There is no single market for agricultural and soft commodities – each commodity has its own unique value chain and combination of production methods, processing/transformations, and consumption patterns; the combinations of which any particular commodity can, and in many cases will, vary significantly by geography.
Prices for these commodities are influenced by:
Where any particular enterprise falls within the value chain from producer to consumer, the influence and impact of any one or more of these factors will vary.
With the majority of agricultural and soft commodity wholesale prices at or near 5 year lows, and the outlook projecting more of the same, almost all market participants are facing significant challenges in maintaining profitability. From producers to processors, any company that operates in the ags and softs market must remain vigilant and constantly adjust to these rapidly changing market conditions, including uncertainty driven price volatilities, in order to ensure a profitable operation.
Driven in large part by economic growth in the Asia Pacific region, and particularly China, in the last decade, investors began pouring money into developing the infrastructures to support increasing demand for commodities of all types, from ags to energy. However, with the slowing of the Chinese economy in the last year, falling from double digit growth to possibly well below 7%, the supply chain in almost all commodities has become significantly overbuilt, with supply of almost all commodities outpacing demand and driving global prices to record lows.
In over-invested and over-supplied markets, particularly those as complex as the ags and softs markets, price movements will be particularly unpredictable. With uncertain prospects for increasing demand in the short- or mid-term, prices will be increasingly sensitive to regional events, such as extreme weather, or the tenuous market forecasts that are seemingly based on nothing more than speculation. Further, given that production in many of these agricultural and soft commodities are centered in developing countries, political instability and limited market liquidity can further contribute to significant price movements.
Over the last decade the markets in ags and softs have seen a period of consolidation, with many companies pursuing growth, and price risk amelioration, by acquisition of assets both up and down the ags and softs supply chain. Trading companies, initially focused on securing supplies from producing regions and transporting those to the areas of increasing demand, such as China, began to acquire upstream assets such as farms, plantations, elevators, and export facilities. Large food processors too saw advantage in controlling the inputs to their business and acquired trading companies, merchant ag firms, and many upstream market components as well.
These growing companies that now trade, manage, and transform physical agricultural commodities, the merchants, food processors, and consumer package goods (CPG) companies, face a particularly complex value chain, potentially including:
Maximizing the value of both the physical assets within that value chain and the commodities as they traverse it is a complex balance that requires the most up-to-date market information and a set of technology tools that provide the analytical capabilities necessary to maximize value for any given portfolio of assets and commodities. Throughout that value chain, daily and intraday decisions must be made as to how to manage supplies, facilities, and product movements to ensure profitability in a market in which small movements in commodity prices can have a profound effect on profitability. These decisions can include, among many:
These firms must have the ability to react quickly to price changes, not only for inputs or finished products, but also for intermediate products. Comprehensive and real-time visibility into current market conditions, inventory levels and valuations, and operational costs, combined with advanced analytics will allow these companies to determine the highest value for each intermediate and finished product and make a rapid determination as to whether to sell the intermediate or to continue processing/improving into stocks of finished goods. (Learn more in "Using Commodity Analytics to Solve Challenges in Procurement.")
Maintaining the balance between costs and realized value requires a fundamental set of tools for optimizing profitability – price feeds, market intelligence solutions, and the appropriate software systems that can track, value, and measure profitability throughout the value chain all backed by a full set of advanced analytic tools that allows producers, traders, and merchants to test any number of scenarios related to inventory requirements, costs, and market prices.
Though traditional CTRM software can capture and value many of the transactions associated with wholesale purchases or trading of agricultural and soft commodities, these systems have proven inadequate in managing the complex value chains found in most of these markets. With the development of the first generation of commodity management (CM) solutions, merchants, food processors, and CPGs did have a solution for tracking and managing commodity trades, inventory movements, and transformations. Additionally, these systems were able to provide valuable information and feedback regarding cost management and some of the optionality that might exist along that value chain.
The first generation of CM systems were primarily architected as transaction based systems and were not designed to address sophisticated analytics. The growing complexity and globalization of the ags and softs markets has stressed these early generation systems’ abilities to model, manage, and provide the critical real-time data, information, and analytics required by commodities businesses.
Companies seeking to maximize profitability in today’s market are faced with a complex and expensive challenge of aggregating, via a complex integration infrastructure, huge amounts of data. And even with such a data aggregation infrastructure, CM solutions can only provide a portion of the analytics that are necessary to truly optimize profitability at the enterprise level.
With more than a decade of experience in developing and deploying CTRM and CM solutions in the agricultural and softs markets, we at Eka understand that optimizing bottom line performance requires the ability to quickly aggregate and analyze an ever-increasing volume of data at near real-time speed. Simple integration or data aggregation approaches that rely on point-to-point data transfers are insufficient in this market and may in fact provide misleading analysis due to missing or outdated data as processes are initiated or files are transferred and aggregated.
Our approach to providing the analytics capabilities necessary to prosper in this market are based upon two main tenants:
Commodity Analytics Cloud, enables the complex data aggregation capabilities necessary to "see," in near real-time, across all requisite systems in the enterprise. Additionally, the platform uses preconfigured and user definable apps to provide rapid analysis and insights into the critical issues facing business users, spanning multiple categories including P&L, procurement, risk, and supply chain.
We have developed a number of apps to answer the most commonly posed questions in the commodity space. Utilizing an in-memory processing grid, the system can process extremely large data sets in a very short time to support near real-time decision making. Utilizing some of the tools of a Big Data solution, including the use of NoSQL, the product aggregates data from any number of sources: internal systems such as CTRM, ETRM, ERP, CRM, and spreadsheets, plus external sources such as market curves and weather data.
Although many CTRM vendors have started to build dashboards and KPIs into their platforms, these solutions are not sufficient as they only provide analysis for data that can be accessed through the CTRM. Transaction-based solutions should not be considered true analytics solutions as they were not designed to handle the increasing quantities and types of data.
Commodity Analytics Cloud brings together large amounts of disparate information from multiple sources that includes CTRM and ETRM software platforms, and also includes data from other internal and external sources such as ERP, CRM, spreadsheets, market curves, and weather data. The platform enables users to run complex forecasting models and scenarios to answer the "what if" questions.