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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)?
| 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. |
To explain why companies are having more trouble predicting what products will sell, even though they have better technology and tools than before.
The author builds their explanation by walking us through a business puzzle and then solving it step by step:
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.
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.
From our passage analysis, we can see that:
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.
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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.
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