Rohit Aggarwal is Chief Operating Officer at DecisionNext, a leading AI platform that enables companies to optimize the buying or selling of commodities at the best possible time and price. He leverages a strong background in supply chain and product management as well as experience directly leading very large teams to execute complex multi-disciplinary projects and deliver business results. Rohit previously held product and operations management roles at both Google and Amazon.
You’ve worked at Amazon and more recently at Google. What were some of your key highlights from these experiences?
At Amazon, I had the opportunity to manage a diverse team of 250 cross-functional employees in order to launch best-in-class operations facilities. I also supported the implementation of innovations such as same-day delivery, robotics, and other emerging technologies. Then at Google, I used my skills to bridge the gap between product and operations. This involved building applications from scratch to manage a new style of fulfillment process, among other new offerings.
Can you explain how DecisionNext leverages AI and machine learning to improve commodity price and supply forecasting?
DecisionNext uses artificial intelligence and machine learning to consume thousands of data sets and find historical and current relationships between key factors. It then learns from this information and builds relevant models for any commodity. In agriculture and natural resource markets, our tools help customers forecast prices better, make smarter decisions, reduce risk, and increase profits across global supply chains. We are also working on using Large Language Models (LLMs) to simplify complex global decisions with risk-aware solutions.
What are the key benefits of using DecisionNext’s AI platform compared to traditional forecasting methods?
Global commodity product buyers and sellers often resort to rules of thumb and spreadsheets to simplify a complex system worth billions of dollars in transactions. This leaves significant money on the table. These spreadsheets have worked wonders and supported hundreds of businesses. However, as workforce dynamics change and global markets become more unpredictable, they are becoming less effective. DecisionNext has spent years perfecting an AI platform that turns global complexities into actionable recommendations at scale—greatly improving financial performance.
Our customers have subject matter experts that have been in a particular space or industry for 30 years or more. And as new generations come in, it’s extremely important to retain all of that experience in a usable way. DecisionNext helps with that by building comprehensive libraries of decisions, integrating expert opinions, and learning from the past.
In doing so, the DecisionNext platform reduces risk and uncertainty in business decisions across business units and individuals while establishing a scalable way to make those decisions. It also improves profitability in day-to-day transactions, long-term positions, and future-looking strategic planning.
What role does dynamic data play in DecisionNext’s AI-driven decision-making process, and how is this data integrated and utilized?
Dynamic and up-to-date data is extremely important when it comes to building best-in-class models. That said, the speed and complexity with which the data can be processed and modeled is not the only factor. For example, how does a model know the weight of the most recent data point (say a shock in the system) and that it needs to treat it differently? Our users can interact with the models through patented technology to input their opinions and build what-if analysis to use data that the model or system simply cannot know yet. This allows our customers to gain new insights that would otherwise not be possible. They are also able to better understand the impact of global shifts in supply or new trading regulations, among an infinite number of other potential situations.
In what ways has DecisionNext’s AI platform revolutionized business decisions in the commodities market?
Our best-in-class platform has revolutionized the standard approach to pricing, supply and demand forecasting by providing our users with more than just a forecast. With our tool, they can quickly understand risk, uncertainty and can analyze complex decisions with a few clicks of a mouse. DecisionNext has a number of use cases across supply chains in both agriculture and mining. These include procurement and sales price optimization, business planning, geographic and product arbitrage, least cost formulation and risk management, among many others.
How does DecisionNext ensure the accuracy and reliability of its AI-forecast models for commodities trading?
We ensure the accuracy and reliability of our AI-forecast models through intensive backtesting. DecisionNext has built a rigorous system that is able to rapidly test thousands of model structures and provide the user with a full understanding of how accurate models have been. This can be done in an easy-to-understand way that also allows us to use that accuracy to predict uncertainty in the future as well.
Could you share an example or case study of how DecisionNext has helped a company navigate market volatility using your AI tools?
With DecisionNext, a large iron ore producer increased its profits by an average 6-8% on spot sales. Our solution helped them optimize pricing strategy and reduce the time required to make key decisions around geographic arbitrage. Similarly, we’re able to help cattle producers make the same decision on where and when to sell the beef coming from their carcasses.
In both cases, DecisionNext provided an accurate and defensible short- and long-term forecast to optimize sales planning strategy. Our visualization tools enabled the producers to rapidly assess multiple sales strategies side by side to best mitigate risk, streamline decision-making, and more effectively increase margins.
Without DecisionNext, companies are forced to rely on historical averages, futures markets (if available), and experience to price goods. Although effective in the past, with our increasingly volatile commodities markets, companies are leaving millions of dollars on the table.
Can you discuss the significance of having interactive forecasting models for users, and how does DecisionNext ensure these models are user-friendly?
The old, outdated “black box” model of forecasting doesn’t tell people why the forecast is what it is. It also can’t help with how to translate the forecast into actionable decisions. So in this scenario, users may not use even a perfect forecast and go back to old methods.
DecisionNext helps its customers gain a better understanding of both market risk and business risk and why the two should be interconnected when it comes to forecasting. DecisionNext provides complete visibility into data sources and model structures along with strategic clarity and direction.
All of this is delivered through a user-friendly dashboard, designed for ongoing engagement.
In what ways has the pandemic and recent geopolitical events influenced the development and use of AI in commodities trading at DecisionNext?
COVID-19 upended the global meat value chain, and one customer that was particularly impacted by the crisis comes to mind. With large quantities of frozen food destined for soon-to-be-dormant foodservice channels, the customer utilized DecisionNext analytics to rapidly and optimally liquidate inventory as lockdowns spread across the US and also plan how and when to rebuild said inventories.
Using the DecisionNext platform, the customer built out and compared four complex sales and procurement alternatives to see the expected market outcomes and compare risks. They were able to successfully liquidate excess inventory across multiple cuts, and these transactions provided a 5X return against the DecisionNext software investment in a single month.
What future advancements in AI and machine learning do you foresee impacting the commodities market, and how is DecisionNext preparing for them?
DecisionNext is at the forefront of the effort to leverage AI and machine learning to make commodities markets more efficient, profitable, and sustainable. As the world continues to grapple with massive challenges like climate change and political instability, intelligent technology will be an increasingly important component in how we successfully navigate them. We are honored to be trusted by our customers and partners to provide a platform to help make that happen.
Thank you for the great interview, readers who wish to learn more should visit DecisionNext.
