually conflict with each other and they often act and react upon one another. As in logistics competitiveness as well as in many decision-making problems, the relationships among criteria are interdependent. Moreover, different dimensions and criteria weights also affect the evaluation results. By considering these situations, in order to handle these criteria interactions, Mahalanobis distance (MD) based TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) method has been developed and it has been applied to evaluate the logistics competitiveness of the OECD countries. Evaluation of the correlation between criteria develops the consideration outcomes (regarding sorting) to a certain degree with the traditional TODIM method.

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“…Based on the empirical findings in the survey (Klumpp et al 2014), two new regression models for dependent variables were developed: one regarding the dependent factor of recommendation for the truck drivers job towards others (Figure 5); and another regarding the dependent factor if drivers assume that they will be able to continue their job until retirement under the current working conditions (Figure 6). Both dependent factors are a representation of the truck drivers work motivationand in each case, three statistically significant independent and influencing factors were identified from all variables within the survey.…”

Section: Motivation Modelling For Truck Drivingmentioning

confidence: 99%

Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements

Klumpp

2017

International Journal of Logistics Research and Applications

134

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Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human-machine interaction concepts. Due to scarce high-skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human-artificial collaboration systems in logistics ex ante for investment decision purposes, a multi-dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient 'trusted' collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.

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Logistics Dynamics and Demographic Change

Cited by 3 publications

References 64 publications

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

Logistics competitiveness of OECD countries using an improved TODIM method

Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements

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