Climate change is making California’s fire seasons more severe, but the conditions that lead to any single
fire remain consistent: dry weather, overgrown brush, wind speed, and wind direction. Private companies
and public agencies are racing to develop technology to monitor these conditions, in the hopes of
understanding how wildfires spread—and predicting them before they happen.
There’s demand for wildfire forecasting from both the public sector and commercial interests. As of now
much of the innovation is coming from technology companies looking to serve insurers grappling with
increasingly costly and erratic blazes—a trend that could determine not only how predictive software is
used but how it’s designed. These dynamics are on display with Kettle, a startup that’s created a predictive
system by using artificial intelligence to help it design reinsurance policies that protect insurance companies
against wildfire risk.
Reinsurers have traditionally used a technique called stochastic modeling, which analyzes historical data to
determine the likelihood of random events. That doesn’t work when the changing climate system behaves
in ways that humans have not yet seen, Engler says.
Kettle is looking to analyze enormous amounts of geospatial imagery to find emerging patterns. It pulls in
data from satellites and weather data maps to predict the areas in California most at risk for wildfire.
Many insurers have responded to a trend of record-breaking fire seasons by raising rates or refusing to cover
some areas. The problem has gotten bad enough that California’s insurance commissioner has issued
moratoriums on canceling or refusing to renew insurance in some ZIP codes hit by a fire emergency for
one year after the area burns.
The predictive tech is tied to a novel business model for reinsurance. Typically, insurers work with
multiple reinsurance companies to cover their entire portfolio. Kettle models the risk of an insurer’s entire
portfolio, then offers to sell fire-specific policies that cover a fraction of those homes in the areas where
Kettle’s model has the most certainty about burn patterns. It says 26 carriers have asked it to model their
risk.
The company recently used historical data to see how well its model would have performed during the 2020
fire season. It examined the 14 largest wildfires in California that year and found that 11 of them occurred
in areas Kettle’s software labeled as top 10% most likely areas to experience wildfire in 2020; all 14 fires
were in the top 20%.
The company’s models account for long-term uncertainty by simulating millions of theoretical scenarios.
“You can’t predict every single gust of wind—every single, you know, plant leaf and what moisture density
it is,” Engler says. But there are “emergent patterns that we see in these larger conflagrations with
everything going wrong that you can start to hone into, and those are the areas that we’re going to find to
be most dangerous over time.”
Other entities with interests in California’s wildfires have their own spins on predictive technology. State
Farm Insurance, MetLife Inc., and other insurance companies use software from startups such as Cape
Analytics LLC and Zesty.AI that also use AI and decades of satellite imagery to understand microlevel risk
factors. This level of detail could allow them to examine features of individual homes—like scruffy
vegetation in the combustible zone within 10 feet of a house—and price policies more in line with their
risk. The U.S. Forest Service is incorporating AI models into its wildfire-fighting strategy, piloting RADRFire, a product that uses infrared technology and satellite imagery to help firefighters track fires through
smoke and haze.
OM Assignment 3
Having the kind of sophisticated rendering of risk that Kettle is developing could also help firefighters and
local officials create shorter-term evacuation plans, determine where to build fire breaks, and better allocate
resources. Engler and Manning say they envision sharing their findings with utility companies, firefighters,
and the Forest Service. But for now they’re solely focused on insurers, a decision that influences the shape
of their product. For instance, Kettle uses its new predictions only twice a year because that’s how often
reinsurers sell new policies, even though it gathers new data constantly.
(Based on the Article from Bloomberg Businesweek “A wildfire-Predicting Startup Tries to Help Insurers
Cope With Climate Chang
Q1. How the Kettle company utilizes time-series data (or historical data) and why the traditional model
doesn’t fit into the current wildfire predictions in California? Given the situation, do you have any
suggestion? I expect you should write at least 500 words, but no more 600 words as well. (12 pts)
OM Assignment 3
Q2. Harlen Industries ‘s actual and forecast demand are following:
Week Forecast
Demand
Actual
Demand
1 140 137
2 140 133
3 140 150
4 140 160
5 140 180
6 150 170
7 150 185
8 150 205
First, please compute MAD. After that using RSFE, compute the tracking signal for week 8. Based on
your tracking signal, comment on Harlen’s forecasting method on week 8. Discuss whether Harlen
Industries’ forecasting method provides good predictions. (8 pts)
Sample Solution