importance of assignment problem in operation research

Assignment Problem: Meaning, Methods and Variations | Operations Research

After reading this article you will learn about:- 1. Meaning of Assignment Problem 2. Definition of Assignment Problem 3. Mathematical Formulation 4. Hungarian Method 5. Variations.

Meaning of Assignment Problem:

An assignment problem is a particular case of transportation problem where the objective is to assign a number of resources to an equal number of activities so as to minimise total cost or maximize total profit of allocation.

The problem of assignment arises because available resources such as men, machines etc. have varying degrees of efficiency for performing different activities, therefore, cost, profit or loss of performing the different activities is different.

Thus, the problem is “How should the assignments be made so as to optimize the given objective”. Some of the problem where the assignment technique may be useful are assignment of workers to machines, salesman to different sales areas.

Definition of Assignment Problem:

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Suppose there are n jobs to be performed and n persons are available for doing these jobs. Assume that each person can do each job at a term, though with varying degree of efficiency, let c ij be the cost if the i-th person is assigned to the j-th job. The problem is to find an assignment (which job should be assigned to which person one on-one basis) So that the total cost of performing all jobs is minimum, problem of this kind are known as assignment problem.

The assignment problem can be stated in the form of n x n cost matrix C real members as given in the following table:

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Unbalanced Assignment Problem: Definition, Formulation, and Solution Methods

Table of Contents

Are you familiar with the assignment problem in Operations Research (OR)? This problem deals with assigning tasks to workers in a way that minimizes the total cost or time needed to complete the tasks. But what if the number of tasks and workers is not equal? In this case, we face the Unbalanced Assignment Problem (UAP). This blog will help you understand what the UAP is, how to formulate it, and how to solve it.

What is the Unbalanced Assignment Problem?

The Unbalanced Assignment Problem is an extension of the Assignment Problem in OR, where the number of tasks and workers is not equal. In the UAP, some tasks may remain unassigned, while some workers may not be assigned any task. The objective is still to minimize the total cost or time required to complete the assigned tasks, but the UAP has additional constraints that make it more complex than the traditional assignment problem.

Formulation of the Unbalanced Assignment Problem

To formulate the UAP, we start with a matrix that represents the cost or time required to assign each task to each worker. If the matrix is square, we can use the Hungarian algorithm to solve the problem. But when the matrix is not square, we need to add dummy tasks or workers to balance the matrix. These dummy tasks or workers have zero costs and are used to make the matrix square.

Once we have a square matrix, we can apply the Hungarian algorithm to find the optimal assignment. However, we need to be careful in interpreting the results, as the assignment may include dummy tasks or workers that are not actually assigned to anything.

Solutions for the Unbalanced Assignment Problem

Besides the Hungarian algorithm, there are other methods to solve the UAP, such as the transportation algorithm and the auction algorithm. The transportation algorithm is based on transforming the UAP into a transportation problem, which can be solved with the transportation simplex method. The auction algorithm is an iterative method that simulates a bidding process between the tasks and workers to find the optimal assignment.

In summary, the Unbalanced Assignment Problem is a variant of the traditional Assignment Problem in OR that deals with assigning tasks to workers when the number of tasks and workers is not equal. To solve the UAP, we need to balance the matrix by adding dummy tasks or workers and then apply algorithms such as the Hungarian algorithm, the transportation algorithm, or the auction algorithm. Understanding the UAP can help businesses and organizations optimize their resource allocation and improve their operational efficiency.

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Operations Research

1 Operations Research-An Overview

• History of O.R.
• Approach, Techniques and Tools
• Phases and Processes of O.R. Study
• Typical Applications of O.R
• Limitations of Operations Research
• Models in Operations Research
• O.R. in real world

2 Linear Programming: Formulation and Graphical Method

• General formulation of Linear Programming Problem
• Optimisation Models
• Basics of Graphic Method
• Important steps to draw graph
• Multiple, Unbounded Solution and Infeasible Problems
• Solving Linear Programming Graphically Using Computer
• Application of Linear Programming in Business and Industry

3 Linear Programming-Simplex Method

• Principle of Simplex Method
• Computational aspect of Simplex Method
• Simplex Method with several Decision Variables
• Two Phase and M-method
• Multiple Solution, Unbounded Solution and Infeasible Problem
• Sensitivity Analysis
• Dual Linear Programming Problem

4 Transportation Problem

• Basic Feasible Solution of a Transportation Problem
• Modified Distribution Method
• Stepping Stone Method
• Unbalanced Transportation Problem
• Degenerate Transportation Problem
• Transhipment Problem
• Maximisation in a Transportation Problem

5 Assignment Problem

• Solution of the Assignment Problem
• Unbalanced Assignment Problem
• Problem with some Infeasible Assignments
• Maximisation in an Assignment Problem
• Crew Assignment Problem

6 Application of Excel Solver to Solve LPP

• Building Excel model for solving LP: An Illustrative Example

7 Goal Programming

• Concepts of goal programming
• Goal programming model formulation
• Graphical method of goal programming
• The simplex method of goal programming
• Using Excel Solver to Solve Goal Programming Models
• Application areas of goal programming

8 Integer Programming

• Some Integer Programming Formulation Techniques
• Binary Representation of General Integer Variables
• Unimodularity
• Cutting Plane Method
• Branch and Bound Method
• Solver Solution

9 Dynamic Programming

• Dynamic Programming Methodology: An Example
• Definitions and Notations
• Dynamic Programming Applications

10 Non-Linear Programming

• Solution of a Non-linear Programming Problem
• Convex and Concave Functions
• Kuhn-Tucker Conditions for Constrained Optimisation
• Quadratic Programming
• Separable Programming
• NLP Models with Solver

11 Introduction to game theory and its Applications

• Important terms in Game Theory
• Saddle points
• Mixed strategies: Games without saddle points
• 2 x n games
• Exploiting an opponent’s mistakes

12 Monte Carlo Simulation

• Reasons for using simulation
• Monte Carlo simulation
• Limitations of simulation
• Steps in the simulation process
• Some practical applications of simulation
• Two typical examples of hand-computed simulation
• Computer simulation

13 Queueing Models

• Characteristics of a queueing model
• Notations and Symbols
• Statistical methods in queueing
• The M/M/I System
• The M/M/C System
• The M/Ek/I System
• Decision problems in queueing

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Home » Management Science » Transportation and Assignment Models in Operations Research

Transportation and Assignment Models in Operations Research

Transportation and assignment models are special purpose algorithms of the linear programming. The simplex method of Linear Programming Problems(LPP) proves to be inefficient is certain situations like determining optimum assignment of jobs to persons, supply of materials from several supply points to several destinations and the like. More effective solution models have been evolved and these are called assignment and transportation models.

The transportation model is concerned with selecting the routes between supply and demand points in order to minimize costs of transportation subject to constraints of supply at any supply point and demand at any demand point. Assume a company has 4 manufacturing plants with different capacity levels, and 5 regional distribution centres. 4 x 5 = 20 routes are possible. Given the transportation costs per load of each of 20 routes between the manufacturing (supply) plants and the regional distribution (demand) centres, and supply and demand constraints, how many loads can be transported through different routes so as to minimize transportation costs? The answer to this question is obtained easily through the transportation algorithm.

Similarly, how are we to assign different jobs to different persons/machines, given cost of job completion for each pair of job machine/person? The objective is minimizing total cost. This is best solved through assignment algorithm.

Uses of Transportation and Assignment Models in Decision Making

The broad purposes of Transportation and Assignment models in LPP are just mentioned above. Now we have just enumerated the different situations where we can make use of these models.

Transportation model is used in the following:

• To decide the transportation of new materials from various centres to different manufacturing plants. In the case of multi-plant company this is highly useful.
• To decide the transportation of finished goods from different manufacturing plants to the different distribution centres. For a multi-plant-multi-market company this is useful.
• To decide the transportation of finished goods from different manufacturing plants to the different distribution centres. For a multi-plant-multi-market company this is useful. These two are the uses of transportation model. The objective is minimizing transportation cost.

Assignment model is used in the following:

• To decide the assignment of jobs to persons/machines, the assignment model is used.
• To decide the route a traveling executive has to adopt (dealing with the order inn which he/she has to visit different places).
• To decide the order in which different activities performed on one and the same facility be taken up.

In the case of transportation model, the supply quantity may be less or more than the demand. Similarly the assignment model, the number of jobs may be equal to, less or more than the number of machines/persons available. In all these cases the simplex method of LPP can be adopted, but transportation and assignment models are more effective, less time consuming and easier than the LPP.

Related posts:

• Operations Research approach of problem solving
• Introduction to Transportation Problem
• Procedure for finding an optimum solution for transportation problem
• Initial basic feasible solution of a transportation problem
• Top 7 Best Ways of Getting MBA Assignment Writing Help
• Introduction to Decision Models
• Transportation Cost Elements
• Modes of Transportation in Logistics
• Factors Affecting Transportation in Logistics
• Export/Import Transportation Systems

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Introduction to Operations Research

• First Online: 05 May 2023

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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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von Neumann J (1928) On the theory of games of strategy. Math Ann (in German) 100(1):295–320

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Delve into Operations Research – Importance and Applications

To match up with the fast-paced competitive business world, most companies demand constant study of market conditions, which is done by operations research. But what is operations research ? 

Commonly known as OR, it is a scientific study that involves statistics and mathematics to solve business problems. The scope of operations research is very high in the present business landscape. Operations research consultants or analysts review company problems, processes, loopholes and perform pattern, trend analysis to develop models which will help dial down and provide insights to fix problems. The methods of operations research are commonly used to solve problems associated with work breakdown, project planning, supply chain management , operations management, scheduling and logistics, etc. Furthermore, the process helps solve other tasks that are supported and reliant on ops research tactics. 

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To run a business effectively, knowing how to use operations research techniques holds relevance. The live recorded lectures provided by this NAAC-accredited A+ university’s eminent faculty will offer deep insights into different industries like HR, marketing, retail, finance, and others. Get in touch with Jaro Education to get further information about the course.

Importance of Operations Research

Operations research is a problem-solving and decision-making analytical technique. Many firms use this quantitative approach to problem-solving . When decision-making becomes complex owing to unclear situations or when specified objectives contradict, this strategy is applied. Problems in operation research management are divided into fundamental divisions and are solved mathematically in specified ways. Organisations may develop more successful systems using operations management by carefully forecasting outcomes, evaluating all feasible options, and employing decision tools and processes . The importance of operations research cannot be denied due to the following reasons.

Increases business productivity

Increased productivity is a significant benefit of operation research that attracts numerous firms. The mathematical formulae employed in operations management research provide a number of optimal alternatives for factory size, inventory mix, labour planning, and incorporating new technology, among other things. This guarantees that work is completed more quickly.

Improves decision-making

The mathematical approach to operations management research allows individuals to examine a far larger number of options and restrictions than the usual intuitive method. As a result, operations research enables firms to simply and swiftly analyse many possibilities. As a consequence, you may make a more confident decision while selecting the best option.

Establishes seamless control

Because of operations research, organisations may retain greater control over their staff. Operations management research creates performance criteria and assesses productivity. As a result, corporate managers may track departures from the norm to discover trouble areas. This guarantees that no time is lost and that corrective action is performed right away.

Improves coordination of departments

The significance of operations research extends to the seamless running of all departments. Departments such as marketing and manufacturing can collaborate to boost overall productivity via operation research analysis.

Minimises uncertainty 

Operations research employs tried-and-true methodology and modelling tools. This assists businesses in removing any doubts that may occur. When accurate data is fed into an existing successful problem-solving model, one can dramatically reduce uncertainty for businesses and thus, resolving problems and managing complicated corporate processes becomes easier with dependable data.

Analyses in detail

One of the reasons for operations management’s importance is that it is based on analytics. To examine and solve diverse issues, mathematical and scientific approaches are applied. Operations research uses these methodologies to deliver a complete and insightful study, allowing firms to handle problems thoroughly and comprehensively.

Applications of Operations Research

Risk analysis.

Risk analysis is an operation research application that allows businesses to detect and handle potential problems that could undermine their projects or initiatives. Moreover, risk analysis can be applied to other non-business initiatives like buying a home or event planning. 

Inventory analysis

Inventory is a balance sheet asset that reflects the goods that a company intends to sell its customers in the future. In addition to the finished products, it also comprises raw materials that are used to manufacture such goods and work-in-progress goods.So, inventory analysis assists businesses in determining the appropriate quantity of goods to have in-hand, to meet customer demands while avoiding excessive inventory storage costs.

Strategic planning

Strategic planning is an application of operations research that allows organisation leaders to define their vision for the future and identify the goals and objectives of those organisations. The process involves determining the order in which those objectives should be carried out so that the organisation may achieve its stated vision. Strategic planning is often used to reflect mid-to long-term goals with a life duration of three to five years; which can be extended.

Marketing research

Marketing research is a strategy or collection of practices used by businesses to acquire information in order to comprehend their target market in a better way. Companies utilise this data to enhance their products, improve their user experience, and provide a better product to their consumers. Marketing research is usually performed to find out what people desire and how they react to items or features.

In business, logistics is the management of the flow of items between the point of origin and the point of consumption to meet the requirements of corporations and customers. The resources handled in logistics can include physical items like animals, food, liquids, materials, and equipment. Also, abstract items like data and time can be included in it. 

Revenue management

Revenue management is a systematic analytics method used to forecast customer behaviour at the micro-level, with the goal of optimising product availability and price while increasing revenue growth. In other words, the fundamental goal is to offer the right product to the right buyer at the right price at the right time.

Sales analysis

Sales analysis is the process of evaluating sales data to detect trends and patterns. Sales data may assist companies in making insightful decisions regarding their product, promotions, price, promotions, customer demands, inventory and other elements of the organisation. In some organisations, sales analysis is as basic as frequently checking the sales numbers.

Scheduling is a part of operations research that organises, manages, and optimises work and workloads in a manufacturing or production process. It is used to plan plant and machinery resources, people resources, manufacturing processes, and material purchases.

To sell assets and properties to prospective purchasers, an auction is arranged.  Auctioning is an open purchasing and selling mechanism in which buyers are asked to bid on specific assets and are held by owners and businesses. 

Forecasting

It is a strategy that uses previous data as inputs to create educated predictions about the trajectory of upcoming trends. Forecasting is used by businesses to understand how to allocate budgets or plan for anticipated costs in the future.

Optimisation

Optimisation is a process that ensures the effective and efficient performance of business operations. It helps to reduce current expenditures while increasing operational capabilities. Business operations should thus be optimised regularly to ensure that they are functioning at an ideal level over the years. 

Portfolio management

It is focused on the financial aspect of operations research that deals with supervising a group of investments on a professional or personal level. These investments include mutual funds, bonds, cryptocurrencies, exchange-traded funds and so on. The goal of portfolio management is to assist investors in meeting their long-term financial objectives while also managing their liquidity demands and risk tolerance.

Supply chain management

The administration of the full process of converting raw materials into a finished product is known as supply chain management . It entails connecting a network of suppliers through a centralised management procedure. Each supplier serves as a link in the manufacturing cycle, from manufacturers to sellers.

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Methods of Operations Research

While emphasising the human-technology interaction, operation research integrates tools from machine learning, statistical analysis, optimisation to mathematical modelling. To understand these methods in a better way, let’s dive deeper into it. 

Simulation method

In operations research, simulation methods include recommended models and algorithms that are tried and tested before implementation. It includes implementation analysis, altering variables or limitations to fit solutions to business objectives, assessing the results of operation research methodologies and recommending the solution with the highest weightage from these considerations.

Mathematical modelling & statistical analysis

A detailed operations research analysis of instances and solutions involves statistical approach to analyse and develop mathematical algorithms to solve issues. It entails using data to go deeper, make conclusions, and develop algorithms that give insights and answers in business circumstances. 

Optimisation approach

When there is a discrepancy between prospective alternatives or decisions that an organisation must make, optimisation techniques come into the picture. Optimisation strategies can involve offering a solution to the business challenge while keeping current project limits in mind. However, constraints may be anything that slows decision-making or limits one’s ability to make the optimal option.

Final Thoughts

In the age of artificial intelligence and machine learning, operations research provides endless opportunities to businesses. It is a process that eliminates conflict by solving issues with subjective input that is filtered by mathematical and statistical models to produce ideal answers. The advantages and importance of operations can no longer be denied by organisations. If you want to be a future business leader, you need to have a solid grasp of operations research, inventory management, revenue management and more. To hone those skills, you can consider enrolling on an Online MBA Programme provided by Manipal University Jaipur. This is a comprehensive course for MBA aspirants looking for the right option to ace in their career trajectory, without hampering their regular schedule.

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Operational Research in Health-care Settings

Rajesh kunwar.

Department of Community Medicine, TS Misra Medical College, Department of Community Medicine, Prasad Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

V. K. Srivastava

Origin of the term operational research (OR), also known as operations research, can be traced back to World War II when a number of researches carried out during military operations helped British Forces produce better results with lesser expenditure of ammunition. The world soon realised the potential of this kind of research and many disciplines especially management sciences, started applying its principles to achieve better returns on their investments.

Following World War II in 1948, the World Health Organization (WHO) came into existence with research as one of its core functions. It emphasized the need of identifying health-related issues needing research and thereby generation, dissemination, and utilization of the newly acquired knowledge for health promotion.[ 1 ] In 1978, Alma Ata Declaration acknowledged that primary health care was well known globally but, at the same time, also noted that modalities of its implementation were likely be different in different countries depending on their socioeconomic conditions, availability of resources, development of technology, and motivation of the community. A number of issues were yet to be resolved and researched before primary health care was operationalized under local conditions.[ 2 ]

T HE D EFINITION

The kind of research that Alma Ata Declaration recommended for improvement of health-care delivery is essentially OR. Described as “the science of better,” it helps in identifying the alternative service delivery strategy which not only overcomes the problems that limit the program quality, efficiency, and effectiveness but also yields the best outcome.[ 3 ] In its report on “The Third Ten Years of the WHO,” WHO has highlighted the usefulness of OR in improvement of health-care delivery in terms of its efficiency, effectiveness, and wider coverage by testing alternative approaches even in countries with limited national resources.[ 4 ]

OR has been variously defined. Dictionary of Epidemiology defined it as a systematic study of the working of a system with the aim of improvement.[ 5 ] From a health program perspective, OR is defined as the search for strategies and interventions that enhance the quality and effectiveness of the program.[ 6 ] A global meeting held in Geneva in April 2008 to develop the framework of OR, defined the scope of OR in context to public health as “ Any research producing practically usable knowledge (evidence, finding, information, etc.) which can improve program implementation (e.g., effectiveness, efficiency, quality, access, scale up, sustainability) regardless of the type of research (design, methodology, approach), falls within the boundaries of OR .”[ 7 ]

OR, however, is different from clinical or epidemiological research. It addresses a specific problem within a specific program. It examines a system, for example, health-care delivery system, and experiments in the environment specific to the program with alternative strategies to find the most suitable one and has an objective of improvement in the system. On the other hand, clinical or epidemiological research studies individuals and groups of individuals in search of new knowledge. In addition, ethical issues, which form an integral part of all clinical and epidemiological research, have their role poorly defined in OR, more so if it is based on secondary data.

The keyword in all the definitions is improvement, which is to be brought about by means of research in the operation of an ongoing program. Its characteristics include:

• It focuses on a specific problem in an ongoing programme
• It involves research into the problem using principles of epidemiology
• It tests more than one possible solution and provides rational basis, in the absence of complete information, for the best alternative to improve program efficiency
• It requires close interaction between program managers and researchers
• It succeeds only if the research is conducted in the existing environment and study results are implemented in true letter and spirit.

T HE P ROCESS

In health-care settings, an ongoing health program often fails to achieve its expected objective and the program managers are faced with problems factors responsible for which are not apparent. This is the stage where process of OR is initiated. In a standard OR process, planning begins with organization of a research team, which should have a mix of people with different backgrounds such as epidemiology, biostatistics, health managements, etc., The program managers may not be able to carry out the research themselves because of their work responsibility and in all probability, their biased views. However, they need to have a working relationship with the research team to ensure smooth conduct of the research and ownership of the result by all parties.

According to Fisher et al ., OR is a continuous process of problem identification, selection of a suitable strategy/intervention, experimentation of the selected strategy/intervention, dissemination of the findings, and utilization of the information so derived.[ 8 ] However, it may not always be possible to follow a step by step approach in OR since it is carried out in the existing environment, and many of the activities may be taking place simultaneously. The process involves the following steps [ Figure 1 ].

Process of operational research

Identifying problems

Like any other research, it is essential to have a research question as to the first and foremost step for beginning the process of OR. Discussion with program managers and staff, review of project reports and local documentation, discussion with experts in the field and literature search gives an insight into why the problem is occurring and what are possible solutions; and help in the identification of the research question. OR methods are useful for the systematic identification of problems and the search for potential solutions. Structured approaches to identifying options, such as the strategic choice approach or systematic creativity approaches have great potential for use in low-resource settings.[ 9 ]

Choosing interventions

Choosing appropriate interventions is clearly a crucial step. Effectiveness, safety, cost, and equity should all be considered, and researchers should be familiar with standard textbook methods for assessing these. Finding the best combinations and delivery methods is a major research exercise in its own right. Modeling different intervention strategies before rollout is now ubiquitous in many industries but is less common in healthcare.[ 10 ] Modeling work has been done on ways to reduce maternal mortality and in cervical cancer screening in low-resource settings.[ 11 ]

An appropriate intervention design, depending on available time and resources, should have a written protocol spelling out details of steps to be taken during implementation. Only valid and reliable instruments – be it quantitative or qualitative study-should be used; and wherever possible, a pilot study be carried out to further refine the conduct of the intervention. The contribution that OR and management science can make to design and delivery is not restricted to high technology. Oral rehydration therapy is a “low-tech–low-cost–high-impact” innovation, in which OR was used to explore ways it could be administered using readily available ingredients by laypeople, with an escalation pathway to treatment by health-care professionals when necessary.[ 12 ]

Small-scale projects generally need considerable modifications to work on a larger scale. Classic OR techniques such as simulation modeling can be used in locating services, managing the supply chain, and developing the health-care workforce.

Integrating into health systems

After analysis of the result, the information gathered should be disseminated to stakeholders and decision-makers. The modalities of information utilization should have been predecided and included in the research proposal. Successes in global health programs often result from synergistic interactions between individual, community and national actors rather than from any single “magic bullet.” A greater focus is needed on how interventions should be used in a complex behavioral environment, to better capture the dynamics of social networks, and to understand how complex systems can adapt positively to change. This is a task where OR and management science tools can be useful, as demonstrated by systems analysis of programs for cervical cancer prevention[ 13 ] or agent simulation modeling of spread of HIV in villages.[ 14 ]

E VALUATION

One of the greatest challenges for global health is the measurement and evaluation of performance of projects and programs. The WHO defines evaluation as “ the systematic and objective assessment of an ongoing or completed initiative, its design, implementation, and results. The aim is to determine the relevance and fulfillment of objectives, efficiency, effectiveness, impact, and sustainability .”[ 15 ] It may or may not lead to improvement.

Accelerated Child Survival and Development (ACSD) program, an initiative of UNICEF, was implemented in eleven West African countries from 2001 to 2005 with an objective of reducing mortality among under-fives by at least 25% by the end of 2006. Retrospective evaluation of the program was carried out in Benin, Ghana, and Mali by comparing data of ACSD focus districts with those of remainder districts. It showed that the difference in coverage of preventive interventions in ACSD focus areas before and after program implementation was not significant in Benin and Mali. This probably resulted in failure of ACSD program to accelerate survival of under-fives in-focus areas of Benin and Mali as compared to comparison areas. The inputs obtained from the evaluation of the program if translated into policy or national program would have delivered the desired result of ACSD program implementation.[ 16 ] Evaluation, thus, is fundamental to good management and is an essential part of the process of developing effective public policy. It is a complex enterprise, requiring researchers to balance the rigors of their research strategies with the relevance of their work for managers and policymakers.[ 17 ]

Standard control trial approaches to evaluation are sometimes feasible and appropriate but often a more flexible systems-oriented approach is required, together with modeling to help assess the effectiveness of preventive interventions.[ 18 ] Decision tree modeling can give rapid insights into the operational effectiveness and cost-effectiveness of procedures[ 19 ] and programs.[ 20 ]

O PERATIONAL R ESEARCH IN H EALTH-CARE S ETTINGS : E XAMPLES

The relevance of OR in health-care settings cannot be overemphasized. It has been successfully used all over the world in various health programs such as family planning, HIV, tuberculosis (TB), and malaria control programs to name a few. Its role in causing improvement in various health programs and the development of policies has been acknowledged globally. Sustained OR efforts of several decades helped in developing the Global strategy for control of TB. India and Malawi provide the most successful example of OR in this field.[ 21 ] In India, it was demonstrated by OR that successful implementation of DOTS strategy throughout the country led to reduction in the prevalence of TB, reduction in fatality due to TB and release of hospital beds occupied by TB patients; and thereby a potential gain to the Indian economy.[ 22 ]

For the treatment of TB, about half of TB patients in India rely on the private sector. In spite of it being a notifiable disease, TB notification from private sector has been a challenge. In 2014, Delhi state, by adopting direct “one to one” sensitization of private practitioners by TB notification committee, was able to accelerate notification of TB cases from the private sector.[ 23 ]

In view of the growing burden of multidrug-resistance TB (MDR-TB), an OR was conducted in the setting of Revised National Tuberculosis Programme on patients with presumptive MDR-TB in North and Central Chennai, in 2014 to determine prediagnosis attrition and pre-treatment attrition, and factors associated with it. Prediagnosis and pretreatment attrition were found 11% and 38%, respectively. The study showed that patients with smear-negative TB were less likely to undergo drug susceptibility testing (DST) and more attention was required to be paid to this group for improving DST.[ 24 ]

One of the most successful examples of OR in India is the experimental study carried out in Gadchiroli district of Maharashtra from 1993 to 1998. In their path-breaking field trial, Bang et al . trained village level workers in neonatal care who subsequently made home visits at scheduled intervals and managed premature birth/low birthweight, birth asphyxia, hypothermia, neonatal sepsis, and breastfeeding problems. This led to a significant reduction in neonatal mortality rates in intervention villages.[ 25 ] Encouraged by the success of this field trial, Home-Based Newborn Care has been adopted by many districts in India to combat neonatal mortality.

In leprosy case detection campaign (LCDC), introduced under National Leprosy Eradication Programme of India in 2016, false-positive diagnosis is a major issue. A study carried out in four districts of Bihar found 30% false-positive cases during LCDC. Using “appreciative inquiry” as a tool, Wagh et al . were able to achieve a decline in false-positive diagnosis.[ 26 ]

OR has been successfully used in hospital settings too. In Latin America, unsafe abortions used to be one of the most common causes of high maternal mortality. Billings and Bensons reviewed ten completed OR projects conducted in public sector hospitals of seven Latin American countries. Their findings indicated that sharp curettage replaced by manual vacuum aspiration for conducting abortion reduced the requirement of resources for postabortion care, reduced cost, and length of hospital stay and reduced maternal mortality.[ 27 ]

C ONCLUSION

Following Alma Ata declaration and Millennium Development Goals, all countries of the world have instituted their own National Health Programmes in a bid to improve health of their countrymen. Although health programs are in place, Governments are committed, guidance from the WHO is available, support from NGOs have been garnered, still many countries have not been able to achieve their desired goals. Operational Research is now being used as a key instrument, especially in resource-poor countries, to tap the untapped information. Administrators are using it as a searchlight for discovering what is still in the dark. It is there to stay. It is high time that the scientific community working in health-care settings gets acquainted with the nuances of OR and uses it more often for improving the outcome of health programs and for making them more efficient and effective.

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Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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