You would have to use some alternate method with yearly data and find the yearly demand (=570). And, then 540/4 (-142.5) is the deseanalized average quarterly demand.
Yes you may. But, it will get very complex like that. I would work at montly levels and then further disaggregate the monthly forecast into weeks. And, you could consider this app for monthly forecasting: zerohour.shinyapps.io/ETS_ARIMA_Prophet_monthly/
You are amazing! Thank you so so much for this video. I was struggling to find a way to construct a problem similar to this one and this video helped me immensely!
Let’s assume that you decide to earn money by tickets scalping, that is, buying tickets for some events and then selling them at higher price. Disclaimer: ticket scalping is illegal; don’t try that at home; this is an optimisation exercise only. You can buy tickets at official price of $150. Then you try sell them at any price you want. However, if your price is too high then there will be less people willing to buy tickets from you. Also, the number of buyers is a random variable, so you cannot be sure how many tickets you will be able to sell. The number of tickets you can sell (demand) follows lognormal distribution with mean µ and standard deviation = 0.5. Note: The number of tickets sold will be not an integer, which does not make sense. It is OK for calculations as we talk about expectations, but it is not OK for reporting. Use common sense. Value µ depends on your selected price and follows the following formula: µ=6-YourPrice/70 You can return all tickets you don’t sell, but you will get only one third of their official price. How many tickets should you buy and what should be your selling price? What is a probability to make a profit in the case of the optimal solution? Is it worth to “do business”? Hint: to get random numbers from some distribution in Excel, you need to use inverse functions. For example, for lognormal distribution the formula is =LOGNORM.INV(RAND(), mean, st.dev) Hi Sir can you solve this
Thank you so much for this video. 11 years later and it perfectly covers the content covered in my 300 level college course. I'm approaching finals, and I appreciate the conciseness and simplicity of your explanations.
Thank you very much! Already on the 4th day, I could not master this technique in order to use it in the qualification work of the bachelor. Thanks to you, I was able to calculate everything quickly. Honor and praise!!!
Dear Piyush, thanks for the videos, very useful, I have a question, can I use this method for seasonal data? Why not? what if I used? how the results will affect the forcasting?
To be honest, you can't calculate the costs. And, the costs do not behave as assumed by the model. EOQ has zero practical use and is merely a conceptual idea.
Not sure why. You might want to try this tool that I have developed for forecasting demand for monthly data: zerohour.shinyapps.io/ETS_ARIMA_Prophet_monthly/?_ga=2.50268966.1622986526.1672339493-1036110055.1665609118
