Sales Forecasting in relation to Operations Research

This study aimed to determine the extent to which sales forecasting research has been linked to OR decision making, and the areas in which that link has not been strong. Four tasks followed: mapping the field bibliometrically, synthesising the forecasting methods used, investigating how forecasts feed into OR decision models, and outlining missing areas for future research. The evidence comes from a Scopus search with a 315-document English-language export, a literature mapping, and a bibliometric analysis of a further 255 documents published from 2012 to 2025. The first, second and fourth tasks were well accomplished: the two analyses described the growth, structure and methods of the field consistently, and the gaps are well supported. The link between forecasting and OR can be traced and a few integrated studies were found, but the literature review used abstracts and keywords rather than full texts. The evidence therefore shows areas where proof of decision value is low, and it does not measure the impact that better forecasts have on decisions. The field has moved beyond the question of how accurate a sales forecast is. The next question is what impact forecasts have on decisions about inventory, pricing, assortment and resources, and what those decisions deliver in profitability, service and customer outcomes. Answering it requires the tools of OR, namely optimisation and decision models, together with managerial judgement and responsible use of the data on which forecasts rely. A forecast is only as good as the decisions it helps to improve. Several limitations apply. The analysis is based on one database (Scopus), on English-language articles and reviews in journals, and on bibliographic metadata, so relevant studies outside these parameters are missing. Only titles, abstracts and keywords were examined in the literature review, and full texts were not checked. Keyword-based analysis is sensitive to thresholds and spellings, and some records in the corpus, such as electricity load forecasting, churn or cryptocurrency forecasting, are only loosely related to sales forecasting. The figures of 316, 315 and 255 documents come from different analytical stages, and the 2026 data cover a partial year. Bibliometric analysis can describe patterns but cannot establish causal relationships, and most studies focus on specific industries, so their findings may apply only partially elsewhere.

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