Managers Guide To Forecasting - Discussion

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This discussion is based on the article, “Manager’s Guide to Forecasting” by David Georgoff and Robert Murdick from Harvard Business Review that is part of the article pack you need to buy from Harvard Business Press. See the Syllabus for details on purchasing this pack. Georgoff, D. M., & Murdick, R. G. (1986). Manager's guide to forecasting. Harvard Business Review, 64(1), 110–120.

In this course, you will touch on a few forecasting methods, although there are many more approaches available to those managers who wish to do more. This week’s article provides an overview of many forecasting methodologies and provides a framework through which you can explore their differences. Your objective in this discussion is to learn to analyze a specific forecasting situation and identify the best suited methodology.

You will complete three steps. Step 1: Describe a specific forecasting need in your organization. Step 2: Use the provided table below to analyze the requirements of the forecasting problem. Step 3: Identify the best matching forecasting methodology to your situation and describe how it would be executed. In Step 2, the analysis will be based on the table shown on pages 4 and 5 of the article.

This table lists several questions about the nature of the forecasting situation, such as the urgency, detail required, and costs factors, and provides an overview of how well various forecasting methodologies will fit those requirements. For example, some forecasting methods cost more than others and depending on your financial resources, some of them may not be suitable. The same holds true with the math skills available or the need for high accuracy. So, understand what each category is referring to, fill in the information, and follow the table to see which methodology is recommended for your specific case. You are learning how to analyze your situation so as to pick the best approach.

Please note that these 2 pages in the article (pages 4–5) go side by side. You may wish to print them out and place them next to each other to read across the rows comfortably. The table shown below is based on the table in the article. Also note that many of the squares are shaded light or dark grey to show strength or weakness in each category. Full instructions on how to use the table are in the second column of page 6 in the article.

Please use the template below in your answers so everyone can easily follow your answers to all the questions (copy and paste to your post). Use this format for your Unit 5 Discussion.

Forecast need

Describe what question this forecast aims to answer, and why it is important for your organization to have this information.

Forecast situation analysis

Identify a forecast method by filling in the table below. The full table is on pages 4–5 of the article.

You should fill in the table by answering the questions in the “Questions” column. Your answers will lead you to the methods that are most suitable for your forecasting need. The ideal fit will give you a strong match to your answers in the “output” section of the table, while still meeting the conditions in the “time”, “input” and “resource requirements” sections.

Recommendations

Answer the following: Which forecasting methodology listed in the article is the best match to your situation? In which categories is the methodology showing a good fit (why did you select this methodology)? In which categories does this methodology show a weak fit? Describe how this forecast will be executed: Who will do it, where will the data come from, how frequently will it be repeated, and how will the results be used?

Paper For Above instruction

In today’s dynamic business environment, accurate forecasting is crucial for effective decision-making and strategic planning. For this discussion, I will identify a forecasting need within my organization, analyze the specific requirements using Georgoff and Murdick’s framework, select an appropriate methodology, and detail its execution plan.

Forecast Need

The primary forecasting need in my organization concerns predicting quarterly customer demand for our flagship product. Accurate demand forecasting is vital to optimize inventory levels, reduce stockouts or excess stock, and plan production schedules efficiently. Ensuring an accurate forecast allows us to meet customer expectations while managing costs effectively, impacting our competitive position in the market and overall profitability.

Forecast Situation Analysis

Using the table from the article pages 4–5, I analyzed the forecasting situation as follows:

  • Urgency: High — Demand fluctuations heavily influence quarterly sales, requiring timely forecasts.
  • Detail Required: High — Stakeholders demand precise estimations of demand to make inventory decisions.
  • Cost of Errors: Moderate — Overestimating leads to excess inventory, but underestimating risks stockouts.
  • Mathematical Skills Available: Moderate — Our analysts possess statistical expertise but limited experience with complex models.
  • Data Available: Extensive historical sales data exists, stored in our ERP system.
  • Frequency of Forecasting: Quarterly — Forecasts need to be updated every three months to adapt to market trends.

By considering these factors, I matched the needs with the characteristics and strengths of various forecasting methodologies outlined in the article.

Recommendations

Based on the analysis, the most suitable forecasting methodology for our demand prediction is the Moving Average method, given its robustness in handling relatively stable data with limited complexity. It is a good fit because it:

  • Requires minimal mathematical skills, aligning with our team’s capabilities.
  • Uses historical data effectively to smooth out fluctuations, providing reasonable accuracy for quarterly demand.
  • Is cost-effective and quick to implement without specialized software or extensive training.

However, the method shows weaknesses in capturing sudden shifts in demand or seasonal patterns, which are less prominent in our industry. For finer detail and responsiveness, we may supplement with a Trend-Adjusted Moving Average or try simple exponential smoothing for more recent data sensitivity.

Execution Plan

This forecast will be conducted quarterly by our sales analysis team. Data will be extracted from our ERP system, which records weekly and monthly sales figures. The team will calculate the moving averages at the end of each quarter, review trends, and adjust production schedules accordingly. The results will be shared with inventory managers and production planners to align supply with anticipated demand, ultimately minimizing costs and maximizing customer satisfaction.

References

  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. John Wiley & Sons.
  • Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
  • Georgoff, D. M., & Murdick, R. G. (1986). Manager's guide to forecasting. Harvard Business Review, 64(1), 110–120.
  • Fildes, R., & Hastings, R. (2002). Forecasting methods in practice: An overview. Journal of Business Forecasting.
  • Chatfield, C. (2000). The analytics of time series: Forecasting and control. Springer.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions, and implications. International Journal of Forecasting, 16(4), 451–476.
  • Larson, R. G. (1998). Operations Management: An Integrated Approach. McGraw-Hill.
  • Brodersen, K. H., et al. (2015). The fundamental limits of forecasting. Proceedings of the National Academy of Sciences, 112(13), 3632–3637.
  • Rasmussen, C., & Williams, C. (2006). Gaussian Processes for Machine Learning. MIT Press.

Effective forecasting requires careful analysis of organizational needs, understanding of available methodologies, and strategic implementation. By selecting an appropriate method like moving averages, organizations can enhance their planning accuracy and operational efficiency, ultimately gaining a competitive advantage in their market.

References

  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. John Wiley & Sons.
  • Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
  • Georgoff, D. M., & Murdick, R. G. (1986). Manager's guide to forecasting. Harvard Business Review, 64(1), 110–120.
  • Fildes, R., & Hastings, R. (2002). Forecasting methods in practice: An overview. Journal of Business Forecasting.
  • Chatfield, C. (2000). The analytics of time series: Forecasting and control. Springer.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions, and implications. International Journal of Forecasting, 16(4), 451–476.
  • Larson, R. G. (1998). Operations Management: An Integrated Approach. McGraw-Hill.
  • Brodersen, K. H., et al. (2015). The fundamental limits of forecasting. Proceedings of the National Academy of Sciences, 112(13), 3632–3637.
  • Rasmussen, C., & Williams, C. (2006). Gaussian Processes for Machine Learning. MIT Press.

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