dc.contributor.author |
Nanayakkara, Udayanga Ranmal |
|
dc.contributor.author |
Poravi, Guhanathan |
|
dc.date.accessioned |
2020-05-24T12:20:58Z |
|
dc.date.available |
2020-05-24T12:20:58Z |
|
dc.date.issued |
2019 |
|
dc.identifier.citation |
Nanayakkara, U R and Poravi, G (2019) ’Review of Black Orthodox Tea Suggestion Techniques’ In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT) Pune, India. 29-31 March 2019. pp. 1 -5 IEEE DOI: 10.1109/I2CT45611.2019.9033619 |
en_US |
dc.identifier.uri |
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9033619 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/411 |
|
dc.description.abstract |
Tea Auction Center (TAC) is the place where the majority of the tea is sold. To enhance the quality and constancy, this auctioning procedure comes with a couple of problems. One noteworthy problem is that there need of 3 weeks cataloguing period. Because of this cataloguing period, the tea manufacture ends up paying the tea grower for their green tea leaves before the prepared tea is sold at the auction. But to secure the planter being underpaid in the process the Tea Board (Tea controlling the body of the government) has introduced an equation. Due to cataloguing and manufacturing time, manufactured tea is sold in next month, therefore the tea factory average in the equation is the last month average. The risk of using the last month average as this month average is if any sudden price drops occurred when the catalogued tea is sold, the manufacturers incorporate a great risk of getting a large trading loss, therefore it's of immense importance to minimize the amount of trading loss. If the factory can identify which grades of tea is going to get more attraction in the market and change its manufacturing process in order yield more outcome from prevailing market conditions minimizing the trading loss impact to the factory.In this research project, the researcher attempts to summarize all the researches done in the domain. |
en_US |
dc.subject |
Tea auction |
en_US |
dc.subject |
regression;modelling |
en_US |
dc.subject |
Predictive models |
en_US |
dc.subject |
Artificial neural networks |
en_US |
dc.subject |
Time series analysis |
en_US |
dc.title |
Review of Black Orthodox Tea Suggestion Techniques |
en_US |
dc.type |
Article |
en_US |