by Sejuti Das

Analytics is set to transform commodity trading in India, albeit gradually

Sep 20, 2016
Agriculture IndustryAnalyticsBig Data

While predictive analytics presents a huge opportunity for commodity trading companies to tap actionable insights and make profits in a volatile environment, the adoption is still in a nascent stage.

Driven by climatic conditions, demand curve, inflation and geopolitical factors, volatility is a constant phenomenon in the world of commodity trading. The prices of commodities such as wheat, corn, oil, natural gas etcetera. rise rapidly, only to fall precipitously and then to rise again. With such instability, the risk of managing commodity prices is a top concern for companies in this sector.

According to a recent EY global survey of 1600 IT leaders of commodity companies over a third of the respondents believed that increased volatility in commodities was the greatest economic risk to their businesses over the next 6 to 12 months. The implications include negative cash flows, lack of margin optimization and significant earnings loss. It can also have a negative impact on the brand image of the company.

There are some companies which have been using passive methods to deal with this fluctuation issue, such as classifying commodities in groups and categorizing unfavorable price movements. But with the continued negative impact, Indian companies are looking to deploy analytics to understand the patterns, lowering costs, improving cash flow predictability and stabilizing operating results. As the budgets are tight companies are only investing in the most critical areas to survive the uncertainty in the market.

Commodity prices fell throughout the year in 2015, according to a report by Accenture, which is forcing Indian companies to maximize efficiency to make better and faster decisions.

According to Manav Garg, CEO and Founder at Eka Software, a commodity analytics firm, the commodity industry is a data intensive business. As markets fluctuate and uncertainty prevails, the need for fact-based decision-making is stronger than ever. He said, “Making decisions in critical moments to stay ahead of the competition has become the norm of the day. Using analytics in commodity industry will precisely help in processing large volumes of data at higher speeds and will provide advanced analytics to make timely decisions for business operations.”

“Globally we are seeing a lot of adoption of commodity analytics, but the market in India hasn’t been hit as much as other markets like US, Europe, Australia, Asia, and Canada,” said Garg.

In India, analytics in commodity future trading is relatively new and faces restrictions in terms of limited participation. But, it’s not far behind the curve. According to a NASSCOM report, Indian big data anaytics market is expected to touch $16 billion by 2025.  And, NASSCOM believes that any Indian company belonging to the Commodity Trading and Risk Management (CTRM) and Energy Trade and Risk Management (ETRM) industry will need analytics in its business operations. Indian leaders can no longer afford to wait for long to understand market patterns, analyze the alternatives, and make decisions.

Analytics can help in gathering data from internal systems, such as CRM and spreadsheets, and external sources, to answer the most important questions of business. Garg also mentioned, “Commodity trading firms having a presence in India, such as Cargill, Louis Dreyfus, Hindalco, RIL etcetera are looking to adopt analytics solutions for their business operations.”

Other industry leaders we spoke to agreed that analytics will be the future of Indian commodity management. Bhushan Akerkar, CIO at Hindalco Industries India, an aluminum rolling company, said that analytics is playing a major role in sectors like healthcare, banking, financial and education, but Indian companies dealing with commodity haven’t yet accepted analytics as the main tool of doing business.

“We at Hindalco use analytics for gathering data, creating reports like management information report and quantitative analysis report. But the application of analytics isn’t much used for predictive examination,” said Arekar. “Although commodity sector is very nascent in terms of analytics, it definitely has a lot of potential in improving operational efficiency, drive new revenue and gain competitive advantages over business rivals.”

He believes that predictive analysis of big data can anticipate business opportunities and help in making decisions faster to drive profits. It can be used to understand the patterns indicative of future situations and behaviors, and it suggests actions that will be beneficial for the business.

According to Sudipta Ghosh, Partner at PwC, Indian commodity trading companies are using analytics only for basic requirements like managing cash flow, hedges or investments, but the penetration of major advanced predictive or qualitative analytics is not much. He explained that an organization uses analytics to gain speed, agility and efficiency. But that requires the right skill-sets which this sector does not invest in.

“Commodity sector in India is always looking towards gaining profits in the long term and therefore agility and speed aren’t their major concerns,” said Ghosh. “Secondly, companies dealing with food, beverages, machinery, agriculture, fertilizers, chemicals, metals and mining, energy and utility etcetera, prefer not hiring new set of skills for deploying newer technologies. They would rather train their existing people and bring them at par, which is a slow process. And that is why analytics has not caught on as much as in other verticals.”

While there seems to be immense potential for the commodity sector to deploy predictable analytics for business benefits, it appears it will be a while before companies will actually use this technology to its true potential.