How Much Stock Should a Grocer Keep to Avoid Running Out with Less Than 1% Risk?

In summary, the grocer should stock 4 items in order to have a 0.01 chance of running out within a week.
  • #1
joemama69
399
0

Homework Statement



On the average, a grocer sells 4 of a certain article per week. How many of these should he have in stock so that the chance of his running of stock within a week will be less than 0.01? Assume Poisson distribution.



Homework Equations





The Attempt at a Solution



So I set λ = 4, plugged it into e^(-λ)λ^(x) / x! set it <0.01 and looked for an x that brought the equation to <0.01.

I was unsure how solve this for x, because of the x! in the bottom, so I just started with x = 0 and plugged and chugged till I came across x = 10, P(X=10)=0.0053 < 0.01.

However this is incorrect. Any suggestions?
 
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  • #2
joemama69 said:
So I set λ = 4, plugged it into e^(-λ)λ^(x) / x!
What event does that represent the probability of in the context of this question?
Also, there is a possible ambiguity in the question. If the storekeeper stocks N items and gets requests for exactly N then technically she has 'run out', but it is not a concern to her unless there is a demand for N+1 or more.
 
  • #3
Thanks for the quick reply...

From my limited knowledge of Poisson distribution, lambda represents the average rate of success, in the case of the problem at hand, an average of 4 successful sales per week.

So my thought was that given the average rate of sales = 4 per week, find the amount of stuff he should stock, 'x', so that its probability is less that 0.01.

P(X=x)<0.01 given average 4 sales per week
 
Last edited:
  • #4
joemama69 said:
From my limited knowledge of Poisson distribution, lambda represents the average rate of success, in the case of the problem at hand, an average of 4 successful sales per week.
Yes, except that I would take it as the rate of opportunities to sell, i.e. requests for the item. The actual number of sales will depend on the number stocked.
So my thought was that given the average rate of sales = 4 per week, find the amount of stuff he should stock, 'x', so that its probability is less that 0.01.

P(X=x)<0.01 given average 4 sales per week
Sure, but that's the probability of exactly x requests for the item. There could be more.
 
Last edited:
  • #5
joemama69 said:

Homework Statement



On the average, a grocer sells 4 of a certain article per week. How many of these should he have in stock so that the chance of his running of stock within a week will be less than 0.01? Assume Poisson distribution.



Homework Equations





The Attempt at a Solution



So I set λ = 4, plugged it into e^(-λ)λ^(x) / x! set it <0.01 and looked for an x that brought the equation to <0.01.

I was unsure how solve this for x, because of the x! in the bottom, so I just started with x = 0 and plugged and chugged till I came across x = 10, P(X=10)=0.0053 < 0.01.

However this is incorrect. Any suggestions?

If X~Po(4) is the demand and the grocer starts the week with N in stock, then he requires P(X > N) < 0.01, or P(X ≤ N) > 0.99.
 

Related to How Much Stock Should a Grocer Keep to Avoid Running Out with Less Than 1% Risk?

1. What is the Poisson Distribution?

The Poisson Distribution is a probability distribution that predicts the likelihood of a certain number of events occurring within a specific time or space interval. It is often used to model rare or random events, such as the number of customers entering a store in a day or the number of accidents on a highway in a month.

2. What are the key characteristics of the Poisson Distribution?

The key characteristics of the Poisson Distribution are that it is discrete, meaning it only deals with whole numbers, and it is unbounded, meaning the range of possible outcomes is infinite. It also assumes that the events occur independently of each other and the average rate of occurrence is constant over time or space.

3. How is the Poisson Distribution calculated?

The Poisson Distribution is calculated using the formula P(x) = (e^-λ * λ^x) / x!, where λ is the average rate of occurrence and x is the number of events. This formula gives the probability of x events occurring within a given time or space interval.

4. What is the difference between the Poisson Distribution and the Normal Distribution?

The Poisson Distribution is used for rare or discrete events, while the Normal Distribution is used for continuous events. Additionally, the Poisson Distribution is unbounded and deals with whole numbers, while the Normal Distribution is bounded and can deal with any real number.

5. In what real-world scenarios can the Poisson Distribution be applied?

The Poisson Distribution can be applied in various real-world scenarios, such as estimating the number of phone calls received by a call center in an hour, predicting the number of product defects in a batch, or calculating the number of website visits in a day. It is also commonly used in finance, biology, and sports analytics.

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