SylabUZ
| Course name | Business Prognossis and Symulation |
| Course ID | 06.9-WM-ZiIP-ANG-D-04_20 |
| Faculty | Faculty of Mechanical Engineering |
| Field of study | Management and Production Engineering |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MSc degree |
| Beginning semester | winter term 2023/2024 |
| Semester | 1 |
| ECTS credits to win | 3 |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Laboratory | 15 | 1 | - | - | Credit with grade |
| Lecture | 15 | 1 | - | - | Exam |
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.
Basic knowledge of: manufacturing processes, economics, statistics. Thorough knowledge of Excel.
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.
Conventional lecture. Computer laboratory
| Outcome description | Outcome symbols | Methods of verification | The class form |
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.
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.
1. Lapin L.L., StatisticfFor Modern Business Decision. Harcourt Brace Javanovich Inc., New York, 1987.
Modified by dr inż. Tomasz Belica (last modification: 12-04-2023 23:05)
