Decision Analysis for Managers

Assignment: Case Study Analysis - AMECO Relocation Decision Problem

This case study is based on a real case; of course, anonymized for obvious reasons. The case study considers the problem of Agricultural Machinery Exporters Company (AMECO), a company considering relocating its manufacturing facilities from the UK to an overseas country. In making its decision, the company needs to take into account a number of political risks that it will face if it decides to go ahead with the relocation. The decision problem is made complex by the large number of combinations of possible events that can occur and the challenges that arise from the need to structure the problem in a way which makes analysis of the problem tractable. Note that all of the monetary values presented in the case have already been expressed as present values to avoid the additional complication of applying discounted cash flow analysis to the data. Carefully read the case study (attached) and answer the question that follow.

Question (approximately 500 words in total, excluding decision tree diagram)

Using decision tree analysis, structure the decision problem faced by AMECO and recommend the alternative or strategy that the company should pursue to maximize expected savings. Explain/justify your recommendations.

Guidance notes:
- Present your decision tree diagrams, clearly showing all probabilities and net values at the end of the branches
- Follow the conventions of constructing decision trees, such as the basic shapes distinguishing decision nodes from chance nodes
- Explain/justify your recommendations

More guidance notes on applying decision tree analysis on the AMECO decision problem

A decision tree model can be used to analyze this decision. Because of the size of the problem, it is suggested that you break down the decision trees into four sub-trees as follows:

Decision Tree 1: Depicts the decision that face the company in planning for the first 5 years of potential operation in Almeria

Decision Tree 2: Depicts the decision for second 5 years if savings in the first 5 years are high

Decision Tree 3: Depicts the decision for second 5 years if savings in the first 5 years are medium

Decision Tree 4: Depicts the decision for second 5 years if savings in the first 5 years are low Decision trees 2, 3 and 4 are best constructed first so that the optimal expected savings that they indicate can be ‘rolled back' and added to the savings for the first five years in decision tree 1.

Assignment 2: From Data to Decisions: Data Analytics

Data-driven decision-making (DDDM) is the process of using data to inform your decision-making process. Data analytics is at the heart of DDDM. Data analytics refers to the process and practice of analysing data to answer questions or to extract meaningful insights that an organization can use to inform its strategy and, ultimately, reach its objectives. Therefore, data only has value if it is turned into information. In the context of the DIK pyramid (Wallace, 2007) (see seminar 1), managers can then use this information in combination with their experience and judgement to create knowledge and ultimately improve their decision-making.

This introduction sets the context of this assignment. Your task is to independently apply data analytics techniques that you learnt in the seminars to extract meaningful insights or information from data. In the context of the DIK pyramid, you then draw out some knowledge or what you have learnt from the data. In particular, follow the steps below.

Step 1: Develop some 3 - 5 questions that you seek to answer from data.

Step 2: Find some data (see guidance on data sources in Box 1 below), download it onto an Excel spreadsheet, analyse the data and interpret the results with a view to answering the questions that you set out in step 1. Note, your analysis should include (but not limited to) the following:
- A selection of descriptive analytics (numerical measures) appropriate for your data, questions or information required from the data - see seminar 1
- A selection of descriptive analytics (data visualization) appropriate for your data, questions or information required from the data - see seminar 2
- Predictive analytics (regression analysis) - see seminar 4
- And anything else you learnt in any of the seminars.

Step 3: Write a short report, structured around your answers to the questions you set out in step 1 (approximately 1,000 words, excluding Tables, Figures/charts and references).

In your report, include some tables summarising the results of your analysis (step 2) and some data visualisation in the form of figures or charts (step 2). Also ensure that you clearly tell us your source of data (e.g., if it is from Statistica, MarketLine or Financial Times) or your own sources. If you use publicly available data (e.g., from the World Bank, office of national statistics of your country, etc), ensure you appropriately acknowledge your data source.

Box 1: Guidance on data and sources

A note on sources of data: The "UK Data Service" is one of the University library's databases for data from a wide range of sectors. Researchers may access open data collections without the need to register or login:

Go to university library website; under Find, click Databases, then find UK Data Service. Alternatively, this link will take you directly to all Databases (A-Z Databases (lincoln.ac.uk). Then find the relevant database (UK Data Service) and login using your usual University login details and you will be ready to search your data.

Of course, you may use your own data sources and you are encouraged to do so e.g., if you have access to data from an organisation that you previously worked for or are familiar with. However, ensure it is good quality data. The quality of University library recommended data sources above is guaranteed. Finally, ensure you have a reasonable sample size of data (a sample size of least 30 to around 100 observations or lines of data; you can use larger datasets, if you like - the more the better!). Needless to say, the data should be relatively recent (perhaps during the last 20 years). You will upload your Excel spreadsheet containing your data on blackboard separately as part of your submission.

If you are interested in financial data, particularly stock market data.

For example, following gives you access to historical stock market data for companies trading on the London Stock Exchange's FTSE 100.