Bob Pierce, PhD is co-founder and Chief Science Officer at DecisionNext. His work has introduced superior mathematical evaluation to completely new markets and industries, enhancing the way in which corporations have interaction in strategic resolution making. Previous to DecisionNext, Bob was Chief Scientist at SignalDemand, the place he guided the science behind its options for producers. Bob has held senior analysis and improvement roles at Khimetrics (now SAP) and ConceptLabs, in addition to tutorial posts with the Nationwide Academy of Sciences, Penn State College, and UC Berkeley. His work spans a spread of industries together with commodities and manufacturing and he’s made contributions to the fields of econometrics, oceanography, arithmetic, and nonlinear dynamics. He holds quite a few patents and is the creator of a number of peer reviewed papers. Bob holds a PhD in theoretical physics from UC Berkeley.
DecisionNext is a knowledge analytics and forecasting firm based in 2015, specializing in AI-driven value and provide forecasting. The corporate was created to handle the restrictions of conventional “black field” forecasting fashions, which frequently lacked transparency and actionable insights. By integrating AI and machine studying, DecisionNext offers companies with higher visibility into the components influencing their forecasts, serving to them make knowledgeable choices primarily based on each market and enterprise threat. Their platform is designed to enhance forecasting accuracy throughout the availability chain, enabling prospects to maneuver past intuition-based decision-making.
What was the unique concept or inspiration behind founding DecisionNext, and the way did your background in theoretical physics and roles in numerous industries form this imaginative and prescient?
My co-founder Mike Neal and I’ve amassed a number of expertise in our earlier corporations delivering optimization and forecasting options to retailers and commodity processors. Two major learnings from that have have been:
- Customers must consider that they perceive the place forecasts and options are coming from; and
- Customers have a really onerous time separating what they assume will occur from the probability that it’ll truly come to go.
These two ideas have deep origins in human cognition in addition to implications in the best way to create software program to unravel issues. It’s well-known {that a} human thoughts just isn’t good at calculating chances. As a Physicist, I realized to create conceptual frameworks to have interaction with uncertainty and construct distributed computational platforms to discover it. That is the technical underpinning of our options to assist our prospects make higher choices within the face of uncertainty, that means that they can’t know the way markets will evolve however nonetheless need to determine what to do now in an effort to maximize earnings sooner or later.
How has your transition to the function of Chief Science Officer influenced your day-to-day focus and long-term imaginative and prescient for DecisionNext?
The transition to CSO has concerned a refocusing on how the product ought to ship worth to our prospects. Within the course of, I’ve launched some everyday engineering tasks which are higher dealt with by others. We at all times have a protracted checklist of options and concepts to make the answer higher, and this function offers me extra time to analysis new and progressive approaches.
What distinctive challenges do commodities markets current that make them significantly suited—or resistant—to the adoption of AI and machine studying options?
Modeling commodity markets presents a captivating mixture of structural and stochastic properties. Combining this with the uncountable variety of ways in which individuals write contracts for bodily and paper buying and selling and make the most of supplies in manufacturing leads to an extremely wealthy and sophisticated discipline. But, the mathematics is significantly much less nicely developed than the arguably less complicated world of shares. AI and machine studying assist us work by means of this complexity by discovering extra environment friendly methods to mannequin in addition to serving to our customers navigate complicated choices.
How does DecisionNext stability the usage of machine studying fashions with the human experience essential to commodities decision-making?
Machine studying as a discipline is continually enhancing, nevertheless it nonetheless struggles with context and causality. Our expertise is that there are features of modeling the place human experience and supervision are nonetheless essential to generate sturdy, parsimonious fashions. Our prospects typically take a look at markets by means of the lens of provide and demand fundamentals. If the fashions don’t mirror that perception (and unsupervised fashions usually don’t), then our prospects will typically not develop belief. Crucially, customers is not going to combine untrusted fashions into their everyday resolution processes. So even a demonstrably correct machine studying mannequin that defies instinct will grow to be shelfware extra doubtless than not.
Human experience from the client can be essential as a result of it’s a truism that noticed information is rarely full, so fashions characterize a information and shouldn’t be mistaken for actuality. Customers immersed in markets have vital information and perception that isn’t obtainable as enter to the fashions. DecisionNext AI permits the person to enhance mannequin inputs and create market situations. This builds flexibility into forecasts and resolution suggestions and enhances person confidence and interplay with the system.
Are there particular breakthroughs in AI or information science that you just consider will revolutionize commodity forecasting within the coming years, and the way is DecisionNext positioning itself for these adjustments?
The appearance of practical LLMs is a breakthrough that can take a very long time to totally percolate into the material of enterprise choices. The tempo of enhancements within the fashions themselves remains to be breathtaking and tough to maintain up with. Nonetheless, I believe we’re solely at first of the highway to understanding the very best methods to combine AI into enterprise processes. A lot of the issues we encounter may be framed as optimization issues with sophisticated constraints. The constraints inside enterprise processes are sometimes undocumented and contextually fairly than rigorously enforced. I believe this space is a big untapped alternative for AI to each uncover implicit constraints in historic information, in addition to construct and clear up the suitable contextual optimization issues.
DecisionNext is a trusted platform to unravel these issues and supply easy accessibility to essential info and forecasts. DecisionNext is creating LLM primarily based brokers to make the system simpler to make use of and carry out sophisticated duties throughout the system on the person’s course. This can enable us to scale and add worth in additional enterprise processes and industries.
Your work spans fields as numerous as oceanography, econometrics, and nonlinear dynamics. How do these interdisciplinary insights contribute to fixing issues in commodities forecasting?
My numerous background informs my work in 3 ways. First, the breadth of my work has prohibited me from going too deep into one particular space of Math. Somewhat I’ve been uncovered to many alternative disciplines and might draw on all of them. Second, excessive efficiency distributed computing has been a by means of line in all of the work I’ve finished. Lots of the strategies I used to cobble collectively advert hoc compute clusters as a grad scholar in Physics are utilized in mainstream frameworks now, so all of it feels acquainted to me even when the tempo of innovation is speedy. Final, engaged on all these completely different issues conjures up a philosophical curiosity. As a grad scholar, I by no means contemplated working in Economics however right here I’m. I don’t know what I’ll be engaged on in 5 years, however I do know I’ll discover it intriguing.
DecisionNext emphasizes breaking out of the ‘black field’ mannequin of forecasting. Why is that this transparency so essential, and the way do you assume it impacts person belief and adoption?
A prototypical commodities dealer (on or off an change) is somebody who realized the fundamentals of their trade in manufacturing however has a ability for betting in a unstable market. In the event that they don’t have actual world expertise within the provide facet of the enterprise, they don’t earn the belief of executives and don’t get promoted as a dealer. In the event that they don’t have some affinity for playing, they stress out an excessive amount of in executing trades. Not like Wall Road quants, commodity merchants usually don’t have a proper background in likelihood and statistics. With a purpose to acquire belief, we have now to current a system that’s intuitive, quick, and touches their cognitive bias that provide and demand are the first drivers of huge market actions. So, we take a “white field” strategy the place all the things is clear. Often there’s a “relationship” part the place they give the impression of being deep below the hood and we information them by means of the reasoning of the system. As soon as belief is established, customers don’t usually spend the time to go deep, however will return periodically to interrogate vital or stunning forecasts.
How does DecisionNext’s strategy to risk-aware forecasting assist corporations not simply react to market situations however proactively form their methods?
Commodities buying and selling isn’t restricted to exchanges. Most corporations solely have restricted entry to futures to hedge their threat. A processor would possibly purchase a listed commodity as a uncooked materials (cattle, maybe), however their output can be a unstable commodity (beef) that always has little value correlation with the inputs. Given the structural margin constraint that costly amenities need to function close to capability, processors are compelled to have a strategic plan that appears out into the long run. That’s, they can’t safely function fully within the spot market, and so they need to contract ahead to purchase supplies and promote outputs. DecisionNext permits the processor to forecast your entire ecosystem of provide, demand, and value variables, after which to simulate how enterprise choices are affected by the total vary of market outcomes. Paper buying and selling could also be a element of the technique, however most vital is to know materials and gross sales commitments and processing choices to make sure capability utilization. DecisionNext is tailor made for this.
As somebody with a deep scientific background, what excites you most in regards to the intersection of science and AI in remodeling conventional industries like commodities?
Behavioral economics has remodeled our understanding of how cognition impacts enterprise choices. AI is remodeling how we will use software program instruments to assist human cognition and make higher choices. The effectivity positive aspects that shall be realized by AI enabled automation have been a lot mentioned and shall be economically vital. Commodity corporations function with razor skinny margins and excessive labor prices, in order that they presumably will profit tremendously from automation. Past that, I consider there’s a hidden inefficiency in the way in which that the majority enterprise choices are made by instinct and guidelines of thumb. Selections are sometimes primarily based on restricted and opaque info and easy spreadsheet instruments. To me, essentially the most thrilling end result is for platforms like DecisionNext to assist remodel the enterprise course of utilizing AI and simulation to normalize context and threat conscious choices primarily based on clear information and open reasoning.
Thanks for the nice interview, readers who want to be taught extra ought to go to DecisionNext.
