By now, it is well understood that to remain competitive in today's market and to more effectively manage trading and risk, commodities companies require advanced commodity analytics. Combined with knowledge from industry experts, data scientists have developed advanced analytics to predict future outcomes. Looking at historical data to determine patterns and relationships, advanced analytics guides users to make the correct decisions based on objectives, business rules, and operating constraints.
With advanced commodity analytics, business users can quickly find answers to these types of questions:
Learn more about these and other use cases in "A Machine Learning Approach for Cash Flow Prediction" and "Using Predictive Analytics to Manage Commodities in Extreme Weather."
Business users who analyze data are trying to answer questions; the answers to these questions invariably lead to more questions. Advanced commodity analytics provides users the ability to drill down and dig deeper. The best advanced commodity analytics solutions will combine commodity specific intelligence with the correct architecture platform to traverse the huge volume of data common to the commodities industry.
An advanced analytics platform should enable users to define settings such as key performance indicators (KPI), decision horizons, and allocation strategies; then run scenarios and compare the results of various strategies to determine optimal allocations. Efficient algorithms will consider various blending and routing options simultaneously to determine how best to meet business objectives. By plugging in applicable inputs to an optimization engine, business users gain answers to their questions.
A decision support system must have the facility to conduct "what-if" scenarios where a scenario is defined as a unique set of user defined settings, performance metrics definition, business rules, input data, and goals. The system must be able to create a new scenario and duplicate a scenario, as well as to compare various scenarios by performance metrics.
As part of the set of inputs, users can specify criteria that must be met, such as tolerance ranges for quality and preferences to fulfill sales orders with stored inventory first. Constraints can be defined in the areas of fulfillment, counterparty, allocation, blending, and transportation.
With the correct analytics platform, site operators can answer these types of questions:
Using advanced analytics enables commodities companies to:
Commodities companies strive to fulfill sales contracts in the most profitable, efficient way. They need to minimize transportation costs and deliver products at the correct quality levels, all while meeting delivery schedules, incoterms, pricing requirements, freight quotations, etc.
portfolio optimization - the process of choosing the proportions of various assets.
Some of the inputs to a model for portfolio optimization include:
With portfolio optimization, users gain insight into automated allocations including:
The right analytics solution will enable commodities companies to optimize decisions around purchase orders, stock orders, routes, and blending. When users can evaluate scenarios, they can determine optimal allocations.
Eka's Commodity Analytics Cloud platform delivers industry-specific apps that cover P&L, procurement, risk, and supply chain. Unlike generic business intelligence tools, Eka's advanced analytics platform has been built with the specific needs of commodities companies in mind.