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PREDICTIVE MODELING
Supply chain modeling presents a massive combinatorial problem. Utilizing numerous constraints and controlling for issues such as multicollinearity and bias/variance tradeoff, our genetic algorithms (GAs) "evolve" models over many generations to closely approximate desired characteristics of a particular target company or entity.
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For 70% of the sectors in the S&P 500, a company is its supply chain: customers are revenues, and suppliers are costs. Thus we call supply chain analysis "the fundamentals behind the fundamentals." Ignoring supply chain risk is to ignore fundamental business risk.
PREDICTIVE MODELING
We employ different techniques on different data sets. The example above represents four years of quarterly forecasts for Taiwan public companies, utilizing monthly data as reported to the Taiwan Stock Exchange. Over the past 19 quarters, we have generated over 6,000 unique forecasts, with a ~95% accuracy rate in successfully predicting upside and downside surprises relative to Street expectations. Viewed as independent Bernoulli trials, the probability of achieving these results over this time period is quite small.
BIO
Supply chain analysis has been my passion for 20 years. In 2005, I left my position as a research analyst at Citi to found Connexiti, a company which quickly became the market leader in supply chain data, research, and analytics. Connexiti was purchased by Bloomberg, L.P. in 2010, and lives today as the SPLC function on the Bloomberg terminal, where it is used by thousands of investors, industry experts, and academics daily. I founded RAD Advisors in 2012 to provide executive-level supply chain consulting to the buy side and sell side investment community, FinTech companies, and industry.
WELCOME TO RAD ADVISORS
See Changing Business Conditions In The Supply Chain.
RAD Advisors quantifies supply chain risk. Our models integrate thousands of supply chain relationships to uncover changing business conditions before Wall Street does.