FORECASTING. Operations Management


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1 2013 FORECASTING Brad Fink CIT 492 Operations Management
2 Executive Summary Woodlawn hospital needs to forecast type A blood so there is no shortage for the week of 12 October, to correctly forecast, a 3week moving average, a 3week weighted moving average and an exponential smoothing forecast will be completed. An undisclosed business which will be referred to as case 4.2 has provided data for a plotted graph to determine any trends, cycles or random variations in sales. Again a moving average and weighted moving average will be utilized to see the differences. Telco Batteries, Inc., has provided monthly sales for the current year. The General Manager wants a simple plot, a Naïve forecast, a moving and weighted moving forecast for the month of January of the next year. The Omaha Emergency Medical Clinic has given the past six weeks of patient demand and would like to see what a forecast in week seven may be. A weighted moving forecast will be performed to give the best possible data available. Dell uses the CR5 computer chip in some of their laptops. With twelve months of data on the price of this chip, Dell would like to see a 2month and 3month moving average plotted on a graph to determine which is best, as well as a exponential smoothing average. Coffee Palace s manager, Joe Felan has determined that the price on a cup of Mocha Latte influences the sales, based on his observations, Joe wants to know what the number of cups sold might be if a cup costs $2.80. Marty and Polly Starr runs a bed and breakfast with a bar. The number of guest for the past four weeks have be provided and the Starr s would like to know what to expect in bar sales if the number of guest reaches twenty. 1
3 Contents Woodlawn Hospital...3 Case Telco Batteries, Inc....9 Omaha Emergency Medical Clinic...13 Dell...15 Coffee Palace...18 Marty & Polly Starr Summary
4 Woodlawn Hospital Running short on supplies of type (A) blood, Woodlawn Hospital needs to ensure the supply is on demand for the weekend of October the 12 th. The best possible solution is to conduct a 3 week moving forecast. To accomplish this, the past five weeks of blood in pints have been provided in Table 1, which shows the calculations, with the October 12 result. To find how much type (A) blood is needed, take the pints used for the weeks of Aug 31 to Sep 14, add the pints used and dived the sum by 3; this will give a slightly higher number of pints than what was actually used during that time frame. This is not a bad thing, a little more is better than ending up with a shortfall. To complete this cycle Oct 12 will have the sum of weeks 21 Sep to 5 Oct divided by 3 giving a forecast of 374 pints of type (A) blood needed. To check and see if the forecast could be more precise or at least see another point of view, the E.R. Administrator has decided to get a weighted moving average. A weighted moving average indicates the subjective importance placed on past or recent data. Weights can be from 0.0 to 1.0; the higher the weight, then the higher importance it has on the most recent data. 12 Oct Forecast: 3Week Moving Average Period Pint Used 3Week Moving Avgerage 31Aug Sep Sep Sep Sep Oct Oct 374 Table 13Week Moving Average In this case, the weights being used are 0.1 for three weeks ago, 0.3 for two weeks past and 0.6 for the previous week. Just like Table 1, the administrator wants a forecast for the week of 12 Oct. The first thing needed is to find the total of the weights, (0.1, 0.3 and 0.6) summed up the result is 1.0. Since the three previous weeks are being used, the first calculation will begin with 21 Sep. Simply multiply the pints of blood used during 31 Aug by 0.1 and add that to the pints used during 7 Sep multiplied by 0.3 and add that to the pints used during 14 Sep, divide the result by the total weight of one which gives the final forecast of 399 pints of type (A) blood needed, again slightly higher than that actually used. Table 2 continues the calculations until the 12 Oct results are finished 3
5 Woodlawn Hospital. 12 Oct Forecast: 3Week Weighted Moving Average Period Pint Used Weights 3Week WMA 31Aug Sep Sep Sep Sep Oct Oct 373 As shown in the Table to the left, the 12 Oct forecast has provided a result of 373 pints of type (A) blood needed by using the 3week weighted moving average. To recap, the formula used was: (381*0.1) + (368*0.3) + (374*0.6) 1 = 373 Table 23week Weighted Moving Average/Forecast To get a visual idea of how this looks the E.R. administrator wants a graph, but does not want to show his superiors extreme spikes and drops. An exponential smoothing graph showing the forecast will do exactly that. Again a weight is needed, for this graph the weight of 0.2 is being used. The formula which will capture the smoothing average is ( ). In which, is the new forecast, is the previous period s forecast, is the smoothing (weighted) constant of (0.2), and is the previous period s actual demand. Easier yet, for those who despise math, the new forecast = the last period forecast *(last period actual demand last period forecast). Figure 1 shows the smoothing graph for Woodlawn Hospital. 4
6 Woodlawn Hospital Pints Used 31Aug 7Sep 14Sep 21Sep 28Sep 5Oct 12Oct Smoothing Forecast Figure 1 Woodlawn Hospital s Smoothing Graph Pints Used Smoothing Forecast Figure 1 is represented using the data from Table 3. Notice the forecast could not be done on the first week; the week of 7 Sep forecast will begin with the previous week s actual demand. Using the formula and working down the formula will look as so: 14 Sep forecast equals (31 Aug pints actually used 7 Sep forecast, or (14 Sep forecast = ( ). Period Pint Used Smoothing Forecast 31Aug Sep Sep Sep Sep Oct Oct 374 Table 3 Smoothing Average 5
7 Demand FORECASTING Case 4.2 Having been asked to plot a graph on data which has been provided in Table 4, whether or not any trends, cycles or random variations has also been requested. Taking the data that was providing, an easy graph was completed and is displayed in Figure 2. Year Demand Table 4 Data provided for graphing The starting point (year 1), has a reoccurring cycle at the end of year 11 and beginning of year 12, this pattern appears to cycle every 12 years. All other graphing data shows no real trends or cycles, there are a few random variations but nothing out of the ordinary. Trend, Cycle, Random Variation Chart Trendline 4 Demand Year Figure 2 Trend Chart Along with the graph in Figure2, a 3Year moving forecast has also been requested. While looking at the 3Year moving forecast below Figure 3, take notice how it has smoothed the appearance of the actual demand forecast. To achieve this 3Year moving average had to be completed, which can be seen in Table inside Figure 3. Taking the sum of the demand in years 1, 2 and 3 then divide that by three, the result will be year four s moving average. To complete this simply move down the line, the important thing to pay attention to is the moving average is equal to the previous 3 year demands divided by 3, Table 5 shows the completed calculations. 6
8 Case 4.2 Demand 3 Year Moving Forecast Year Demand Moving Forecast Figure 33 Year Moving Average & Forecast The last forecast needed to weigh all options is the weighted average. By placing a weight standard on each year s demand, a forecast may seem more realistic than that of the 3Year moving forecast. The weights associated with the demands normally are numbers ranging 0 to 1.0, with the biggest number being associated with the nearest month, since a 3Year average is still in effect, there will be three different weighted numbers. The predetermined numbers given are (0.1, 0.3 and 0.6). Again using the formula (0.6*last month demand)+(0.3*demand 2 months ago)+(0.1*demand 3 months ago) / 1, which is represented in Figure 4. After doing all the equations, the weighted average can then be placed into the graph in Figure 3. The weighted average that is placed into the graph will be referred to as the weighted forecast in Figure 4. Taking a good look at all three graphs, the Trend graph, 3Year moving forecast and the weighted forecast, the weighted forecast gives a better depiction of a good accurate forecast. Following the green graphing line, it is not only a medium of the actual demand and the moving 7
9 Demand FORECASTING Case 4.2 forecast. Notice that it is not as smooth as the 3Year forecast and yet it does not give a presence of drastic declines as does the actual demand. Taking a good look at all three graphs, the Trend graph, 3Year moving forecast and the weighted forecast, the weighted forecast gives a better depiction of a good accurate forecast. Following the green graphing line, it is not only a medium of the actual demand and the moving forecast. Notice that it is not as smooth as the 3Year forecast and yet it does not give a presence of drastic declines as does the actual demand. 14 Weighted Forecast Time in Years Trend Demand Moving Forecast Weighted Figure 4 Weighted Forecast 8
10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sales FORECASTING Telco Batteries The monthly sales have been provided for a one year time frame, the general manager wants to see a graph for the years sales, so he has asked for a simple sales chart. Taking the data given by the GM, in Table 5 and monthly sales chart has been provided shown in Figure 5. Month Sales Jan 20 Feb 21 Mar 15 Apr 14 May 13 Jun 16 Jul 17 Aug 18 Sep 20 Oct 20 Nov 21 Dec 23 Table 5 Telco Sales Sales Chart Figure 5 Telco Batteries Monthly Sales Chart Sales After looking at the sales the GM wanted to see what a simple forecast of sales would be for the next January, a Naïve forecast is the simplest method of determining a forecast by taking the last month s sales and placing the value as next month s forecast, Table 6 shows January sales forecast using the Naïve method. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Sales Table 6 Telco Jan Naïve Forecast By taking the sales during December, a store can use that data in predicting the next month s sales as is with the last January in Table 6 equaling that of the previous month in December. Now that the Telco GM has seen the sales and the Naïve forecast, he wanted to see a forecast with a little more accuracy. To do this a 3month moving forecast has been calculated, and plotted for his viewing. Figure 6 will show both the 3month moving average inserted into the 3 month moving forecast chart. 9
11 Telco Batteries Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 3Month Moving Forecast Actual Sales 3Month Moving Month Sales 3Month Jan 20 Moving Feb 21 Average Mar 15 Apr May Jun Jul Aug Sep Oct Nov Dec Jan 21 Figure 6 Telco Batteries 3Month Moving Average/Forecast By taking the sum of sales for Jan(20), Feb(21) and Mar(15) that value needs to be divided by three, this will give the 3month moving average for April, this will continue until the end, so the Jan 3month forecast will be (Oct + Nov + Dec) divided by 3, or ( )/3 which will give Jan a 3month moving forecast of 21. Now that the GM has seen how the 3month moving forecast correlates with the actual sales, he has also asked to see what a 6month forecast would look like, To help in accomplishing this some weights have been assigned to each six previous months, these weighted numbers will be within a normal weight range of zero to one. In the case of Telco Batteries, the weights that correspond to each month is (.1,.1,.1,.2,.2 and.3); the first series of 0.3 being applied to the most recent month and working down to 0.1 that applies to the farthest month. All these weights summed up have a value of 1.0 which will be used in the division of the formula. Figure 7 will show the data table along with the forecast chart to show a more precise upward trend compared to that of the 3month moving forecast in Figure 6. To reach these results the weights are multiplied to corresponding sales for that month. An example of how this is achieved 10
12 Telco Batteries the following formula is used; (the actual sales for Jan*0.1)+(Feb*0.1)+(Mar*0.1)+(Apr*0.2)+(May*0.2)+(Jun*0.3)/1. Remember that the divisor was the sum of all numbers of weights. This will be the results for the 6month weighted forecast for the month of July, the next forecast for August will be the same except the formula will skip the sales of January and start with February. 6Month Weighted Forecast Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Sales Month Weighted Average Figure 76Month Weighted Average and Forecast With the exception of the January forecast being off slightly between the 3month moving forecast and the weighted forecast there is really no difference, this is why the GM has requested a smoothing forecast, this should give him what the forecast suggests, a smoothing effect to the forecast from the two previous forecasts already done. Figure 8 will show both data table and chart to show Telco Batteries smoothing forecast chart. 11
13 Telco Batteries 25 Smoothing Forecast with Trend Line 20 Sales Sales Smoothing Trend Line 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Figure 8 Telco Batteries Smoothing Average and Forecast Chart While just looking at Figure 8, the GM has noticed the forecast is much smoother while giving almost the same exact forecast as in the prior two forecast, except the smoothing forecast has both a gradual decline in sales forecasts as well as a gradual spike in sales. With the smoothing forecast complete he needs to see a trend projection. Figure 8 shows the same forecasting chart as Figure 7 with the addition of the trend line based off the monthly sales, the trend line for Telco Batteries shows a slow but gradual positive trend in sales for the year. 12
14 Omaha Emergency Medical Clinic Marc Schniederjans needs a forecast the patient demand for week seven using the data from weeks 1 thru 6. After looking at all options, the weighted average/forecast will present more emphasis on the data since the data given is so short. The data that Marc has given can be seen in Table 5. ACTUAL WEEK NO. OF PATIENTS The values in the forecast Table 6 will be for week seven, also this will be determined from a 4 Week weighted average. The weighted numbers for the Table 5 data are (0.333 on the present period, 0.25 one period ago, 0.25 two periods ago, and three periods ago). ACTUAL WEEK NO. OF PATIENTS Table 5 Data for a Weighted Forecast Weighted 3 Periods 2 Periods 1 Period Ago Ago Ago Present Table 6 Week 7 Weighted Forecast After completing the 7Week weighted forecast, a confirmation check is performed with weighted number extremely out of the normal weighted range of 0 to 1.0, the number used were (20 replacing 0.333, 15 replacing 0.25, 15 replacing 0.25 and 10 replacing 0.167) the results are presented in Table 7. Total Week 7 Forecast based of the weighted numbers (0.33, 0.25, 0.25 and 0.167) 13
15 Omaha Emergency Medical Clinic ACTUAL WEEK NO. OF PATIENTS Weighted 3 Periods Ago 2 Periods Ago 1 Period Ago Present FORECASTING Using the numbers outside the normal weighted range the new 7 week forecast shows a 6,066% increase which is substantially similar to the total weights periods of 60. Total Now that the integrity check on weighted number beyond the normal range is complete a different set of numbers are used, this time within the normal weighted range. The numbers being used to replace the original respectively are, (0.40, 0.30, 0.20, and 0.10). Table 8 will show the results of the changes. ACTUAL WEEK NO. OF PATIENTS Weighted 3 Periods Ago 2 Periods Ago 1 Period Ago Present Total Weighted Periods Table 8 Weighted Forecast confirmation check Total Since all number are within the normal weighted range in Table 8, and with such minor differences, the forecast is approximately 5% greater, which coincides with the data given, the difference is only off by 2 forecasted patients. 14
16 Dell Dell Computers has been tracking the cost of the CR5 chip used in some of their laptops for the past year, the tracking list has been provided and a 2month moving average is needed as well as plotting the information in a 2month moving forecast, the data is provided in Table 9 with the 2 month moving average. In order to help Dell get the 2month moving average, the values in January and February are summed and divided by 2, this will be the 2month moving average or forecast for the month of March. This process will continue progressively until the months of November and December, these two months will be for the next January. Table 9 displays the finished 2month moving average which is needed in order to do a plotted chart shown in Figure 9. $1.95 $1.90 $1.85 $1.80 $1.75 $1.70 $1.65 $1.60 $1.55 $1.50 Jan Feb Mar 2Month Moving Forecast Apr May Jun Jul Aug Sep Oct Nov Dec Jan Price per Chip 2MonthMoving Average Price 2 Month per Chip Month Jan $1.80 Moving Feb $1.67 Average Mar $1.70 $1.74 Apr $1.85 $1.69 May $1.90 $1.78 Jun $1.87 $1.88 Jul $1.80 $1.89 Aug $1.83 $1.84 Sep $1.70 $1.82 Oct $1.65 $1.77 Nov $1.70 $1.68 Dec $1.75 $1.68 Jan $1.73 Table 10 2Month Moving Average Figure 92Month Moving Forecast Along with the 2month moving average and forecast, Dell also needed to see what a 3month moving average and forecast chart would look like compared to the 2month mmoving average and forecast. Figure 10 shows Dell exactly what they need to help predict future cost of the CR5 computer chip. 15
17 Dell $1.95 $1.90 $1.85 $1.80 $1.75 $1.70 $1.65 $1.60 $1.55 $ Month Moving Forecast Price per Chip 2MonthMoving Average 3Month Moving Average Figure 103Month Moving Forecast The 3month moving average is done with the same process as the 2month average with the exception that three months of values are added and the divided by three. Taking a look at the difference between the two averages, it would appear that the 3month moving average is just slightly higher than that of the 2month moving average. Since it is easier to predict a near term forecast than it is for a farther out forecast, the 2month average better predicts the cost of the CR5 chips. Dell has additionally requested an exponential smoothing forecast for each month. The weighted numbers are within a normal weighted range. To do this each month will be calculated using each set of numbers which are: ( =0.1, =0.3 and =0.5). In addition to these factors, the beginning forcast for January will be $1.80. Table 11 will show the final calculations of the smoothing forecast and using the Mean Absolute Deviation (MAD) formula, we will be able to tell which weighted factor involved is best. Using the formula for the forecast (New Forecast= Last forecast + (Actual DemandLast Forecast), the error can now be calculated by subtracting the new forecast from the actual demand, if the value is a negative simply treat it as an absolute number. Once all columns are completed, add all the values under the error column which will then be divided by the number of periods, in this case twelve. Since the MAD computation has the lowest value of $0.068, this will be the best smoothing forecast scenario, which is using the 0.5 weighted score number. 16
18 Dell Month Price Forecast per Chip Error Forecast Error Forecast Error Jan $1.80 $1.80 $0.00 $1.80 $0.00 $1.80 $0.00 Feb $1.67 $1.80 $0.13 $1.08 $0.13 $1.80 $0.13 Mar $1.70 $1.79 $0.09 $1.76 $0.06 $1.74 $0.04 Apr $1.85 $1.78 $0.07 $1.74 $0.11 $1.72 $0.13 May $1.90 $1.79 $0.11 $1.77 $0.13 $1.78 $0.12 Jun $1.87 $1.80 $0.07 $1.81 $0.06 $1.84 $0.03 Jul $1.80 $1.80 $0.00 $1.83 $0.03 $1.86 $0.06 Aug $1.83 $1.80 $0.03 $1.82 $0.01 $1.83 $0.00 Sep $1.70 $1.81 $0.11 $1.82 $0.12 $1.83 $0.13 Oct $1.65 $1.80 $0.15 $1.79 $0.14 $1.76 $0.11 Nov $1.70 $1.78 $0.08 $1.75 $0.05 $1.71 $0.01 Dec $1.75 $1.77 $0.02 $1.73 $0.02 $1.70 $0.05 $0.86 $0.86 $0.81 MAD (Total/12) Table 11  Exponential Smoothing Chart using weighted Numbers (0.1, 0.3 and 0.5) Using MAD, the Error total divided by 12 rates the best choice. 17
19 Coffee Palace Joe Felan suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Joe has gathered the following data, which show the numbers of these coffees sold over six different price values. Using this data, Joe would like to know the forecast if the price for a cup of coffee were $2.80. The data Joe has provided is in Table 12 below. PRICE NUMBER SOLD $ $ $ $ $ $ Table 12 Coffee Palace Historical Data Using Table 12, the forecast will be determined by using a simple linear regression method based off the price of coffee at $2.80. The forecast will be performed in a few steps, so taking one step or mathematical equation at a time will be the easiest way about it. First taking the data from Joe s observation, an updated Table will need to be done. Table 13 will help with formulating the steps. PRICE NUMBER (x) SOLD (y) x² xy , , , , ,944 Total: , ,783 Table 13 x, y Factor Chart To find the values for x², simply start from top to bottom under the price column and square that particular value, for example 2.70² will be the first value for the x² column which is Continue down all 6 rows until complete. The next step is to find the xy value, by multiplying the value in price by the value in number sold the xy value will be completed. Now that all six rows in Table 13 are complete, the next phase is ready for calculation. By adding all the values in the price column, the result is For all following equations the 18
20 Coffee Palace value for n will be the total number of observations which is six. Now that the value x and value n as been determined, by using the next formula the value for can be computed. = Value of dependent variable, (Sales), or ( ) a = yaxis intercept b = Slope of regression line x = Independent variable (2.80) The next step is to find the mean average ( ) of all the prices, to do this add all six prices and dive that sum by (n), the next equation will give the final result. =, or = 3.26; The next equation to be completed is finding the mean average of the number of cups sold, the process is the same as the previous equation. = = 550; To continue the data in Table 13 will be used to find the value of the slope of regression (b). Taking the sum of (xy) and subtracting the total observations multiplied by ( ), divide that value by the sum in x² minus the number of observation multiplied by the result will be that in the equation below. = To find the value of the dependent variable follow the equation below, remember that the value of (b) is a negative number. a = b = 550( )*3.26, a = Now that all data needed is complete, the last step is to find the value of. This will be calculated using the value of (a), (b) and the price per cup that Joe wants forecasted, (2.80). The equation below will finish the last step before making a plotted forecast. Sales = a + bx = ( ) * 2.80, = 677 With all information available, a scatter plot can now show Joe the forecast based on the data he provided. Looking at the forecast in Figure 11, it clearly proves Joe s theory that the cost of a cup of Mocha latte does in fact impact the sales. With the economy being what it is, there are 19
21 Coffee Palace fewer customers willing to spend over $3.00 per cup, and while charging only $2.00, the profits do not justify selling a cup at such a low price Coffee Palace Mocha Forecast Regression Line Cups Sold Cups sold at $2.80 y = x Number sold 0 $0.00 $1.00 $2.00 $3.00 $4.00 $5.00 Price Per Cup Figure 11 Regression Forecast for Mocha Latte Selling a cup of Mocha Latte at $2.80 will bring in gross sales of $1, The difference between selling at $2.00 is $64, but after expenses, the overall profits suggests this would be a good move, not to mention that selling a cup at $2.70 is by far the biggest gross sales of $2,052. Joe should make this move in order to collect data for another analysis. 20
22 Marty and Polly Starr Marty and Polly Starr have provided the data in Table 14 that pertains to the number of guest registered in their bed and breakfast. The data was obtained from a four week period which they consider an appropriate time frame to forecast the bar sales for twenty guests. To give the Starr s an accurate forecast; a linear regression will be used. Week Guests (x) Bar Sales (y) 1 16 $ $ $ $380 Table 144Week Guest to Bar Sales (x) and (y) factors for the Linear Regression Calculations Table 14 gives the base of information needed in order to perform all further calculations; Table 15 shows the progression of the required data. Notice that the bottom row gives the total of the number of weeks, guest and bar sales, while the furthest two columns have multiplied the number of guest and bar sales giving a result of (xy). Again the far right column has taken the number of guest and squared it giving the values for (x²). Week Guests (x) Bar Sales (y) xy x² 1 16 $330 $5, $270 $3, $380 $6, $380 $5, Totals: 4 60 $1,360 $20, Table 15 Linear Regression Chart In a five step mathematical equation, the first step is to find the value of ( ), this is the mean average for the number of guest, the following equation below will show what the process looks like. =, or = 15; 21
23 Marty and Polly Starr Step two is to find the value of ( ), this will be the mean average for the bar sales, the equation for this is below and can easily be followed. = = 340; Step number three is to find the value of (b), this is known as the slope of the regression line, which is the next equation below. = In step four, the yaxis intercept value will be determined by using the end results for (, b and ) from steps one through three in the next equation below. After step four is complete, all results can be seen in Figure 14. a = b = 340(14)*15, a = 130 Using the formula a + bx, the linear regression that relates the bar sales to the number of guest can represented by: Sales = a + bx, or Sales = x The Starr s want to know what the sales in the bar might be if the forecasted number of guests reaches twenty. Again, using the x 15 y 340 b 14 a = 130 Figure 14 Linear Regression Results equation above and substituting (x) for (20), the equation now becomes; (14 * 20) which will give the estimated bar sales of $410. This equates to about $20.5 in bar sales per guest. 22
24 Summary Forecasting happens in smart businesses worldwide on a daily basis, for determining how much blood to order as in the case of Woodlawn Hospital. A poor forecast in this situation could result in lost lives. Of course a good forecast is only as good as the data that is required to make such a prediction. While there are several different ways to make a forecast, choosing the right method is vital to the accuracy needed. With Woodlawn Hospital the best choice was a moving forecast only because the information needed was good and accurate. With past raw data, not only can a forecast can be produced, but looking at any trends in sales, what the cycle of sales might look like or even looking into the random variations can be studied by plotting the information on a graph. A good manager can then adjust any ordering of products based on this type of information to avoid having an overstock of certain items which could take valuable space for another item which traditionally sells better at a particular time of year. In many cases a linear regression method of forecasting can help business owners decide what might happen to sales if the price per items were either raised or lowered, which was the case of Joe from Coffee Palace. With linear regression forecasting future sales based on other forecasts are possible to help determine sales in specific departments just like the Starr s bed and breakfast, in which the bar was one of the specific departments they were focusing on. No matter which method of forecasting is being done, the bottom line is there is no good forecast without a good foundation of data that is used for support. A good manager or analyst knows, good data in equals good data out, not to mention, having a strong accurate record of past history makes compiling that data much easier for forecasting, not to mention faster and more understandable. Without the fore mentioned, forecasting truly is like trying to pick information out of someone s brain, an impossible task for anyone. 23
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