FINAL EXAM 17th 10am to 12 am melb time
PowerPoint Presentation MGMT90141 Business Analysis and Decision Making Week 1 – Introduction to Business Analytics and Linear Programming Dr Lusheng Shao Senior Lecturer of Operations Management Department of Management and Marketing Faculty of Business and Economics The Spot 10.030 Lusheng.shao@unimelb.edu.au 1 Subject Overview 10 lectures and tutorials + 1 revision + 1 oral presentation 2 recommended textbooks Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., and Martin, K. (2019), An Introduction to Management Science: Quantitative Approaches to Decision Making, 15th edition, South-Western Cengage Learning Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, J.D., and Cochran, J. (2020), Statistics for Business Economics, 14th edition, South-Western Cengage Learning LMS Lecture slides Lecture recordings All subject related documents Studying for MGMT90141 3 Each week Prepare for lecture and tutorial: Read through equivalent book chapter(s) Watch lecture recordings After lecture and tutorial: Re-read, try problems for any part of lecture and tutorial that is not clear Try to apply the quantitative methods in real situations Discuss with your lecturer if you find any interesting and/or difficult subject-related matters Assessments A 2000 word group assignment, due Week 5 (15%) A 4000 word group assignment, due Week 11 (30%) A 10 minute oral presentation, due Week 12 (5%) A 3-hour online examination, end of semester (50%) Studying for MGMT90141 4 Assignment 1 (2000 words) – Specifications The assignment is designed to allow you to demonstrate that you can effectively collect, review, and analyze the relevant academic literature on a particular topic, which is same as the topic for your assignment 2. Each group has to select a company (manufacturing-based OR service-based) with which you are familiar, select an optimization problem being faced by the company (e.g., media selection at Telstra, employee scheduling in HSBC, make-or-buy at Toyota, resource allocation at the University of Melbourne, distribution network design at Pickfords, etc.), and then collect and review 10 international journal articles, from the past ten years, applying mathematical modelling for the problem to be studied. You should analyze the strengths of these applications and their “value-add” to the decision making process, and also identify the knowledge gaps and limitations, if any, and suggest for improvements. 5 Assignment 1 – Expected Contents of Report Introduction – company background, description of the business optimization problem, justification of selection, and identification of the company’s requirements or evaluation criteria, etc. Methodology – description of the method used to collect the journal articles, such as databases, searching and filtering criteria, etc. Strengths – analysis of the strengths of the articles with respect to the evaluation criteria. Weaknesses – analysis of the weaknesses of the articles with respect to the evaluation criteria. Discussions and Conclusions, including suggestions for improvement. References Appendices – including the minutes/notes of meetings. 6 Assignment 1 – Marking Criteria CriteriaPossible Mark Identification and description of the problem to be analyzed2 Collection and discussion of relevant academic literature3 Analysis of the strengths of these applications and their “value-add” to the decision making process4 Identification of the knowledge gaps and limitations, if any, and suggestion for improvements4 Structure and presentation. Use of appropriate language, spelling, grammar, and punctuation2 Total15 7 Assignment 1 – Word Limits and Submission Due Word limits – The total length of the report is a maximum of 2,000 words (excluding figures, tables, references, and appendices). Submission due – 5pm on 4 September 2020 8 Assignment 1 – Useful Online Sources Databases http://www.sciencedirect.com http://www.scopus.com Professional associations in decision sciences and business analytics https://www.informs.org https://www.theorsociety.com/ http://www.scienceofbetter.org/ 9 Assignment 2 (4000 words) – Specifications The assignment is designed to allow you to demonstrate that you can effectively analyze the business optimization problems, apply the mathematical modelling approaches such as LP and IP to formulate the problems, use the Excel Solver to obtain the optimal solutions, generate the sensitivity analysis report, and suggest courses of action to the focal company. Each group has to base on the same company and optimization problem selected for Assignment 1, and then apply the LP and/or IP to formulate the optimization problem with real or hypothetical data. 10 Assignment 2 – Expected Contents of Report Introduction – company background, description of the business optimization problem, justification of selection, etc. Literature review – summary of the findings from Assignment 1. Methodology – description and illustration of the mathematical modelling approach for the problem. Implementation – formulation of the problem by using the mathematical modelling approach, application of the Excel Solver to optimize the mathematical model, and execution of the sensitivity analysis. Discussions and Conclusions – suggestions for courses of action to the selected company as well as the evaluation of the mathematical modelling approach. References Appendices – including the minutes/notes of meetings. 11 Assignment 2 – Marking Criteria CriteriaPossible Mark Identification and description of the problem to be analyzed3 Summary of literature review2 Description and application of the mathematical modelling approach to analyze and solve the problem16 Suggestions for courses of action to the company, and critical evaluation of the mathematical modelling approach7 Structure and presentation. Use of appropriate language, spelling, grammar, and punctuation2 Oral presentation5 Total35 12 Assignment 2 – Word Limits and Submission Due Word limits – The total length of the report is a maximum of 4,000 words (excluding figures, tables, references, and appendices). Submission due – 5pm on 23 October 2020 13 Part one (25%): 1 compulsory question Part two (75%): 3 out of 4 questions All questions are equally weighted Examination 14 To explain what is business analytics To introduce the linear programming elements, formulation, and solution approaches To differentiate between the minimization and maximization linear programming problems To recognize the sensitivity analysis of the linear programming models Objectives of Lecture 15 Introduction to Business Analytics What is Analytics? INFORMS Definition INFORMS is the world’s largest professional association dedicated to promoting best practices and advances in operations research, management science, and analytics to improve operational processes, decision-making, and outcomes Analytics is defined as the scientific process of transforming data into insight for making better decisions. The use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions Supported by various tools such as Microsoft Excel and various Excel add-ins, commercial statistical software packages such as SAS or SPSS, and more complex business intelligence suites that integrate data with analytical software. Business Analytics – Introduction Descriptive: categorize, characterize, consolidate and classify data to convert it into useful information for the purposes of understanding and analysing past and current business performance and make informed decisions. Predictive: analyze past performance in an effort to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. Prescriptive: use optimization to identify the best alternatives to minimize or maximize a single or multiple objective. The mathematical and statistical techniques of predictive analytics can also be combined with optimization to make decisions that take into consideration the uncertainty in the data. Business Analytics – Elements Topics covered in this subject Prescriptive Linear Programming Integer Programming Decision Analysis Predicative Descriptive statistics Probability basics Probability distributions Descriptive Simple linear regression Multiple linear regression Introduction to Linear Programming In the world of management science or operations research (MS/OR), programming refers to modelling and solving a problem mathematically. Linear Programming (LP) is widely used mathematical technique designed to help managers plan and make the decisions necessary to allocate resources. Many business decisions involve trying to make the most effective use of an organization’s resources. Resources typically include machinery, labour, money, time, and raw materials. These resources may be used to produce products or provide services. Linear Programming – Introduction A set of decision variables: which are used to represent the quantity of each product to be produced or the amount of service to be offered. An objective function: which measures the extent to which alternative feasible decisions achieve the aim that is pursued. A set of constraints: which define in mathematical terms the values of the decision variables that are feasible. Linear Programming – Elements Max or Minc1x1 + c2x2 + … + cnxn Subject to:a11x1 + a12x2 + … + a1nxn b1 a21x1 + a22x2 + … + a2nxn b2 ……………………………………… am1x1 + am2x2 + … + amnxn bm All x1, …, xn 0. c1, …, cn: Objective function coefficients a11, …, amn: Constraint coefficients b1, …, bm: Right-hand-sides (RHS) x1, …, xn:Decision variables Linear Programming – General Form Deterministic model All ci, aij, bj (for i = 1, …, n; j = 1, …, m) are known with certainty. Implications of linearity Divisibility: All variables are continuous. For example, we could produce whole tons of products or proportions of tons. Proportionality: Value of the function is in direct proportion to the values of the decision variables. For example, if we increase the cost per unit shipped by 10%, then we will increase the total cost of the shipments by 10% . Linear Programming – Assumptions LP Formulation – Shader Electronics The Shader Electronics Company produces two products: (1) the Shader x-pod, a portable music player, and (2) the Shader BlueBerry, an internet-connected colour telephone. Each x-pod takes 4 hours of electronic work and 2 hours in the assembly shop. Each BlueBerry requires 3 hours in electronics and 1 hour in assembly. During the current production period, 240 hours of electronic time are available, and 100 hours of assembly department time are available. Each x-pod sold yields a profit of $7; each BlueBerry produced can be sold for a $5 profit. Shader’s problem is to determine the best possible combination of x-pods and BlueBerrys to manufacture to reach the maximum profit. Linear Programming – Maximization Step 1: Fully understand the managerial or optimization problem being faced Hours required to produce one unit Departmentx-podsBlueBerrysAvailable hours Electronic43240 Assembly21100 Profit per unit$7$5 How many x-pods and BlueBerrys should Shader manufacture to reach the maximum profit, while not exceeding the limited amount of electronic and assembly time? Linear Programming – Formulation Step 2: Define the decision variables x1 = number of x-pods to be produced x2 = number of BlueBerrys to be produced Step 3: Identify the objective function Maximize profit or z = 7x1 + 5x2 Step 4: Identify the constraints 4x1 + 3x2 ≤ 240 (Electronic department resource constraint) 2x1 + 1x2 ≤ 100 (Assembly department resource constraint) x1 and x2 ≥ 0 (Non-negativity) Linear Programming – Formulation Maximize profit or z = 7x1 + 5x2 Subject to4x1 + 3x2 ≤ 240 2x1 + 1x2 ≤ 100 x1 and x2