predictions of automobile sales in the US for the month of March 2012. The prediction is to take into account the historic data (provided) and current marketing environment. At first‚ two approaches of the analytical (quantitative) method were used – moving average and exponential smoothing. The objective of doing so was to get an idea of the prediction based on historic data only. Once that was done‚ the marketing environment was taken into consideration - to see how it would effect the predictions made
Free Exponential smoothing Moving average
DEMAND FORECASTING Demand forecasting is the process of predicting future average sales on the basis of historical data samples and market intelligence. The volatility of demand from an average level is supplied from the safety inventory. Any forecast is likely to be wrong‚ so the focus should be on understanding the range of potential forecast errors and the level of safety inventory that will cater for peak demand. An important additional calculation is forecast bias. This is the cumulative
Premium Forecasting Future Prediction
for patterns in data -pattern vs noise‚ and noise is random and has zero average ideally -in real world. Noise is random but probably not with zero mean -time series usually decomposed into different affects (seasonality‚ tend‚ noise‚ etc) d(t) =(L+ back fit your data to see if you have a good forecast what would you do 1. Plot the data in excel forecast approaches were in order with charts moving average-look at last few periods and update each new period with the new data (ie
Premium Time series analysis Time series Moving average
Littlefield Technologies Game Strategy- Group 28 I. PROJECT MANAGEMENT: We can apply multiple project management concepts to planning the project‚ scheduling the project‚ and controlling the project. First‚ the project was planned and scheduled by setting a goal of completion. Considering the group’s total allotted time‚ our goal was to have the description of the game strategy completed 48 hours before the deadline‚ and to work collaboratively on the statistical spreadsheet 24 hours before the
Premium Capacity utilization Project management Moving average
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
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