# Tag Info

10

I agree with @QianZhang's answer (nice theoretical properties, easy to implement), and I would add that there is some theoretical justification too. If demands come from customer arrivals, then Poisson is a reasonable demand distribution since customer arrivals are well modeled by Poisson (often). And if the mean is large enough, then normal is a good ...

9

In my understanding, using normal/Poisson distribution for customer demand is mainly for two reasons. These distributions have nice properties for theoretical analysis in supply chain models These distributions are easier to implement or already been implemented for computational concerns

9

Time Series Data Library The time series data library from Rob Hyndman has hundreds (~648+) of time-series data. It depends on what you call "demand" and what you require as a "sufficiently long period of time." The entire dataset has been migrated to an R package called tsdl. It is also available on GitHub. You can find descriptions of the data in the ...

4

This is indeed a newsvendor problem. The fact that D is very uncertain only makes it more so. If we were in normal times, the standard approach would be: Use your historical data to calculate $\hat{\mu}$, an estimate for the mean demand. (Sounds like $\hat{\mu} = 1.05[\text{last year's demand}]$ is the go-to estimate for your relative.) If your historical ...

4

Question: Why do we normally assume normal distribution/Poisson distribution for customer demand in a supply chain? Answer : Based on my experience in the industry, I have seen that generally, business users use simple thumb rules-based methods for safety stock or inventory models. The next level of sophistication for these users is usage of Normal or ...

3

Theoretically it is quite naturally convincing to assume that demand time points are independent. In other words knowing that an item was bought in t1 does not give one any good clue in understanding next sales time t2. It is like process renews/regenerate itself after each event and hence time between events are independent of each other. This particular ...

2

There are quite a few wrinkles here, specific to the current sources of uncertainty. Will the suppliers be giving discounts (to get orders after coming off a shutdown)? Discounts would reduce the risk of over-ordering. What normally happens with excess inventory (hold it, sell it at a discount, scrap or donate it, ...)? If they sell off the excess to a ...

1

My professor helped me for solving this error like below: clc clear cov_x=[2.5 0.1 0.2;0.1 0.4 0.3;0.2 0.3 0.9]; E_x=[10 6 5]; [V, landa]=eig(cov_x); E_w=V'*E_x'; var_w= landa; W=repmat(E_w,1,1000)+sqrt(var_w)*(randn(3,1000)); X=V*W; A= X(1,:); B= X(2,:); C=X(3,:); %D= A'*B.^2*C.^3'; for k=1:1000 D(:,k)=A(:,k)'*B(:,k).^2*C(:,k).^3; end %now we use ...

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