Rohit Aggarwal is Chief Operating Officer at DecisionNext, a number one AI platform that allows corporations to optimize the buying or selling of commodities at the very best possible time and price. He leverages a robust background in supply chain and product management in addition to 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 each Google and Amazon.
You’ve worked at Amazon and more recently at Google. What were a few of your key highlights from these experiences?
At Amazon, I had the chance to administer a various team of 250 cross-functional employees with a purpose to launch best-in-class operations facilities. I also supported the implementation of innovations corresponding to 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 constructing applications from scratch to administer a brand new variety of achievement process, amongst other latest offerings.
Are you able to explain how DecisionNext leverages AI and machine learning to enhance commodity price and provide forecasting?
DecisionNext uses artificial intelligence and machine learning to eat 1000’s of information sets and find historical and current relationships between key aspects. 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 higher, make smarter decisions, reduce risk, and increase profits across global supply chains. We’re also working on using Large Language Models (LLMs) to simplify complex global decisions with risk-aware solutions.
What are the important thing advantages of using DecisionNext’s AI platform in comparison with traditional forecasting methods?
Global commodity product buyers and sellers often resort to rules of thumb and spreadsheets to simplify a fancy system value billions of dollars in transactions. This leaves significant money on the table. These spreadsheets have worked wonders and supported a whole lot of companies. Nonetheless, as workforce dynamics change and global markets turn into more unpredictable, they have gotten 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 material experts which were in a selected space or industry for 30 years or more. And as latest generations are available, it’s extremely essential to retain all of that have in a usable way. DecisionNext helps with that by constructing comprehensive libraries of choices, 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 technique 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 the way is that this data integrated and utilized?
Dynamic and up-to-date data is incredibly essential relating to constructing best-in-class models. That said, the speed and complexity with which the info may be processed and modeled isn’t the one factor. For instance, how does a model know the load of probably the most recent data point (say a shock within the system) and that it must treat it in a different way? Our users can interact with the models through patented technology to input their opinions and construct what-if evaluation to make use of data that the model or system simply cannot know yet. This enables our customers to realize latest insights that will otherwise not be possible. Also they are capable of higher understand the impact of world shifts in supply or latest trading regulations, amongst an infinite variety of other potential situations.
In what ways has DecisionNext’s AI platform revolutionized business decisions within the commodities market?
Our greatest-in-class platform has revolutionized the usual approach to pricing, supply and demand forecasting by providing our users with greater than only a forecast. With our tool, they’ll quickly understand risk, uncertainty and may analyze complex decisions with a number of clicks of a mouse. DecisionNext has quite a few use cases across supply chains in each agriculture and mining. These include procurement and sales price optimization, business planning, geographic and product arbitrage, least cost formulation and risk management, amongst many others.
How does DecisionNext make sure the accuracy and reliability of its AI-forecast models for commodities trading?
We make sure the accuracy and reliability of our AI-forecast models through intensive backtesting. DecisionNext has built a rigorous system that’s capable of rapidly test 1000’s of model structures and supply the user with a full understanding of how accurate models have been. This may be done in an easy-to-understand way that also allows us to make use of that accuracy to predict uncertainty in the long run as well.
Could you share an example or case study of how DecisionNext has helped an organization navigate market volatility using your AI tools?
With DecisionNext, a big iron ore producer increased its profits by a median 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 capable of help cattle producers make the identical decision on where and when to sell the meat coming from their carcasses.
In each 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, corporations are forced to depend on historical averages, futures markets (if available), and experience to cost goods. Although effective up to now, with our increasingly volatile commodities markets, corporations are leaving thousands and thousands of dollars on the table.
Are you able to discuss the importance of getting interactive forecasting models for users, and the way 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’s. It can also’t help with the right way to translate the forecast into actionable decisions. So on this scenario, users may not use even an ideal forecast and return to old methods.
DecisionNext helps its customers gain a greater understanding of each market risk and business risk and why the 2 must be interconnected relating to forecasting. DecisionNext provides complete visibility into data sources and model structures together with strategic clarity and direction.
All of that is delivered through a user-friendly dashboard, designed for ongoing engagement.
In what ways has the pandemic and up to date geopolitical events influenced the event and use of AI in commodities trading at DecisionNext?
COVID-19 upended the worldwide meat value chain, and one customer that was particularly impacted by the crisis involves mind. With large quantities of frozen food destined for soon-to-be-dormant foodservice channels, the client utilized DecisionNext analytics to rapidly and optimally liquidate inventory as lockdowns spread across the US and in addition plan how and when to rebuild said inventories.
Using the DecisionNext platform, the client built out and compared 4 complex sales and procurement alternatives to see the expected market outcomes and compare risks. They were capable of 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 the way is DecisionNext preparing for them?
DecisionNext is on the forefront of the trouble to leverage AI and machine learning to make commodities markets more efficient, profitable, and sustainable. Because the world continues to grapple with massive challenges like climate change and political instability, intelligent technology shall be an increasingly essential component in how we successfully navigate them. We’re honored to be trusted by our customers and partners to offer a platform to assist make that occur.
