meaning of network analysis in operation research

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Chapter 3: network analysis.

Many real-world problems in various fields can be formulated as optimization problems on networks. A network consists of nodes and edges that connect two nodes of the network to each other. A network is called directed if all edges can only be traversed in one direction. In an undirected network, every edge may be traversed in either direction. An undirected network can always be reduced to a directed network (why?). Network optimization problems can generally be solved with (continuous) linear programming. However, using the special structure of such problems, one can develop algorithms that reach the optimal solution much more quickly than linear programming algorithms. In this chapter, we discuss some of the most important models from network theory. In Section 3.1, we consider the shortest-path problem. We discuss Dijkstra’s algorithm for finding the shortest path between nodes in a network with nonnegative distances. Section 3.2 deals with the maximum-flow problem, in which the maximum amount of flow (for example, liquid or traffic) that can go from one node in the network to another node is sought, taking into account capacity restrictions on the edges between the nodes. In Section 3.3, we discuss the minimum spanning tree problem. We discuss Prim’s algorithm, which can be used to calculate how to connect a given number of nodes by a spanning tree at the lowest cost. We also briefly touch upon the related Steiner tree problem. Section 3.4 deals with the Euler circuit problem, where in an undirected graph, a path is sought that passes through every edge of the graph exactly once and ends at the starting point of the path. We then discuss the Chinese postman problem, in which costs are attached to the edges of the graph and a circuit is sought with the lowest total cost. Finally, in Section 3.5, we briefly discuss the general minimum-cost flow model that can be used for a large number of network optimization problems; in particular, we mention the assignment problem and the associated Hungarian solution method.

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Operations Research

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A network may be defined by a set of points, or “nodes,” that are connected by lines, or “links.” A way of going from one node (the “origin”) to another (the “destination”) is called a “route” or “path.” Links, which may be one-way or two-way, are usually characterized by the time, cost, or distance required to traverse them. The time or cost of traveling in different directions on the same link may differ.

A network routing problem consists of finding an optimum route between two or more nodes in relation to total time, cost, or distance. Various constraints may exist, such as a prohibition on returning to a node already visited or a stipulation of passing through every node only once.

Network routing problems commonly arise in communication and transportation systems. Delays that occur at the nodes ( e.g., railroad classification yards or telephone switchboards) may be a function of the loads placed on them and their capacities. Breakdowns may occur in either links or nodes. Much studied is the “traveling salesman problem,” which consists of starting a route from a designated node that goes through each node ( e.g., city) only once and returns to the origin in the least time, cost, or distance. This problem arises in selecting an order for processing a set of production jobs when the cost of setting up each job depends on which job has preceded it. In this case the jobs can be thought of as nodes, each of which is connected to all of the others, with setup costs as the analogue of distances between them. The order that yields the least total setup cost is therefore equivalent to a solution to the traveling salesman problem . The complexity of the calculations is such that even with the use of computers it is very costly to handle more than 20 nodes. Less costly approximating procedures are available, however. More typical routing problems involve getting from one place to another in the least time, cost, or distance. Both graphic and analytic procedures are available for finding such routes.

Competitive problems deal with choice in interactive situations where the outcome of one decision maker’s choice depends on the choice, either helpful or harmful, of one or more others. Examples of these are war, marketing, and bidding for contracts. Competitive problems are classifiable as certain, risky, or uncertain, depending on the state of a decision maker’s knowledge of his opponent’s choices. Under conditions of certainty, it is easy to maximize gain or minimize loss. Competitive problems of the risk type require the use of statistical analysis for their solution; the most difficult aspect of solving such problems usually lies in estimating the probabilities of the competitor’s choices; for example, in bidding for a contract on which competitors and their bids are unknown.

The theory of games was developed to deal with a large class of competitive situations of the uncertainty type in which each participant knows what choices he and each other participant has. There is a well-defined “end state” that terminates the interaction ( e.g., win, lose, or draw), and the payoffs associated with each end state are specified in advance and are known to each participant. In situations in which all the alternatives are open to competition, or some of their outcomes are not known in advance, operational gaming can sometimes be used. The military have long constructed operational games; their use by business is more recent.

Search problems involve finding the best way to obtain information needed for a decision. Though every problem contains a search problem in one sense, situations exist in which search itself is the essential process; for example, in auditing accounts, inspection and quality control procedures, in exploration for minerals, in the design of information systems, and in military problems involving the location of such threats as enemy ships, aircraft, mines, and missiles.

Two kinds of error are involved in search: those of observation and those of sampling. Observational errors, in turn, are of two general types: commission , seeing something that is not there; and omission, not seeing something that is there. In general, as the chance of making one of these errors is decreased, the chance of making the other is increased. Furthermore, if fixed resources are available for search, the larger the sample (and hence the smaller the sampling error), the less resources available per observation (and hence the larger the observational error).

The cost of search is composed of setup or design cost, cost of observations, cost of analyzing the data obtained, and cost of error. The objective is to minimize these costs by manipulating the sample size (amount of observation), the sample design (how the things or places to be observed are selected), and the way of analyzing the data (the inferential procedure).

Almost all branches of statistics provide useful techniques for solving search problems. In search problems that involve the location of physical objects, particularly those that move, physics and some fields of mathematics ( e.g., geometry and trigonometry) are also applicable.

A “reversed-search” problem arises when the search procedure is not under control but the object of the search is. Most retailers, for example, cannot control the manner in which customers search for goods in their stores, but they can control the location of the goods. This type of problem also arises in the design of libraries and information systems, and in laying land and sea mines. These, too, are search problems, and solution techniques described above are applicable to them.

Frontiers of operations research

Operations research is a rapidly developing application of the scientific method to organizational problems. Its growth has consisted of both technical development and enlargement of the class of organized systems and the class of problems to which it is applied.

Tactics and strategy are relative concepts. The distinction between them depends on three considerations: (1) the longer the effect of a decision and the less reversible it is, the more strategic it is; (2) the larger the portion of a system that is affected by a decision, the more strategic it is; and (3) the more concerned a decision is with the selection of goals and objectives, as well as the means by which they are to be obtained, the more strategic it is.

Strategy and tactics are separable only in thought, not in action. Every tactical decision involves a strategic choice, no matter how implicit and unconscious it may be. Since the strategic aspects of decisions are usually suppressed, an organization’s strategy often emerges as an accidental consequence of its tactical decisions.

Operations research is becoming increasingly concerned with strategic decisions and the development of explicit strategies for organizations so as to improve the quality of their tactical decisions and make even the most immediate and urgent of these contribute to its long-run goals.

Introduction to Operations Research

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Operations research is a multi-disciplinary field that is concerned with the application of mathematical and analytic techniques to assist in decision-making. It employs techniques such as mathematical modelling, statistical analysis, and mathematical optimization as part of its goal to achieve optimal (or near optimal) solutions to complex decision-making problems.

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What is Operations Research (OR)? Definition, Concept, Characteristics, Tools, Advantages, Limitations, Applications and Uses

• Post last modified: 20 July 2022
• Reading time: 25 mins read
• Post category: Operations Research

What is Operations Research (OR)?

Operations Research (OR) may be defined as the science that aims for the application of analytical and numerical techniques along with information technology to solve organisational problems. There are various definitions of OR in the literature.

Table of Content

• 1 What is Operations Research (OR)?
• 2 Operations Research Definition
• 3 Concept of Operations Research
• 4 History of Operations Research
• 5.1 OR as a decision-making approach
• 5.2 OR as a scientific approach
• 5.3 OR as a computer-based approach
• 6 Objectives of Operations Research
• 7.1 Linear Programming
• 7.2 Simulation
• 7.3 Statistics
• 7.4 Assignment
• 7.5 Queuing Theory
• 7.6 Game Theory
• 7.7 Non-linear Programming
• 7.8 Dynamic Programming
• 7.9 Goal Programming
• 7.10 Network Scheduling
• 8.1 Increased productivity
• 8.2 Optimised outcomes
• 8.3 Better coordination
• 8.4 Lower failure risk
• 8.5 Improved control on the system
• 9.1 High costs
• 9.2 Dependence on technology
• 9.3 Reliance on experts
• 9.4 Unquantifiable factors
• 10.1 Resource distribution in projects
• 10.2 Project scheduling, monitoring and control
• 10.3 Production and facilities planning
• 10.4 Marketing
• 10.5 Personnel management
• 10.6 Supply chain management

Operations Research Definition

Some of the well-known operations research definitions are as:

Moarse and Kimbal (1946) defined OR as a scientific method of providing the executive department a quantitative basis for decision-making regarding the operations under their control.

According to Churchman, Ackoff and Arnoff (1957), OR is the application of scientific methods, techniques and tools to operational problems so as to provide those in control of the system an optimum solution to the problem.

McGraw-Hill Science & Technology Encyclopaedia states that OR is the application of scientific methods and techniques to decision-making problems.

Britannica Concise Encyclopaedia defines OR as the application of scientific methods to the management and administration of military, government, commercial, and industrial processes.

Decision-making problems arise when there are two or more alternative courses of action, each resulting in a different outcome. The goal of OR is to help select the alternative that will maximise the use of available resources and lead to the best possible outcome.

In this article, we introduce the topic of operation research that will allow students to gain an insight into the basic concepts of operations research. This will give them a better understanding of the upcoming chapters.

Concept of Operations Research

Decision-making is not a simple task in today’s socio-economic environment. Complex problems such as transportation, queuing, etc., are routinely presented and dealt with at the operational level. Moreover, higher attention is now being paid to a wide range of tactical and strategic problems.

Decision makers cannot afford to take decisions by simply taking their personal experiences or intuitions into account. Decisions made in the absence of suitable information can have seri- ous consequences. Being able to apply quantitative methods to deci- sion-making is, therefore, vital to decision-makers.

OR is a field of applied mathematics that makes use of analytical tools and mathematical models to solve problems and aid the management in decision-making. OR is an approach that allows decision makers to compare all possible courses of action, understand the likely outcomes and test the sensitivity of the solution to modifications or errors.

OR helps in making informed decisions, allocating optimal resource and improving the performance of systems. According to Ackoff (1965), the development (rather than the history) of OR as a science consists of the development of its methods, concepts, and techniques.

OR is neither a method nor a technique; it is or is becoming a science and as such is defined by a combination of the phenomena it studies.

History of Operations Research

The beginning of OR as a formal discipline can be traced back to 1937 when A.P. Rowe, Superintendent of the Bawdsey Research Station in the British Royal Air Force, sought British scientists to assist military leaders in the use of the recently developed radar system to detect enemy aircraft.

A few years later, the British Army and the Royal Navy also incorporated OR, again for assistance with the radar system. All three of Britain’s military services had set up formal OR teams by 1942. Similar developments took place in other countries (of which the most significant are those of the United States, in terms of further development of the discipline).

Once World War II ended, several British operations researchers relocated to government and industry. By the 1950s, the United States government and industry also incorporated OR programmes. In India, it was in 1949 when an Operation Research unit was set up at Regional Research Laboratory, Hyderabad, that OR came into being.

An OR team was also established at Defense Science Laboratory to resolve inventory, purchase and planning issues. The 1950s saw continual growth in the application of OR methods to non-defence activities in India. In 1953, the Indian Statistical Institute, Calcutta established an OR unit for national planning and survey-related issues. OR also became useful in the Indian Railways to resolve ticketing issues, train scheduling problems and so on.

Since then, OR, as a formal discipline, has expanded continuously in the last 70 years and is widely recognised as a central approach to decision-making in the management of different domains of an organisation.

With accessibility to faster and flexible computing facilities, OR has expanded further and is widely used in industry, finance, logistics, transportation, public health and government. One needsCharacteristics of Operations Research to bear in mind that OR is still a fairly new scientific discipline, despite its rapid evolution. This means that its methodology, tools and techniques, and applications still continue to grow rapidly.

Characteristics of Operations Research

OR aims to find the best possible solution for any problem. Its main goal is to help managers obtain a quantitative basis for decision-making. This results in increased efficiency, more control and better coordination in the organisation when fulfilling the required objectives.

OR is an interdisciplinary field involving mathematics and science. OR uses statistics, algorithms and mathematical modelling to provide the best possible solutions for complex problems. OR basically involves optimising the maxima or minima functions.

For example, a business problem could be the maximisation of profit, performance or yield or it could be related to minimising risk and loss. OR has various characteristics based on the different objectives for which it is used.

The characteristics of operations research (OR) are explained as follows:

OR as a decision-making approach

All organisations are faced with situations where they need to select the best available alterna- tive to solve a problem. OR techniques help managers in obtaining optimal solutions for their problems.

Additionally, OR techniques are also used by managers to understand the problems at hand in a better manner and make effective decisions. It is important to note that OR techniques help in improving the quality of decisions.

OR helps in finding bad answers to problems having worse answers. It means that for many problems, OR may not be able to give perfect replies but can help in improving the quality of decisions.

OR as a scientific approach

OR uses multiple scientific models along with tools and techniques to resolve complex problems while eliminating individual biasness. The scientific method involves observing and defining a problem, formulating and testing the hypothesis and analysing the results of the test. The results of the test determine whether the hypothesis should be accepted or rejected.

OR as an interdisciplinary approach: Since OR focuses on complex organisational problems, it includes expertise from different disciplines such as mathematics, economics, science and engineering. Having different experts ensures that the problem is analysed from different perspectives and alternative strategies are evolved for the selected problem.

Some of the complex problems that can be solved using OR include deciding or choosing optimal dividend policies, investment portfolio management, auditing, balance sheet and cash flow analysis, selection of product mix, marketing and export planning, advertising, media planning and packaging, procurement and exploration, optimal buying decisions, transportation planning, facilities planning, location and site selection, production cost and methods, assembly line, blending, purchasing and inventory control, etc.

OR as a systems approach: In OR, important interactions and their influence on the organisation as a whole are considered for decision-making. OR looks at problems from the perspective of the organisation:

• To determine the potential for enhancing the performance of the system as a whole
• To measure the impact of alterations in variables on the whole system
• To find reasons for the malfunctioning of the system as a whole

OR as a computer-based approach

OR solves business problems using mathematical models, manipulating large amount of data and performing computations on these large data sets. It is almost impossible to do such computations and manipulations manually. Therefore, most OR-based problems are solved using computers.

Objectives of Operations Research

Operations research in an organisation is responsible for managing and operating as efficiently as possible within the given resources and constraints. In case of complex problems as listed in the previous section, normal analysis does not work and in such cases, OR approach helps an organisation in reaching a viable solution.

OR is basically a problem-solving and decision-making tool used by organisations for enhancing their productivity and performance. Apart from this, certain other objectives of OR are as follows:

• Solving operational questions
• Solving queries related to resources’ operations such as human resource scheduling, machine and material scheduling, utilisation of funds, etc.
• Making informed decisions
• Improving the current systems
• Predicting all possible alternative outcomes
• Evaluating risks associated with each alternative

OR is needed for the following reasons:

• If the problem is a recurring one, it may make sense to create a model to make decision-making faster and better. OR provides a readymade model or process in such cases to help create a suitable model.
• OR provides an analytical, logical and quantitative basis to represent the problem
• OR models help in making sound decisions and decreases the risk of flawed decisions

Tools of Operations Research

OR is widely used in industries, businesses, governments, military establishments and agriculture. Most importantly, OR techniques are used by organisations. All the business decision areas, such as planning production and facilities, scheduling projects, minimising procurement costs, and selecting a product mix, which require optimisation of an objective, fall under the domain of operation research. OR uses a variety of tools to solve different business problems.

The most commonly used tools of OR are discussed below:

• Linear Programming

Organisations use the Linear Programming (LP) technique to determine the optimal solutions that may be defined as either most profitable or least cost solutions. Businesses use LP techniques to assign jobs to machines, select product mix, select advertising media, select an investment portfolio, etc.

Simulation is another important OR tool wherein an expert con- structs a model that replicates a real business scenario. Simulation is extremely useful in cases where actual market testing is risky or impossible due to various reasons such as high expenditure.

It has widely been used in a variety of probabilistic marketing situations. For example, finding the Net Present Value (NPV) distribution of the market introduction of a product.

Statistics allows an organisation to evaluate the risks present in all the domains of the business. It enables an organisation to predict future trends and thus makes informed business decisions. The OR team compares different trade-offs and chooses the best alternative.

For example, statistics is used in solving various real-life problems such as deterministic optimisation. Some of the problems where statistics serve as the primary vehicle for OR include decision theory, optimal strategies for search engine marketing, credit scoring, queuing theory, stochastic programming and inventory management.

The assignment method deals with the issue of how to allocate a fixed number of facilities to different tasks in the most optimal manner. The aim is to minimise the cost/time of completing a number of tasks by a number of agents (person or equipment). For example, assigning method can be used to assign specific workers to specific tasks.

Queuing Theory

If a problem involves queuing, the Queuing or Waiting Line theory is used. Using this tool, the expected number of people waiting in line, expected waiting time, expected idle time for the server and so on can be calculated. Queuing theory can be used to solve problems related to traffic congestion, repair and maintenance of broken machines, air traffic scheduling and control, scheduling bank counters, etc

Game Theory

Game theory is useful in decision-making in cases where there are one or more opponents (or players) with conflicting interests. Just as in a game, where the success of one person is influenced by the choices made by the opponent, in the game theory, the actions of all the players influence the outcomes.

For example, game theory is used for selecting war strategies and military decisions, bidding at auctions, negotiations, product pricing, stock market decisions, etc.

Non-linear Programming

Non-linear problems are similar to linear problems except that they have at least one non-linear function or constraint. Non-linear models become useful in cases where the objective function of some of the constraints is not linear in nature.

For instance, a non-linear programming is used for making optimal decisions in the production process, optimising fractionated protocols in cancer radiotherapy, training recur- rent neural networks in time series prediction problems, etc.

Dynamic Programming

Dynamic programming models deal with problems in which decisions need to be made over multiple stages in a sequence and the current decisions affect both present and future stages.

For example, dynamic programming is used by Google Maps to find the shortest path between a source and a destination. It is also used in networking to sequentially transfer data from one sender to various receivers.

Goal Programming

Goal programming tools allow organisations to handle multiple and incompatible objectives. These models are quite similar to linear programming models with the difference being that goal programming can have multiple objectives whereas linear programs have only one.

For example, goal programming can be applied to corporate budgeting, financial planning, working capital management, financing decisions, commercial bank management, accounting control, etc.

Network Scheduling

Network scheduling methods are useful in planning, scheduling and monitoring projects of large scales common in construction industry, information technology, etc.

For example, network scheduling is used for assembly line scheduling, inventory planning and control, launching new advertisement campaigns, installing new equipment, controlling projects, etc.

Advantages of Operations Research

The field of OR contains robust tools that can be applied in a variety of fields such as transportation, warehouse, production management, assignment of jobs, etc. The application of OR tools and techniques helps in making the best decisions with the available data.

There are many advantages of OR, as shown in Figure:

Increased productivity

OR helps in increasing the productivity of organisations to a huge extent. The use of OR for effective control of operations allows the managers to take informed decisions. Effective and precise decision-making leads to improvement in the productivity of an organisation.

OR tools also help increase the efficiency of various routine tasks in an organisation such as inventory control, workforce-related, business expansion, technology upgrades, installation etc. All these ultimately contribute towards productivity improvement.

Optimised outcomes

Management is responsible for making various important decisions about the organisation. OR tools can be used by the management to find out various alternative solutions to a problem and selecting the best solution. Selection is based on the profits accrued and costs incurred.

Better coordination

OR can be used to synchronise the objectives of different departments which results in achieving the goals of all departments. Managers belonging to different departments become aware of the common objectives of the organisation, which ensures that different departments coordinate towards achievement of the said goals.

For example, OR helps in coordinating the goals of the marketing department with the production department schedule.

Lower failure risk

Using OR tools and techniques, managers can find all the alternative solutions and risks associated with a given problem. Prior information with respect to all the possible risks helps in reducing the risks of failure.

Improved control on the system

Managers can apply OR to take better control of the work since it provides comprehensive information about any given course of action. Since OR informs managers about the expected outcome, they can determine what standards of performance need to be expected from employees.

They can compare the actual performance of the employees with the standard performance and, therefore, control them in a better manner. It also enables managers to prioritise tasks in terms of their importance.

Limitations of Operations Research

There is no doubt with respect to the practical utility and usability of OR and its applications in real life. However, OR also suffers from several limitations as shown in Figure:

High cost is one of the biggest limitations of OR. It not only needs expensive technology to create mathematical equations but also experts to perform simulations. Therefore, while OR does provide effective solutions to a particular problem, it comes with a high cost attached.

Dependence on technology

OR is heavily reliant on technology. Computers are generally needed to model and analyse OR problems. Since technology is quite costly as well as subject to failure, its use is severely restricted.

Reliance on experts

OR requires a team of experts from different fields to perform the assessments. Hiring multiple experts can be costly. In addition, maintaining good communication and coordination among experts and making all experts work together is a critical task.

Unquantifiable factors

It is known that OR tools are based on mathematical models that include various information based on quantifiable factors. It means that the efficacy of a solution provided by OR tool depends on quantifiable factors.

However, there are certain important unquantifiable factors that cannot be included in the models. When this happens, solutions can often be inexact, inaccurate and therefore, inefficient.

Applications and Uses of Operations Research in Management

The list of OR applications is notable, given its considerable involvement in various managerial and decision-making processes at several organisational levels. It can be applied in a wide range of industries to help with complex problems in planning, policy-making, scheduling, forecasting, resource allocation, process analysis, etc.

It may be employed by virtually any industry to determine the best solution to any problem. Various human activities that need optimisation of resources can use OR.

The following are some areas where OR may be applied:

Resource distribution in projects

Various OR tools are used to determine which resources are to be allocated to which activities. For instance, OR can help in determining the allocation of ‘n’ number of jobs among two machines. Similarly, OR can also be applied to determine and allocate materials, workforce, time and budget to projects.

Project scheduling, monitoring and control

OR is applied to activities involving scheduling, inventory control, improvement of workflow, elimination of bottlenecks, business process re-engineering, capacity planning and general operational planning.

OR tools such as the Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT) are used for scheduling the different activities involved in a project. In addition, these tools are also used for continuous monitoring and control of the project.

Production and facilities planning

OR can be applied for activities involving site selection, factory size, facility planning, inventory forecasts, calculation of economic order quantities, computing reorder levels, maintenance policies, replacement policies, manpower planning, and assembly line scheduling, etc. All the important decisions and planning work related to facilities, manufacturing and maintenance can be completed using OR tools.

Application of OR can be done in budget allocation for advertising, choice of advertising media and product launch timing. For instance, how should a company allocate its budget for advertising a newly launched product on two TV channels, TV1 and TV2 within a given budget. A company may also use OR techniques to find out how many units of each product in a product mix should be produced to maximise demand.

Personnel management

OR also finds application in manpower planning, scheduling of training programs, wage administration, etc.

Finance and accounting: The application of OR in finance is concerned with effective capital planning, cash flow analysis, capital budgeting, credit policies, investment analysis and decisions, establishing costs for by-products and developing standard costs, portfolio management, risk management, etc.

Supply chain management

The application of OR in Supply Chain Management involves decision-making regarding the transportation of goods for the purpose of manufacturing and distribution. This further involves the selection of the shortest optimal routes so that the goods can be transported to maximum locations at minimum costs.

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
• CSR Reporting
• Voluntary Codes in CSR
• What is Corporate Ethics?

Lean Six Sigma

• What is Six Sigma?
• What is Lean Six Sigma?
• Value and Waste in Lean Six Sigma
• Six Sigma Team
• MAIC Six Sigma
• Six Sigma in Supply Chains
• What is Binomial, Poisson, Normal Distribution?
• What is Sigma Level?
• What is DMAIC in Six Sigma?
• What is DMADV in Six Sigma?
• Six Sigma Project Charter
• Project Decomposition in Six Sigma
• Critical to Quality (CTQ) Six Sigma
• Process Mapping Six Sigma
• Flowchart and SIPOC
• Gage Repeatability and Reproducibility
• Statistical Diagram
• Lean Techniques for Optimisation Flow
• Failure Modes and Effects Analysis (FMEA)
• What is Process Audits?
• Six Sigma Implementation at Ford
• IBM Uses Six Sigma to Drive Behaviour Change
• Research Methodology
• What is Research?
• What is Hypothesis?
• Sampling Method
• Research Methods
• Data Collection in Research
• Methods of Collecting Data
• Application of Business Research
• Levels of Measurement
• What is Sampling?
• Hypothesis Testing
• Research Report
• What is Management?
• Planning in Management
• Decision Making in Management
• What is Controlling?
• What is Coordination?
• What is Staffing?
• Organization Structure
• What is Departmentation?
• Span of Control
• What is Authority?
• Centralization vs Decentralization
• Organizing in Management
• Schools of Management Thought
• Classical Management Approach
• Is Management an Art or Science?
• Who is a Manager?

Operations Research

• What is Operations Research?
• Operation Research Models
• Linear Programming Graphic Solution
• Linear Programming Simplex Method
• Linear Programming Artificial Variable Technique

Duality in Linear Programming

• Transportation Problem Initial Basic Feasible Solution
• Transportation Problem Finding Optimal Solution
• Project Network Analysis with Critical Path Method

Project Network Analysis Methods

Project evaluation and review technique (pert), simulation in operation research.

• Replacement Models in Operation Research

Operation Management

• What is Strategy?
• What is Operations Strategy?
• Operations Competitive Dimensions
• Operations Strategy Formulation Process
• What is Strategic Fit?
• Strategic Design Process
• Focused Operations Strategy
• Corporate Level Strategy
• Expansion Strategies
• Stability Strategies
• Retrenchment Strategies
• Competitive Advantage
• Strategic Choice and Strategic Alternatives
• What is Production Process?
• What is Process Technology?
• What is Process Improvement?
• Strategic Capacity Management
• Production and Logistics Strategy
• Taxonomy of Supply Chain Strategies
• Factors Considered in Supply Chain Planning
• Operational and Strategic Issues in Global Logistics
• Logistics Outsourcing Strategy
• What is Supply Chain Mapping?
• Supply Chain Process Restructuring
• Points of Differentiation
• Re-engineering Improvement in SCM
• What is Supply Chain Drivers?
• Supply Chain Operations Reference (SCOR) Model
• Customer Service and Cost Trade Off
• Internal and External Performance Measures
• Linking Supply Chain and Business Performance
• Netflix’s Niche Focused Strategy
• Disney and Pixar Merger
• Process Planning at Mcdonald’s

Service Operations Management

• What is Service?
• What is Service Operations Management?
• What is Service Design?
• Service Design Process
• Service Delivery
• What is Service Quality?
• Gap Model of Service Quality
• Juran Trilogy
• Service Performance Measurement
• Service Decoupling
• IT Service Operation
• Service Operations Management in Different Sector

Procurement Management

• What is Procurement Management?
• Procurement Negotiation
• Types of Requisition
• RFX in Procurement
• What is Purchasing Cycle?
• Vendor Managed Inventory
• Internal Conflict During Purchasing Operation
• Spend Analysis in Procurement
• Sourcing in Procurement
• Supplier Evaluation and Selection in Procurement
• Blacklisting of Suppliers in Procurement
• Total Cost of Ownership in Procurement
• Incoterms in Procurement
• Documents Used in International Procurement
• Transportation and Logistics Strategy
• What is Capital Equipment?
• Procurement Process of Capital Equipment
• Acquisition of Technology in Procurement
• What is E-Procurement?
• E-marketplace and Online Catalogues
• Fixed Price and Cost Reimbursement Contracts
• Contract Cancellation in Procurement
• Ethics in Procurement
• Legal Aspects of Procurement
• Global Sourcing in Procurement
• Intermediaries and Countertrade in Procurement

Strategic Management

• What is Strategic Management?
• What is Value Chain Analysis?
• Mission Statement
• Business Level Strategy
• What is SWOT Analysis?
• What is Competitive Advantage?
• What is Vision?
• What is Ansoff Matrix?
• Prahalad and Gary Hammel
• Strategic Management In Global Environment
• Competitor Analysis Framework
• Competitive Rivalry Analysis
• Competitive Dynamics
• What is Competitive Rivalry?
• Five Competitive Forces That Shape Strategy
• What is PESTLE Analysis?
• Fragmentation and Consolidation Of Industries
• What is Technology Life Cycle?
• What is Diversification Strategy?
• What is Corporate Restructuring Strategy?
• Resources and Capabilities of Organization
• Role of Leaders In Functional-Level Strategic Management
• Functional Structure In Functional Level Strategy Formulation
• Information And Control System
• What is Strategy Gap Analysis?
• Issues In Strategy Implementation
• Matrix Organizational Structure
• What is Strategic Management Process?

Supply Chain

• What is Supply Chain Management?
• Supply Chain Planning and Measuring Strategy Performance
• What is Warehousing?
• What is Packaging?
• What is Inventory Management?
• What is Material Handling?
• What is Order Picking?
• Receiving and Dispatch, Processes
• What is Warehouse Design?
• What is Warehousing Costs?

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operations research (OR)

• Sarah Lewis

Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis.

The process of operations research can be broadly broken down into the following steps:

• Identifying a problem that needs to be solved.
• Constructing a model around the problem that resembles the real world and variables.
• Using the model to derive solutions to the problem.
• Testing each solution on the model and analyzing its success.
• Implementing the solution to the actual problem.

Disciplines that are similar to, or overlap with, operations research include statistical analysis , management science, game theory, optimization theory, artificial intelligence and network analysis. All of these techniques have the goal of solving complex problems and improving quantitative decisions.

The concept of operations research arose during World War II by military planners. After the war, the techniques used in their operations research were applied to addressing problems in business, the government and society.

Characteristics of operations research

There are three primary characteristics of all operations research efforts:

• Optimization- The purpose of operations research is to achieve the best performance under the given circumstances. Optimization also involves comparing and narrowing down potential options.
• Simulation- This involves building models or replications in order to try out and test solutions before applying them.
• Probability and statistics- This includes using mathematical algorithms and data to uncover helpful insights and risks, make reliable predictions and test possible solutions.

Importance of operations research

The field of operations research provides a more powerful approach to decision making than ordinary software and data analytics tools. Employing operations research professionals can help companies achieve more complete datasets, consider all available options, predict all possible outcomes and estimate risk. Additionally, operations research can be tailored to specific business processes or use cases to determine which techniques are most appropriate to solve the problem.

Uses of operations research

Operations research can be applied to a variety of use cases, including:

• Scheduling and time management.
• Urban and agricultural planning.
• Enterprise resource planning ( ERP ) and supply chain management ( SCM ).
• Inventory management .
• Network optimization and engineering.
• Packet routing optimization.
• Risk management .

Continue Reading About operations research (OR)

• The big picture of Operations Research

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