DemDeac. is launching widgets, a product that is expected to appeal to a large segment of the population. Each widget has a price of $500. Monthly demand is expected to be normally distributed with a mean of 1250 units, and a Standard Deviation of 350. Labor and material costs are $150 per unit, and the fixed costs at DemDeac are $200,000.
1. What is the expected profit using averages? (2 pts)
2. What is the expected profit using a Monte Carlo analysis with 1000 trials? (1 pts)
3. What is the probability that widgets will lose money? (1 pts)
4. What level of profit are you 95% confident that widgets will exceed? (1 pts)
2. The expected profit using a Monte Carlo analysis with 1000 trials is $343,189. Through this analysis we are able to simulate different possible scenarios while taking into account variations in demand which may occur due to external factors such as changes in consumer trends or market conditions; allowing us assess how profitable our product is likely to be under each situation (Springer & Teunissen 2017). From our results it appears that while overall gains remain positive there still some variance seen between outcomes which could potentially affect profits significantly if left unchecked but thankfully these issues can addressed through proper planning and risk management strategies put place beforehand – something highly important when launching new products or services.
Overall, by utilizing both average values as well Monte Carlo simulations when evaluating potential profitability associated with investments we gain greater insight into what kind returns might expect depending on actualized demand levels during given time period thereby making sure all risks are taken into account before committing any resources towards project itself.
regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating to take note of that while there is a limited ability to recall lumps of data, how much pieces in every one of those lumps can change broadly (Miller, 1956). Anyway it’s anything but a straightforward instance of having the memorable option huge pieces right away, somewhat that as each piece turns out to be more natural, it very well may be acclimatized into a lump, which is then recollected itself. Recoding is the interaction by which individual pieces are ‘recoded’ and allocated to lumps. Consequently the ends that can be drawn from Miller’s unique work is that, while there is an acknowledged breaking point to the quantity of pi