Teknik Peramalan Bisnis

Authors

Nadia Azalia
UIN KHAS Jember
Lilik Farida
UIN Kiai Haji Achmad Siddiq Jember
Izzul Ashlah
UIN Kiai Haji Achmad Siddiq Jember
Sofiah
UIN Kiai Haji Achmad Siddiq Jember

Keywords:

teknik peramalan bisnis, teknik proyeksi bisnis

Synopsis

Peramalan merupakan bagian integral dari aktivitas pembuatan keputusan manajemen. Sebuah organisasi menentukan tujuan, kemudian mencari faktor lingkungan yang dianggap mempengaruhi pencapaian tujuan; dan menentukan tindakan untuk mencapai tujuan itu. Kebutuhan kepada peramalan adalah meningkatkan upapa manajemen untuk menekan ketergantungan mereka terhadap perubahan dan mencoba bersikap lebih ilmiah berkaitan dengan perubahan lingkungan.

Buku ini membahas alat (tool) untuk membuat perkiraan (proyeksi) peristiwa di masa yang akan datang menggunakan pendekatan statistika, yaitu menggunakan metode times series smoothing, metode dekomposisi, metode regresi, metode autoregressive/moving average, dan pendekatan normatif peramalan teknologi. 

References

Alderson, R. C.,, and W. C. Sproull, 2001, Requirement Analyis, Need Forecast- ing, and Technology Planning, Using the Honeywell PATTERN Tech- nique, In Industrial Applications of Technological Forecasting, ed. M. Cetron, John Wiley & Sons, New York.

Box, G. E. P., and G. M Jenkins, 2006, Time Series Analysis: Forecasting and Control, Revised Edition, Holden-Day, SanFrancisco, California.

Chambers, J.C., S. K. Mullick, and D.D Smith, 2011, How to Choose the Right Forecasting Technique, Harvard Business Review, July – Augustus.

Christ, C F., 2006, Econometric Models and Method, John Wiley & Sons, New York.

Cox, D.R., 2001, Prediction by Exponentially Weighted Moving Averages and Related Method, Journal of The Royal Statistical Society, Series B, Vol. 23. No. 2. Pp 413 – 422.

Forecasts, New York, McGraw-Hill Book Company.

Freund, J. E., 2012, Mathematical Statistics, Prentice Hall Book Coy., Engelwood Cliffs, New Jersey.

Freund, J. E., and F. J. Williams, 2009, Modern Busiess Statistics, Prentice-Hall Engelwood Cliffs, New Jersey.

Goldberger, A. S., 2004, Econometric Theory, John Wiley & Sons, New York. Pindyck, R. S., and D. L. Rubenfeld, 2006, Ekonometric Models and Economic

Gross, A. C. and W. W. Ware, 2005, Profiles of the Future: Energy Prospect in 2050, Business Horizons, June, pp. 5 – 18.

Holt, C. C., F. Modigliani, J. F. Muth, and H. A. Simon, 2000, Prentice Hall Inc., Planning Production and Inventories and Work Force, Engel- wood Cliffs, New Jersey.

Locke, F. M., 2016, Business Mathematics, John Wiley & Sons, New York. Koosis, D. J., 2012, Business Statistics, John Wiley & Sons, New York.

Macauley, F. R., 2001, The Smoothing of Time Series, National Bureau of Eco- nomic Research, USA.

Makridakis, S. A., and S. Wheelwright, 2007, An Interactive Forecasting System, The American Statistician, November.

Makridakis, S., and S. Wheelwright, 2008, Interactice Forecasting, Third Edition, Holden-Day, San Francisco, California.

Nelson, C. R., 2003, Applied Time Series Analysis, Holden-Day, San Francisco, California.

Robert, E., 2009, Explanatory and Normative Technological Forecasting: A Criti- cal Appraisal, MIT Working Paper, No. 378-69.

Shiskin, J, 1997, Test and Revisions of Bureauof the Cencus Methods of Ad- jutments, Bureau of the Cencus, Technical Paper no. 5.

Shiskin, J, A. H. Young and J. C. Musgrave, The X-11 Variant of the Cencus II Methos Seasonal Adjustment Program, Bureau of the Cencus, Technical Paper no. 15.

Spurr, W. A., and C. P. Bonini, 2007, Statistical Analysis for Business Decisions, Homewood III, Irwin.

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