Exploring the role of drones and UAVs in logistics and supply chain management: a novel text-based literature review

Figure 1. Overview of the research process.

Overview of a five-step research process: (1) Collecting materials, (2) Visualising and analysing raw data, (3) Deploying topic modelling, (4) Analysing and classifying topics, and (5) Summarising key insights from the literature.

Table 7. Dominant topics and their top keywords (the numbers are the percentages contribution in each topic) using whole data set.

Table 8. Linear temporal trend of meaningful and stable topics related to drones applications (ns: $p - value > 0.05;^{} P < 0.05;^{ } P < 0.01;^{ * *} P < 0.001; R > 0$ : Positive Slope; R<0: Negative Slope).

Display Table

Figure 2. Distribution of papers during years (1978 to 2022).

Graphical representation of the distribution of papers from 1978 to 2022, excluding 2023 due to incomplete data.

Figure 3. Top-10 high-frequency venues.

List of the top-10 high-frequency publication venues for drone applications in LSC.

Figure 4. Number of papers used any of the top-10 keywords.

Representation of the number of papers using any of the top 10 high-frequency words in titles, abstracts, and keywords. The figure illustrates extensive use of ‘vehicle routing problems’ as a main keyword, appearing both as the singular term ‘routing’ and in various combinations such as ‘routing protocol(s).

Figure 5. Wordcloud of the top-50 high-frequency keywords.

Wordcloud of the top-50 high-frequency keywords in which the searched words and top words are highlighted as expected.

Figure 6. Number of citations given to each paper and the year each paper was published.

Visualization depicting the number of citations given to articles based on their publication year, using log scale transformation. Notably, the figure reveals a significant surge in literature growth in the last seven years.

Figure 7. Coherence scores for finding the optimal number of topics using both maximisation and minimisation approaches.

Graph illustrating coherence scores for determining the optimal number of topics. Application of the coherence criterion across a range from 1 to 50 topics reveals that the optimal number of topics is identified as 10.

Figure 8. Weights of topics gained by the LDA topic modelling approach.

Visualization depicting the weights of topics identified through the LDA topic modelling approach. The six classes of topics include: (1) hyperspectral imaging, (2) drone transportation, (3) IoT, (4) humanitarian drone, (5) FANET, and (6) medical services.

Figure 9. Weights of topics gained by the LDA topic modelling approach with respect to years of publications.

Visualization illustrating the weights of topics gained through the LDA topic modelling approach with respect to years of publications. The average weight of selected topics between 2017 to 2022 is presented.

Figure 10. Yearly trend of the 5 topics with a significant trend $(p - value < 0.05)$ .

Graph depicting the yearly trend of 10 topics analysed from 1978 to 2023.

Figure 11. Mindmap of finalised topics.

Mindmap depicting finalised topics from the 5 with significant trends. One topic displayed a decreasing trend (‘cold’), while the remaining 4 topics showed increasing trends (‘hot’).

Table 9. Dominant topics and their most frequent keywords using only logistics and supply chain domain data.

Table 10. Final dominant topics of logistics and supply chain domain.

Figure 12. Frequency of top-10 keywords in logistics and supply chain papers.

Graph illustrating the frequency of the top-ten keywords in logistics and supply chain papers, highlighting that machine learning is the most frequent keyword/method employed in the domain.

Figure 13. Frequency of the top-ten high-frequency words in logistics and supply chain papers.

Graph illustrating the frequency of the top-ten high-frequency words in logistics and supply chain papers, with the data indicating that the word ‘system’ is the most frequently used.

Figure A1. Frequency of words in title, abstract, and keywords.

Visualisation of word frequency in titles, abstracts, and keywords. only 10% of unique words had a frequency greater than 100, aligning with Zipf's rule, demonstrating an inverse relationship between word frequency and their rating.

Figure A2. Wordcloud of the top-50 high-frequency words in title, abstract, and keywords.

Wordcloud representation of the top 50 high-frequency words in titles, abstracts, and keywords. The words are counted after the stemming process in data preparation, highlighting frequent terms such as ‘network’, ‘system’, ‘route’, and ‘delivery’ in the literature.

Figure A3. Number of papers that used any of the top-10 high-frequency words in title, abstract, and keywords.

Visualisation detailing the frequency of the top ten high-frequency words in titles, abstracts, and keywords. The data illustrates that the initial four words (‘network’, ‘system’, ‘route’, and ‘delivery’) are more commonly utilised than others.

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