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10-18-2024
Provide a critical insight into various numerical methods for forecasting that have wide applications in project management
ASSIGNMENT I FRONT SHEET
ONL719 Business Analytics for Project Management
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Programme: MBA (online)
Level: Post Graduate
Academic Year: 20/21
Semester: NA
Module title: Business Analytics for Project Management
Assignment no. 1
Module code: ONL719
Word guide: 1000
Percentage Weighting of this assignment for the module: 35%
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Notes for students:
Electronic copy of assignment must be submitted through Turnitin.
Students should ensure that they comply with Glyndwr University’s plagiarism policy.
Students should make correct use of the Harvard referencing method.
Learning Outcomes Tested in this Assignment:
Provide a critical insight into various numerical methods for forecasting that have wide applications in project management
Overall Comment:
Mark (%)
Would students please note that achievement of the learning outcomes for this assessment is demonstrated against the assessment criteria shown below (which are not necessarily weighted equally). All marks/grades remain indicative until they have been considered and confirmed by the Assessment Board
Assessment Criteria
Marks Awarded
Marks Available
MBA (Online) - Business Analytics for Project Management - Assignment 1 - November 2020
Moving average, exponential smoothing and trend forecasting.
Using data and a project of your choice. Critically evaluate the use of the following numerical forecasting methods in a project management context: Moving average, exponential smoothing, trend forecasting.
You must apply your findings in a practical context using a project of your choice and address how the data can be utilised to predict future trends. Projects can be found online, in journal articles or a may be project that you are currently working on.
1. You must include an introduction to your project.
2. An overview of the numerical methods used: Moving average, exponential smoothing and trend forecasting.
3. Provide an example of the data that might be utilised and illustrate an understanding of SPSS analysis.
4. Discuss strengths and limitations of the numerical methods used.
5. Apply your findings in a project management context.
6. A prediction of future trends.
Masters level Marking Criteria
No work has been submitted in the time allowed, or the work submitted demonstrates little or no understanding of the task or the subject matter. This may be evident where the work is substantially incoherent, irrelevant or lacking in factual content, or where these shortcomings are present in combination such that the work as a whole is unsound. Major errors of fact, or evidence of substantially poor cognitive or other relevant skills will also lead to a fail.
Fail:
Marks below 30%
The work shows some knowledge and required skills are present to a degree. There may be appreciable error or omission of facts, poor structure, misdirection to the task, or poor conceptualisation or illustration of the work. Evidence of analysis and evaluation is weak. There will be indications in the work that the candidate is capable of improving it by further application to the task
Fail:
Marks in the range
30% – 39%
The work contains sufficient descriptive information. There is some analysis and explanation with appropriate illustration and example, and some attempt to evaluate. The work will generally be coherent and relevant, it will contain some useful proposals or solutions related to familiar solutions and there will be some attempt at originality. It will be communicated clearly.
Pass:
Marks in the range of
40% – 49%
The work contains all the necessary contextual information. There will be adequate analysis, explanation and conceptualisation, with appropriate illustration and example, and sound attempts to evaluate and judge. The work will be substantially coherent and will contain relevant and feasible proposals or solutions related to familiar situations, some responses to uncertainty or ambiguity and some acknowledgements of the implications of change.
Pass:
Marks in the range of
50% – 59%
The work will contain complete explanations using most available information. There will be substantial analysis; the ability to recognise evidence, use ideas, conceptualise, evaluate and judge in familiar situations will be clearly demonstrated. Proposals or solutions will be contextually relevant and useful, with substantial evidence of the skill necessary to operationalize them in a variety of situations, including those in which uncertainty, ambiguity or change are present. The work will provide evidence of originality and of useful knowledge transfer to novel situations. It will be coherent and convincing.
Pass:
Marks in the range of
60% - 69%
The work will clearly demonstrate the ability to analyse accurately, reliably and fully, all relevant information; to use evidence; to conceptualise, evaluate and judge; to propose and operationalise effective solutions, and to show substantial originality and creativity in a variety of familiar situations or in the face of ambiguity, uncertainty or change. It will demonstrate valuable knowledge transfer and propose feasible solutions for a wide range of situations. Evidence of the ability to innovate will be present.
Pass:
Marks in the range of 70% and above
Sample Answer - Plagiarised
Provide a critical insight into various numerical methods for forecasting that have wide applications in project management
Using data and a project of your choice. Critically evaluate the use of the following numerical forecasting methods in a project management context: Moving average, exponential smoothing, trend forecasting.
Introduction to the Project
In the contemporary business landscape, effective project management relies heavily on accurate forecasting methods to ensure timely decision-making and resource allocation. This assignment explores the use of Moving Average, Exponential Smoothing, and Trend Forecasting in the context of a project aimed at launching a new software application within a tech company. The ability to predict future trends based on historical data is essential for managing project timelines, budgeting, and resource allocation.
Overview of Numerical Methods
Moving Average
The Moving Average method is one of the simplest forecasting techniques. It smooths out fluctuations in data by creating an average over a specific number of past data points. For instance, if we have monthly sales data, a three-month moving average would average the sales of the previous three months to predict the next month’s sales. This method is particularly effective in identifying trends over time, especially in stable environments.
Exponential Smoothing
Exponential Smoothing is a more advanced forecasting technique that applies decreasing weights to older data points, thus prioritising recent observations. This method allows for a more responsive prediction model, accommodating sudden changes in trends. For example, in the context of a software application launch, if there was a spike in user sign-ups in the past month, exponential smoothing would significantly impact the forecast for the upcoming month.
Trend Forecasting
Trend forecasting identifies the direction of data trends over time. This method often involves the use of regression analysis to determine the relationship between variables. In our software application project, trend forecasting can help predict user growth based on historical sign-up data, allowing project managers to strategise marketing efforts effectively.
Example Data Utilised and SPSS Analysis
For this assignment, we will consider hypothetical monthly user sign-up data for a new software application over the past year, as follows:
Month Sign-Ups
Jan
50
Feb
60
Mar
70
Apr
80
May
100
Jun
120
Jul
140
Aug
150
Sep
160
Oct
180
Nov
200
Dec
220
Using SPSS, we can apply the Moving Average and Exponential Smoothing methods to the above data. The SPSS outputs would provide us with a forecast for future months, along with confidence intervals that indicate the reliability of these forecasts.
Continued...
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