Bob Pierce, PhD is co-founder and Chief Science Officer at DecisionNext. His work has brought advanced mathematical evaluation to thoroughly latest markets and industries, improving the best way firms engage in strategic decision making. Prior to DecisionNext, Bob was Chief Scientist at SignalDemand, where he guided the science behind its solutions for manufacturers. Bob has held senior research and development roles at Khimetrics (now SAP) and ConceptLabs, in addition to academic posts with the National Academy of Sciences, Penn State University, and UC Berkeley. His work spans a spread of industries including commodities and manufacturing and he’s made contributions to the fields of econometrics, oceanography, mathematics, and nonlinear dynamics. He holds quite a few patents and is the writer of several peer reviewed papers. Bob holds a PhD in theoretical physics from UC Berkeley.
DecisionNext is a knowledge analytics and forecasting company founded in 2015, specializing in AI-driven price and provide forecasting. The corporate was created to handle the restrictions of traditional “black box” forecasting models, which frequently lacked transparency and actionable insights. By integrating AI and machine learning, DecisionNext provides businesses with greater visibility into the aspects influencing their forecasts, helping them make informed decisions based on each market and business risk. Their platform is designed to enhance forecasting accuracy across the availability chain, enabling customers to maneuver beyond intuition-based decision-making.
What was the unique idea or inspiration behind founding DecisionNext, and the way did your background in theoretical physics and roles in various industries shape this vision?
My co-founder Mike Neal and I actually have amassed a number of experience in our previous firms delivering optimization and forecasting solutions to retailers and commodity processors. Two primary learnings from that have were:
- Users have to imagine that they understand where forecasts and solutions are coming from; and
- Users have a really hard time separating what they think will occur from the likelihood that it would actually come to pass.
These two concepts have deep origins in human cognition in addition to implications in the right way to create software to unravel problems. It’s well-known that a human mind shouldn’t be good at calculating probabilities. As a Physicist, I learned to create conceptual frameworks to have interaction with uncertainty and construct distributed computational platforms to explore it. That is the technical underpinning of our solutions to assist our customers make higher decisions within the face of uncertainty, meaning that they can not understand how markets will evolve but still have to make your mind up what to do now with the intention to maximize profits in the longer term.
How has your transition to the role of Chief Science Officer influenced your day-to-day focus and long-term vision for DecisionNext?
The transition to CSO has involved a refocusing on how the product should deliver value to our customers. In the method, I actually have released some each day engineering responsibilities which might be higher handled by others. We all the time have a protracted list of features and concepts to make the answer higher, and this role gives me more time to research latest and progressive approaches.
What unique challenges do commodities markets present that make them particularly suited—or resistant—to the adoption of AI and machine learning solutions?
Modeling commodity markets presents a captivating mixture of structural and stochastic properties. Combining this with the uncountable number of how that individuals write contracts for physical and paper trading and utilize materials in production ends in an incredibly wealthy and complex field. Yet, the maths is considerably less well developed than the arguably simpler world of stocks. AI and machine learning help us work through this complexity by finding more efficient ways to model in addition to helping our users navigate complex decisions.
How does DecisionNext balance using machine learning models with the human expertise critical to commodities decision-making?
Machine learning as a field is always improving, nevertheless it still struggles with context and causality. Our experience is that there are points of modeling where human expertise and supervision are still critical to generate robust, parsimonious models. Our customers generally take a look at markets through the lens of supply and demand fundamentals. If the models don’t reflect that belief (and unsupervised models often don’t), then our customers will generally not develop trust. Crucially, users is not going to integrate untrusted models into their each day decision processes. So even a demonstrably accurate machine learning model that defies intuition will turn into shelfware more likely than not.
Human expertise from the shopper can be critical since it is a truism that observed data isn’t complete, so models represent a guide and mustn’t be mistaken for reality. Users immersed in markets have necessary knowledge and insight that shouldn’t be available as input to the models. DecisionNext AI allows the user to enhance model inputs and create market scenarios. This builds flexibility into forecasts and decision recommendations and enhances user confidence and interaction with the system.
Are there specific breakthroughs in AI or data science that you simply imagine will revolutionize commodity forecasting in the approaching years, and the way is DecisionNext positioning itself for those changes?
The arrival of functional LLMs is a breakthrough that can take a protracted time to totally percolate into the material of business decisions. The pace of improvements within the models themselves continues to be breathtaking and difficult to maintain up with. Nonetheless, I believe we’re only firstly of the road to understanding the most effective ways to integrate AI into business processes. A lot of the problems we encounter may be framed as optimization problems with complicated constraints. The constraints inside business processes are sometimes undocumented and contextually reasonably than rigorously enforced. I believe this area is a large untapped opportunity for AI to each discover implicit constraints in historical data, in addition to construct and solve the suitable contextual optimization problems.
DecisionNext is a trusted platform to unravel these problems and supply easy accessibility to critical information and forecasts. DecisionNext is developing LLM based agents to make the system easier to make use of and perform complicated tasks inside the system on the user’s direction. This can allow us to scale and add value in additional business processes and industries.
Your work spans fields as diverse as oceanography, econometrics, and nonlinear dynamics. How do these interdisciplinary insights contribute to solving problems in commodities forecasting?
My diverse background informs my work in 3 ways. First, the breadth of my work has prohibited me from going too deep into one specific area of Math. Somewhat I’ve been exposed to many alternative disciplines and might draw on all of them. Second, high performance distributed computing has been a through line in all of the work I’ve done. Most of the techniques I used to cobble together ad hoc compute clusters as a grad student in Physics are utilized in mainstream frameworks now, so all of it feels familiar to me even when the pace of innovation is rapid. Last, working on all these different problems inspires a philosophical curiosity. As a grad student, I never contemplated working in Economics but here I’m. I don’t know what I’ll be working on in 5 years, but I do know I’ll find it intriguing.
DecisionNext emphasizes breaking out of the ‘black box’ model of forecasting. Why is that this transparency so critical, and the way do you’re thinking that it impacts user trust and adoption?
A prototypical commodities trader (on or off an exchange) is someone who learned the fundamentals of their industry in production but has a skill for betting in a volatile market. In the event that they don’t have real world experience in the availability side of the business, they don’t earn the trust of executives and don’t get promoted as a trader. In the event that they don’t have some affinity for gambling, they stress out an excessive amount of in executing trades. Unlike Wall Street quants, commodity traders often don’t have a proper background in probability and statistics. With a view to gain trust, we now have to present a system that’s intuitive, fast, and touches their cognitive bias that offer and demand are the first drivers of enormous market movements. So, we take a “white box” approach where every thing is transparent. Normally there’s a “dating” phase where they give the impression of being deep under the hood and we guide them through the reasoning of the system. Once trust is established, users don’t often spend the time to go deep, but will return periodically to interrogate necessary or surprising forecasts.
How does DecisionNext’s approach to risk-aware forecasting help firms not only react to market conditions but proactively shape their strategies?
Commodities trading isn’t limited to exchanges. Most firms only have limited access to futures to hedge their risk. A processor might buy a listed commodity as a raw material (cattle, perhaps), but their output can be a volatile commodity (beef) that always has little price correlation with the inputs. Given the structural margin constraint that expensive facilities must operate near capability, processors are forced to have a strategic plan that appears out into the longer term. That’s, they can not safely operate entirely within the spot market, and so they must contract forward to purchase materials and sell outputs. DecisionNext allows the processor to forecast all the ecosystem of supply, demand, and price variables, after which to simulate how business decisions are affected by the total range of market outcomes. Paper trading could also be a component of the strategy, but most significant is to know material and sales commitments and processing decisions to make sure capability utilization. DecisionNext is tailor made for this.
As someone with a deep scientific background, what excites you most concerning the intersection of science and AI in transforming traditional industries like commodities?
Behavioral economics has transformed our understanding of how cognition affects business decisions. AI is transforming how we will use software tools to support human cognition and make higher decisions. The efficiency gains that can be realized by AI enabled automation have been much discussed and can be economically necessary. Commodity firms operate with razor thin margins and high labor costs, in order that they presumably will profit greatly from automation. Beyond that, I imagine there may be a hidden inefficiency in the best way that almost all business decisions are made by intuition and rules of thumb. Decisions are sometimes based on limited and opaque information and straightforward spreadsheet tools. To me, essentially the most exciting final result is for platforms like DecisionNext to assist transform the business process using AI and simulation to normalize context and risk aware decisions based on transparent data and open reasoning.
