Reference no: EM133082631
Analytics and Decision Making Case Studies
Learning Outcome 1: Explore & evaluate forecasting applications via case studies
Learning Outcome 2: Investigate forecasting at different levels of business and challenges
Learning Outcome 3: Understand how forecasting helps the consumer
Case Study 1: Amazon
Forecasting Levels: E-Business
» Strategic Forecasting (Macro Level Planning):
• Where should the business be more active geographically?
• What will be the retail demand long-term (5-10 years)?
» Operational Forecasting (Purchase Orders):
• How many units for each product should be purchased from the vendors? With what lead time?
• In which what country/region should they be stored?
» Tactical Forecasting (Promotional Activities):
• W hat promotions should be run? For what products?
• How should promotions be run?
• What products should be liquidated? W hen?
• Question: What is the main difference between related time series and categorical variables?
Activity 1
I. A forecast that projects a company's sales is a(n):
A. Economic Forecast
B. Technological Forecast
C. Demand Forecast
D. Associative Forecast
II. The first step in time-series analysis is to:
A. Perform preliminary regression calculation
B. Calculate a moving average
C. Plot the data on the graph
D. Identify relevant correlated variable
Case Study 2: Walt Disney Parks & Resorts
Activity 2
I. Define Strategic, Operational and Tactical decisions. Provide at least two examples for each of these.
II. What are seasonal variations in data and why would businesses want to understand them? Provide two examples of businesses that have a strong seasonality.
III. What forecasting horizon is most efficient for time series? Therefore what level of decisions are time series typically used for?
Activity 3
I. Describe three general strategic business outcomes that can be achieved through accurate forecasting.
II. Could you use a time-series model together with your knowledge of local environment variables?
III. Give examples of industries (or companies) in which demand forecasting is dependent on the demand for other products.
Case Study 3: Aviation Industry
I. W hich of the following forecasting method is suitable for launching new products?
A. Causal models
B. Moving average methods
C. Exponential smoothing
D. Market research
E. Judgmental methods
II. Decisions relating to the sales and operations planning (aggregate planning) involve:
A. Long-term forecasting
B. Short-term forecasting
C. Medium-term forecasting
D. Medium and long-term forecasting
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