forecasting linear programming modelling

Assignment Task

forecasting linear programming modelling

This individual report will require to you analyze 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.

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Background/Context

 forecasting linear programming modelling

SCENARIO

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.

 forecasting linear programming modelling

 

WHAT YOU ARE REQUIRED TO DO:

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 take a critical approach to the evaluation of your body of evidence.

 forecasting linear programming modelling

Task 2:  In your spreadsheet, apply the following FIVE updating schemes: Naïve Forecasting, Updating the Mean, Moving Average of Length k, Weighted Moving Average of length k and Simple Exponential Smoothing.

forecasting linear programming modelling

In your report, provide a summary table to present the forecast for the next time period under each of the schemes.  Critically evaluate the updating schemes and the values you introduce into your modelling, ie: k, weights, alpha, initial value, method of optimisation etc. and the implications of any decision you have had to make.

 

Task 3:  Critically 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.

forecasting linear programming modelling

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, and the business implications of such factors.

forecasting linear programming modelling

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?

 

 forecasting linear programming modelling

 

MARKING CRITERIA

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.

 

 forecasting linear programming modelling

 

 

 

HOW YOUR WORK SHOULD BE PRESENTED AND SUBMITTED

You need to submit two files for this piece of coursework:

 forecasting linear programming modelling

1.        Written Report

You are required to submit a written report with a maximum word limit of 1750 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.

forecasting linear programming modelling

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.

forecasting linear programming modelling

This supporting spreadsheet should be submitted on Studyspace under ‘Spreadsheet Submission’ in the Individual Assignment tab.

 

FEEDBACK ON YOUR WORK

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)

Section/element Allocated Marks
Presentation, structure, flow, grammar, spelling etc. 5%
Task 1: Diagnostic Analysis of Stationary Data 10%
Tasks 2/3: Application and evaluation of modelling of the stationary data 40%
Tasks 4/5: Diagnosis, application and evaluation of modelling of the non-stationary data 35%
Task 6: Other factors to consider 10%

 

Grade Band Weight Fail/Marginal Fail D (40-49)/3rd C Grade/2.2 B Grade/2.1 A Grade/1st
0/5/15/25/35 45 55 65 75/85/95/100
Presentation, structure, flow, grammar, spelling 5% 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 10% 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, not critical 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 critical 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 40%

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 organization 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 organization 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, lack of critical approach

Correct schemes/models used, some attempt at optimization where appropriate. Well organized.

 

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 35%
Task 6: Other factors considered 10% 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.

forecasting linear programming modelling

 

FEEDBACK ON THE WRITTEN ELEMENTS OF THE MODULE WILL BE BASED ON UNDERGRADUATE L6 GRADE CRITERIA:

CLASS % LETTER GRADE OVERALL DESCRIPTION GUIDELINE GRADE DESCRIPTIONS
1st 85-100 A+ Outstanding

Your work meets all of the criteria described below for the A and A- grades. On top of that, it shows exceptional scholarship, with very effective critical evaluation and synthesis of ideas and information.  Your work shows originality and challenges existing approaches.

You have used a range of detailed evidence accurately and thoughtfully.

Your work shows that you have followed good academic practice in terms of citation and referencing, presentation format and clear, accurate English.

1st

75-85

 

 

70-74

A

 

 

A-

Excellent

 

 

Very Good

Your work shows a comprehensive and up-to-date knowledge and understanding of the material covered in this module, and of the way in which key concepts relate to one another.   Your work shows a detailed appreciation of the way in which some aspects of the material covered are uncertain or contradictory.

Your work takes a critical approach throughout and uses a good range of evidence, reasoned argument and reflection.

Your work shows a mature and independent approach to problem-solving.  You have created appropriate arguments and have used well-justified, imaginative and innovative approaches to explore them.

Your work shows that you have followed good academic practice in terms of citation and referencing, presentation format and clear, accurate English.

2.1

67-69

64-66

60-63

 

B+

B

B-

Good

Your work shows a broad, up-to-date knowledge and understanding of the material covered in this module and of the way in which key concepts relate to one another.  You also show awareness of how some aspects of the material are uncertain or contradictory.

Your work takes a critical approach and uses a range of evidence, reasoned argument and reflection.

Your work shows an independent approach to problem-solving.  You have created appropriate hypotheses and have used well-justified approaches to explore them.

Your work shows good academic practice in terms citation and referencing, presentation format and clear, accurate English.

2.2

57-59

 

54-56

50-53

C+

 

C

C-

Satisfactory

Your work shows good knowledge and understanding of the material covered in this module.  You also show some awareness of how some aspects of the module are uncertain or contradictory.

Your work generally takes a critical approach, but is not always entirely confident in tackling important concepts or applying key ideas and theories.

Your work shows that you can operate independently to identify problems and use appropriate approaches to explore them.

Most of your work follows good academic practice in terms of citation and referencing, presentation format and clear, accurate English.

3rd

47-49

44-46

40-43

D+

D

D-

 

Adequate

Your work shows that you have gained knowledge and understanding of the core material covered in this module and a basic awareness of the complexity of the subject.

Your work tends to be descriptive, and your analysis is oversimplified.

There is some evidence in your work that you have applied the methods and tools covered in the module appropriately to resolve straightforward problems and/or practical issues.

Your work shows some evidence of good academic practice in terms of citation and referencing, presentation format and clear, accurate English, but this is not always consistent throughout.

Marginal Fail 35-39 F5 Unsatisfactory

Your work shows only a limited knowledge and understanding of the material covered in this module.  It suggests that you have not recognized the complexity of the subject.

Your work is descriptive and shows little attempt to analyse ideas or arguments. You make some assertions without sufficient evidence to back up your arguments.

Your work does not apply what we learnt appropriately to problems and/or practical issues. Your work has not followed good academic practice in terms of citation and referencing, presentation format and clear, accurate English.

Fail 34 and below F4 Poor

Your work shows little knowledge or understanding of the material covered in the module.

Your work is descriptive and shows no attempt to analyse ideas or arguments.  You make assertions without putting forward the evidence to back them up.

Your work suggests that you have not understood the methods and tools covered in the module well enough to apply them to ideas or problems. Your work does not meet most of the Learning Outcomes for this module. Your work has not followed good academic practice in terms of citation and referencing, presentation format and clear, accurate English.

Avoiding plagiarism

When you write an essay, report or dissertation you should always cite the published sources to which you quote, refer to or use as evidence, otherwise you are likely to be committing plagiarism, which is a form of academic misconduct with potentially very serious consequences. References need to be made both within the text and in a list at the end.

The aim in doing this is to ensure that somebody reading your work can easily find these sources for themselves. This applies to whether you are using a book, a report, a journal article or an Internet site. You will probably know from your own experience how much easier it is to find a reference when a reading list or bibliography is clear and unambiguous.There is help available from the library and online, including a range of videos such as those given below:

https://mykingston.kingston.ac.uk/library/help_and_training/Pages/referencing.aspx.

http://www.citethemrightonline.com/basics

Do remember you can submit your work as many times as you like before the final deadline. It is a good idea to check your Originality Report and ensure that any potential plagiarism is eradicated for your work by rewriting in your own words and referencing correctly. The staff on the BLASC desk in the LRC will be able to advise on this. Here you can find out how to access your Originality Report:

 

https://studyspace.kingston.ac.uk/bbcswebdav/institution/Support/Student_Guide_to_Turnitin_v2.pdf?target=blank

 

Additional helpful resources can be found here:

http://www.youtube.com/watch?v=1yYf8AihndI

The best way to avoid academic misconduct or plagiarism is to use your own words at all times; do not cut and paste from other work.

Illness or other mitigating circumstances

By submitting an assignment you are declaring yourself fit to take the assessment therefore please make sure that if you are unwell you understand our mitigating circumstances process. The most important thing to do is keep us informed if you are experiencing problems! See our regulations on this link: http://www.kingston.ac.uk/aboutkingstonuniversity/howtheuniversityworks/policiesandregulations

Group work and academic misconduct

Work submitted by a group is the responsibility of the group as a whole. In the unfortunate event of the work being judged to have been plagiarized, the only circumstance in which it is possible that the responsibility for the misconduct would only fall on the group member who actually committed it, would be if there were clear evidence that that member had dishonestly misled the rest of the group as to the source of his her contribution. This would require clear and contemporaneous evidence of group discussions of the sort which should be available if groups follow the advice given about keeping a log of group proceedings. If the group work is simply allocated amongst the members of the group without any sort of group review of the outcomes, then all the group members are taking on themselves the risk that some element of the work is tainted by academic misconduct. If you are unclear about any of this, you should refer to the University’s guide to Plagiarism for further explanation.