Over the last twenty or more years, global wholesale commodity markets have grown and evolved substantially and in the process, a sizeable new software category has been established. That software category is widely known as Commodity Management (CM) software and, at the highest level, it can be defined as those software applications, architectures and tools that support the business processes associated with managing commodities. CM software therefore comprises a broad set of functions that can vary considerably depending on which commodities are traded, what assets are employed in the business, where those assets are located, and what the nature of the company’s business strategy and associated business processes. CM software continues to evolve quite rapidly in lockstep with the industry. In past years, CM focused squarely on trading and risk management as CTRM software, but in recent years it has been extended into the supply chain with solutions such as shipping and stockyard bulk handling, for example.
As the 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 or purchase commodities and raw materials. As a result, the requirements that users place on CM software are also changing from essentially an after the trade recording and reporting system, to one that provides real intelligence and value back to the business.
This whitepaper briefly examines the history of CM software and looks at how new demands are being placed on the software to make it increasingly of benefit to decision-making and optimization in the business. It also examines how trading and related data has also grown in volume and latency, and changed in complexity, such that CM solutions are challenged to transform big data into actionable insights and strategies for the business. In particular, it looks at how advanced, predictive analytics will be delivered, deployed and used to get the most value from the exponential growth in data being captured.