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Part of the book series: Studies in Computational Intelligence ((SCI,volume 1014))

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Abstract

Supply chain is a hot topic with strong links in the industry and business applications. The overflowing information generation, increasing complexity of businesses, digitalisation of the supply chain, and introduction of advanced analytics capabilities are all topical issues in the supply chain. Visualization of supply chain inform action in this regards is more than ever important and critical: it provides an easy way to understand and act upon solutions for decision makers, reduces the cognitive load and brings strategic benefits to the business. The development of data analytics and visualization techniques have been booming while little attention was given in the academic literature to structure the landscape and draft the road for further development. The present paper addresses this gap by providing a comprehensive review of the current literature in the use of visualisation in this growing area of supply chain and logistics. The paper employs the PRISMA methodology to identify the main theme, particular areas of development and suggests the future directions for research. Such a structural view developed on the basis of top academic and industry publications, leverages its contribution by provision of a brief structural view of available directional developments and links them to practical applications.

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Correspondence to Catherine Xiaocui Lou .

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Appendix A

Appendix A

The adjusted PRISMA framework checklist and its implementation in the study

Section/topic

#

Checklist item

Title

1

Identify the report as a systematic review, meta-analysis, or both

Structured summary

2

Provide a structured summary including background; objectives; data sources; and other details of the study

Rationale

3

Describe the rationale for the review in the known context

Objectives

4

Provide an explicit statement of questions/aims being addressed

Protocol

5

Indicate if a review protocol exists and provides its details

Eligibility criteria

6

Specify study characteristics and report characteristics used as criteria for eligibility, giving rationale

Information sources

7

Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search

Search

8

Present full electronic search strategy, including limits used

Study selection

9

State the process for selecting studies, such as screening and eligibility

Data collection process

10

Describe method of data analysis

Data items

11

List and define all analytical items for which data were sought

Risk of bias

12

Describe methods used for assessing risk of bias and synthesis of information

Summary measures

13

Describe the principal summary measures

Synthesis of results

14

Describe the methods of handling data and combining results of studies

Risk of bias across studies

15

Specify any assessment of risk of bias that may affect the cumulative evidence

Additional analyses

16

Describe methods of additional analyses

Study selection

17

Give numbers of studies screened

Study characteristics

18

For each study, present characteristics for which data were extracted and provide the citations

Risk of bias within studies

19

Discuss the risk of bias for reviewed studies

Results of individual studies and synthesis of results

20

21

A brief of individual studies and summary

Risk of bias across studies

22

Discuss risk of bias across studies

Additional analysis

23

Give results of additional analyses

Summary of evidence

24

Summarize the main findings, including their relevance to key stakeholders

Limitations

25

Discuss limitations at study

Conclusions

26

Provide a general interpretation of the results in the context of other evidence, and implications for future research

Funding

27

Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review

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Lou, C.X., Bonti, A., Prokofieva, M., Abdelrazek, M. (2022). Supply Chain and Decision Making: What is Next for Visualisation?. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Banissi, E. (eds) Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1014. Springer, Cham. https://doi.org/10.1007/978-3-030-93119-3_26

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