SCM 485 Exam 1 Review Forecast Notes Supply Chain Management Sequence of activities and organizations involved in producing and delivering a good or service SCM Define by Council of Supply Chain Management Professionals (CSCMP) Supply Chain Management encompasses the planning and management of all activity involved in sourcing and procurement‚ conversion‚ and all logistics management activities. Importantly‚ it also includes coordination and collaboration with channel partners‚ which can
Premium Time series analysis Moving average Forecasting
several factors that should be considered when making this analysis. It has been provided that the costs of reconditioning the current equipment will be $50‚000 fixed costs and $1‚000‚000 variable costs. Additionally‚ purchasing new equipment will produce fixed costs of $200‚000 and variable costs of $500‚000 and outsourcing will have no fixed costs with variable costs of $300‚000. These figures can be input into the breakeven cost volume analysis module in POM for Windows and it can be determined
Premium Manufacturing Costs Cost
to write the name‚ student # and section # for each student in the group on the cover page of the assignment 1. Suppose you/your group is the owner of a company that produces e-readers. The present production rate is 1000 e-readers /day and the selling price is $210/unit. It requires 200 workers working 8 hours/day to produce the e-readers and they are paid $20/hour. The material cost is $100/unit and overhead cost is $50‚000/day. a) What is the unitless multi-factor (labor + material + overhead)
Premium Management Strategic management Marketing
FORECASTING FORECASTING The Role of the Manager Planning Organizing Staffing Leading Controlling Future ? Data Information • Short-range • Medium-range • Long-range Features Common to All Forecasts Forecasting techniques generally assume that same underlying causal system that existed in the past will continue to exist in the future. Forecasts are rarely perfect. Forecasts for groups of items tend to be more accurate than forecasts for individual items. Forecast
Premium Regression analysis Forecasting Time series
period) MAD ≈ 32.403 Exponential Smoothing or ES (adjusted for trend and seasonality) MAD ≈ 13.258 6 Q2: Forecast for January – April 2012 Month Mean Base Period MR SR ES January 0.61 49 825.27 745.12 720.56 February 0.88 50 1107.05 1082.68 1039.50 March 0.87 51 1105.66 1078.04 1027.69 April 1.05 52 1296.43 1310.32 1240.31 7 Q3: Best Forecast: Exponential smoothing forecast has lowest MAD Disadvantages: the exponential smoothing forecast should be updated
Premium Forecasting Regression analysis Time series analysis
203.50 3. EXPONENTIAL SMOOTHING: α=0.1 Ft+1 = α Xt + (1 - α ) Ft Let the starting point forecast be Jan sales‚ F1=200. And X1=200 Forecast for Feb‚ F2=200. Actaul Sales‚ X2=135 F3 (forecast for March)= α *X2+(1- α)*F2=0.1*135+0.9*200 In EXCEL: For the cell‚ F3 enter =$E$16*C2+(1-$E$16)*F2 where $E$16 is value of 0.1‚ which is the smoothing constant‚ α. So‚ the forecast for Dec is 205.56 The following shows the plot for Forecast by 3 MA‚ 5MA and Exponential Smoothing‚ 0.1 In last
Premium Moving average Exponential smoothing
RMSE/Mean* MAPE RMSE RMSE/Mean* Winter’s exponential smoothing 6.65% 4.65 7.74% 5.92% 6.73 28.74% Decomposition with exponential smoothing trend 5.45% 3.97 6.61% 4.63% 5.77 24.64% ARIMA(1‚0‚0)(2‚0‚0) 7.23% 5.11 8.51% 8.02% 6.56 28.01% *Mean of NHS for the historical period is 60.08 and for the holdout period is 23.42 The best model should be the one with the smallest error. Among these three time-series models‚ the decomposition with exponential smoothing trend has the smallest MAPE and RMSE
Premium Time series analysis Mean absolute percentage error Time series
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar‚ but more general term. Both might refer to formal statistical methods employing time series‚ cross-sectional or longitudinal data‚ or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example‚ in hydrology
Premium Forecasting
production planning and budgeting‚ cash budgeting‚ analyzing various operating plans. 6. There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. 7. Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8. MAD‚ MSE‚ and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model‚ forecast with
Premium Exponential smoothing
(A1) Forecast Demand (F1) 1 50 50 2 42 50 3 56 48 4 46 50 5 49 The first forecast F1 was derived by observing A1 and setting F1 equal to A1. Subsequent forecasts were derived by exponential smoothing. Using the exponential smoothing method‚ find the forecast time for period 5. (Hint: You need to first find the smoothing constant‚ α.) To find α: 50= 50 + α(42-50) -8α = -2 α = 0.25 F5 = 50 + 0.25(46 – 50) F5 = 49
Free Exponential smoothing Moving average Time series analysis