Assignment Briefing (Level 5)
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Module Name
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Business Decision Modelling
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Module Code
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BB5112
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Assignment Title
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Assignment 2
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Type of Submission
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Online through Canvas
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Weighting of the assignment in the overall module grade
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70%
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Word Count/Time allocation (for presentations)
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No limit
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Issue Date
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3rd March 2025
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Submission Date
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17th April 2025
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Date of Feedback to Students
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17th May 2025
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Where feedback can be found
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CANVAS
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Employability skills
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Professional
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Creative
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Thoughtful
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Resilient
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Proactive
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Literacy
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✔
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Communication
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✔︎
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Critical Thinking
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✔︎
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Relationship building
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✔︎
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Adaptability
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✔︎
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Numeracy
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✔︎
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Storytelling
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✔︎
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Critical Writing
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✔︎
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Networking
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✔︎
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Commercial Awareness
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✔︎
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Creativity
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✔︎
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Soft skills
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✔︎
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Presentation
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✔︎
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Problem Solving
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✔︎
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Teamwork
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✔
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Digital Skills
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✔︎
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Project Management
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✔︎
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How these skills are being developed in this assessment
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Through the development of forecasting models to examine both stationary and trended time series. The steps taken will be developed in workshops and will result in a mechanism to generate appropriate forecasts associated with supplied datasets accompanied by a comprehensive report. Workshops will involve peer discussion in development of the forecasting models but the submission must be an individual piece of work.
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Data Driven Decision Making/Business Decision Modelling. TB2 Assignment 2
Individual Report– Forecasting
Consider the two time series data sets below in Tables 1 and 2 where n=24 in both series. These datasets are also available on the Assignment Two page on Canvas as dataset1.xlsx and dataset2.xlsx. You are required to do the following:
TASK 1 (15 marks)
1. Conduct a diagnostic analysis on both datasets. From these diagnostics Identify which time series you think is stationary and which you think exhibits trend and seasonality. You should justify your conclusions with both visual and numerical evidence.
TASK 2 (35 marks)
2. Using the dataset you feel is stationary carry out the following:
a. Use the moving average (MA) approach to smooth the data using a moving average of period k = 2, 4, 6 and produce a forecast for period n+1 i.e. period 25. Determine which scheme appears to perform. best;
b. Use Solver to produce a weighted moving average for k = 2, 4, 6 with weights optimised on both MAPE and RMSE to produce a forecast for period n+1. Which scheme appears to perform. the best?
c. Compare your results obtained in a. and b. above,
d. Conduct a similar exercise using exponential smoothing, initially with alpha = 0.2 and 0.8. Then use Solver to optimise the value of alpha based on both MAPE and RMSE to produce a forecast for period n+1.
TASK 3 (35 marks)
3. Using the dataset you feel exhibits Trend and Seasonality use an additive decomposition model to:
a. Extract a seasonal index for each quarter,
b. Deseasonlise the data for each quarter,
c. Produce a deseasonalised and seasonalised forecast for each period,
d. Produce a deseasonalised and seasonalised forecast for periods n+1, n+2, n+3 and n+4,
e. Plot the actual, deseasonalised and seasonalised data and forecasts on a single graph and comment on the results;
TASK 4 (15 marks)
4. Consolidate your results in 1 – 3 above into a short report, which should include a critical evaluation of the methods you have used and consideration of the potential impact on business strategy of effective use of the forecasting process.
Instructions
a) Upload two spreadsheets with the solutions for each dataset,
b) Upload a Word document containing your report,
c) The piece of work is individual,
d) The submission date for this assignment is by 23.59 on 17th April 2025.