Description
DB – Learning Module 04: Forecasting Applications
In his module, you will focus on the importance of forecasting as it relates to operations management. 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.
Discussion Topic
Read through the Case Study entitled “Highline Financial Services, Ltd.” in Chapter 3 of your textbook. Examine the historical trends this company has experienced for the three products (A, B, C) discussed over the two years shown.
Address the following requirements:
- Prepare demand forecasts for the next four quarters for all three products, describe the forecasting method you chose and explain why that forecasting method is best suited to the 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?
Directions:
- Discuss the concepts, principles, and theories from your textbook. Cite your textbooks and cite any other sources if appropriate.
- Your initial post should address all components of the question with a 500 word limit.
Learning Outcomes
- Recommend the best forecasting method for a specific business issue.
- Apply forecasting techniques to specific problems found in an operations management setting.
Readings
Required:
- Review Chapter 3 in Operations Management
- Review Chapter 3 PowerPoint Presentation
- Orji, U., Güven, Ç., & Stowell, D. (2025). Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features. https://doi.org/10.48550/arXiv.2502.08376.
Recommended:
- Genov, E., Ruddick, J., Bergmeir, C., Vafaeipour, M., Coosemans, T., Garcia, S., & Messagie, M. (2024). Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems. https://doi.org/10.48550/arXiv.2407.03368.
- Palakshappa, A., Maradithaya, S., & V, C. (2025). A Machine Learning Method to improve Supplier Delivery Appointments in Supply Chain Industries : A Case Study. Brazilian Journal of Operations & Production Management, 22(1), 2040 .
