1. SylabUZ
2. Wydział Mechaniczny
3. semestr zimowy 2022/2023
4. Management and Production Engineering - drugiego stopnia z tyt. magistra inżyniera
5. Business Prognossis and Symulation

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Business Prognossis and Symulation - opis przedmiotu

Cel przedmiotu

Skills and competences: statistical data analysis, time series, the use of econometric models, quality models, methods for modelling discrete and continuous processes,  use of methods of forecasting and simulation of processes in the enterprise.

Wymagania wstępne

Zapisz zmiany

Basic knowledge of: manufacturing processes, economics, statistics. Thorough  knowledge of Excel.

Zakres tematyczny

Lecture

W1  Production process. Manufacturing company. The importance of forecasts for the company. Basic concepts. Classification of forecasting methods. Forecasting process. Making managerial decisions. Forecasting methods. Forecasting horizon.

W2  Measures for the quality of forecasts. Errors of forecast ex post and ex ante.

W3  Quantitative forecasting methods. Forecasts based on time models. Formation of time series. Models of time series with trends.

W4  Analytical models. Linear exponential smoothing models. Autoregression and moving average (ARMA and ARIMA) models.

W5  Methods based on econometric models. Stages of formation of an econometric model. Single equation  econometric models.

W6  Qualitative forecasting methods. Forecasting based on heuristics. Analogue models. Models with leading variables. Models of cohort analysis. Market tests

W7  Simulation of continuous and discrete processes.

W8  Application scenarios in forecasting.

Laboratory

L1  Application of the method of least squares in forecasting. Determination of the regression line. Implementation of MNK Excel (LINEST).

L2  Extrapolation of a linear function of trend. Determination of the point and interval forecasts.

L3  Forecasting using non-linear trend model. Linearization of a function.

L4  Forecasting based on time series. Random and seasonal fluctuations. Forecasting  based on adaptation models. A naive method. Methods: simple moving average and weighted average.

L5  Exponential smoothing models (Brown's, Holt's and Winters')

L6  Econometric models.

L7  Heuristic forecasting methods.

Metody kształcenia

Conventional lecture. Computer laboratory

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture: lecture credit is awarded after passing a written exam which verifies the knowledge of the issues included in the lecture curriculum.

Laboratory: graded credit, based on the component ratings of current tests.

Literatura podstawowa

1. Hanke J.E. Reitsch, Business Forecasting, Prentice Hall, Upper Saddle River, 1998.

2. Makridakis S., S.C. Wheelwright, V.E. McGee, Forecasting, John Wley, New York 1983

3. Chambers J.C. Mullick S.T., Smith D.D. How to chose the right forecasting technique. Harvard Business Review, Vol. 4., 1991.

 

Literatura uzupełniająca

1. Lapin L.L., StatisticfFor Modern Business Decision. Harcourt Brace Javanovich Inc., New York, 1987.

Uwagi

Zmodyfikowane przez dr inż. Tomasz Belica (ostatnia modyfikacja: 25-04-2022 09:38)