Probability to identify highest margin product. Assume the following scenario. 
I can 
    Sell P1 for a profit of  14% 
    or sell it at a Loss of -7%

    Sell P2 for a profit of 11%
    Or sell it at a loss of -6%

    Sell P3 for a profit of 7%
    or sell for a loss of -1%

Considering the profit margins provided above and max loss rate, at which they need to be cleared by the end of month. 
As a seller, stocking which of the above is more profitable to the business. 
Assuming all products will be sold at the mentioned P/L levels. 
If I consider PL ratio, 


*

*For P1, it would be 14:7 ~ 2   : 1 

*For P2, it would be 11:6 ~ 1.9 : 1

*For P3, it would be 7:1  ~ 7   : 1


Clearly the higher ratios isnt going to aid in determining the ideal product. 
How could I identify the right product?
 A: Here is a simple model. Assume that we buy $n$ units of a given product and each unit is sold by the end of the month with probability $p$, independent of all other units. Then the number of sold items $X$ has a Binomial$(n,p)$ distribution. If we have a profit of $a > 0$ and a loss of $b > 0$ for each unit, our "total profit per unit" by the end of the month is 
$$
\frac{a X - b(n-X)}{n} 
$$
This converges to the expectation $a p - b(1-p)$, as $n \to \infty$, by the law of large numbers.
In the example above, for product P1, we get $f(p_1) = 14p_1 - 7(1-p_1) = 21p_1 - 7$ and for P2 we have $g(p_2)=11p_2 - 6(1-p_2) = 17p_2 - 6$. The functions $f$ and $g$ are two straight lines. Assume that $p_1 = p_2 = p$, i.e., the products have equal chance of being sold. This is the plot of the two functions:

The intersection is at $p=1/4$. So if you expect to sell less than 25% of your inventory you should go with P2. If you expected to sell more that 25%, you should go with P1. A better choice is to not sell either if you expected to sell $\le 33\%$ (i.e. where $21p - 7 = 0$), and sell P1 otherwise. Below $\approx 33\%$, both products have negative expected return.
Two other ideas for more realistic modeling:


*

*You can also view the problem as portfolio optimization, if you have expectation and variances of the returns on each product. You can use the variance as a measure of risk in that case and try to maximize the expectation of a portfolio subject to a given threshold on the risk of the portfolio. 

*You can model the problem as a multi-armed bandit. Each product is an arm that you can pull and get a reward (think of a slot machine). You  try to simultaneously figure out the best arm to pick (i.e., estimate the expected return of each arm and find out which one has the largest return) as well as maximize your overall earning during the exploration part. There is the so-called exploration-exploitation trade-off. A famous algorithm to solve the problem is the upper confidence bound (UCB) algorithm. How to incorporate your known data so far (e.g., the profit/loss figures you have) is an interesting problem. There might be variants of the bandit problem that consider the notion of risk as well, otherwise it is worth thinking about.

A: You should just calculate the expected revenue of each of the items, and take the one with the highest expected revenue. All with a fixed investment. Either $N$ or $100$ to your liking.
Then the probability of loss/profit comes into play, if I understand you correctly, you mean $p_1$, the probability that $P_1$ will be sold with profit it $2/3$ from your PL ratio?!
Then you end up with $100 \cdot (p_1*1.14 + (1-p_1)*0.93)$, etcetera.
Then just take the maximum for all $3$ cases.
