Using forecasting models in business operations
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Describe how a domestic fast food chain like McDonald's with plans for expanding into China would be able to use a forecasting model.
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Solution Summary
The expert describes how a domestic fast food chain like McDonald's with plans for expanding into China would be able to use a forecasting model.
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Forecasting Model
What is forcasting?
Primary function of forcasting is to predict the future using mathematical model ( time series related or other) data.
Where is forecasting used?
Forecast can demand for products and services
Forecast availability/ need for manpower
Forecast inventory and material needs daily
Characteristics of Forecasts
A good forecast includes a mean value and standard deviation and accuracy range (high and low).
A good forecast includes:
Short term ( Days/Weeks) - short term sales, shift schedule and resource requirement
Intermediate ( Weeks/ Months) - Product family sales; labor needs and resource requirements.
Long term ( Months/ Years) - Capacity needs; long term sales patterns, growth trends
Subjective Forecasting Methods
One method of forcasting is the "subjective forecasting". This usually require sales force composites and an aggregation of sales personnels estimates. It required a collection of the customer surveys and a jury of executive opinion. As part of the subjective forecasting is the "Delphi Method", which means that individual opinions are compiled and anonymously shared among group. Their opinion requests are repeated until an overall group consensus is reached.
A domestic food chain MacDonalds that want to expand into China should considered the "subjective forecasting". One step of the food chain MacDonalds is to estimates their total sales personnels costs over China. They also need to collect customers surveys and a jury of executive opinion about the pros and cons of the expansion into China. Individuals opinions are collected and a vote must be agree among the groups.
Objective Forecasting methods
There are two primary methods: causal models and time series methods.
Causal ...
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