Courses

  1. Quantitative Methods -1a

The purpose of the course is to introduce you to the management of uncertainty in managerial decisions. In the first session, we discuss situations where uncertainty plays a role in the decisions involved and where the assessment of uncertainty becomes imperative. We then move to certain technical aspects like axioms of probability, probability computations, probability distributions, summary measures of probability distributions etc. In our judgement, it is essential to have good understanding of these technical aspects in order to be able to use them in concrete situations.

The technical part of the course is being developed through a series of problems. It may seem to you that many of the problems can be intuitively solved. We would urge you not to do that. We have given hints with many problems. Try to use them and see whether or not you can have a framework for handling such problems. This may appear cumbersome in the beginning. However, if you follow this, you would develop skills to deal with more complex situations. The concepts are intuitive, but to master them you need systematic study. You would get all our help in this process of learning.

  1. Quantitative Methods -1b

This is the second part of the introductory sequence in probability and statistics. Participants will be exposed to basic notions of sampling and sampling distribution, hypotheses testing, confidence intervals, analysis of variance and simple and multiple linear regression analysis.

  1. Operations Management I

Operations Management (OM) deals with the management of resources in the production of goods and services. Central to OM thinking is the paradigm of competing through excellence in operations (i.e., manufacturing and service sectors). In recent years, several strategies have emerged for improving productivity and quality, reducing costs and delivery times, and enhancing the flexibility and innovative abilities of the firm. Moreover, competitiveness and consequently the growth of the manufacturing sector are being recognized as the key factors in enhancing the status of the Indian economy.

Operations Management is taught in two parts: OM I & OM II. Together, they complete the picture on competitiveness through manufacturing and service operations. OM I lays the foundation on the factors of competition, i.e., cost/productivity, quality, delivery & flexibility. The course presents a detailed description and analysis of the different types of production and service operations. While OM I lays the foundation of Operations based competitiveness, OM II completes the conceptual understanding and focuses on the decision perspective of Operations.

The objective of the course is to develop an understanding of the role of operations management in a firm's success, and to develop abilities to structure and solve operations related problems analytically. The course will also try to explore the interaction of Operations Management with other functional areas of a firm.

  1. Operations Management II

Operations Management (OM) deals with the management of resources in the production of goods and services. Central to OM thinking is the paradigm of competing through excellence in operations (i.e., manufacturing and service sectors). In recent years, several strategies have emerged for improving productivity and quality, reducing costs and delivery times, and enhancing the flexibility and innovative abilities of the firm. Moreover, competitiveness and consequently the growth of the manufacturing sector are being recognized as the key factors in enhancing the status of the Indian economy.

Operations Management is taught in two parts: OM I & OM II. Together, they complete the picture on competitiveness through manufacturing and service operations. OM I lays the foundation on the factors of competition, i.e., cost/productivity, quality, delivery & flexibility. The course presents a detailed description and analysis of the different types of production and service operations. While OM I lays the foundation of Operations based competitiveness, OM II completes the conceptual understanding and focuses on the decision perspective of Operations.

The objective of the course is to develop an understanding of the role of operations management in a firm's success, and to develop abilities to structure and solve operations related problems analytically. The course will also try to explore the interaction of Operations Management with other functional areas of a firm.

  1. Quantitative Methods 2

This course introduces techniques to model and solve managerial decision making problems under certainty, uncertainty, and risk. The emphasis of the course is on hands-on problem solving. Participants are expected to identify managerial situations amenable to the techniques introduced, solve such models, and interpret the solutions obtained to answer domain-specific questions.

  1. Flexicore – Manufacturing Operations Management

The 15 session flexicore course on Manufacturing Operations Management follows the 25 sessions of the core Operations Management courses. The purpose of the course is to understand different issues and tradeoffs involved in managing the manufacturing supply chain. The course seeks to achieve this by focussing on three broad modules. In the first module, a single "station" is considered as the unit for discussion. The next module uses the single station as a building block and expands the discussion to a "production line and work-cells." This is followed by topics focussed on analysing the entire "supply chain." Strategic and contemporary issues in manufacturing such as using manufacturing for competitive advantage and green manufacturing and sustainability are briefly introduced.

  1. Flexicore – Service Operations Management (SOM)

The objective of this course is to enable students to understand the challenges of the service sector and service organizations, and to address the same with frameworks primarily from the Operations Management core course.

The defining concept of services is that it is delivered at the time consumption. Consequently, some of the significant issues in the context of services are managing capacity, congestion, quality and demand. We address these issues in different contexts and decision making levels.

  1. Context: The course offers a mix of cases from different contexts like public service, healthcare, hospitals, hotels and transport.
  2. Decision makers: The cases have been chosen to highlight decision problems faced by various types of decision makers like government, company heads and planners.
  1. Advanced Mathematical Modeling for Managerial Decisions

The course aims to achieve the following objectives:

  1. Train participants to develop mathematical models to model real-world managerial

problems, and to use OR/MS tools to solve them. This will be done through modeling real-world problems, drawn from diverse areas of management: Product Line Design from Marketing; Paper recycling from Public Systems; Disentangling bankruptcies and Investments for electricity generation from Finance; Supply chain network design, Supplier selection, Assembly line balancing, Job scheduling, Freight allocation with lane cost balancing problem, Milk collection problem, and hub & spoke network design problem from Operations/Logistics & Supply Chain; Bandwidth packing, Selecting Telecommunication carriers from Telecommunications.

  1. To train participants understand the differences between alternate model choices, and

identify one that is computationally more efficient.

  1. To train participants in the use of a Mathematical Modeling Language, namely AMPL (A Mathematical Modeling Language), for modeling and solving large problems arising in real world.

 

  1. Advanced Methods of Data Analysis

The advent of fast inexpensive computers has profoundly impacted the way data is analyzed and business decisions are made. In this course we provide a glimpse of some of the modern methods of data analysis. We discuss the techniques along with the simple and elegant statistical principles on which they are based. The advantages of use of these techniques are illustrated by application of some of these techniques to real-life business scenarios and data sets.  

 

  1. Bayesian Method of Data Analysis

Course objective: The aim will be to provide a firm basic understanding of Bayesian methods to be able to apply to practical data analysis problems.

 

  1. Business Analytics

This course on Business Analytics will familiarize students in applying modeling approaches to solve business decision making problems using statistical and data mining approaches. The course will encompass various functional areas of management. After completing this course, you will be able to apply predictive modeling to business decision situations.

 

  1. Coordinating the Crowd

The course "Coordinating the Crowd" aims to build an understanding of centralized and decentralized coordination mechanisms. The course would focus on the value of coordination in different contexts ranging from Hierarchies to Platforms and Markets and ways to unlock the value. This course is likely to benefit students who desire to take up entrepreneurial or managerial opportunities in marketplace operations or governing multi-sided platforms. 

 

  1. Elephants and Cheetahs: Systems, Strategy and Bottlenecks

‘Elephants and Cheetahs: Systems, Strategy and Bottlenecks' is an unconventional treatment of Operations Strategy. It presents the dimensions of competition in the operations domain through the lens of the natural world and systems thinking. The fundamental premise of the course is that systems are not just a collection of interconnected parts – the whole is greater than the sum of parts (Aristotle). A well designed system is thus like a living entity with its own structural characteristics, peculiarities, capabilities, constraints and vulnerabilities.  

The course builds on the core OM courses taught in first year and would be beneficial for participants who are planning a career in management consulting or general management roles. The relationships between the firm level strategy and operations strategy are explored and the interlinkages between marketing, HR and operations strategy are highlighted, leading up to a balanced scorecard approach to tracking organizational performance.

 

  1. Forecasting Techniques for a Practitioner

This course introduces the theory and methods of univariate time series analysis for applications in economics and finance. The objective of the course is to give participants enough technical background to enable them to model and forecast macroeconomic and financial time series. After introducing fundamental concepts in time series analysis, the course covers the theory of stationary ARMA processes. This forms the bulk of the course. Moving on from considering covariance stationary processes, the course next introduces the econometrics of unit roots. The remaining portion consists of studying some specific unit root processes e.g. ARIMA models and variations, and models with conditional heteroskedasticity (ARCH and GARCH).

 

  1. Managerial Applications of OR

Learning Objectives:

  1. To provide an opportunity to the participants to experience the joy of solving real-life, complex, meaningful administrative situations.
  2. To demonstrate the power of modelling in responding to the challenges in a given context.
  3. To illustrate the use and applicability of formal, operational research methods in resolving real-life situations.
  4. To appreciate the trade-off required in balancing an optimal vs. appropriate solutions.
  5. To sensitise the participants to the implementation challenges of a solution recommended by a formal procedure in a real-life context.

 

  1. Operations Strategy

The course is designed in five modules.  A brief description of the modules is given below:

 

Module 1 – Linkages between operations and competitive strategy

  • Defined competitive position and implications to operations
  • Excellence in operations and its implications to redefine competitive positioning

 

Module 2 – Tactical Decisions

  • Decisions related to managing set of time, sourcing and scheduling in the overall context of competitive positioning of operations.

 

Module 3 – Elements of Operations Strategy

  • Issues related to the capacity, facilities, planning systems, HR interfaces
  • Factory within factory
  • Implementation experience of operations strategy enablers in the Indian context

 

Module 4 – Competing Through Operations

  • Agility and Innovation enabled operations
  • Mass Customization and its Limitations

 

Module 5 – Emerging Scenario

  • Green Manufacturing
  • Managing global facilities

 

  1. Revenue Management and Dynamic Pricing

Objective of this course is:

  • To provide an insight into concepts of Revenue Management.
  • To provide an understanding of how Revenue Management systems improve revenue.
  • To acquaint the participants with various tools and techniques used in Revenue Management.
  • To discuss the practice of revenue management in various industries.
  • To discuss the technical and organizational challenges for implementing Revenue Management Systems.

 

  1. Statistical Methods in Data Analysis

Companies and regulators alike are placing more and more emphasis on objective decision making based on data. Strategic decisions of companies depend on findings from data analysis tasks such as estimating market share of a product, deciphering the latent reasons underlying a consumer's basket of product preferences, prediction of defaults on a bank's loan portfolio etc. Therefore, design, collection and analysis of data have been playing an increasingly crucial role in decision making.

This course introduces participants to some modern statistical techniques in data analysis.  While the course is primarily application focused, it will provide adequate theoretical background so that participants can confidently formulate and apply these techniques in real life situations.

 

  1. Supply Chain Management

Supply chain management is a critical area for competitive advantage, based on the paradigm that coordination and collaboration across supply chain flows is as important as functional expertise. Enhancing the supply chain perspective from across functions in an organization to across enterprises to across borders is the challenge. Effective logistics management involving transportation, storage and handling is an integral part of supply chain management. This course focuses on the significance of supply chain management, issues in coordination, demand management, variety management, sourcing, information technology, logistics from both the perspective of the supply chain beneficiary and the service provider in a global context.

 

  1. Supply Chain Thinking

This course highlights the potential of supply chain thinking to create value and the importance of adapting supply chain strategy as the business environment evolves.

 

  1. The Art and Craft of Decision Making

Learning Objectives:

  • To structure a problem, generate alternatives and recommend a choice.
  • To appreciate the sources of risk (in a decision making context) and approaches to mitigate the same.
  • To appreciate the decision choices and the consequences from multiple stakeholders' point of view in tactical and strategic decisions.
  • To examine and understand individual's perceptions and beliefs related to decision making.

 

  1. Why Projects Fail? Uncertainty, Complexity and Risk in Projects

Course Objectives:

  • Acquaint the participants with three types: Variations, foreseen and unforeseen uncertainties;
  • Discuss issues related to risk identification, risk analysis, and risk response planning in projects;
  • Mitigating cost and time overruan;
  • Develop knowledge about system, task, and organizational level complexities;
  • Discuss the various statistical principles in risk management and introduce new concepts of cumulative.
  • Impact factor and cumulative likelihood factors, risk cost, risk time, corrective cost, corrective time, expected cost, and expected time;
  • Discuss advanced concepts like simulation, Dependency Structure Matrix, selection-ism  and learning;
  • Discuss the difference between PRM in ordinary projects and technology start-up projects;
  • Discuss various legal issues including statutory reporting of project risk management; and
  • Discuss Indian and international cases of application of PRM in several industries.

 

  1. Working with Networks

The present course equips participants with tools to analyse the structure of networks and algorithms for effective decision making about problems posed on networks.

  1. Food Supply Chain Management (FSCM)

This course will focus on:

  1. Understanding of core supply chain management concepts as applicable to the food sector.
  2. Enhancing strategic decision-making skills through analysis and integrated perspectives.
  1. Advanced Probability for Management (AP)

The course builds the theory of probability confining the discussion to the discrete sample space avoiding the measure theoretic approach. Besides getting a reasonably good understanding of the important concepts related to probability theory, the students are exposed to the mathematical rigour of proving theorems. Also it helps them to learn how to formulate a mathematical problem and solve it.

  1. Classical Operations Management (OM)

This course provides the basic theory and methodology inputs required for understanding key issues in Operations Management.  The objective of the course is to expose the students to the classical themes and material in OM and prepare them for research in OM.  The course comprises of the following modules:

  1. Linear Algebra (LA)            

This is an introductory course in Linear Algebra. The aim is to provide a strong foundation in concepts to help participants understand and apply the ideas in their area of research.

  1. Operations Research (OR) 

This is an introductory doctoral level course in Mathematical Programming. The emphasis of this course is on understanding the theory of mathematical programming. While the subject of Operations Research is much more diverse than mathematical programming, we focus on fundamentals of the deterministic linear and network programming in this course.

  1. Algorithms on Graphs and Networks (G&N)

The course aims to introduce students to graph and network algorithms. The takeaways from this course will be useful to students in a variety of courses in logistics and supply chain management.

  1. Applied Multivariate Analysis (AMA)

This course gives a balanced emphasis on theory and applications. It covers the following broad areas: Multivariate Normal Distribution and Related Inference Problems, Assessing Normality, Outlier Detection, Multiple Linear Regression Analysis, Variable Selection Problems, Multicollinearlity, Heteroscedasticity, Regression Plots, Regression Diagnostics, Model Specification Tests, Auto correlated and Longitudinal Data Analysis.

  1. Applied Regression Analysis (ARA)

This course is designed to provide a comprehensive exposition on the scope and applicability of regression modelling techniques in solving real-life problems. In doing so, the aim will be to inculcate a sound understanding of both the underlying theoretical aspects of modelling as well as various issues that are encountered in applying the models in real-life scenarios. Real datasets and cases from diverse areas (like business administration, economics, engineering and social, biological and ecological sciences) will be analysed which will help the participants in reinforcing their methodological and conceptual understanding. It is expected that by the end of the course, the participants will gain a thorough understanding of various aspects of regression models and their applicability in analyzing datasets they may encounter during their FPM coursework/programme and beyond. Since all applications will be carried out in the R programming language, this course can also aid the participants in learning this important statistical programming language at some length.

  1. Applied Statistical Inference (ASI)

This course will explore the concepts of statistical inference with applications in management research in mind. This course will start with basic inference but will also cover situations where assumptions about situations being ‘nice' do not work, and one needs to go beyond the obvious. Estimation techniques, both theoretical and empirical, will be covered. Asymptotic as well as data-driven estimates will be derived. Examples will be discussed in detail. The theoretical discussions will be backed up by hands-on training to apply the methodology to data sets using R. Both standard packages and non-standard coding will be discussed.

  1. Approximate Methods in Solving Real World Complexities (AMS)

Exact approaches in solving problems are highly dependent on definitive problem structuring and on computational sophistication. They generate superior solutions, but with huge computational time and overhead. In solving real-world problems, very often heuristic procedures are applied as a trade-off for acceptable, but quick solutions. Meta-heuristic procedures are standardized and advanced procedures that operate iteratively to generate improved solutions under dynamic system variations. In fact, most of the problems in real world are prone to dynamic and uncertain changes that are difficult to solve using standard and bespoke heuristics. This course discusses a host of meta-heuristic algorithms that can effectively address the real world complexities and inter-dependencies. Discussions shall cover some of the distinctive characteristics of these meta-heuristics such as learning, self-correction and adaption.

  1. Bayesian Methodology for Business Research (BMBR)

Application of Bayesian methodology in solving business research problems is a fast growing area of research.  In this course we will start from the scratch assuming no prior knowledge of Bayesian Methodology. Before getting into deeper issues of Bayesian modelling, we plan to devote adequate number of sessions at the beginning to acquaint the students with the basic tools and concepts of Bayesian inference. In this course, our emphasis will be on the modelling aspect of business data arising in different functional areas of management from a Bayesian perspective. In this context, we will discuss hierarchical Bayesian models, model checking (both data model consistency and model selection) and implementation of the methodologies through Bayesian computation.

  1. Convexity & Optimization (CO)

Convex analysis is the analysis of properties of convex functions and convex sets in a normed vector space. In optimization, convexity plays a very important role in proving optimality results in both linear and nonlinear optimization. For instance, the concept of a separating hyperplane between two disjoint convex sets helps establish the sufficiency of KKT conditions for optimality of convex programming problem. However, to prove the existence of a separating hyperplane between two disjoint convex sets requires knowledge of continuous functions, affine transformations, dimension of sets, hyperplanes and uses other topological properties of sets such as closure, relative interior, relative boundary and compactness, amongst others. This course is aimed at establishing these results from basic results in set theory and topology. Among the topics discussed are basic properties of convex sets (extreme points, facial structure of polytopes), separation theorems, duality and polars, propertes of convex functions, mimima and maxima of convex functions over a convex set and various optimization problems.

  1. Game Theory for Operations Management (GTOM)

Game Theory deals with problems of strategic interaction between two or more players, wherein each player needs to decide its best action, while anticipating the reaction from the other(s). In business, such strategic interactions occur at various levels. If the decision making within a firm is decentralized, then such interactions may manifest between two of its functions; for example, between marketing and production for price and leadtime decisions (Pekgun et al., 2008). This also often manifests between two retailers deciding the stocking (newsvendor) quantity of a limited shelf-life product for the next period (Lipman and McCardle, 1997), or between two manufacturers/service providers for price and delivery leadtime (So, 2000), or between a retailer and a manufacturer in a supply chain (Tsay and Agarwal, 2000; Camdereli and Swaminathan, 2005; Wang and Zipkin, 2009), or between two supply chains (Liu & Tyagi, 2011). The objective of this course is to prepare students to analyze such problems of strategic interactions that are pertinent to Operations Managers. It also covers such problems that lie at the interface between Operations and other functions like, IT (Camdereli and Swaminathan, 2005); Marketing (Pekgun et al., 2008; Goic et al., 2011); Environment (Orsmedir et al., 2015; Zhou et al., 2016; Park et al., 2015); and Finance (Dada and Hu, 2008; Lai et al., 2011; Lai et al., 2012).

The course assumes no prior background on Game Theory. It will, therefore, begin with the basic concepts of elimination of dominated strategies and Nash Equilibrium to arrive at the outcome of a game. We will discuss four classes of games: static games of complete information; dynamic games of complete (perfect/imperfect) information; static games of incomplete information; and dynamic games of incomplete information. Corresponding to these four classes of games, we will discuss the four notions of equilibrium in games: Nash equilibrium, subgame-perfect Nash equilibrium, Bayesian Nash equilibrium, and perfect Bayesian equilibrium. After developing the idea of corresponding equilibrium concept, we will study one or two problems of strategic interactions arising in each of the four categories of the games, which are relevant to Operations/Supply chain Managers. We will see how to arrive at the corresponding equilibrium for each of the games, and derive useful insights for Operations managers. To this end, the course will also introduce Bilevel Mathematical programming & its solution methods for Stackelberg Games (2-stage Dynamic games with complete and perfect information).

  1. Large Scale Optimization (LSO)

Real world optimization problems often tend to be large Integer Program/ Mixed Integer Program (IP/MIP) problems, often to an extent that even the standard IP/MIP solvers, which use Branch & Bound and Branch and Cut algorithms, fail to solve them in reasonable time. In this course, students learn how to take advantage of the often hidden special structures of such problems either by relaxation or by decomposition into relatively easier/smaller problems, which can be solved efficiently using their special structures. The challenge then is how to recover the solution to the original problem from the solution to its relaxation/decomposition. To this end, the introduces several decomposition techniques, namely, Cutting Plane Method, Lagrangian Relaxation, Benders Decomposition, Column Generation, and Dantzig-Wolfe Decomposition methods. The course also introduces linearization techniques for non-linear IP/MIP problems and their solutions using cutting plane techniques. Towards the end, the course also introduces Stochastic Optimization and Database Optimization Interface.

This is an applied course, and hence its focus is more on understanding and applications of the techniques rather than on formal proofs. The course introduces several practical applications from Hub-and-Spoke Network Design, Facility Location, Telecommunication Network Design, etc.

  1. Non-linear Optimization (NLO)

The course introduces students to the fundamentals of non-linear optimization and then builds on it to introduce other advanced topics in the area of optimization. It enables students to enhance their understanding of optimization methods that may be suitable for problems with complexities such as non-linearity, non-convexity, discontinuity and non-differentiability.

Around 50% of the course focuses on the conventional techniques for solving non-linear optimization problems. 20% of the course focuses on non-traditional optimization techniques. Remaining 30% of the course discusses extensions of single objective optimization to multiobjective optimization, bilevel optimization and robust optimization.

  1. Problem Solving With Heuristics (PSH)

Many real-world optimization problems belong to the class of NP-hard problems, which mean that there are no methods that guarantee optimal solutions to large instances of such problems within reasonable time. However obtaining good quality solutions to such problems are important in practice, and research has focused on developing heuristic methods for such problems. In this course the participant is exposed to the current state of knowledge about heuristic techniques to solve large instances of combinatorial optimization problems.

  1. Queuing Models (QM)

The participants will be able to appreciate the various queuing modelling constructs and solution algorithms as an analytical toolkit. Further, the participant will be able to develop customized models to analyse the performance of a practical system, and obtain design insights. No prior working knowledge of measure theory or stochastic processes is required. However, participants should have a prior course on basic probability theory.

  1. Revenue Management and Dynamic Pricing (RMDP)

Revenue Management and Dynamic Pricing (RMDP) is the method of selling right product to the right customer at the right price at the right time. It is the scientific way of dynamically managing prices, inventories, and capacities of perishable services. Although core of RM is related to OR/Statistics, it has relationship with economics, marketing, information technology, human resources and legal dimension. In this doctoral courses, we plan to discuss those topics that cuts across four disciplines, PQM (OR/OM/Statistics), economics, marketing and information technology. Conceptually the course focuses on two three aspects, economics of pricing, optimization of perishable resources and forecasting of demand of perishable products.  We discuss several aspects related to  design of revenue management system. At end we discuss emerging research areas on the topic.

  1. Real Analysis (RA)

The course analyses basic concepts in certain areas of mathematics and prepares students to take advanced courses. The topics covered include : structure of the real number system, infinite sequence- convergence and divergence, subsequence – Bolzano-Weierstrass Theorem, Cantor intersection property, Cauchy sequences, infinite series - convergence and divergence, tests for convergence, Metric Spaces - limits, continuity, Compactness – Heine-Borel theorem,  connectedness and uniform continuity.

  1. Statistics II (Stat-II)

The course will provide an understanding of the statistical methods that are useful for carrying out research in management.

  1. Stochastic Processes (SP)

The objective of this course is to provide the theoretical foundation for modelling and analysis of variety of processes in service and manufacturing environments that are characterized by uncertainty. Topics include birth and death processes, Markov chains, Markov processes, renewal theory, martingales and optimal stopping, processes with independent increments (e.g. Poisson, Wiener processes), Brownian motion and the theory of weak convergence, application of stochastic processes in logistics, inventory, manufacturing, marketing, and finance.

  1. Systems Analysis and Simulation (SAS)

To introduce the participant to the idea of simulation in management, and to expose them to the latest software and statistical techniques in simulation. The broad topics that will be covered are: Introduction to Simulation, Building Simulation Models, Input Modelling, Generating Random Input, Output Analysis, Comparing and Optimizing Systems, and Variance Reduction.

  1. Survey of Statistical Methods Used in Management Research (SSMMR)

This is close to a comprehensive review of major statistical methods that are used extensively in management research. This course should serve the purpose of exposing the student to these prolifically used statistical/empirical methods. While all attempts have been made to make the course comprehensive enough to include major techniques, it is not necessarily exhaustive. Additionally, this is a generic survey course to provide exposure to the methods to FPM students. Students are advised to acquire additional expertise in any specific topic by choosing advanced courses offered by various relevant academic Areas of the institute.

  1. Time Series Analysis (TSA)

This course introduces the theory and methods of time series analysis for research in economics and finance. The objective of the course is two-fold. First is to give participants enough technical background to enable them to read research papers in applied time series analysis. The second is to introduce select advanced topics useful for analysis of macroeconomic and financial time series. After introducing fundamental concepts in time series analysis, the course covers the theory of stationary ARMA processes and reviews the relevant asymptotic distribution theory. This forms the bulk of roughly half the course and the basis for studying Vector Autoregressions (VARs) which is discussed next. Moving on from considering covariance stationary processes, the course next introduces the econometrics of unit roots. The core of the remaining portion consists of studying linear combinations of unit root processes, i.e. Cointegrated Systems (VECMs) and models with conditional heteroskedasticity (GARCH). We end the course by introducing State Space representations of time series models and Bayesian methods.

  1. Analysis of Data (AD)

This course exposes participants to the various concepts and applications of probability theory and statistical methods. This is also an input for other courses in the programme.

  1. Designing Operations to Meet Demand (DOMD)

Organizational processes aim to match supply with demand that vary both in a predictable and an unpredictable manner. Supply is often inflexible to keep up with the rapidly changing demand fluctuations. From an organizational perspective, shortage of supply leads to revenue loss and excess supply leads to additional wasteful investment. Operations management is about efficiently matching supply and demand. This course helps to understand tactical decision making by obtaining these inputs from practice and how production processes contribute to competitive advantage of organizations.

  1. Modeling for Decisions (MD)
  • This course exposes participants to the various quantitative models for decision making.
  • This is also an input for other courses in the programme.

 

  1. Setting and Delivering Service Levels (SDSL)

 Course Objective:

  • "Operations Management (OM)" deals with the management of resources in the production of goods and services. Central to OM thinking is the paradigm of competing through excellence in operations (in both manufacturing and service sectors). In the PGPX, Operations Management is taught over two terms: Designing Operations to Meet Demand (DOMD) in Term I and Setting and Delivering Service Levels (SDSL) in Term II. Together they complete the picture on competitiveness through manufacturing and service operations. DOMD lays the foundation with a detailed description and analysis of the different types of manufacturing and service operations.
  • The course SDSL builds on the foundation laid by DOMD and completes the conceptual framework for analyzing manufacturing and service operations. This course focuses on tactical and intermediate level decision making. In contrast to the process perspective of DOMD, in SDSL we provide a decision perspective and address long, medium and short term planning and control issues. This course stresses the role of OM as a service provider. In addition, the course also provides exposure to some basic management tools and prepares future managers to use the results of analyses for improving firm's operational performance.

 

The objective of the two courses together is to develop an understanding of the role of Operations Management in a firm's success and to develop the ability to structure and solve operations related problems analytically.

  1. Business Analytics (BA)

This course on Business Analytics will familiarize students in applying modeling approaches to solve business decision making problems using statistical and data mining approaches. The course will encompass various functional areas of management. After completing this course, you will be able to apply predictive modeling to business decision situations.

  1. Data Science for Business

The objective of the course is to introduce various methods from the domains of machine learning and optimization that will be useful to make business decisions when faced with large amount of data. The objectives of the course are as follows:

1. Train the participants on handling both small and large amount of data and perform tasks such as classification and predictive modeling. The training will be useful in automating business operations decisions with the use of data.
2. Train the participants on using important data analysis and optimization libraries that are available off-the-shelf.
3. Give an insight to the participants on how data-driven ideas are being used to develop artificial intelligence technologies to enhance human potential and solve challenging problems using machines.

  1. Elephants and Cheetahs: Systems, Strategy and Bottlenecks (EC)

‘Elephants and Cheetahs: Systems, Strategy and Bottlenecks' is an unconventional treatment of Operations Strategy. It presents the dimensions of competition in the operations domain through the lens of the natural world and systems thinking. The fundamental premise of the course is that systems are not just a collection of interconnected parts – the whole is greater than the sum of parts (Aristotle). A well designed system is thus like a living entity with its own structural characteristics, peculiarities, capabilities, constraints and vulnerabilities.  

The course builds on the core OM courses and would be beneficial for participants who are planning a career in management consulting or general management roles. The relationships between the firm level strategy and operations strategy are explored and the interlinkages between marketing, HR and operations strategy are highlighted, leading up to a balanced scorecard approach to tracking organizational performance.

  1. Logistics Management (LM)

Objectives:

  • To evolve an operational and conceptual framework to design and manage an effective logistics system in organizations.
  • To demonstrate the competitive potential of logistics systems, in the context of supply chains.
  • To illustrate the strategies for efficient warehouse design and improving delivery performance.
  1. Perspectives on Operations Management (POM)

Objective:

  • To understand, reflect and appreciate landscape changes in manufacturing function.
  • To illustrate the emergent inter-dependencies of manufacturing and other functional areas.
  • To demonstrate the changing role of manufacturing as a source of competitive advantage of organizations.
  1. Quality Management (QM)

The broad aim of this elective course is to introduce the participants to the main concepts and ideas of quality management that are essential for business managers.

  1. Supply Chain Management (SupCM)

Supply chain management is designed as an in-depth practitioner-oriented course to enable the participants to understand the elements of supply chain, challenges in managing a supply chain to enhance business competitiveness.

  1. Understanding and Assessing Risk (UAR)

This course introduces the participants to the assessment of risk in various business situations through use of quantitative and simulation modeling techniques.

  1. Operations Management (OM)

The objective of the course is to develop an understanding of the role of operations management in a firm's success and to develop an ability to structure and solve operations related problems analytically. The course will also explore the interaction of Operations Management with other functional areas of a firm.

  1. Statistical Data Analysis (SDA)

This course exposes the participants to the basic concepts (and applications) of Statistical Theory and Methods. Specifically, participants will be exposed to the basic notions of Probability, Random Variable (and their Distributions), Sampling and Sampling Distributions, Confidence Intervals, Hypotheses Testing, Analysis of Variance (One & Two way) and Regression Analysis (Simple and Multiple).

  1. Special topics introducing contemporary practices or research areas in operations

This course introduces big and impactful ideas in operations management. Since the course is designed for the ten sessions we plan to discuss three or four such ideas. Each one of these ideas will be discussed with a focus on (a) introduction of the idea, (b) the anatomy of the idea and (c) its applicability to general management.

  1. Operations Management I (OM I)

Operations Management (OM) deals with the management of resources in the production of goods and services. Central to OM thinking is the paradigm of competing through excellence in operations (i.e., manufacturing and service sectors). In recent years, several strategies have emerged for improving productivity and quality, reducing costs and delivery times, and enhancing the flexibility and innovative abilities of the firm. Moreover, competitiveness and consequently the growth of the manufacturing sector are being recognized as the key factors in enhancing the status of the Indian economy.

Operations Management is taught in two parts: OM I & OM II. Together, they complete the picture on competitiveness through manufacturing and service operations. OM I lays the foundation on the factors of competition, i.e., cost/productivity, quality, delivery & flexibility. The course presents a detailed description and analysis of the different types of production and service operations. While OM I lays the foundation of Operations based competitiveness, OM II completes the conceptual understanding and focuses on the decision perspective of Operations.

The objective of the course is to develop an understanding of the role of operations management in a firm's success, and to develop abilities to structure and solve operations related problems analytically. The course will also try to explore the interaction of Operations Management with other functional areas of a firm.

  1. Operations Management II

 

  1. Probability Statistics I (PS I)

This course exposes participants to the basic concepts of probability theory and its applications in managerial decision making. This is also an input for other courses in the programme.

  1. Probability Statistics II (PS II)

This is the second part of the introductory sequence in Probability and Statistics. Participants will be exposed to the concepts of confidence intervals, hypotheses testing, analysis of variance and linear regression models.

  1. Quantitative Techniques – (Decision Making) DT

This course exposes participants to the various quantitative models, tools, techniques for decision making.

The emphasis of this course is on decision making in the context of corporation or an organization. This course exposes the participants to the various quantitative models for decision making. In the first part of this course,  (called deterministic models) we provide an input how a management situation can be formulated as optimization problem and solved in the computer. In the second part of the course (called stochastic Model) we will provide inputs how to analyze sequential decisions. We also provide inputs on how simulation works in the decision making context. At the end of each part, we discuss an actual case where such models have been used making impact in the bottom line.

Central to the decision making context is critical to the thinking that ‘Making decisions is just one of the many things that corporations do but is a critical activity'. The process by which the decisions are taken, differentiate successful organizations from unsuccessful organizations. The successful organizations have a history of making decisions that are timely opportunistic and that generally work out in the long run.

  1. Business Analytics

This course on business analytics will guide the students to apply statistical analyses in solving business problems. The course will introduce the students to some data analysis techniques, which they will apply in solving real life problems. The pedagogy would involve a mix of theoretical training, some coding and case analysis. After completing this course, a student should be able to apply statistical modelling and clustering techniques to business situations.

  1. Business Statistics and Research Methods (BSRM)

This course introduces the participants to the basic concepts (and applications) of statistical theory and methods. Specifically, participants will be exposed to the notions of Probability, Random Variable (and their Distributions), Sampling and Sampling Distributions, Point Estimation, Confidence Intervals, Hypotheses Testing, Analysis of Variance (One & Two way) and Regression Analysis (Simple and Multiple).

  1. Decision Modelling (DM)

Module: Decision Modelling under Certainty

The course deals with the use of linear programming techniques to model business situations, and use the results obtained to take business decisions. The emphasis of the course is on hands-on problem solving. After the course, participants are expected to be able to structure business problem situations and model them as linear programs; and to use software to solve such models and interpret the solutions obtained to answer domain-specific questions.

Module: Decision Modelling under Uncertainty

The objective of the course is to provide techniques to help structure decision problems and analyze them quantitatively. The techniques covered in the course, whether used formally or informally, would help you think clearly about objectives, alternatives, consequences, and uncertainties, and enable you to integrate judgments with other types of information in a logical manner.

  1. Logistics & Supply Chain Management (LSCM)

Logistics is a support function to business. The operational areas of logistics function can be divided into two segments: (i) sourcing to production and (ii) production to market. Supply Chain management (SCM) involves concept to cash conversion. The decisions in logistics function influence the choices available to a firm / organization in production (capacity, location, planning) and inventory management in effective SCM. In addition, the business requirements would impact design, execution and maintenance of logistic and SCM function.

This 10-session course on Logistics and Supply Chain Management is aimed to provide an overview of the logistics function and supply chain in the business context. The emphasis is on concepts. The course is designed in three interconnected modules: (i) decision areas in logistics, (ii) process orientation, and (iii) emerging practices.

  1. Operations Management (OM)

"Operations Management (OM)" deals with the management of resources in the production of goods and services. Central to OM thinking is the paradigm of competing through excellence in operations (i.e. manufacturing and service sectors). In recent years, several strategies have emerged for improving productivity and quality, reducing costs and delivery times, and enhancing the flexibility and innovative abilities of the firm. Moreover, the competitiveness and consequently the growth of the manufacturing sector are being recognized as the key factors in enhancing the status of the Indian economy.

Operations Management course has four modules: Process Analysis, Internal and External coordination, Process Improvements, and Managing Service Operations. Together they complete the picture of competitiveness through manufacturing and service operations.

  1. Process Analysis first lays the foundation on the factors of competition and then presents a detailed description and analysis of different types of production and service operations.
  2. Internal and External coordination covers the techniques required to actually run the system and enhance the competitiveness of the firm.
  3. Process Improvements module focuses on achieving the competitiveness through quality initiatives. 
  4. Module on Managing Service Operations talks about the growing importance of service industry in India and focuses on selected key issues in services sector especially quality of services and customer services.

 

The overall objective of these four modules together is to develop an understanding of the role of operations management in a firm's success and to develop the ability to structure and solve operations related problems analytically. The course will also try to explore the interaction of Operations Management with other functional areas of a firm.

  1. Project Management (PM)

This course focuses in providing a broad framework of project management in ten sessions.

Text Book: Project Management for Business and Technology, Nicholas and Steyn, Fourth edition.

Course objective:

a) Management of Resources Understanding of Time Cost Resource management issues

b) Understanding of the tools and techniques for effective management of resources and cost in projects

c) Understand the concept of uncertainty in Project Management by applying PERT (Program Evaluation Review Techniques )

d) Understand three types of uncertainties, variation, foreseen and unforeseen and chaos

e) Discuss the concept of PRM (Project Risk Management) , RC (Risk Cost), RT (Risk Time), Corrective Cost and Corrective Time in Expected value Method

f) Look some of the cases why project fail

g) Understand the new concept of project management called Diamond Framework of Project Management with four dimension like NTCP (Novelty, Technology, Complexity, and Pace)

h) Bring in one outstanding success stories of project management in India

  1. Advanced Analytics for Management
  2. Advanced Quality Management
  3. Art and Craft of Decision Making
  4. Cutting Edge Analytics
  5. Food Supply Chain Management
  6. Fundamentals of Operations
  7. Logistics Management
  8. Manufacturing Strategy
  9. Project Management
  10. Restaurant Management
  11. Revenue Management and Dynamic Pricing
  12. Risk - Modeling and Management
  13. Strategic Analytics: Programme on Quantitative Data Analytics and its application in Business & Marketing
  14.  Supply Chain Management
  15. Uncertainty, Complexity and Risk in Projects
  16. Warehouse Design and Management
  17. Workshop on Manufacturing
 
Staging Enabled