Analyze On The Forecasting Demand

Jul 2005, Volume 4, No.7 (Serial No.25) China-USA Business Review, ISSN 1537-1514, USA Analysis on the Forecasting Demand of Front-line Worker Zhenzhu Zhang∗ Tianjin University Abstract: The importance of personnel forecast is pointed out in this paper, and also the author expounds comparative analysis through instances among several methods of personnel forecast Key words: forecasting methods time series analysis causal forecasting Introduction of Forecasting Methods The normal forecasting methods are divided into three types, which are qualitative forecasting, time series forecasting and causal forecasting [1] Qualitative forecasting is used in the environment that lacks the statistic history information or the turn in the course of events [2] The primary methods are manager’s opinion, jury of executive opinion, sales force composite, consumer market survey, Delphi method, and so on The method of time series analysis is used in condition that has enough statistic history information The types are set out in Table Table The Methods of Time Series Analysis The name of forecasting method Last-value Account method Forecasting value = Last value [F] [L] Character Application No relativity between value Unstable time series Average F = Average of all values Notable relativity between value Quite stable time series Moving average F = Average of the latest n values Relativity between contiguous value, the data’s number reflect the degree of stabilization Moderate stable time series Adjusting α to adapt the different stabilization Time series from unstable to quite stable Change slowly or change by chance The equal value of probability distributing changes upwards or downwards Exponential smoothing F = α*L + (1-α)*(Last Forecasting Value) [LF] α ∈ ( 0,1) α: Smoothing constant F = α* L + (1-α)*(LF) + (Trend estimate) [T] T = β (The latest trend)[TL] + (1-β) (Last Exponential smoothing with trend ∗ ( ) time trend estimate) β ∈ 0,1 TL = α (L - Reciprocal second time value) + (1-α) (Last time forecasting value - Reciprocal second time forecasting value) β: Trend smoothing constant Zhenzhu Zhang (1974-), female, Ph.D candidate of School of Management, Tianjin University, Tianjin, China, Postcode: 300072; Main research field: Supply Chain Management; Tel: 13011332163; E-mail: zhangzhenzhu_74@126.com 64 Analysis on the Forecasting Demand of Front-line Worker There is another method belonging to the time series analysis, which is called ARIMA or Box-Jenkins method It is so complicated that is achieved by software It is applied in the problem, which has visible time variety Causal forecasting confirms the linear or nonlinear relations between dependent variable and independent variable through XY (Scatter) chart of observation data When there are many independent variables causing the variety of dependent variable, we call it as multi-linear regression On the great mass of application, the nonlinear relation can be changed into linear relation through forecasting the relation of dependent variable and independent variable [3] In the course of applying the forecasting, the work named model diagnose needs to choose or improve on the original model according to the series of target value The target value which is used to judge the model is mainly as mean absolute deviation (MAD), mean square (MSE), R2, Adj.R2 and so on The iterative work to diagnose model make the target value achieve the ideal precision Then, the opposite model of forecasting gains optimization In this paper, we use MAD and MSE as the target value Their formulas are as follows [4]: MAD the sum of forecasting error / the time of forecasting; MSE the square sum of forecasting error / the time of forecasting Analyzing from Example The company A is a non-shop that sells commodities for pregnant woman and baby through call center to order and confirm the price There, the products are mainly comprised by high quality eatable nurture, clothing, toy, washing commodity, interrelated books, magazines and remembrance Every year, the company posts the catalogs of product to plenty of users or potential consumers The users are told to purchase through the telephone number, which is printed on the catalog and then is connected to call center Usually, we estimate the number of client representation in the specific period of time through the statistic of call numbers In this paper, we use the above forecasting model to analyze the forecasting of front-line worker from this example Now we know the sum of call numbers in every quarter in the last three years as follows Table The Every Quarter’s Total Call Numbers and Sales of Company A in the Last Three Years The first year Name The second year The third year 4 Call number 6809 6465 6569 8266 7257 7064 7784 8724 6992 6822 7949 9650 Sale century 4894 4703 4748 5844 5192 5086 5511 6107 5052 4985 5576 6647 We use excel and the Software-Crystal Ball 2000 to process modular arithmetic In Table 2, the method of causal forecasting uses sales as independent variable and the linear regression equation as follows: y a b x, through this model, we use the method of least squares to confirm, then we get a = -1223.86; b = 1.6324 The result is in Table Table The Target Value of Every Forecasting Method The name of forecasting method MAD MSE 65 Analysis on the Forecasting Demand of Front-line Worker Last-value 295 145909 Average 400 242876 Moving average 437 238816 Exponential smoothing 324 157836 Exponential smoothing with trend 345 180796 Cell linear regression 35 1838 Note: In Exponential smoothing α = 0.5; In Exponential smoothing with trend α = 0.3, β = 0.3 From Table 3, we know that the call numbers have obvious season fluctuation and are closely correlative with distribution So the target value of linear regression is far less than the others The method of causal forecasting is more appropriate than the other methods in this case References: Shuangzeng Hu, Ming Zhang Logistics System Engineering, Tsinghua University Press, 2000: 58-60 Chunshan Feng, Jiachun Wu, Jiang Fu Research on the Integrate Exertion of the Qualitative and Quantitative Forecasting Method (Natural Science Edition), Journal of Donghua University, 2004(6): 114 Yongnin Jia Apply the Method Forecasting to the Decision-making, Railway Communication Signal, 2004(5): 12 Frederick S Hillie, Mark S Hiller Introduction to Management Science, China Financial & Economic Publishing: 550-562 (Edited by Dragon, Joy and Sun) 66 ... observation data When there are many independent variables causing the variety of dependent variable, we call it as multi-linear regression On the great mass of application, the nonlinear relation... relation through forecasting the relation of dependent variable and independent variable [3] In the course of applying the forecasting, the work named model diagnose needs to choose or improve on the. .. follows [4]: MAD the sum of forecasting error / the time of forecasting; MSE the square sum of forecasting error / the time of forecasting Analyzing from Example The company A is a non-shop that