Companies that must determine well in advance of the selling season how many units of a new product to manufacture...
GMAT Reading Comprehension : (RC) Questions
Companies that must determine well in advance of the selling season how many units of a new product to manufacture often underproduce products that sell well and have overstocks of others. The increased incidence in recent years of mismatches between production and demand seems ironic, since point-of-sale scanners have improved data on consumers' buying patterns and since flexible manufacturing has enabled companies to produce, cost-effectively, small quantities of goods. This type of manufacturing has greatly increased the number of new products introduced annually in the United States. However, frequent introductions of new products have two problematic side effects. For one, they reduce the average lifetime of products; more of them are neither at the beginning of their life (when prediction is difficult) or at the end of their life (when keeping inventory is expensive because the products will soon become obsolete). For another, as new products proliferate, demand is divided among a growing number of stock-keeping units (SKU's). Even though manufacturers and retailers can forecast aggregate demand with some certainty, forecasting accurately how that demand will be distributed among the many SKU's they sell is difficult. For example, a company may be able to estimate accurately the aggregate number of shoes it will sell, but it may be uncertain about which specific types of shoes will sell more than other types.
Which of the following most accurately describes the function of the last sentence in the passage (Last 2 lines)?
1. Passage Analysis:
Progressive Passage Analysis
| Text from Passage | Analysis |
|---|---|
| Companies that must determine well in advance of the selling season how many units of a new product to manufacture often underproduce products that sell well and have overstocks of others. | What it says: When companies have to decide production quantities early, they guess wrong - making too few popular items and too many unpopular ones. What it does: Introduces the main problem/topic Source/Type: Author's statement of fact about business reality Connection to Previous Sentences: This is our opening premise - no previous context to connect to yet What We Don't Know Yet: Why this happens, whether it's getting worse, what solutions exist Visualization: Company planning in January for summer sales: • Predicts: 1000 sandals, 500 boots • Reality: 1500 sandals sold out, 300 boots left over Reading Strategy Insight: This opening sentence gives us a clear, concrete business problem to anchor our understanding. |
| The increased incidence in recent years of mismatches between production and demand seems ironic, since point-of-sale scanners have improved data on consumers' buying patterns and since flexible manufacturing has enabled companies to produce, cost-effectively, small quantities of goods. | What it says: This problem is getting worse recently, which seems strange because companies have better data and more flexible production methods now. What it does: Adds the puzzle element - why is the problem worsening despite better tools? Source/Type: Author's observation and analysis Connection to Previous Sentences: • Sentence 1 told us: Companies often guess wrong on production quantities • NOW Sentence 2: This problem is actually getting WORSE, not better • This builds on the opening by adding a timeline and paradox Visualization: Expected: Better tools → Better predictions → Fewer mismatches Reality: Better tools → ? → MORE mismatches (Why?) Reading Strategy Insight: The word "seems ironic" signals the author will explain this puzzle - stay tuned for the resolution. |
| This type of manufacturing has greatly increased the number of new products introduced annually in the United States. | What it says: Flexible manufacturing has led to many more new products being launched each year. What it does: Introduces the key consequence that will explain the puzzle Source/Type: Author's factual claim Connection to Previous Sentences: • Sentence 2 mentioned: Flexible manufacturing should help • NOW Sentence 3: Shows what flexible manufacturing actually caused • This begins to bridge toward explaining the irony What We Know So Far: Problem exists, it's worsening despite better tools, flexible manufacturing creates more product variety Visualization: Old manufacturing: Company launches 50 products per year Flexible manufacturing: Same company now launches 200 products per year Reading Strategy Insight: This sentence is setting up the explanation - more products will be the key to understanding the puzzle |
| However, frequent introductions of new products have two problematic side effects. | What it says: Launching many new products creates two specific problems. What it does: Signals the direct explanation of the irony is coming Source/Type: Author's analytical framework Connection to Previous Sentences: • Sentence 3 told us: Flexible manufacturing → more new products • NOW Sentence 4: More new products → problems (which will explain the irony) • This is the pivot point where the puzzle gets resolved Visualization: More new products → Side effect #1 + Side effect #2 → Harder to predict demand Reading Strategy Insight: Feel confident here - the author is about to give us a clear, organized explanation with exactly two parts |
| For one, they reduce the average lifetime of products; more of them are neither at the beginning of their life (when prediction is difficult) or at the end of their life (when keeping inventory is expensive because the products will soon become obsolete). | What it says: First problem: Products don't last as long, meaning they're stuck in the hard-to-predict early stage or the risky-to-stock late stage. What it does: Provides the first detailed explanation of why more products create prediction difficulties Source/Type: Author's logical analysis Connection to Previous Sentences: • Sentence 4 promised: Two side effects • NOW Sentence 5: Delivers side effect #1 with clear reasoning • This directly explains part of why companies struggle with predictions Visualization: Old scenario: Product lifecycle = 24 months, sweet spot = 18 months New scenario: Product lifecycle = 8 months, sweet spot = 3 months Result: Most products are in unpredictable phases Reading Strategy Insight: This is detailed but not new complexity - it's just elaborating on the framework the author already set up. |
| For another, as new products proliferate, demand is divided among a growing number of stock-keeping units (SKU's). | What it says: Second problem: More products means customer demand gets split up among more choices. What it does: Introduces the second explanation with a key business term (SKUs) Source/Type: Author's logical analysis Connection to Previous Sentences: • Sentence 4 promised: Two side effects • Sentence 5 gave us: Side effect #1 • NOW Sentence 6: Delivers side effect #2 • This completes the analytical framework Visualization: Scenario A: 1000 customers, 10 products → 100 customers per product Scenario B: 1000 customers, 50 products → 20 customers per product Result: Harder to predict demand for each individual product Reading Strategy Insight: The authors just defined SKUs for us - expect this concept to be developed further |
| Even though manufacturers and retailers can forecast aggregate demand with some certainty, forecasting accurately how that demand will be distributed among the many SKU's they sell is difficult. | What it says: Companies can predict total sales pretty well, but can't figure out which specific products will sell how much. What it does: Restates and clarifies the core problem in simpler terms Source/Type: Author's explanatory summary Connection to Previous Sentences: • Sentence 6 mentioned: Demand divided among more SKUs • NOW Sentence 7: Restates this as "can predict total but not individual distribution" • This is NOT new information - it's clarification of what we already learned What We Know So Far: The irony explained - better tools led to more products, which paradoxically made prediction harder Visualization: Total shoes sold this year: 10,000 (predictable) How many sneakers vs. boots vs. sandals: ??? (unpredictable) Reading Strategy Insight: Feel relieved here - this is simplification, not new complexity. The author is helping us understand by restating the technical concept in everyday terms. |
| For example, a company may be able to estimate accurately the aggregate number of shoes it will sell, but it may be uncertain about which specific types of shoes will sell more than other types. | What it says: A shoe company can predict total shoe sales but not whether sneakers or boots will be more popular. What it does: Provides a concrete, relatable example of the abstract concept Source/Type: Author's illustrative example Connection to Previous Sentences: • Sentence 7 explained: Can forecast aggregate demand but not distribution among SKUs • NOW Sentence 8: Gives the exact same idea using shoes as a concrete example • This reinforces understanding rather than adding complexity Visualization: Shoe Company's Prediction: • Total shoes: 50,000 pairs ✓ (confident) • Sneakers: 15,000? 25,000? 35,000? ❓ (uncertain) • Boots: 10,000? 20,000? 30,000? ❓ (uncertain) • Sandals: 5,000? 15,000? 25,000? ❓ (uncertain) Reading Strategy Insight: Perfect ending - the author gives us a concrete example we can all relate to. This should make you feel MORE confident about understanding the passage, not less. |
2. Passage Summary:
Author's Purpose:
To explain why companies are having more trouble predicting what products will sell, even though they have better technology and tools than before.
Summary of Passage Structure:
The author builds their explanation by walking us through a business puzzle and then solving it step by step:
- First, the author introduces the basic problem that companies often make too many of some products and not enough of others.
- Next, they point out something puzzling: this problem is getting worse even though companies have better data and more flexible manufacturing.
- Then, they explain that flexible manufacturing has led to many more new products being launched each year, which creates two specific problems that make prediction harder.
- Finally, they clarify the core issue with a simple example about shoes, showing that while companies can predict total sales, they struggle to figure out how customers will split their buying among all the different product choices.
Main Point:
Better manufacturing technology has actually made demand forecasting harder because it allows companies to create so many more products that customer demand gets spread thin across too many choices, making it impossible to predict which specific items will be popular.
3. Question Analysis:
The question asks us to identify the function of the last sentence in the passage: "For example, a company may be able to estimate accurately the aggregate number of shoes it will sell, but it may be uncertain about which specific types of shoes will sell more than other types."
This is a function question, so we need to understand what role this sentence plays in the author's argument structure.
Connecting to Our Passage Analysis:
From our passage analysis, we can see that:
- The sentence immediately before (sentence 7) made an abstract claim: "Even though manufacturers and retailers can forecast aggregate demand with some certainty, forecasting accurately how that demand will be distributed among the many SKU's they sell is difficult."
- The last sentence provides "a concrete, relatable example of the abstract concept"
- Our analysis noted this sentence "reinforces understanding rather than adding complexity" and "gives the exact same idea using shoes as a concrete example"
- The visualization showed exactly how this works: companies can predict total shoes (50,000 pairs) but struggle with specific types (sneakers? boots? sandals?)
Prethinking:
Based on our analysis, the last sentence serves as a concrete illustration of the abstract principle stated in the previous sentence. The author moved from technical business language ("aggregate demand," "SKUs") to an everyday example (shoes) that anyone can understand. This is a classic example/illustration function in argumentative writing.
Why It's Wrong:
- The sentence doesn't argue that aggregate demand is more important than distribution; it shows both are relevant but different in predictability
- The passage actually emphasizes that distribution problems are what's causing the main business difficulties
- This choice misreads the example as making a value judgment rather than illustrating a distinction
Common Student Mistakes:
- Did you think the shoe example was ranking which type of forecasting matters more?
→ Focus on what the example demonstrates rather than what it prioritizes - Did you confuse "can forecast accurately" with "is more important"?
→ The passage shows capability differences, not importance rankings
Why It's Wrong:
- The sentence supports rather than refutes any assertions about flexible manufacturing
- There's no contradiction or disagreement being expressed in this example
- The shoe example reinforces the problems caused by product proliferation, which stems from flexible manufacturing
Common Student Mistakes:
- Did you think the example was contradicting previous points?
→ Trace how this example continues the same logical thread as the previous sentence - Were you looking for conflict where there is actually support?
→ Notice how the example clarifies rather than disputes the preceding analysis
Why It's Right:
- The sentence directly illustrates the assertion from sentence 7 about companies' forecasting abilities
- It provides a concrete, relatable example of the abstract concept of aggregate vs. distributed demand forecasting
- The example format ("For example") explicitly signals its illustrative function
Key Evidence: "Even though manufacturers and retailers can forecast aggregate demand with some certainty, forecasting accurately how that demand will be distributed among the many SKU's they sell is difficult" - this assertion is directly illustrated by the shoe company example that follows.
Why It's Wrong:
- The sentence doesn't provide solutions or ways companies address forecasting difficulties
- It demonstrates the problem rather than showing how companies solve it
- No methods, strategies, or solutions are mentioned in the example
Common Student Mistakes:
- Did you read the shoe example as showing what companies do to fix the problem?
→ The example illustrates the problem itself, not any solutions - Were you expecting the passage to end with solutions?
→ This passage focuses on explaining why the problem exists, not how to solve it
Why It's Wrong:
- The sentence reinforces rather than contradicts the author's points about demand distribution difficulties
- There's no exception being noted; the shoe example perfectly fits the general rule described
- The example supports the assertion about SKU distribution challenges rather than providing a counterexample
Common Student Mistakes:
- Did you think the shoe example was showing a special case?
→ It's actually a typical case that demonstrates the general principle - Were you confused by "but it may be uncertain" as indicating an exception?
→ This "but" shows the contrast between aggregate and distributed forecasting, not an exception to the rule