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 interact in strategic determination 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 educational 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 worth and provide forecasting. The corporate was created to deal with the constraints of conventional “black field” forecasting fashions, which regularly lacked transparency and actionable insights. By integrating AI and machine studying, DecisionNext gives companies with higher visibility into the elements influencing their forecasts, serving to them make knowledgeable choices based mostly on each market and enterprise danger. 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 thought or inspiration behind founding DecisionNext, and the way did your background in theoretical physics and roles in varied industries form this imaginative and prescient?
My co-founder Mike Neal and I’ve amassed loads of expertise in our earlier corporations delivering optimization and forecasting options to retailers and commodity processors. Two main learnings from that have have been:
- Customers have to consider that they perceive the place forecasts and options are coming from; and
- Customers have a really arduous time separating what they assume will occur from the probability that it’s going to really come to cross.
These two ideas have deep origins in human cognition in addition to implications in learn how to create software program to resolve issues. It’s well-known {that a} human thoughts is just not good at calculating chances. 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 choices within the face of uncertainty, which means that they can’t know the way markets will evolve however nonetheless should determine what to do now with a purpose 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 each day engineering obligations which are higher dealt with by others. We at all times have an extended listing of options and concepts to make the answer higher, and this position offers me extra time to analysis new and modern approaches.
What distinctive challenges do commodities markets current that make them notably 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 discipline. But, the maths is significantly much less nicely developed than the arguably less complicated world of shares. AI and machine studying assist us work by way of this complexity by discovering extra environment friendly methods to mannequin in addition to serving to our customers navigate complicated choices.
How does DecisionNext steadiness the usage of machine studying fashions with the human experience crucial to commodities decision-making?
Machine studying as a discipline is continually enhancing, however 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 usually have a look at markets by way of the lens of provide and demand fundamentals. If the fashions don’t replicate that perception (and unsupervised fashions usually don’t), then our prospects will usually not develop belief. Crucially, customers won’t combine untrusted fashions into their each day determination processes. So even a demonstrably correct machine studying mannequin that defies instinct will turn into shelfware extra doubtless 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 data and perception that isn’t accessible as enter to the fashions. DecisionNext AI permits the consumer to reinforce mannequin inputs and create market eventualities. This builds flexibility into forecasts and determination suggestions and enhances consumer confidence and interplay with the system.
Are there particular breakthroughs in AI or information science that you simply consider will revolutionize commodity forecasting within the coming years, and the way is DecisionNext positioning itself for these modifications?
The arrival of useful LLMs is a breakthrough that may take a very long time to totally percolate into the material of enterprise choices. The tempo of enhancements within the fashions themselves continues to be breathtaking and troublesome to maintain up with. Nevertheless, I feel we’re solely initially of the street to understanding one of the best 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 resolve the suitable contextual optimization issues.
DecisionNext is a trusted platform to resolve these issues and supply quick access to crucial info and forecasts. DecisionNext is creating LLM based mostly brokers to make the system simpler to make use of and carry out difficult duties throughout the system on the consumer’s course. This can 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. Somewhat I’ve been uncovered to many various disciplines and may draw on all of them. Second, excessive efficiency distributed computing has been a by way 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 totally 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 crucial, and the way do you assume it impacts consumer 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 aspect 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. As a way to achieve belief, we’ve to current a system that’s intuitive, quick, and touches their cognitive bias that offer and demand are the first drivers of enormous market actions. So, we take a “white field” method the place all the things is clear. Often there’s a “courting” section the place they give the impression of being deep below the hood and we information them by way 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 necessary or stunning forecasts.
How does DecisionNext’s method to risk-aware forecasting assist corporations not simply react to market circumstances 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 danger. A processor would possibly purchase a listed commodity as a uncooked materials (cattle, maybe), however their output can also be a unstable commodity (beef) that usually has little worth correlation with the inputs. Given the structural margin constraint that costly services should 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 completely within the spot market, they usually should contract ahead to purchase supplies and promote outputs. DecisionNext permits the processor to forecast your entire ecosystem of provide, demand, and worth 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 necessary is to grasp 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 reworking conventional industries like commodities?
Behavioral economics has reworked our understanding of how cognition impacts enterprise choices. AI is reworking how we are able to use software program instruments to help human cognition and make higher choices. The effectivity positive factors that will probably be realized by AI enabled automation have been a lot mentioned and will probably be economically necessary. 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 almost all enterprise choices 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 consequence is for platforms like DecisionNext to assist rework the enterprise course of utilizing AI and simulation to normalize context and danger conscious choices based mostly on clear information and open reasoning.
Thanks for the nice interview, readers who want to study extra ought to go to DecisionNext.
