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Posted: December 29th, 2022

Electronic Manufacturing Inc.

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:

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Prepare demand forecasts for the next four quarters for all four products.
Demand forecasts are predictions of the future demand for a product or service. They are used by businesses to inform production and inventory planning, marketing and sales strategies, and resource allocation. Demand forecasts can be based on a variety of factors, including past sales data, market trends, economic conditions, and consumer behavior.

There are several methods that can be used to create demand forecasts, including:

Historical analysis: This method involves analyzing past sales data to identify trends and patterns that can be used to predict future demand.

Market research: This method involves collecting data from consumers and industry experts through surveys, focus groups, and other research methods to inform demand forecasts.

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Our custom dissertation help and thesis writing services meet your unique requirements. Unlike companies using pre-written content, we craft every dissertation, research paper, or essay from scratch based on your specific instructions and grading rubric. A subject-specialized writer will deliver a well-researched dissertation or thesis tailored just for you. Beyond essays, our academics and scholars offer PowerPoint presentations, cover letters, editing, tutoring, and business reports—comprehensive dissertation help and research paper support for all your academic needs.

Econometric modeling: This method involves using statistical and economic models to predict demand based on a variety of economic indicators, such as GDP, inflation, and employment.

Judgmental methods: This method involves using the expert judgment of managers or industry experts to make predictions about future demand.

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It is important to note that demand forecasts are not always accurate, and can be affected by a variety of factors, such as changes in consumer preferences, competition, and economic conditions. As a result, businesses should regularly review and update their demand forecasts to ensure that they remain accurate and relevant.
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 Ace homework tutors - 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 OverviewIn
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.
ReadingsRequired:
Chapter 3 in Operations Management Chapter 3 PowerPoint slides Click for more options 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).

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