SBM1108 Business Decision Analysis UOS CODE SBM1108 SUMMARY

COURSE CONVENOR COURSE TUTOR ASSUMED KNOWLEDGE APPROXIMATE WORKLOAD

PRE-REQUISITE (course name) Learning Outcomes

UOS NAME Business Decision Analysis

CREDIT POINTS 6

STATUS Advanced

SBM1108 is an elective unit and aims to equip graduates with the necessary skills and conc and quantitative methods that need to be applied in business decision making. Due to th environmental complexity of business firms, having efficient decision making capabil organizational survival and growth. Advanced decision making and problem solving are d important attributes of APIC’s graduates. This unit will introduce students to modelling and skills that are required for business problems related to projects. Students engage with a var approaches and their associated tools, and will be able to apply these capabilities to different b in different industries.

Dr Rakesh Khanal

Command of the contemporary project management knowledge Self Study Team Work >60 >60 hours hours Recommended all core and SBM1103 and SBM1104 Weekly Lectures & Tutorials 60 hours

      

Readings >30 hours

Conceptualize, formulate and represent a business problem or opportunity as a decision model Identify potential alternative solutions for a business problem Clarify objectives and develop performance matrices Use decision-making techniques such as forecasting, optimization, and regression Apply quantitative decision making techniques Develop business decision models using software tools Perform sensitivity analysis

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TARGET COMPETENCIES (Project and Program Management)

Target competencies in this unit of study comprise the following: Basic tools  Literacy in terms of the latest concepts, tools and techniques in data analysis  Ability to collect data, summarize them and prepare descriptive statistics reports  Know how to implement an assessment scheme effectively and efficiently  Ability to communicate the results and demonstrate deficiencies in data analysis procedures in an organization Modeling and analysis  Ability to delineate data gaps and obtain consensus from the relevant sections regarding missing data in an organization  Ability to model a decision making situation, formulate the situation with appropriate tools and techniques  Ability to analysis the results and prescribe appropriate actions accordingly Appraisal and continuous improvement  Ability to continually evaluate effectiveness and efficiency of data analysis procedures in an organization  Ability to identify performance shortcomings, prioritize these and take action to address the same  Ability to communicate results with stakeholders

TARGET COMPETENCIES (Personal and Socio-cultural)

MODES OF DELIVERY ASSESSMENT



Generic: All competencies that are common to all professionals (including cognitive and communication abilities, problem solving and analytical mindset)  Leadership: Ability to direct, motivate & manage individuals & teams.  Commitment: Ability to dedicate to tasks & to project outcomes.  Attitude: Ability to create the right frame of mind that promotes integrity & support for achievement of project goals within a social context.  Self Direction: Ability to manage within and without guidelines & processes, and to work without supervision.  Learning: Ability to commit to continuous improvement in knowledge, skills & attitude, & to creating new knowledge developing skills & approaches.  Cultural Empathy: Ability to respect for & accommodation of individual lifestyle, beliefs & norms.  Creativity & Innovation: Capacity to generate new ideas/approaches & make them happen.  Lectures and Tutorials two (2) hours per week  Team-based learning and project work two (2) hours per week  Reflective learning, in tandem with team and project learning. Theoretical Knowledge  Formal written end-of -semester test - 2 hours  40% of Total Grade Team Project Presentation & Assessment  Team project submissions (formatted as per specification for the same) 60% of Total Grade  Project submissions comprise 3 assignments; there is a deadline for each assignment.

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PRINTED MATERIALS

PRESCRIBED FOR THE COURSE Learning material (lecture notes, slides, case study and other material provided online). Albright, S. C. & Winston, W. L. (2015) Business Analytics: Data Analysis & Decision Making, Stamford: Cengage Learning.

SELECTED REFERENCES Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. L., Fry, M.J. & Ohlmann, J.W. (2015), Quantitative Methods for Business, Stamford: Cengage Learning. Journal Articles Perić, T., Babić, Z. and Veža, I. (2013). ‘Vendor selection and supply quantities determination in a bakery by AHP and fuzzy multi-criteria programming’, International Journal of Computer Integrated Manufacturing, Vol. 26, No. 9, pp. 816–829. Vyve, M. V. (2012). ‘Fixed-charge transportation on a path: optimization, LP formulations and, separation’, Springer and Mathematical Optimization Society. Ogryczak, W., and ´Sliwi´nski, T. (2011). ‘On Dual Approaches to Efficient Optimization of LP Computable Risk Measures for Portfolio Selection’, Asia-Pacific Journal of Operational Research, Vol. 28, No. 1, pp. 41–63, World Scientific Publishing Co. & Operational Research Society of Singapore. Raut, R. D., Bhasin, H. V. and Kamble, S. S. (2010). ‘Exploring Critical Criteria for Supplier Selection by CNG/LPG kit ,Manufacturers in India-Selection of Suppliers for Compressed Natural Gas and Liquefied Petroleum Gas Kit Manufacturers: A Case Study and Proposed Methodology’, International Journal of Business Insights and Transformation, Vol3, Issue 2, pp. 35-45. Narisetty, A. K., Richard, P. J.P., Ramcharan, D., Murphy, D., Minks, G. and Fuller, J. (2008). ‘An Optimization Model for Empty Freight Car Assignment at Union Pacific Railroad’, Interfaces, Issue 2, pp 89-102, INFORMS. Urbanovich, E., Young, E. E., Putertnan, M. L., and Fattedad, S, O. (2003). ‘Early Detection of High-Risk Claims at the Workers' Compensation Board of British Columbia’, Interfaces 33, pp. 15-26, INFORMS. Brown, G., Keegan, J., Vigus, B. and Wood, K. (2001). ‘The Kellogg Company Optimizes Production, Inventory, and Distribution’, Interfaces 31, pp. 1-15, INFORMS.

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WEB SITES

No single Web site presents all the necessary knowledge that students need to learn and apply.

Software

EXCEL

Students may use special-purpose software as well.

WEEKLY SCHEDULES Week 1

2

3

4

5

6

7

Activities

Topic Decision making process, Data types, tools

Model building , assignment problem, make or buy decision, shortest route Ch 1 Questions: Case study Reading

Introduction to Decision modelling Ch 2 Questions: Case study Reading Payoff tables and Decision trees Ch 6 Questions: Various techniques- Regret table etc. Case study Reading Sampling and sampling Assignment 1 LR due distributions Ch 7 Questions: Case study Reading Probability basics and discrete Ch 4, 5 Questions: distributions Decision making with Case study & without probabilities Reading Normal, Binomial, Poisson, conditional probability , Bayes’ theorem Linear programming Ch 14 Questions: Case study Reading Graphical solution techniques and Sensitivity analysis Risk and sensitivity analysis in decision making Linear programming Ch 13, 14 Questions Case study Reading

8

Distribution & network models

9

Regression analysis as a causal forecasting model

Ch 14 Questions: Case study Reading Transport and production scheduling Assignment 2 due Ch 10,11 Questions: Case study Reading

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Week

Activities

Topic Simple Linear Regression

10

Simulation Models

11

Moving average model and other smoothing methods of forecasting

12 Modelling Appraisals

13

Ch 15, 16 Questions: Case study Reading Introduction to Simulation Modelling Ch 12 Questions Case study Reading Time Series Analysis and Forecasting models Critique of application or model for its suitability in a business scenario, e.g., LP for assignment and product-mix; Decision tree for sports scheduling Assignment 3 due

FINAL EXAM

Academic Integrity and Honesty Following are details and a link to the APIC academic integrity and honesty policy. All students are encouraged to familiarize themselves with the policy, together with other relevant policies, prior to commencing their studies. APIC believes that academic integrity is based on honesty in all scholarly endeavors. Students must conduct themselves in their academic studies honestly and ethically and are expected to diligently acknowledge the work of others in all academic activities. A failure to uphold the College’s policies and standards of academic honesty and integrity may result in a finding of academic misconduct which can incur serious penalties including a loss of marks, failure of an assessment, failure of the unit, or expulsion from the College. Academic misconduct includes cheating, collusion, plagiarism, and other conduct that deliberately or inadvertently claims ownership of an idea or concept without acknowledging the source of the information. This includes any form of activity that negates the academic integrity of the student or another student and his or her work. Detailed information about relevant terms, penalties, and the processes for investigating allegations of academic misconduct, and for appealing a finding is provided in the college’s policy.

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