person who walks through a cafe’s door. The sale of each entrée represents one customer. They forecast monthly guest counts‚ retail sales‚ banquet sales‚ and concert sales at each café. To evaluate managers an set bonuses‚ a 3-year weighted moving average is applied to cafe sales. "Menu planning". Using multiple regressions‚ managers can compute the impact on demand of other menu items if the price of one item is changed. 2 What variables‚ besides time‚ can influence guest count?
Premium Regression analysis Linear regression Forecasting
Eight Steps to Forecasting • Determine the use of the forecast □ What objective are we trying to obtain? • Select the items to be forecast • Determine the time horizon of the forecast □ Short time horizon – 1 to 30 days □ Medium time horizon – 1 to 12 months □ Long time horizon – more than 1 year • Select the forecasting model(s) |Description |Qualitative Approach |Quantitative Approach
Premium Time series analysis Forecasting Moving average
5 310 June 6 175 July 7 155 Aug 8 130 Sept 9 220 Oct 10 277.5 Nov 11 235 Dec 12 1. 3 Month Moving Averages: The forecast for April is average of Jan‚ Feb and Mar shipments‚ =(200+135+195)/3‚ enter D5=SUM(C2:C4)/3 in EXCEL file. Copy and paste this column. So‚ forecast for Dec. shipments is 244.17 2. Similarly‚ for Five Month Average will be E7=SUM(C2:C6)/5= 207.4; copy and paste the formula till the end. So‚ forecast for Dec is 203.50 3. EXPONENTIAL
Premium Moving average Exponential smoothing
Executive Summary Dumitri Mironescu is the owner of a limousine company in Las Vegas which currently consists of 17 vehicles. During the year of 2012‚ Dumitru decided that it was time to replace three of the company’s 17 vehicles. In addition‚ Dumitru wanted to add two new vehicles to his fleet of limousines. Dumitru submitted a business plan to the bank to finance his purchases. After reviewing his business plan‚ the bank was not comfortable with the company’s revenue forecast and needed further
Premium Forecasting Future Time series analysis
ending with week 11‚ forecast registration using a two-week moving average. Moving Average Forecast Ft = ie F3 = = = 21.5. Carrying this down the table through to week 11 gives: Week 1 2 3 4 5 6 7 8 9 10 11 Registrations 22 21 25 27 35 29 33 37 41 37 Forecast 21.5 23 26 31 32 31 35 39 39 (3 marks) c) Starting with week 5 and ending with week 11‚ forecast registrations using a four-week moving average. Moving Average Forecast Ft = ie F5 = = = 23.75. Carrying this down the
Free Exponential smoothing Moving average Forecasting
have risen by a high number of 40% over a two year period and are unpredictable‚ an exponential smoothing method would be most appropriate. This method weights highest on the most recent years sales history. For the detergent intermediates‚ a moving average method would be most appropriate because sales have been stable overall in the past years. There is no need to give the most recent periods a higher weight of importance. For the specialty chemicals division‚ it would be most effective to complete
Premium Exponential smoothing Forecasting Data analysis
customer. 2. Quantitative Method- Used when situation is “stable” and historical data exists. Used for existing products and current technology. Involves mathematical techniques. E.G.‚ forecasting sales of color televisions. Naïve approach‚ moving averages‚ exponential smoothing‚ trend projection‚ linear regression. Time Series Forecasting- Set of evenly spaced numerical data. Obtained by observing response variable at regular time periods. Forecast based only on past values‚ no other variables
Premium Forecasting Moving average Time series
Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method. Exponential smoothing 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
Premium Exponential smoothing Forecasting Time series analysis
Chapter 4_class exercise True/False 1. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product. Answer: TRUE 2. A time-series model uses a series of past data points to make the forecast. Answer: TRUE 3. Cycles and random variations are both components of time series. Answer: TRUE 4. One advantage of exponential smoothing is the limited amount of record keeping involved. Answer: TRUE 5. If a forecast is consistently greater
Premium Time series analysis Moving average Time series
thousands of dollars) for the years 2009 through 2012 havebeen $48‚000‚ $64‚000‚$67‚00 and $83‚000‚ respectively a) What sales would you predict for 2013‚ using a simple four-year moving average? F2013 = = $65‚500 $65‚000 is the forecast for 2013 b) What sales would you predict for 2013‚ using a weighted moving average 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
Free Exponential smoothing Moving average Time series analysis