FIN 440 Week 1 Risk and Measuring Uncertainty Example

Four Ways a Cold-Storage Warehouse Can Lose Money: Frequency, Severity, Expected Loss and Spread for Forklift Injuries, Power Failures, Ammonia Leaks and Fire

[Student Name]

University of Phoenix

FIN/440: Risk Management and Insurance Planning

Week 1 Assignment

[Instructor Name]

[Date]

Peach State Cold Storage, its probabilities and its loss figures are composites written for a model paper; risk concepts and research come from the sources listed.

Peach State Cold Storage, a composite refrigerated warehouse near the Port of Savannah, holds about 40 million pounds of frozen meat, seafood and vegetables for grocery chains and exporters in rooms kept at minus 10 degrees. It employs 80 people, runs a fleet of forklifts around the clock and cools its rooms with an anhydrous ammonia system. Revenue is about $28 million a year and operating profit about $3.1 million. After a competitor in another state lost a building to fire, the owners asked their new risk manager what could hurt them most. The answer depends on whether one asks what happens often or what could end the company, and those are different questions. This paper measures the exposures to answer both.

Risk, Uncertainty, Peril and Hazard

Knight (1921) distinguished risk, where outcomes are unknown but their probabilities can be estimated, from uncertainty, where even the probabilities are unknown. Insurance works with risk. A peril is the cause of a loss: fire, a power failure, a toxic release, an injury. A hazard increases the chance or size of loss: frost on a loading dock floor, an aging compressor, a sprinkler blocked by pallets. Pure risks offer only loss or no loss, while speculative risks, such as changing food prices, offer gain as well (Rejda et al., 2020). This paper deals with pure risks.

Exposure One: Forklift Injuries

Forklifts operating on icy floors in cold rooms injure workers. Industry experience and the company's records suggest about 0.06 recordable injuries per worker a year that lead to workers' compensation claims, or about 4.8 claims a year among 80 workers, with an average cost of about $28,000 each. Expected annual loss is about $134,000. Because claims are frequent and roughly independent, the yearly total is fairly predictable; treating the count as following a Poisson distribution, the standard deviation is about $61,000.

Exposure Two: A Long Power Failure

If utility power fails for more than about 12 hours and backup generators fail too, product temperatures rise and food must be destroyed. The company estimates a 10 percent chance each year of such an event, mainly from hurricanes, with a loss of about $1.2 million in spoiled customer goods, for which it is liable, and cleanup. Expected loss is $120,000 a year, but the standard deviation is about $360,000, three times the average.

Exposure Three: An Ammonia Release

Ammonia is an efficient refrigerant but toxic. A major release could injure workers and neighbors, force an evacuation and bring regulatory penalties. The company estimates a 1 percent annual chance of a release serious enough to cause a loss of about $3 million. Expected loss is $30,000 a year, but the standard deviation is about $300,000, ten times the expected loss.

Exposure Four: Fire

Cold-storage fires are rare but often total losses, because insulated panels can burn intensely and frozen products and building are destroyed together. With modern sprinklers, the company estimates a 0.2 percent annual chance of a major fire with a loss near $25 million in building, equipment, customer goods and lost income. Expected loss is only $50,000 a year, the second smallest of the four, but the standard deviation is about $1.1 million, and a single fire would cost about eight years of operating profit.

What the Numbers Show

Ranked by expected loss, forklift injuries come first and fire near the bottom. Ranked by threat to survival, the order reverses. The forklift exposure is frequent, small and predictable; the fire exposure is rare, huge and almost unpredictable for one firm. The power failure and ammonia exposures sit between. Bernstein (1996) traced how the measurement of probability transformed decisions about risk, but the measurements here show its limit for a single firm: knowing that a fire happens once in 500 years does not reveal whether it happens this year.

A Risk Map

On a grid that sets how often a loss happens against how large it is, forklift injuries sit in the high-frequency, low-severity corner, where prevention and retention work best. Fire sits in the low-frequency, high-severity corner, where transfer through insurance is essential. Power failure and ammonia release sit in the middle, calling for prevention plus insurance.

Why an Insurer Can Carry What the Company Cannot

The law of large numbers states that as the number of similar, independent exposures grows, the average loss per exposure approaches its expected value. An insurer covering 1,000 similar warehouses against fire would expect about two fires a year and could predict its average loss per warehouse fairly closely, because the standard deviation of the average falls with the square root of the number of exposures. Peach State, with one building, cannot pool its fire risk. The insurer can, which is why paying a premium above the $50,000 expected loss can still be a sound decision for the company.

Limits of the Estimates

The probabilities used here are estimates from industry data and judgment, not facts. The fire probability in particular could be off by a factor of two or more, and correlated events, such as a hurricane causing both a power failure and a fire, would make losses larger than the separate figures suggest.

Correlated Losses

The four exposures are not independent. A hurricane can cut power, flood generators, injure workers during cleanup and, through electrical faults on restart, cause a fire. Treating each exposure separately understates the chance of a very bad year. The risk manager therefore added one combined scenario: a direct hurricane hit causing a two-day outage, spoilage of $1.2 million, $400,000 of storm damage and two injury claims, about $1.7 million in all. Planning for that scenario, rather than for each exposure alone, sets the size of cash reserves and the limits the company will need.

What to Track

The risk manager will track injury rates and claim costs monthly, generator test results and fuel supply, ammonia system inspections and near-miss reports and sprinkler impairments. These measures reduce the hazards that drive each exposure.

Conclusion

Peach State's four exposures differ more in spread than in expected cost. Forklift injuries cost the most on average but are predictable; fire costs little on average but could destroy the company in one night. Measuring both expected loss and standard deviation shows which risks to manage and retain and which to transfer, the subject of the weeks ahead.

References

Bernstein, P. L. (1996). Against the gods: The remarkable story of risk. John Wiley & Sons.

Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin.

Rejda, G. E., McNamara, M. J., & Rabel, W. H. (2020). Principles of risk management and insurance (14th ed.). Pearson.