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Assignment Briefing (Level 5)
Module Name
Managerial Decision Making
Module Code
Assignment Title
Individual Business Forecasting Report
Type of Submission
Weighting of the assignment in the overall module grade
Word Count
Issue Date
Wednesday 18th January 2017
Submission Date
Tuesday 21st February 2017
Date of Feedback to Students
Tuesday 21st March 2017
Where feedback can be found
Assignment Task
This individual report will require to you analyse a set of data using the forecasting and linear programming modelling approaches covered in the module. You will be tested both on your ability to apply the correct set of quantitative analytical techniques to a set of forecasting data, but also your ability to diagnose the data, select appropriate models, interpret the output generated and evaluate your analysis appropriately so that the data you produce can be used for future managerial decision making.
Jake Fearne is the owner of Garrett Magazines, which produces two food magazines:
• Tom’s Treats, published monthly and which Jake Fearne thinks the circulation data is stationary
• Cass’s Cakes, published quarterly and which Jake Fearne thinks the circulation data is non-stationary
Circulation data for each magazine is provided in GM Circulation Data.xlsx.
Write a brief report discussing your answers to the tasks below using the GM circulation data and submit your quantitative analysis on a supporting spreadsheet. The aim of this assignment is to model and analyse the data appropriately and interpret your output to provide recommendations to the owner of Garrett Magazines on how to manage the business going forward.
For the Tom’s Treats magazine data:
Task 1: Carry out appropriate diagnostic analysis to confirm whether you agree with Jake Fearne’s conclusion that the data for Tom’s Treats is stationary. Make sure you provide a body of evidence to back up your reasoning.
Task 2: In your spreadsheet, apply the following FOUR updating schemes: Naïve Forecasting, Updating the Mean, Moving Average of Length k and either Weighted Moving Average of length k OR Simple Exponential Smoothing.
In your report, provide a summary table to present the forecast for the next time period under each of the schemes and write a short statement to justify the values you introduce, ie: k, weights, alpha, method of optimisation.
Task 3: Evaluate each updating scheme you have used in task 2 using statistical and graphical analysis. Recommend which one updating scheme should be used to forecast the next time period value. In your discussion explain why you have selected your chosen scheme over the other methods and discuss how suitable you think your model is.
For the Cass’s Cakes magazine data:
Task 4: Carry out diagnostic analysis and build an appropriate model to forecast the circulation data for Cass’s Cakes. Make sure you are clear about which time series components you believe this data exhibits and back up your reasoning with graphical and statistical analysis. Present your forecast for the next time period.
Task 5: Evaluate your model in task 4 using statistical and graphical analysis and discuss any factors that the manager should consider when forecasting data that is exhibiting this/these time series components.
For all magazine data:
Task 6: Given your analysis of Garrett Magazine’s circulation data what factors, issues or developments should Jake Fearne consider for the future?
The marking criteria guidelines are published in advance so you know how you will be judged for this piece of work and are available on Studyspace.
You need to submit two files for this piece of coursework:
1. Written Report
You are required to submit a written report with a maximum word limit of 1250 words. As this is not a formal management report, you are not required to do a summary, introduction etc. Rather, your report should be laid out into sub-sections to reflect the tasks above, although the sections will not be of equal size. The report is for the manager, so keep the audience in mind when you write up your analysis. You should be to the point, supplementing any statements, conclusions or comments with statistics and/or graphs where appropriate. Any graphs or tables used to illustrate a particular point should be included in the main body of your report, labelled clearly and referenced within the text. Remember however, that your report will be marked alongside your spreadsheet so there is no need to include screenshots of the models themselves.
The written report should be submitted online via Turnitin on Studyspace in one document.
2. Supporting Spreadsheet
All of the quantitative analysis eg: diagnostic analysis, application and evaluation of the models, graphs and/or tables you generate, should be included in a separate spreadsheet file organised appropriately. The work in this file will be reviewed alongside your written report so it should be clear what you have done. This means that tables and charts should have headings (especially if you use them in the written report), and tabs in the worksheet named accordingly.
This supporting spreadsheet should be submitted on Studyspace under ‘Spreadsheet Submission’ in the Individual Assignment tab.
Your formal feedback will be published online no later than Tuesday 21st March 2017. However, the module is designed so that you can get regular feedback during class whilst we cover the Business Forecasting topic, and you are strongly advised to build up the spreadsheet models we cover in class for reference during your assignment.
Allocation of Marks (as per the full marking criteria provided below)
Allocated Marks
Presentation, structure, flow, grammar, spelling etc.
Task 1: Diagnostic Analysis of Stationary Data
Tasks 2/3: Application and evaluation of modelling of the stationary data
Tasks 4/5: Diagnosis, application and evaluation of modelling of the non-stationary data
Task 6: Other factors to consider
Grade Band
Fail/Marginal Fail
D (40-49)/3rd
C Grade/2.2
B Grade/2.1
A Grade/1st
Presentation, structure, flow, grammar, spelling
Significant grammatical errors, lack of spellcheck used, flow to report
Acceptable structure, maybe minimal grammatical errors that do not affect the flow of the work
Satisfactory structure and acceptable use of English
Good structure that clearly presents summary discussion, good flow, free of grammatical errors
Excellent presentation and structure, well laid out and leads the reader from start to conclusion.
Task 1: Assessment of non-stationary data
Incorrect analysis of poor graphs and/or statistics
Graphs and/or statistics used but poorly interpreted or incorrect conclusions
Correct graphs/statistics used, interpretation could be fuller. Not presented as a body of evidence, or analysis not related to the context
Good interpretation of graphs and statistics that consider inconsistencies, provide correct and reasonable conclusions related to the context
Comprehensive and insightful use of graphs and statistics that support the conclusions reached and relate this to the context, presented as a body of evidence
Task 2/3: Application and evaluation of stationary data modelling
Poor application of models, errors in modelling.
Little to no evaluation, poor analysis, incorrect conclusions or application of methods
Correct schemes/models used, may be errors in application or poor organisation of models.
Some evaluation, minimum required to reach some basic conclusions regarding effectiveness
Correct schemes/models used. Models selected are basic and require little additional analysis. Reasonable organisation of analysis.
Satisfactory evaluation, some comparison statistics and graphs provided. Basic discussions that demonstrate reasonable awareness of pros and cons of models and evaluation methods
Correct schemes/models used, some attempt at optimisation where appropriate. Well organised.
Comprehensive evaluation that compares models effectively using graphs and statistics. Some comparative discussion provided, but could be more extensive.
Correct schemes/models used, optimised correctly. Analysis is organised and presented effectively and efficiently.
Critical and comprehensive graphical and statistical evaluation that discusses the multiple considerations in forecasting. Models are compared and contrasted effectively to provide a justified conclusion
Task 4/5: Diagnosis, application and evaluation of non-stationary data modelling
Task 6: Other factors considered
Little or no other factors considered, discussion not relevant
Some discussion that is relevant to the business and forecasting context
Competent discussion over other factors relating to the business and forecasting context
Discussion concludes the preceding report, and is relevant and related to the conclusions presented
Excellent conclusion to the report, discussing related factors that should be considered within this context. Logical and relevant.

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