Bob Pierce, PhD is co-founder and Chief Science Officer at DecisionNext. His work has introduced superior mathematical evaluation to thoroughly new markets and industries, enhancing the way in which firms interact in strategic choice 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 variety 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 an information analytics and forecasting firm based in 2015, specializing in AI-driven value and provide forecasting. The corporate was created to handle the constraints of conventional “black field” forecasting fashions, which frequently lacked transparency and actionable insights. By integrating AI and machine studying, DecisionNext gives companies with higher visibility into the components influencing their forecasts, serving to them make knowledgeable selections based mostly 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 variety of expertise in our earlier firms delivering optimization and forecasting options to retailers and commodity processors. Two major learnings from that have had been:
- Customers must consider that they perceive the place forecasts and options are coming from; and
- Customers have a really exhausting time separating what they assume will occur from the probability that it’ll really come to move.
These two ideas have deep origins in human cognition in addition to implications in how one can create software program to resolve issues. It’s well-known {that a} human thoughts will not be good at calculating possibilities. As a Physicist, I realized to create conceptual frameworks to interact with uncertainty and construct distributed computational platforms to discover it. That is the technical underpinning of our options to assist our prospects make higher selections within the face of uncertainty, which means that they can not know the way markets will evolve however nonetheless need to determine what to do now so as to maximize income sooner or later.
How has your transition to the position 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 day after day engineering tasks which are higher dealt with by others. We all the time have an extended listing of options and concepts to make the answer higher, and this position provides 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 an interesting 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 complex subject. But, the mathematics is significantly much less properly developed than the arguably easier world of shares. AI and machine studying assist us work via this complexity by discovering extra environment friendly methods to mannequin in addition to serving to our customers navigate advanced selections.
How does DecisionNext stability the usage of machine studying fashions with the human experience crucial to commodities decision-making?
Machine studying as a subject is consistently enhancing, nevertheless it nonetheless struggles with context and causality. Our expertise is that there are points of modeling the place human experience and supervision are nonetheless crucial to generate strong, parsimonious fashions. Our prospects typically have a look at markets via the lens of provide and demand fundamentals. If the fashions don’t mirror that perception (and unsupervised fashions typically don’t), then our prospects will typically not develop belief. Crucially, customers won’t combine untrusted fashions into their day after day choice processes. So even a demonstrably correct machine studying mannequin that defies instinct will turn into shelfware extra probably than not.
Human experience from the shopper can also be crucial as a result of it’s a truism that noticed information isn’t full, so fashions symbolize a information and shouldn’t be mistaken for actuality. Customers immersed in markets have necessary information and perception that isn’t obtainable as enter to the fashions. DecisionNext AI permits the consumer to reinforce mannequin inputs and create market situations. This builds flexibility into forecasts and choice suggestions and enhances consumer 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 modifications?
The appearance of purposeful LLMs is a breakthrough that can take a very long time to completely percolate into the material of enterprise selections. The tempo of enhancements within the fashions themselves continues to be breathtaking and troublesome to maintain up with. Nonetheless, I feel we’re solely initially of the street to understanding the most effective methods to combine AI into enterprise processes. Many of the issues we encounter may be framed as optimization issues with difficult constraints. The constraints inside enterprise processes are sometimes undocumented and contextually reasonably than rigorously enforced. I feel this space is a large 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 resolve these issues and supply easy accessibility to crucial info and forecasts. DecisionNext is growing LLM based mostly brokers to make the system simpler to make use of and carry out difficult duties inside the system on the consumer’s path. It will permit 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. Fairly I’ve been uncovered to many alternative disciplines and may draw on all of them. Second, excessive efficiency distributed computing has been a via line in all of the work I’ve completed. Lots of the strategies I used to cobble collectively advert hoc compute clusters as a grad pupil 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 evokes a philosophical curiosity. As a grad pupil, 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 crucial, and the way do you assume it impacts consumer belief and adoption?
A prototypical commodities dealer (on or off an alternate) is somebody who realized the fundamentals of their trade in manufacturing however has a talent for betting in a risky 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. In contrast to Wall Road quants, commodity merchants typically don’t have a proper background in likelihood and statistics. In an effort to achieve belief, now we have to current a system that’s intuitive, quick, and touches their cognitive bias that provide and demand are the first drivers of enormous market actions. So, we take a “white field” strategy the place every part is clear. Normally there’s a “courting” part the place they appear deep below the hood and we information them via the reasoning of the system. As soon as belief is established, customers don’t typically spend the time to go deep, however will return periodically to interrogate necessary or stunning forecasts.
How does DecisionNext’s strategy to risk-aware forecasting assist firms not simply react to market situations however proactively form their methods?
Commodities buying and selling isn’t restricted to exchanges. Most firms solely have restricted entry to futures to hedge their threat. A processor may purchase a listed commodity as a uncooked materials (cattle, maybe), however their output can also be a risky commodity (beef) that usually 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 not safely function fully within the spot market, they usually need to contract ahead to purchase supplies and promote outputs. DecisionNext permits the processor to forecast all the ecosystem of provide, demand, and value variables, after which to simulate how enterprise selections are affected by the complete vary of market outcomes. Paper buying and selling could also be a part of the technique, however most necessary is to know materials and gross sales commitments and processing selections 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 reworked our understanding of how cognition impacts enterprise selections. AI is remodeling how we will use software program instruments to help human cognition and make higher selections. The effectivity positive factors that might be realized by AI enabled automation have been a lot mentioned and might be economically necessary. Commodity firms function with razor skinny margins and excessive labor prices, so that they presumably will profit vastly from automation. Past that, I consider there’s a hidden inefficiency in the way in which that almost all enterprise selections are made by instinct and guidelines of thumb. Choices are sometimes based mostly on restricted and opaque info and easy spreadsheet instruments. To me, probably 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 selections based mostly on clear information and open reasoning.
Thanks for the good interview, readers who want to study extra ought to go to DecisionNext.