{"ModuleCode":"DSC3216","ModuleTitle":"Forecasting For Managerial Decisions","Department":"Decision Sciences","ModuleDescription":"Managerial success rests strategically on the ability to forecast the demand for the goods and services that a firm provides. Demand forecasting drives the effective planning of the supply chain: personnel requirements, capital investment, production schedules, logistics etc.This module surveys forecasting techniques and their applications. These encompass traditional qualitative (e.g. front line intelligence, Delphi method) and quantitative techniques (e.g. regression, time series) as well as emerging techniques based on neural networks. Concepts such as trends, seasonality and business cycles will be discussed. Their value in improving forecasts will be illustrated. 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\n\tIt aims to portray forecasting as art and science, integrating sensibility and methodology. The course will make productive use of forecasting tools available in MS Excel (and dedicated add-ins), SPSS and Minitab. The finality is the applicability of the forecasting results towards managerial decision-making.
\n\t
\n\tBusiness Analytics
\n","Order":2},{"ID":"6e5f053b-8835-4692-be49-41f07234cfff","Title":"Syllabus","Description":"\n\t1. Overview
\n\tWhy & how of forecasting?
\n\tReal-life illustrations of the value and limitations of forecasting
\n\t
\n\t2. Qualitative & Quantitative Forecasting Concepts
\n\tDelphi Method, Jury of Opinion, Ground Intelligence
\n\tEconometric and Time Series methods
\n\t
\n\t3. Time Series Methods
\n\tMoving Average
\n\tExponential Smoothing methods for levels, trends and seasons
\n\tAdditive & Multiplicative decomposition models
\n\t
\n\t4. Econometric Methods
\n\tReview of simple regression
\n\tExtension to multiple regression
\n\tRegression diagnostics / Remedial actions
\n\tModel building using lagging, co-incident, leading variables
\n\t
\n\t5. The Box-Jenkins Methods
\n\tARIMA model
\n\t
\n","Order":6},{"ID":"7e5f053b-8835-4692-be49-41f07234cfff","Title":"Practical Work","Description":"\n\tHands-on experience is the best way to appreciate the value and limitations of forecasting. Be prepared to explore (new) softwares and investigate their utility towards developing sensible forecasts.
\n\n\tFor practical assignments, concentrate on making an interesting presentation with organised facts. The freshness of your presentation is at least as important as your analytics. You want to hold and engage your lecturer/classmates, not bore them with facts and narratives.
\n\n\tThroughout the course, the students are asked to write up a 10-page Business Case at their own choice of topics with small set of data and then make use of the techniques learnt in the class to solve the problem. They also have to present the case plus solution in the class. Besides, the students are also required to work on a 30-pages project with assigned areas and hand in by the end of the term (with presentation).
\n\t
\n\tSimple Case Write-up 15%
\n\tProject 35%
\n\tPresentation and Class Participation 10%
\n\tFinal Quiz 40%