GF510 Unit 3 Likelihood and Impact Matrix Example

Reviewed by Chester Goodwin, MBA Risk Analysis and Management Purdue University Global Free custom sample in 24 to 48h

This GF510 Unit 3 likelihood and impact matrix writes its scales down before rating anything. Purdue Global GF 510, Risk Analysis and Management, asks MS in Finance students to build a rating scale others could apply the same way. For the composite shell manufacturer Ledgerwood Pools, which earns about $28 million of operating income, the sample sets five likelihood bands as annual probabilities and five impact bands as shares of that income, with parallel safety and regulatory descriptors. It rates twelve items with evidence and a confidence note, ranks them on a heat map with stated tie rules, and notices that a plant fire scores last despite a severe impact, so it adds a board review rule.

CourseGF510 Risk Analysis and Management
UnitUnit 3
Paper typeLikelihood and impact matrix
LengthAbout 1,030 words, 6 pages double-spaced
FormatAPA 7 student paper
SchoolPurdue University Global
ProgramMS in Finance
UpdatedOctober 2026

Free sample paper for GF510 Unit 3

1

Scales First, Ratings Second: A Unit 3 Likelihood and Impact Matrix for Twelve Exposures at a Pool Maker

[Student Name]

Purdue University Global

GF510: Risk Analysis and Management

Unit 3 Likelihood and Impact Matrix

[Instructor Name]

[Date]

Ledgerwood Pools and every figure are composite; the framework and articles cited are real.

What this part is doingSaying the scales come before the ratings tells the grader the matrix can be checked, not just read.
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Why the Scales Come First

A rating of "high" means one thing to the plant manager and another to the treasurer unless both are reading the same definitions. The bands below are written in numbers wherever the case allows, so that two people placing the same exposure should land in the same cell (Hopkin, 2018).

Likelihood Bands

Likelihood is stated as the probability that the event occurs at least once in the next twelve months.

1. Rare: below 5 percent.

2. Unlikely: 5 to 15 percent.

3. Possible: 15 to 35 percent.

4. Likely: 35 to 65 percent.

5. Almost certain: above 65 percent.

Impact Bands

Financial impact is tied to Ledgerwood's scale, measured as a share of its annual operating income of about $28 million. A parallel description lets safety and regulatory consequences be placed on the same rows, so that an exposure whose main harm is not financial still lands in a band.

1. Insignificant: below 1 percent, under about $0.28 million; first-aid injury; a regulator's letter.

2. Minor: 1 to 5 percent, up to $1.4 million; a recordable injury; a notice of violation.

3. Moderate: 5 to 10 percent, up to $2.8 million; a lost-time injury; a fine below $100,000.

4. Major: 10 to 20 percent, up to $5.6 million; a serious injury; a fine plus a mandated process change.

5. Severe: above 20 percent, more than $5.6 million; a fatality; suspension of an operating permit.

Where an exposure has both a financial and a non-financial consequence, it takes the higher of the two bands.

The Ratings

E1. Resin price swings: likely, major. Resin prices rose by more than a fifth twice in the last five years, which supports a probability near 50 percent. A 25 percent rise would add about $10 million to costs; if price increases recover 70 percent after a lag, the net hit is about $3 million. Confidence: medium.

E2. Dealer credit losses: likely, minor. Some dealer fails in most years. Write-offs in the worst of the last five years came to about $0.6 million. Confidence: high.

E3. Resin supplier outage: possible, severe. The supplier's plant has had two unplanned shutdowns in a decade, one lasting three weeks. A month-long outage in spring would halt two lines and cost about $6 million of lost margin. Confidence: medium, because the duration is uncertain.

E4. Serious crash in oversize transport: possible, major. Two preventable accidents last year, neither serious, support a meaningful chance of a worse one. Impact is placed on the safety descriptor, serious injury, with a financial loss near $3.5 million above insurance once downtime and the retention are counted. Confidence: medium.

E5. Plant fire: rare, severe. No fire has occurred, and the plants are sprinklered, but a major fire at the Texas plant would cost far more than $5.6 million even after insurance, through lost sales during rebuilding. Confidence: low on likelihood, high on impact.

E6. Ransomware on production systems: possible, major. Attacks on mid-sized manufacturers are common, and backups share the production network. A week's stoppage in spring would cost about $4.5 million. Confidence: medium.

E7. Demand drop from higher borrowing rates: possible, severe. Orders fell 18 percent in 2023. A further rise in home equity rates could cut orders by a quarter, reducing operating income by about $15 million. Confidence: medium.

E8. Styrene compliance: unlikely, moderate. Readings have come close to permit limits but never exceeded them. A violation would probably bring a fine under $100,000 and corrective work. Confidence: high.

E9. Warranty claims spike: possible, moderate. One production month already shows triple the usual claim rate; a full defective batch could cost about $2 million. Confidence: medium.

W1. Laminator turnover: almost certain, minor. Turnover runs above 40 percent a year, and hiring and training cost about $0.5 million. Confidence: high.

W2. Hurricane interruption in Gulf markets: likely, minor. Most years bring at least one storm that halts installations for a week or two, shifting about $0.6 million of margin into later months or losing it. Confidence: high.

W3. Floating rate on the term loan: likely, minor. A one-point rise on the $80 million loan adds $0.8 million of interest. Confidence: high.

Multiplying the two bands ranks the plant fire last of the twelve, with a score of 5, even though it is one of only three exposures whose impact falls in the severe band, so the matrix needs a rule that sends every severe exposure to the board regardless of its score.

What this part is doingThe highlighted sentence shows the writer testing the matrix against its own output, which graders reward over a matrix taken on trust.
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The Heat Map

Scores are likelihood times impact, from 1 to 25. Ties are broken by impact band, then by the dollar estimate.

1. E1 resin price, score 16.

2. E7 demand drop, 15.

3. E3 resin supplier outage, 15.

4. E6 ransomware, 12.

5. E4 transport crash, 12.

6. W1 laminator turnover, 10.

7. E9 warranty spike, 9.

8. W3 floating rate, 8.

9. E2 dealer credit, 8.

10. W2 hurricanes, 8.

11. E8 styrene compliance, 6.

12. E5 plant fire, 5.

On the five-by-five grid, E1 sits in the likely and major cell, E3 and E7 share the possible and severe cell, and E4 and E6 share possible and major. The top five all lie in the upper right of the grid and carry forward to the register as principal exposures.

Limits of the Matrix

Research on risk matrices warns that multiplying ordinal bands can rank exposures in the wrong order, especially where a low-probability, high-impact event sits beside a frequent, modest one (Cox, 2008). Studies in industry have also found that small changes in band boundaries can move exposures between cells and change priorities (Thomas et al., 2014). Two safeguards answer that concern here: every severe-impact exposure, including the fire, goes to the board whatever its score, and the dollar estimate behind every rating is kept beside it so the register can rank by expected loss when the figures are firm enough.

What this part is doingClosing on the matrix's known weaknesses and the safeguards adopted shows the scale was chosen with its limits in view.
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References

Cox, L. A. (2008). What's wrong with risk matrices? Risk Analysis, 28(2), 497-512. https://doi.org/10.1111/j.1539-6924.2008.01030.x

Hopkin, P. (2018). Fundamentals of risk management: Understanding, evaluating and implementing effective risk management (5th ed.). Kogan Page.

Thomas, P., Bratvold, R. B., & Bickel, J. E. (2014). The risk of using risk matrices. SPE Economics & Management, 6(2), 56-66. https://doi.org/10.2118/166269-PA

What the GF510 Unit 3 assignment asks for

The third GF510 unit asks you to rate the identified exposures on a likelihood and impact scale. Define the scale first: likelihood bands as probabilities over a stated period, impact bands tied to the organization's own size, such as a share of operating income. Add non-financial descriptors so that safety and regulatory consequences can be placed on the same scale. Then rate each exposure, giving the band for each dimension, the evidence behind the placement and how confident you are. Explain how scores are combined and how ties are broken. Present the result as a ranked list or heat map. Finally, test your matrix against its own output and discuss its known limits, citing research on risk matrices. Cite all sources in APA 7 and keep the definitions short enough to apply quickly.

Inside the GF510 Unit 3 example

The sample begins by explaining why written bands keep two managers from placing the same exposure in different cells. Likelihood runs from rare, below 5 percent a year, to almost certain, above 65 percent. Impact runs from insignificant, below about $0.28 million, to severe, above $5.6 million, with matching injury and regulatory descriptors. Each of twelve ratings shows two bands, the evidence and a confidence grade, from the resin supplier's two past shutdowns to laminator turnover above 40 percent. The highlighted sentence points out that multiplying bands ranks the plant fire last. The heat map lists all twelve by score with ties settled by impact band and then dollars, so the demand drop edges out the supplier outage at 15. The limits section cites Cox on ordinal scores and Thomas, Bratvold and Bickel on boundary effects, and sends every severe exposure to the board whatever its score.

GF510 Unit 3 rubric: what earns full points

Credit on a GF510 matrix depends first on whether the scales are defined clearly enough for consistent use; graders check for numbers, a stated time period and descriptors for non-financial harm. Ratings earn the next share when each comes with evidence and a confidence note rather than a bare label. Instructors reward a transparent scoring rule, including how ties are broken, because without it the heat map cannot be reproduced. The analytical high point is critique: a student who spots that the matrix misranks a low-probability, high-impact exposure and adds a safeguard shows understanding beyond mechanics. Citations to research on matrix weaknesses support that critique. Clear presentation of the ranking and accurate APA 7 references complete the rubric for this unit.

Common GF510 Unit 3 mistakes, and how to avoid them

The commonest problem in GF510 Unit 3 is a matrix with labels like "low, medium, high" and no definitions, so every rating is a matter of opinion. Impact scales are often set in absolute dollars that do not fit the firm's size. Non-financial harms, injuries or regulatory action, get forced into financial bands or left out. Ratings appear without evidence, and confidence is never discussed. Ties are broken silently. Many students accept the heat map's output without noticing that a rare catastrophe ranks below a frequent nuisance. If your case is a hospital or a bank, the method is unchanged: write numeric bands tied to that organization, rate with evidence, show how scores combine, and test the result against what the board would want to see.

Get a GF510 Unit 3 example written to your instructions

Send the GF510 Unit 3 prompt, your rubric and the exposures carried over from your identification work. We build a custom matrix whose bands are defined before anything is rated, with evidence and confidence recorded for each placement. No fee attaches to a first custom sample; turnaround sits at 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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GF510 Unit 3 questions, answered

Should the matrix be three by three or five by five?

Both sizes appear in these courses, and the prompt usually settles which. A smaller grid is simpler to apply consistently; five bands allow finer distinctions but demand clearer descriptors. Five were chosen for the sample because the trucking case offered enough data to separate them, and the paper states that choice so a grader can see why it was made.

Is it wrong to multiply likelihood by impact?

Not wrong, but it needs care. Band numbers are ordinal labels, so a score of 12 is not necessarily twice as serious as a score of 6. Louis Anthony Cox's 2008 critique of risk matrices sets out the problems in detail. The sample shows scores for sorting but breaks ties by impact and explains why, which most graders accept.

What if the case gives no probability data?

Then the rating rests on stated judgment, recorded as such with a confidence note. Industry loss data, insurer statistics or regulator reports can often supply a base rate, and the trucking example draws on federal crash statistics for that purpose. The sample marks each placement with its source type, so a reader can see which ratings rest on data and which on estimates.

Where can I find a free GF510 Unit 3 sample paper?

A complete GF510 Unit 3 likelihood and impact matrix is available here, with defined bands, twelve evidenced ratings, a ranked heat map, a critique of matrix scoring and APA 7 references.

Why tie impact bands to operating income?

Because the same dollar loss means different things to firms of different sizes. Expressing bands as a share of Ledgerwood's $28 million operating income makes the scale fit the company and lets it grow with it.