A company is selling a particular brand of tea and wishes to introduce a new flavor. How will the company forecast demand for it ?
The most sensible approach is to look at the actual past demands. Some demands show some kind of trend or cycles, which could be used for our advantage and to forecast more accurately.The common behaviors of the demand is as following:Stationary: here the demand show a smooth pattern where no increase or decrease in the demand. Linear: an steady increase or decrease in the demandNonlinear: Where the demand takes a weird increasing or decreasing slopes.Trends:Seasonal: Where the demand is repeated after a certain periodCycle: this is easily detected graphically where the demand repeats in each cycle.Random: The most annoying type. it maybe meaningless to forecast such kind of behavior. However, the industrial engineer could still simplify the behavior and remove outliers from consideration.Each one of these cases has its own way to forecast.
If a company chooses to raise prices during the holidays, they will sell less of that product. Some consumers reservation price will be lower than the new price so they will not buy the product. This is represented by a movement along the demand curve, NOT a shift of the demand curve.
If there is an increase in demand then a new demand curve appears to the right of the original, but if there is an increase in quantity demanded, then there will only be an increase in price and a new demand curve will not appear.
The alpha factor (a fraction between 0 and 1) is used to determine how much of the previous smoothed estimate will be used and how much of the current seasonal adjusted demand is used to produce the new Smoothed Estimate (ED). Example: if the alpha factor is 0.8, then 20% of the previous period smoothed estimate is combined with 80% of the seasonal adjusted demand to give the new smoothed estimate. Compute the forecast statistic for first order smoothing using the alpha factor selected for the item, forecast alpha = (L - Low, M - Medium, H - High). Estimated Demand 1 = (1 - alpha factor) * old cur_est_dem_1 + SAD * alpha factor. So if the old estimated demand is 90 and the selected alpha factor is .5 then:ED 1 = (1-.5)*90+(107.147*.5) = 98.5735Hope This Helps!!!
If the purchase or acquisition of an item is meant as an addition to stock, it is new demand. It the purchase of an item is meant for maintaining the old stock of capital/ asset intact, it is replacement demand.
Their newness and long lead times make it very difficult to forecast product demand accurately. In many cases, the project may be of special interest because it would give the company an option to break into a new market.
The most sensible approach is to look at the actual past demands. Some demands show some kind of trend or cycles, which could be used for our advantage and to forecast more accurately.The common behaviors of the demand is as following:Stationary: here the demand show a smooth pattern where no increase or decrease in the demand. Linear: an steady increase or decrease in the demandNonlinear: Where the demand takes a weird increasing or decreasing slopes.Trends:Seasonal: Where the demand is repeated after a certain periodCycle: this is easily detected graphically where the demand repeats in each cycle.Random: The most annoying type. it maybe meaningless to forecast such kind of behavior. However, the industrial engineer could still simplify the behavior and remove outliers from consideration.Each one of these cases has its own way to forecast.
forecast are wrong, because of some factors that may occur during the forecasting period. This factors may include, promotion, when company advertise their product without planning for the number of customer they can get during this period, even though, they have planned for the product to be sold during this period, there will still be an error in the forecast, because they may eventually get more customer or low customer.Also introduction of new product may make a forecast to be wrong since there is no historical data, that may suggest what the demand has been in the past, with this forecast may be wrong. Another reason is that customer may not want to buy the product again, this may due to fact that they have alternative product or a product that the price is reduced from the competitor, and if the customer did not buy the product,it will make the forecast to be wrong. One final point is that, unexpected event may make the forecast to be wrong. for example, it is believe that between December and November in the UK, snow must fall, if for a particular year there is no snow and a company has prepared to sell worm cloth, what would happen to that company during the period that snow does not fall,there forecast will definitely be wrong and they may loss.
Forecasting sales would be 3 type. 1. Seasonal ( festivals, events, famous dates, sports events, movie manias.. etc) 2. Demand based ( Completely depends on the demand our product has now and in the future). 3. Creating new waves (People get behind the new and happenings like crazy).
Weather forecast for new York April 2nd to April 8th
Answer this question…A. The price of silver rises due to an increase in consumer demand. B. Only one company offers bus service to the town. C. A new Pizza shop promotes a unique new flavor of ice cream. D. Customers can choose among a limited number of internet providers.I need help really really quick please
Enter two data series that correspond and select them. On the Data tab, in the Forecast group, click Forecast Sheet. In the Create Forecast Worksheet box, pick either a line chart or a column chart for the visual representation of the forecast. Pick an end date, and then click Create. This will create a new worksheet with your forecast and a chart. You can also use the FORECAST function.
If a company chooses to raise prices during the holidays, they will sell less of that product. Some consumers reservation price will be lower than the new price so they will not buy the product. This is represented by a movement along the demand curve, NOT a shift of the demand curve.
A New Flavor - 2009 was released on: USA: 21 August 2009
There are a few potential areas for innovation when it comes to the Snickers and Twix brands. One area could be in the packaging. For example, the company could consider using more sustainable and eco-friendly packaging materials. Another area for innovation could be in the flavor profiles of the products. For example, the company could experiment with new flavor combinations or add new variations of the existing flavors. Additionally, the company could innovate in terms of the product’s nutritional profile. For example, the company could add new products that are high in protein or fiber. Finally, the company could also focus on marketing innovation. For example, the company could create new marketing campaigns that target a specific demographic or focus on a new benefit of the product.
SQUID
All of them except those who never made a forecast. Worst-case examples include the Ford Edsel and the New Coke.