Commodity markets are becoming more complex producing a huge amount of data. As such we find ourselves at an inflection point in the commodity management market where we are seeing a generational shift from first- and second-generation transaction systems to solutions that make management control and decision support a key element of the solution.
The management of commodities and the risks associated with this activity have evolved as both the type and quantity of data to be managed has changed. What started as primarily energy trading and risk management software (ETRM) later became commodity trading and risk management software (CTRM) — these solutions now support more commodities and the functional footprint of these systems has dramatically increased.
These systems started as transaction systems 20+ years ago and in almost all cases are still mainly transaction systems. The ability to handle complex transactions is obviously a primary requirement, but it's not sufficient. The generational shift from transaction only systems to next-generation commodity management software with advanced analytics is a big shift for first- and second-generation vendors — many vendors will be unable to make the shift due to technological, cultural, or managerial reasons.
This generational shift in solutions is being driven by commodity market participants that need to be able to make better, faster, smarter decisions with the huge quantity of data that is available to them.
The next big shift in the commodities industry is the recognition of the need for Smart Commodity Management — those systems that will focus on decision support analytics.
As this software category has evolved, so has the volume and nature of the data that the software captures, manipulates, and stores. Today, big data is an increasingly important aspect of the commodity management world as vast quantities of many types of structured and unstructured data potentially hold the key to profitability and even survival of companies that sell and purchase commodities and raw materials. The amount of data being stored has grown and, with it, the need for this data to be better managed.
As a result, the requirements that users place on commodity management platforms are changing from essentially an after-the-trade recording and reporting system to one that provides real intelligence and value back to the business.
"First- and second-generation commodity management solutions are challenged
to transform big data into actionable insights and strategies for the business."
Advanced analytics will be needed to get the most value from the exponential growth in data being captured.
Everyone is talking about big data — how to capture it, how to manage it, how to store it, how to use it. Businesses that deal in physical commodities, such as agricultural products, metals, oil, and gas are no exception. But what is interesting about the management of commodities is that it has always been a big data business, long before the term became fashionable. (See "How to Leverage Big Data with Next-Gen ETRM/CTRM Software.")
By its very nature commodity trading creates thousands of individual data points:
Behind all these data points are decisions to be made that affect short-term profitability and long-term stability. If we look at a commodity supply chain, simply moving the commodity from point A to point B involves selecting the right storage, the right transport solution, and the right route.
That's after deciding on the right quantity to move, the right market to send it to, and, of course, the right source to acquire it from in the first place. In rapidly moving markets, any of those decisions may need to be reconsidered before the commodity arrives at its final destination.
Then there's the processing to consider as well as the risk management aspect of the business, adding ever more layers of data points. Taken together, these individual dimensions create a significant amount of complexity and produce huge volumes of data.
To remain competitive, commodity companies can no longer rely on traditional first- and second-generation ETRM and CTRM software. Instead, these companies need a Smart Commodity Management platform that uses advanced visualization techniques coupled with user-controlled, analytics.
With powerful analytical capabilities, a Smart Commodity Management solution enables commodity exposed businesses to develop predictive capabilities that facilitate actual decision-making, rather than just monitoring activity. In other words, it's about looking forward, rather than back: making accurate forecasts about the future.