Forecasting
In order for a business to be successful it must come up with the most accurate forecast possible so they can plan for the demands. There are forecasting tools that assist with making calculations to receive the best outcome by your company’s needs. The tools are moving average, weighted moving average and exponential smoothing.

The moving average takes the total of actual demand for previous months then divides by the number of months added. The number of months that is used can be predefined such as using the previous three months. This is the simplest and easiest calculation but often is not accurate since it can have a lag in spotting trends (Murphy).

The weighted moving average is similar to the moving average but it places weights on each period usually with more recent periods weighing more. For example if you are averaging the past three months with the most recent month being the most valuable you would multiply the last month by .3 and then month before that at .2 and then the first month by .1 then by adding them together you would get the average with more of an emphasis on the month with the most weight (Career Education Corporation, 2010).

Lastly there is the exponential smoothing tool. The exponential smoothing tool is a moving average which is ideal for forecasting smaller items. It uses the most recent demand along with the most recent forecast using a weight between 0 and 1 (Bozarth, 2011). In other words the forecast is the current forecast plus an adjustment for the error rate. This tool is very useful to take in considerations trends occurring.

Before a company selects a forecasting technique it should consider that what every technique used can be implemented correctly and maintained. Then the company should adjust parameters so that they match the company’s situations. As well as continually monitoring the forecast for accuracy, if the forecast being calculated is way off it may be best to implement a...

...
Forecasting
HSM/260
January 17, 2014
Janice Gilstorff
Forecasting
Exercise 9.1
Forecasting is a guess of what the financial future holds (production output or sales). In the scenario in the book exercise 9.1 they want you to forecast what the 20X5 figures would be. It does give you some background information, such as the Human services expenses over the past four years.
20X1 [$5,250,000]
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
Weighted movingaverages and movingaverages, just use the data for the past three fiscal years. This would look like this
MovingAverages-
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
20X5 [$6,083,000]
With just the three we already knew the total of $18,250,000. If you divide the total by three you get, $6,083,000.
Weight averages-
20X2 $5,500,000 1=$5,500,000
20X3 $6,000,000 2=12,000,000
20X4 $6,750,000 3=$20,250,000
20X5 $6,300,000 6=$37,750,000
For 20X5 I divided by 6 (which represents the values 1+2+3=6), which equals $6,291,667 or $6,300,000 as a weighted average. From the information gathered a prediction for the forecast can be made.
Exponentialsmoothing:
The alpha method of 0.95 would work here. The formula would look like this: NF=LF + a (LD-...

...Choose one of the forecasting methods and explain the rationale behind using it in real life.
I would choose to use the exponentialsmoothing forecast method. Exponentialsmoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be higher in demand in the present than in the past Taylor (2011). For example, annual sales of toys will probably peak in the months of March and April, and perhaps during the summer with a much smaller peak. This pattern is likely to repeat every year, however, the relative amount of increase in sales during March may slowly change from year to year. During the month of march the sales for a particular toy may increase by 1 million dollars every year. We could add to our forecasts for every March the amount of 1 million dollars to account for this seasonal fluctuation.
Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model.
By looking at the data of other companies the fast food chain would be able to put...

...Demand Forecasting Problems
Simple Regression
a) RCB manufacturers black & white television sets for overseas markets. Annual exports in thousands of units are tabulated below for the past 6 years. Given the long term decline in exports, forecast the expected number of units to be exported next year.
|Year |Exports |Year |Exports |
|1 |33 |4 |26 |
|2 |32 |5 |27 |
|3 |29 |6 |24 |
b) A small hospital is planning for future needs in its maternity wing. The data below show the number of births in each of the past eight years.
|Year |Births |Year |Births |
|1 |565 |5 |615 |
|2 |590 |6 |611 |
|3 |583 |7 |610 |
|4 |597 |8 |623 |
Use simple linear regression to forecast the annual number...

...havebeen $48,000, $64,000,$67,00 and $83,000, respectively
a) What sales would you predict for 2013, using a simple four-year movingaverage?
F2013 =
= $65,500
$65,000 is the forecast for 2013
b) What sales would you predict for 2013, using a weighted movingaverage with
weights of0.50 for the immediate preceding year and 0.3, 0.15, and 0.05 for the
three years before that?
F2013 = 0.50A2012 + 0.3A2011 + 0.15A2010 + 0.05A2009
=0.50(83000) + 0.30(67000) + 0.15(64000) + 0.05(48000)
= 41,500 + 20,100 + 9,600 + 2,400
= $73,600
$73,600 is the forecast for 2013
Q2. Using exponentialsmoothing with a weight of 0.6 on actual values:
a) If sales are $45,000 and $50,000 for 2010 and 2011, what would you forecast for 2012?
(The first forecast is equal to the actual value of the preceding year.)
Actual values are
2010: $45,000
2011: $50,000
α = 0.6
F2012 = 0.60A2011 + 0.40A2010
= 0.60(50000) + 0.40(45000)
=48000
Forecast for 2012 is $48,000
b) Given this forecast and actual 2012 sales of $53,000, what would you then forecast for2009?
Actual value of 2012 = $53,000
F2009 =
Q3. In question 4-1, taking actual 2009 sales of $48,000 as the forecast for 2010, what sales
would you forecast for 2011, 2012, and 2013, using exponentialsmoothing...

... |375,000 |620,000 |
Bob new the his labor cost per hour has increased from average of $13 per hour to an average of $14 per hour, primarily due to a move by management to become more competitive with a new company that had just opened a plant in the area. He also knew that his average cost per barrel of new material had increased from $320 to $360. He was concerned about the accounting procedure that increased his capital cost from $375,000 to $620,000, but earlier discussions with boss suggested that there was nothing that could be done about the allocation.
Bob wanted if his productivity had increased at all. He called Sharon and conveyed the above information to her and asked her to prepare this part of the report.
Discussion Question;
Prepare the productivity part of the report for Mr Richards. He probably expects some analysis of productivity inputs for all factors, as well as a multifactor analysis for both years with the change in productivity (up or down) and the amount noted.
Solution Q 1 [20 marks]
Question # 2 [20 Marks]
Consider the following two techniques for forecasting F1 and F2. The actual and the two sets of forecast are as follows...

... |45000 |
|Energy |5000 |6000 |
|Capital |50000 |50000 |
|Others |2000 |3000 |
2.Assume that in past years,a firm sold an average of 1000 units of a particular product line each year.On the average,200 units were sold in the spring,350 in the summer,300 in the fall and 150 in the winter.Compute the seasonal relatives for each season.If the expected demand in the subsequent year is 1100 units,use the seasonal relatives to forecast the seasonal demand.
3.A specific forecasting model was used to forecast the demand for a product.The forecast and the corresponding demand that subsequentlyy occurred are shown below.Use the MAD and tacking signal to evaluate the accuracy of the model.
|Month |Actual |Forecast |
|October |700 |660 |
|November...

...Practice Problems: Chapter 4, Forecasting
Problem 1:
Auto sales at Carmen’s Chevrolet are shown below. Develop a 3-week movingaverage.
|Week |Auto Sales |
|1 |8 |
|2 |10 |
|3 |9 |
|4 |11 |
|5 |10 |
|6 |13 |
|7 |- |
Problem 2:
Carmen’s decides to forecast auto sales by weighting the three weeks as follows:
|Weights Applied |Period |
|3 |Last week |
|2 |Twoweeks ago |
|1 |Three weeks ago |
|6 |Total |
Problem 3:
A firm uses simple exponentialsmoothing with [pic] to forecast demand. The forecast for the week of January 1 was 500 units whereas the actual demand turned out to be 450 units. Calculate the demand forecast for the week of January 8.
Problem 4:
Exponentialsmoothing is used to forecast automobile battery sales. Two value of [pic] are examined, [pic] and [pic] Evaluate the accuracy of each smoothing constant. Which is preferable? (Assume the forecast for January was 22 batteries.) Actual sales are given below:
|Month |Actual |Forecast |
| |Battery Sales| |
|January |20 |22...

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