Solution Library

Forecasting For Reducing Randomness

Question

Situation

You are a manager at a medium sized manufacturing operation that sells a customized product directly to the consumers and the data pattern is linear but it can have the occasion randomness. The causes of this randomness are never apparent until after the event.

As manager, you are to complete a corporate forecast for this expensive and customized product that trends generally with the current economic situation.

Requirement 1

Complete the corporate forecast for this product using the data below to complete a 3 and 5 year weighted averages for the next year. For the judgmental forecasting component use the current economic situation.

Time Period    Historical Demand       
1                           456       
2                           399       
3                           712       
4                           498       
5                           487       
6                           567       
7                           450    

Questions

What method is best here?  Moving Average or Exponential Smoothing?
What is weighted average?

What is judgmental forecasting and how does it apply here?

The plant is using the chase production option and the ATO process design. The BOM is illustrated below.


 
Requirement 2

Select the better forecasting model and support your answer. From your answer, explode the above BOM for each item to determine the requirements of each item based on the Gross Requirement of 200 units for Item ‘A’; the parent item.

Requirement 3

The factory works on a process layout but the order lot sizing is open for determination. Determine the optimal method for lot sizing.

Questions:

What is BOM?

What is 2 to 1?  1 to 1?

Summary

The question belongs to Operations Management. The question here is about a medium sized plant which customizes products directly to consumers. The company occasionally faces randomness and the manager is required to know the randomness before it happens.

Total Word Count 368

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Comments

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