Electronic Manufacturing Inc. is dedicated to the production of diverse products
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Electronic Manufacturing Inc. is dedicated to the production of diverse products related to computers. Below you will see the production data of four of their products during the past 3 years presented in quarters.
Examine the historical production trends and address the following:
Prepare demand forecasts for the next four quarters for all four products.
Describe the forecasting method you chose and explain why that forecasting method is best suited to this scenario.
Explain why you did, or did not, choose the same forecasting method for each product.
What are the benefits of using a formalized approach to forecasting these products?
Year 1
Products
Quarter
Motherboard
Hard Drive
Video Card
Ink
1
2,550
1,525
1,855
5,250
2
995
1,025
1,285
4,250
3
3,850
3,025
4,240
5,155
4
2,855
3,175
1,970
5,345
Total
10,250
8,750
9,350
20,000
Year 2
Products
Quarter
Motherboard
Hard Drive
Video Card
Ink
1
4,025
3,965
3,525
9,350
2
2,595
3,025
3,025
9,005
3
6,225
3,250
7,320
9,255
4
5,905
4,125
3,480
10,640
Total
18,750
14,365
17,350
38,250
Year 3
Products
Quarter
Motherboard
Hard Drive
Video Card
Ink
1
5,275
4,905
4,850
13,250
2
3,350
4,250
4,325
13,125
3
7,005
4,105
8,150
13,325
4
6,730
5,535
4,625
15,775
Total
22,360
18,795
21,950
55,475
Directions:
Your assignment is required to be four to five pages in length, which does not include the title page and reference pages, which are never a part of the content minimum requirements.
Support your submission with course material concepts, principles, and theories from the textbook and at least three scholarly, peer-reviewed journal articles. Use the Saudi Digital Library to find your resources.
Formatted according to APA 7th edition and Saudi Electronic University writing standards.
It is strongly encouraged that you submit all assignments into Turnitin prior to submitting them to your instructor for grading. If you are unsure how to submit an assignment into the Originality Check tool, review the Turnitin – Student Guide for step-by-step instructions.
Review the grading rubric to see how you will be graded for this assignment.
Module Overview
In this module, you will identify elements of demand forecasting, analyze steps in the forecasting process, and you will focus on the importance of forecasting as it relates to operations management. Demand forecasting may be completed in a simple manner reviewing historical trends or through elaborate systems. This will include the meaningful units in forecasting, how those meaningful units may be different in organizations providing services rather than making products, and how writing down the forecast allows multiple people in multiple roles in the organization to give input to the forecast. We will also look at the impact that a forecasting technique has on receiving the level of detail needed for a forecast. Each organization chooses its own method or methods based on the level of accuracy that may be required.
Learning Outcomes
Appraise the importance of timely forecasts in terms of operations management.
Evaluate the steps to creating accurate forecasts.
Weigh the best forecasting method for specific business challenges.
Readings
Required:
Chapter 3 in Operations Management
Chapter 3 PowerPoint slides Chapter 3 PowerPoint – Alternative Formats – Operations Management
Sousa Agostino, I.R., Vieira da Silva, W., Pereira da Veiga, C., Mendonca Souza, A. (2020). Forecasting models in the manufacturing processes and operations management: Systematic literature review. Journal of Forecasting, 39(7), 1043-1056.
Sharma, H. K., Kumari, K., & Kar, S. (2020). A rough set approach for forecasting models. Decision Making: Applications in Management and Engineering, 3(1), 1-21.
Recommended:
Li, Y., Berry, D., & Lee, J. (2020). How to choose among three forecasting methods: Machine learning, statistical models and judgmental forecasts. International Journal of Applied Forecasting, 2020(58), 7-14.
Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(1).
