Inventory Optimisation and Analytics: A Bibliometric Review with Implications for E-Commerce

This study gave a bibliometric review of the literature on inventory optimisation and analytics and examined its implications for e-commerce. The master dataset is a Scopus BibTeX export containing 6,946 records. Statistics recomputed for the 6,926 documents of 2018–2026 reproduce the Biblioshiny figures, and the 6,946, 6,941 and 6,926 counts are related by simple arithmetic, with the underlying search and screening records unavailable. Three main findings came from the analysis. First, the field has grown strongly: the annual growth rate is 13.54% (calculated with partial-year 2026), and output rose from 470 documents in 2018 to 1,124 in 2025, with 1,298 in the partial year 2026. Second, the research community is global and collaborative, with China, the USA, India and Iran as the leading countries, and operations and production journals, led by Computers and Industrial Engineering (191 documents), as the main outlets. Third, optimisation, inventory control, machine learning and sustainability are closely connected: machine learning and sustainability are motor themes, and the keyword network shows strong links among the modelling, sustainability and decision-making clusters. E-commerce is a small part of this literature. E-commerce terms appear in the title or author keywords of 2.04% of documents and in the title, keywords or abstract of 3.67%, and e-commerce is not a labelled theme on the thematic map. These shares are a keyword proxy for a dataset whose recorded search term was “inventory optimisation” (the full query is unavailable), so they suggest that applications to e-commerce are thinly represented in this dataset, not that e-commerce research is rare. The agenda in Section 6 gives seven directions, with research questions and methods, for these areas. Verifying the search history and screening logs would allow the reported search and screening stages to be confirmed.

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