Essential Technology for Smart Commodity Management
November 10, 2016
Technology is advancing at an astonishing rate. Take, for example, laptop computers. In April 1981, Adam Osborne introduced the first portable computer, the Osborne 1 Personal Business Computer, for $1,795. It featured a 5 inch display, two floppy-disk drives (capacity 92K), and 64K of RAM. Today, you can get the “best” laptop in 2016 for $450. It features a 13.3 inch monitor, 4GB of RAM, and 128GB of storage – no floppy disks required.
Computers are amazing tools. We use them for work, communication, and entertainment. They are essential for making sense of the overwhelming amount of information used in commodity trading and risk management (CTRM). And they are just the beginning.
The next wave of innovation
Commodity trading companies are navigating volatile markets and combating shrinking profit margins. Innovations that enable them to gain efficiencies, improve margins, or make better decisions provide a competitive advantage they cannot ignore.
- In the internet of things (IoT), sensors and actuators are embedded in physical objects and linked via the internet. This could be a person with a heart monitor, a farm animal with a biochip transponder, or ground sensors located in farms. The internet of things is transformative for commodity companies, providing automated responses to changing conditions without human evaluation and intervention. For example, farm sensors provide data about crop conditions to computers that trigger equipment to water fields – all without human interaction. Because sensors collect and send real-time data, commodity traders can check the status of inventory as it moves through the supply chain and more accurately predict the timing and availability of products. IoT monitors track equipment performance as well, providing visibility into equipment usage and possible efficiency improvements.
- Machine learning is the creation of algorithms that learn and adapt, updating analytics based on previous performance. Machine learning models can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. Since commodity markets are characterized by huge volumes of data, these large-scale models are perfect for commodity trading and risk management. Machine learning can be used to make accurate predictions on diverse elements within the commodity value chain: from investment in new plant and fleet right-sizing, to cash-flow management based on information on individual counterparties and invoices.
- Drones are small, maneuverable unmanned aircraft that can be equipped with cameras to capture images. Because of their maneuverability, drones can collect more information in an afternoon than piloted aircraft can collect in a week. The images are better, because drones can fly lower and maneuver around obstacles to take photos at a variety of angles. For commodity traders, drones can be a game changer. In agriculture, they can scout 100% of a field in a day and deliver earlier detection of drought, weeds, pests, disease, and other abnormalities. For bulk commodities, drones can capture images of stockpiles from all angles, importing images into CTRM solutions like Eka’s InSightCM, which create 3D models to determine volumetric data with extreme accuracy. Commodity traders will know exactly how much product they have in stockpiles and can better manage inventory and delivery.
Smart commodity trading companies embrace innovation to make better decisions, increase efficiency and improve margins. Smart technology like Eka’s CTRM, ETRM and Commodity Analytics Cloud integrates data from the internet of things and drones, using machine learning to ensure the most accurate analyses of product quality and quantities throughout the value chain. Companies that embrace innovation can gain a significant advantage over competitors.