Understanding Organisations And People Met 2019 Group Projec

Understanding Organisations And People Met 2019group Project Pres

Understanding Organisations and People (MET) 2019 Group Project & Presentation (Component A) Briefing Sheet Component A, the group presentation, is a major part of the assessment for this module, consisting 40% of your final module mark. It consists of (1) PowerPoint presentation submitted online and (2) a formal oral presentation during the January assessment period. As noted in the module handbook, your team is required to deliver a formal group presentation lasting up to 20 minutes at an assigned time as scheduled during the Assessment Period. A further 5-10 minutes will be allowed for you to answer questions put to you by your tutor/examiner and peers. Students should check with their tutor for dates and times once the exam schedule is provided. The Examinations Team at UWE will arrange for all UOP presentations to be held so they do not conflict with any other exams that you or your teammates may have. All members of the team must be present and must take an active role in the presentation. The presentation is essentially the exam for this module, and any member who fails to attend for any reason will fail this assessment component and will be required to do a resit work in the July Resit Assessment Period. The group project and presentation require significant thinking, planning, research, and organizing; therefore, considerable time must be allocated for its preparation. While the exact amount of time is not fixed, it is approximately 25-35 days of full-time effort, corresponding to roughly 150 hours allocated to the module's lectures, tutorials, and self-study.

The project topic involves groups of 4–5 students exploring a specific management topic of their choice, critically analyzing and evaluating the practices of real organizations by drawing on mainstream and critical literature. The chosen topic must extend from at least two study units covered in the lectures and tutorials, such as Fun at Work, Groups and Teams, Technology, Leadership, or Individual Differences. After selecting a topic, you need to collect primary data from organizations and/or members (via interviews, surveys, etc.) and/or secondary data (from published sources like trade magazines, reports, books, internet) for analysis and interpretation in light of lecture discussions.

Teams will be formed in class, typically of 4-5 members, and must notify the tutor by the end of the session. Unnotified students will be randomly assigned. The presentation will be in class, lasting 20 minutes, using appropriate media (PowerPoint, video, etc.), with all team members actively participating, each presenting at least one aspect. The presentation will be graded based on quality, following provided criteria. As with all university assessments, proper citations within the presentation and references at the end are mandatory; plagiarism will be penalized.

The project emphasizes collaborative effort; all team members are expected to contribute equally. In cases of underperformance or conflict, teams should work to resolve issues, possibly drafting a Team Contract. If conflicts persist, individuals may be dismissed from the team, with written justification and a petition demonstrating that all proactive steps were taken. Dismissed individuals can seek placement in other teams or, if not hired, must submit and deliver an individual presentation. Given the project's complexity, individual completion of such tasks is highly challenging.

Respond to the following:

- Compare the major strengths and weaknesses of the forecasting models presented in Chapter 6.

- Outline the primary conditions in which regression is a useful and applicable forecasting tool. Provide one example of such a condition to support your answer.

Paper For Above instruction

Forecasting models are essential tools in managerial decision-making, providing forecasts that help organizations plan for the future. Chapter 6 covers several forecasting models, each with distinct strengths and weaknesses. Among the prominent models are time series analysis, causal methods, and judgmental forecasting techniques.

Strengths and Weaknesses of Forecasting Models

Time series analysis models, such as moving averages and exponential smoothing, are widely used due to their simplicity and effectiveness in short-term forecasting. They rely on historical data trends, capturing patterns like seasonality and cyclical movements. Their simplicity and ease of implementation are significant strengths, making them accessible for organizations with limited resources. However, they assume that historical patterns will continue, which is a major weakness, especially during periods of structural change or unforeseen events. These models may produce inaccurate forecasts if the underlying patterns shift unexpectedly, as seen during economic crises or technological disruptions.

Causal forecasting models, including regression analysis, incorporate external variables thought to influence the forecasted variable. Their primary strength lies in their ability to account for relationships between variables, enabling more accurate long-term forecasts when this relationship is stable. For example, regression models can predict sales based on advertising expenditure or economic indicators. A key weakness is their dependence on the availability and quality of relevant data. Additionally, causal models assume stable relationships over time, which may not hold true in dynamic environments, leading to potential inaccuracies.

Judgmental forecasting relies on expert opinions and intuition, which can be valuable when historical data is scarce or unreliable, or in novel situations where past data may not be predictive. Its strength is flexibility and adaptability; it can incorporate qualitative factors and insights that quantitative models cannot capture. However, this approach is subjective, potentially introducing bias, and generally less consistent, reducing its reliability for precise forecasting.

Conditions Favoring Regression as a Forecasting Tool

Regression analysis is particularly useful when there are theoretical or empirical reasons to believe that the variable of interest is influenced by one or more independent variables, especially when these relationships are linear or can be approximated linearly. It allows organizations to estimate the degree of impact of each independent variable on the dependent variable, making it a powerful forecasting tool under certain conditions.

One primary condition for regression's utility is the presence of stable, quantifiable relationships between variables over time. For example, a company may want to forecast sales based on advertising spend, assuming that past correlations continue in the future. If historical data shows a consistent linear relationship between advertising expenditure and sales, regression can generate reliable forecasts. Another condition is sufficient and accurate data on predictors, enabling the model to capture the true relationships without being overly affected by noise or outliers.

Example of a Condition for Regression Use

An example condition is when an organization operates in a relatively stable environment where external factors influencing the dependent variable do not fluctuate unpredictably. For instance, a manufacturing firm predicting material costs based on commodity prices, assuming stable market conditions, can utilize regression to forecast future costs. If historical data demonstrates a consistent relationship, such as a high correlation between raw material prices and total material costs, regression analysis provides a valuable forecasting tool to inform procurement planning and budgeting.

Conclusion

Overall, while forecasting models have their individual advantages and limitations, selecting the appropriate model depends on the specific context and data available. Time series models excel in stable, predictable environments but falter with structural breaks. Causal models, particularly regression, are advantageous when relationships are stable and quantifiable, providing deeper insights into variable interactions. Judgmental approaches serve well in uncertain or unprecedented scenarios but lack consistency. Proper understanding of these strengths and weaknesses allows organizations to choose the most suitable forecast model, enhancing decision accuracy.

References

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  • Chatfield, C. (2000). Time-Series Forecasting. Chapman & Hall/CRC.
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications (3rd ed.). Wiley.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Retrieved from https://otexts.com/fpp3/
  • Armstrong, J. S. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476.
  • Gorry, G. A., & Scott-Morton, M. (1971). A framework for management planning and control. Sloan Management Review, 13(1), 1–17.
  • Larson, R. C. (1995). Complexity Theory and the Management of Large-Scale Systems. Journal of Organizational Computing, 5(1), 1-12.
  • Fildes, R., & Hastings, R. (2007). The Handbook of Forecasting: Principles and Practice. Wiley.
  • Makridakis, S., & Maciel, M. (2009). Forecasting: Theory and Practice. Springer.

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