Big Data Insights for Field Commodity Traders enabling Spot decisions

Big Data Insights for Field Commodity Trader

The International Trade in Agro-commodities from time immemorial, from the Barter days until today spread across 41,821 Airports (2013) 4,764 Sea ports in 196 countries around the world has been carried on over centuries over land, Air and Sea ways.

Very large ecosystems of players are stakeholders in this sector defining the economy of countries, economics of families and livelihood of generations. These stake holders include from farmers, farm produce agents, Brokers, Traders, Transporters, Packers, Warehouses, processors, workers, shipping, governments, Public agencies, bankers, Currency traders, Forex, Quality controllers, markets, Equipment & machines, Fertilizers & pesticides, Industries, etc.

This industry has been carrying on business for centuries following various standards, practices and processes all recorded over time by the various participating agencies both in the private and public sector to enable trade successfully.

Some of these agencies in the Public and Private sector have been in existence for centuries and operating with over thousands of offices and tens of thousands of employees enabling millions of transactions globally consolidating and recording data systematically.

These above data from multitude of sources, currently mostly in Digital formats and previous century’s records in ledgers and Forms are archived in Public and private agencies.

That’s Trade data.

In International Physical Commodities Trade, a successful transaction (Buy & Sell decision) is based upon a number of factors that are constantly changing and the optimum choice would be made based on the experience, expertise, knowledge of the Trader. The exposure is very large and so is the risk.

An experienced and seasoned trader takes a balanced view after considering a number of factors across a number of dimensions such as Commodity specs, Harvest Info, Weather, Exchange rates, Shipping rates, Buyers sentiment, Buy rates, Competition, end user market interests, Bank interests, Political situations, Consumer trends, competitive / replacement commodity situation, technology advancement, Ports situation, pests & infestation situation, economic trends, economic News, etc.

All of above seamlessly based on Skills honed over decades. A true Blue Trader, Lives and breathes his commodity. Its a Heart & Soul profession.

“What IF” analysis by the trader is sheer experience and skills based with lots of emphasis on individual “take” and not fully are defendable with Data.

Today, a large extent of this “What IF” analysis can be done by aggregating and analysing data that is available in both the public and private domains from current data, immediate past and archived data / archived libraries country wise, such as Commodity rates, Harvest data, Quality reports, Inspection reports, Freight rates, Shipping Documents, Shipping manifest data, Forex reports, Banking data, Buy/ Sell reports, Market reports, Broker reports, Weather data, News, Political reports, Wage data, etc and give that “One Insight” that “One Recommendation” to buy or Sell.

This Recommendation based upon real time and archived data aggregated from multitude of sources across a long period of time that can also include past patterns, trends and performances of that specific company, Commodity or market would de-Risk and protect investments.

“Parity” based on Big Data Recommendations would be of immense value to various stake holders in commodity trade organizations such as Procurement, Banking, Finance, Sales, shipping, Quality, MIS, Corporate Secretarial, etc.

In today’s complex world successful trades would be possible to those who make informed decisions from real time, aggregated data, both current and historical along with structured information analysed continuously on a global scale.

Managing exposure and de-risking decisions based on recommender systems would help maximise profits.

The Field Traders operating in remote locations would be able to optimise their performance by making Insights based spot decisions.

A Market Scenario: The Cashew Exporter

A cashew exporter in India with 20+ processing factories with year round operations would have to manage his purchase of Raw cashew nuts and Kernel sales both spot and forward very well to be profitable.

The cashew exporter would have to source and procure the raw cashew nuts from the bushes in India, Africa, Indonesia, Madagascar, etc round the year as per the harvest season in the respective countries, ship them to the processing factories in India and sell the processed kernels spot & forward both locally and Internationally.

This above business has many stakeholders, scenarios, exposures and transactions…

The business begins by the exporter studying the Season’s Raw Nut crops origin wise, their expected availability, Quantity, Quality, Yield, weather conditions, Bush prices, Finished Kernel prices, Supply & demand, Shipping availability, Shipping Rates, Banking Limits availability, Production requirements, capacity planning, Raw Nut Parity, Buyers sentiment, Sellers sentiment, Labour availability, Political and other stability factors in Origin countries, historical trends & patterns, etc, prior to committing to procure from a particular origin.

It takes a very experienced Trader with decades and seasons of experience from Raw Nuts to Kernels across countries to assess the risk and make the right choice based on all above factors including live and continuous data/ information as the season moves in.

Today Big Data Insights would be the solution to all stake holders enabling real time Risk assessment and management, end-to-end from Raw Cashew Nut procurement to Cashew kernels exports.

2 thoughts on “Big Data Insights for Field Commodity Traders enabling Spot decisions

  1. Kannan Sreedhar says:

    Incredible rhyme! Hope somebody from the commodity industry finds your blog useful and is willing to adopt to novel concept revealed here. Kudos CK.

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