Abstract—Tea is an age old industry in Sri Lanka which has been highly regulated via Ceylon Tea Board. Country’s tea business has deep roots and is among the major economic contributors. Tea brokers facilitate the weekly tea auction in Colombo year round and perform the role of advisors to the factories in terms of predicting demand of different varieties of tea. However these predictions are not planned well and are run by the instinct of the brokers. This research is focused on extracting seasonal demands from tea auction history records to better facilitate the brokers predict demand of upcoming sales. Initial exploration on the dataset gives better insights to tea auction. This closeness, combined with the domain knowledge of the industry is then applied on the sample dataset using the association rule mining technique. The outcomes reveal information hidden in the tea auction dataset which could not be extracted using traditional analysis and display promising results. Findings suggest that selected manufacturing factories display similarities in some sale months in terms of the price range of the auctioned teas providing clues to possible seasonal demands in the dataset. Most of all, it unveils the tea auction dataset for possibilities of further pattern discoveries of future tea demands.

Index Terms—data mining, association rule mining, tea auction, seasonal demand, business intelligent

Cite: Amitha Caldera, Dilini R. Serasinghe, "Analyzing Tea Auction Trends for Beneficial Seasonal Tea Production in Sri Lanka" Journal of Advanced Management Science, Vol. 5, No. 2, pp. 152-157, March 2017. doi: 10.18178/joams.5.2.152-157