SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering

Transcription

1 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA (Tech) Semester : IX Sub: Marketing Analytics for Strategic Decision Code : MBAB090 Teaching Scheme Evaluation Scheme Lecture Practical Internal Continuous Assessment (ICA) As Credit / Tutorial per Institute Norms (50 Marks) Weightage 00% Prerequisite: Statistical Methods Marketing Management Business Analytics Application of Business Analytics Research Methodology Managing Data with spreadsheet Objectives: To understand the role of analytical techniques and computer spreadsheet models and metrics for enhancing strategic marketing decisions To provide exposure to examples demonstrating the value of data-based marketing in managerial context Outcomes: Enables its participant to put together data, models and analyses and translate concepts into context specific strategic decisions such as Portfolio Analysis, Forecasting, Segmentation and Positioning Enhances skills in viewing marketing processes and relationships systematically and analytically Detailed Syllabus Unit Topics Duration (Hr) Introduction to the marketing response models, their properties and related concepts are discussed in the module Directing Segmentation and Targeting Decisions by profiling data of customer groups Leveraging data to make positioning decisions for attribute based and image based positioning strategies Choosing and applying appropriate forecasting models and making forecasting decisions 5 Strategic analysis of a portfolio of businesses or products TOTAL HRS 5 Prescribed text: Lilien G. L, Rangaswamy A. and Bruyn A. (0). Principles of Marketing Engineering.

2 Trafford Publishers. Reference Books: Sorger, S. (0), Marketing Analytics: Strategic Models and Metrics, CreateSpace Independent Publishing Platform Mark J. (00), Data-Driven Marketing: The 5 Metrics Everyone in Marketing Should Know, Wiley Venkatesan, R.; Farris, P.; Wilcox, R. T. (0), Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands On Learning, Pearson FT Press Winston, W. L.(0), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, Wiley Internet references/ Software s Nil Term work: Class Tests/ Presentations

3 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA(Tech) Semester : IX Sub : Financial Technical Analysis Code : MBAB0905 Teaching Scheme Evaluation Scheme Lecture Practical / Internal Continuous Assessment (ICA) As per Credit Tutorial Institute Norms (50 Marks) Weightage 00% Prerequisite: Financial Management Financial Institutions and Markets Objective: To learn charting techniques which will help them to create and innovate profitable investing and trading strategies. Outcomes: Unit Student will Identify, interpret and analyze the varied financial technical patterns Learn the art of working in Intra-day and Positional Trades Detailed Syllabus Topics Introduction: Basic Philosophy of Technical Approach for Investment, Dow theory, Chart construction, Basic concept of trends, Technical Analysis Applied to Different Trading Mediums and Time Dimensions Pattern study : Major reversal pattern: Head and shoulders, Double top and double bottom Triple top and triple bottom Continues pattern: Triangles, Flags and pennants, Wedge and Rectangle. Candlestick Theory: Candlestick Construction & Analysis, Major Candlestick Reversal and Continuation Patterns Major indicators & oscillators: Simple moving average, Exponential moving average, Relative strength index, Moving average convergence/divergence(macd), Bollinger Bands, and Stochastic Duration (Hr) Total 5 Prescribed text: Murphy, J., (999), Technical Analysis of the Financial Markets, New York Institute of Finance. Nison, S. (00). Japanese Candlestick Charting Techniques, New York Institute of Finance 5

4 Reference Books: Parasuraman,. (0)., Financial Management, Delhi: Cengage Publication Ramanathan, S. (0)., Accounting for Management, Delhi: Oxford Mahakud, J. & Bhole, L.M. (009)., Financial Institutions and Markets, Europe: McGraw-Hill Education Internet references/ Software s Amibroker Technical Analysis Software ( Free available) Spider Term work: Class Tests/ Presentations

5 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA (Tech) Semester : IX Sub : Lean Six Sigma Code : MBAB0906 Teaching Scheme Evaluation Scheme Lecture Practical / Internal Continuous Assessment (ICA) As per Credit Tutorial Institute Norms (50 Marks) Weightage 00% Prerequisite: Operations Management Statistical Methods Quality Management Systems and Practices Objective: Understand six sigma methodology & its application to lean management. Relevant statistical tools and techniques to practice Six Sigma in Lean Management framework. Outcomes: Ability to initiate and manage lean six sigma project life cycle using relevant techniques. Detailed Syllabus Unit Topics Duration (Hr) Overview of Six Sigma & Lean Management: Introduction, Problem Solving Strategy Y = f(x), DMAIC & DMADV (DFSS) process, Lean Management Principles, Type of Waste, Lean Management Tools & Techniques Six Sigma Project selection: Selecting Lean Six Sigma Projects, Building a Business Case, Project Charter, Financial Evaluation Define Phase: Defining a Process, Collect customer data, Translating (Voice of Customer) into Critical to Quality Characteristics (CTQ), Develop problem statements, Establish project metrics, Identify necessary resources, Create a project plan. Measure Phase: Select product or process CTQ characteristics, Define performance standards for Y s (Output Parameter), Identify X s (Independent Parameters), Validate the measurement system for Y s and X s, Collect new data, Establish process capability (sigma level) for Y s. 5 Analyze Phase: Localize the problem, State the relationship to be established, Define the hypothesis or the questions describing the problem, Decide on appropriate techniques to prove the hypothesis, Test the hypothesis using the data collected in the earlier phase, Analyze the results and make conclusions, Validate the hypothesis.

6 6 Improve Phase: Define the problem, Establish the experimental objective, Select the variables and choose the levels for the input variables, Select the experimental design, Run the experiment and collect data, Analyze the data, Draw practical conclusions, Replicate or validate the experimental results. 7 Control Phase: Select the variable to control, Select the type of control to be used, Determine measurement methods and criteria, Calculate the parameters of the control chart, Develop a control plan, Train the people and use the charts. TOTAL 5 Prescribed text: George M.L et. el. The Lean Six Sigma Pocket Tool book. McGraw-Hill. Reference Books: Bedi Kanishka (0). Quality Management. Oxford George M.L. Lean Six Sigma: Combining Six Sigma Quality with Lean Speed. McGraw-Hill Internet references / Software s Hands-on activities. Microsoft Excel / SPSS 9.0 Term work: Class Test/ Presentations

7 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA(Tech) Semester : X Sub : Marketing Analytics - Marketing Mix Models Code : MBAB005 Teaching Scheme Evaluation Scheme Practical / Internal Continuous Assessment (ICA) As Lecture Credit Tutorial per Institute Norms (50 Marks) --- Weightage 00% Prerequisites: Statistical Methods Marketing Management Data Mining & Analytics Business Analytics Application of Business Analytics Marketing Analytics for Strategic Decisions Research Methodology Managing Data with spreadsheet Objective: To learn a metric driven approach for marketing mix decisions To understand the role of analytical techniques and computer models for enhancing marketing mix decision making To provide exposure to examples demonstrating the value of data-based marketing in managerial context Outcomes: Enables its participant to put together data, models and analyses and translate concepts into context specific operational decisions such as advertising and communication decisions, sales force and channel decisions, pricing and sales promotion decisions Enhances skills in viewing marketing processes and relationships systematically and analytically Detailed Syllabus Unit Topics Duration (Hr) Strategic conceptual framework, Positioning New Product Decisions Advertising and Communications Decisions such as budgeting and selecting ad execution themes Sales Force and Channel Decisions: allocating sales revenues, aligning sales territories, designing compensation plans, planning sales calls and locating new stores

8 Pricing and Promotion Decisions : Competitive bidding, Learning curve 5 pricing, Value-in-use analysis, Revenue management analysis, Promotions planning and analysis Total 5 Prescribed text: Lilien G. L, Rangaswamy A. and Bruyn A. (0). Principles of Marketing Engineering, Trafford Publishers. Reference Books: Sorger, S. (0), Marketing Analytics: Strategic Models and Metrics, CreateSpace Independent Publishing Platform Mark J. (00), Data-Driven Marketing: The 5 Metrics Everyone in Marketing Should Know, Wiley Venkatesan, R.; Farris, P.; Wilcox, R. T. (0), Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands On Learning, Pearson FT Press Winston, W. L.(0), Marketing Analytics: Data-Driven Techniques with Microsoft Excel, Wiley Internet references/ Software Term work: Class Tests/ Presentations

9 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA (Tech) Semester : X Sub: New Venture Business Models Code : MBAB006 Teaching Scheme Evaluation Scheme Lecture Practical / Internal Continuous Assessment (ICA) As per Credit Tutorial Institute Norms (50 Marks) --- Weightage 00% Prerequisite: Strategic Management Marketing Management Financial Management Operations Management Human Resource Management. Objectives: Generating Business Models for starting a new venture. Understand business model canvas and its applications. Outcomes: Develop your own business models for implementation. Gain insights on how to stay ahead of competition. Detailed Syllabus Unit Topics Duration (Hr) Entrepreneurial process. Components of effective business model. Tools for effective model generation for Starting a new venture. Business Model Canvas for new ventures. Generating business models; Using business model to beat competition. Strategizing your growth stories: having right team, right marketing and right finances. Planning for success by planning for failure. 5 Success stories on Start-ups. Total 5 Prescribed Text: Thierry. Burger. Helmchen. (0). Entrepreneurship - Creativity and Innovative Business Models.InTech.

10 References : Hisrich, Peters.(0). Entrepreneurship. Tata McGraw Hill. Bruce R.Barringer., Duane Ireland.(0).Entrepreneurship successfully launching new ventures.,pearson. Megginson. (0)Small Business Management - An Entrepreneur s Guide Book,, Irwin McGraw Hill Peter F.Drucker ;(0) Innovation & Entrepreneurship,, Harper & Row Marc J. Dollinger, (0), Entrepreneurship-strategies & Resources, Prentice Hall Anuradha K Rajivan, (997), A Business of Her Own, East West Books Term Work : Class Tests/ Presentations

11 SVKM S NMIMS Mukesh Patel School of Technology Management & Engineering MBA (Tech) Semester : X Sub: Quality Function Deployment (QFD) for New Product Innovations Code : MBAB007 Teaching Scheme Evaluation Scheme Lecture Practical / Internal Continuous Assessment (ICA) As per Total Tutorial Institute Norms (50 Marks) --- Weightage 00% Prerequisite: Management of Technology & Innovation Objective: Identify & capture Voice of Customer (VOC) Deploy Voice of Customer (VOC) by using Quality Function Deployment (QFD) Outcomes: Ability to deploy QFD Methodology for New Product Development Detailed Syllabus Unit Topics Duration (Hr) Introduction: Product Planning and Voice of the Customer, QFD Introduction, Basic QFD - The Four Phases Voice of the Customer (VOC): Importance, Methods of capturing, Creating Effective Statements for VOC, Organizing Customer Needs House of Quality: Organizing Customer Requirements, Evaluating the Competition and Developing a Product Strategy (Team Development of a New Pocket Calculator Product ), Developing Technical Characteristics for the New Product & Establishing Relationships with Customer Requirement, Performing a Technical Evaluation (Product Benchmarking with Competitive Calculators), Identifying and Addressing Interactions, Technical Difficulty and Program Risks, Finalize Product Planning Matrix and Establishing Target Value, Creating and Overall Requirements of Specifications Document from the Product Planning Matrix Concept Development: Concept Development, Concept Evaluation and Selection - Concept Selection Matrix, Concept Synthesis 5 Subsystem/Part Deployment: Deploy Design Requirements, Determining Critical Assembly/Part Characteristics, Importance Ratings & Target Values 6 Process & Production Planning: Deploy Selected Part Characteristics, Evaluate & Select Process Approach, Determine Process Relationships & Critical Process Parameters, Establish Quality Control and Process Control Parameters

12 7 QFD Framework and Process: QFD and the Product Development Process, Planning a QFD Project, Avoiding Pitfalls with QFD TOTAL 5 Prescribed text: Bedi Kanishka (006). Quality Management. Oxford. Reference Books: Nil Internet references / Software s Hands-on activities. If required MS Excel / Similar to be used for calculations. Term work: Class test / presentation