UPSC MainsMANAGEMENT-PAPER-II201910 Marks
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Q5.

Probability: Standard Quality Scooter Production

A company has 3 plants manufacturing 1000 scooters in a month. Plants 'A', 'B' and 'C' manufacture 500, 300 and 200 scooters per month respectively. Plants 'A', 'B' and 'C' in respectively 90%, 92% and 95% scooters are rated standard quality. (i) What is the probability that a scooter selected at random is of standard quality? (ii) If it is known that a scooter selected at random is of standard quality, what is the probability that scooter comes from plant 'B'?

How to Approach

This question tests the application of basic probability concepts in a managerial context. The approach should involve clearly defining the events, applying Bayes' Theorem for part (ii), and presenting the calculations in a structured manner. Key points to cover include understanding conditional probability, total probability theorem, and accurate calculation of probabilities. The answer should demonstrate a clear understanding of statistical principles and their application to operational management.

Model Answer

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Introduction

In modern manufacturing operations, quality control and production efficiency are paramount. Statistical analysis plays a crucial role in assessing product quality and identifying the source of defects. This question presents a scenario involving scooter production across three plants with varying production volumes and quality rates. Applying probability theory allows us to determine the overall probability of a scooter being of standard quality and, conversely, the probability that a standard-quality scooter originated from a specific plant. This is a fundamental application of statistical quality control in operations management.

Part (i): Probability of a Scooter Being of Standard Quality

Let 'S' denote the event that a scooter is of standard quality. We are given the following information:

  • P(A) = Probability that a scooter comes from plant A = 500/1000 = 0.5
  • P(B) = Probability that a scooter comes from plant B = 300/1000 = 0.3
  • P(C) = Probability that a scooter comes from plant C = 200/1000 = 0.2
  • P(S|A) = Probability that a scooter is of standard quality given it comes from plant A = 0.9
  • P(S|B) = Probability that a scooter is of standard quality given it comes from plant B = 0.92
  • P(S|C) = Probability that a scooter is of standard quality given it comes from plant C = 0.95

We can use the law of total probability to find P(S):

P(S) = P(S|A)P(A) + P(S|B)P(B) + P(S|C)P(C)

P(S) = (0.9 * 0.5) + (0.92 * 0.3) + (0.95 * 0.2)

P(S) = 0.45 + 0.276 + 0.19

P(S) = 0.916

Therefore, the probability that a scooter selected at random is of standard quality is 0.916 or 91.6%.

Part (ii): Probability that a Scooter Comes from Plant 'B' Given it is of Standard Quality

We need to find P(B|S), the probability that a scooter comes from plant B given that it is of standard quality. We can use Bayes' Theorem:

P(B|S) = [P(S|B) * P(B)] / P(S)

We already know:

  • P(S|B) = 0.92
  • P(B) = 0.3
  • P(S) = 0.916 (calculated in part i)

Therefore:

P(B|S) = (0.92 * 0.3) / 0.916

P(B|S) = 0.276 / 0.916

P(B|S) ≈ 0.3011

Therefore, if a scooter selected at random is of standard quality, the probability that it comes from plant 'B' is approximately 0.3011 or 30.11%.

Conclusion

In conclusion, we have successfully applied probability principles to analyze the quality control scenario presented. The probability of a randomly selected scooter being of standard quality is 91.6%, and the probability that a standard-quality scooter originated from plant 'B' is approximately 30.11%. These calculations are vital for operational decision-making, allowing management to identify areas for improvement in production processes and quality control measures at each plant. Continuous monitoring and statistical analysis are essential for maintaining high-quality standards and optimizing production efficiency.

Answer Length

This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.

Additional Resources

Key Definitions

Bayes' Theorem
Bayes' Theorem is a mathematical formula for calculating conditional probability. It describes the probability of an event, based on prior knowledge of conditions that might be related to the event. It is expressed as: P(A|B) = [P(B|A) * P(A)] / P(B)
Conditional Probability
Conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), which reads as "the probability of event A given event B."

Key Statistics

According to a 2023 report by Statista, the global scooter market was valued at approximately $22.8 billion and is projected to reach $35.7 billion by 2028.

Source: Statista (2023)

The automotive industry contributes approximately 7.1% to India's GDP as of 2023.

Source: Society of Indian Automobile Manufacturers (SIAM) - Knowledge Cutoff 2024

Examples

Toyota Production System (TPS)

Toyota's TPS heavily relies on statistical process control (SPC) to identify and eliminate defects in manufacturing. By continuously monitoring production processes and analyzing data, Toyota ensures high-quality standards and minimizes waste.

Frequently Asked Questions

What if the production volumes of each plant were not known?

If the production volumes were unknown, we would need additional information to calculate the probabilities P(A), P(B), and P(C). Without these probabilities, we could not apply the law of total probability or Bayes' Theorem.

Topics Covered

StatisticsOperations ManagementProbabilityBayes TheoremQuality Control