May 14, 2021

Human Resource Management has been an indispensable part of an organization. Be it monitoring high productivity levels regularly, maintaining talent pool, recruiting, training, compensating, career development, or succession planning, the list goes on. HR management is critical for the symbiotic relationship of the employees and the employers. An organization grows to the extent as its employees do.

But it is a known fact that humans are prone to make mistakes if they do any work sans the aid of logical or scientific methodologies. Several detailed research in HRM reveal that recruitment, training, allocation, career development, succession planning, workforce planning and other processes in many companies are based on sole human judgments. 

The problem with this is that it increases the possibility for errors, ego clashes, bias, or halo-effect to creep into the critical aspects of HRM. The consequent actions that are done unintentionally or intentionally may lead to resentment and the loss of morale among employees, leading to increased attrition rates in severe cases. Another problem with manual efforts is that capital investments in HRM activities could be much higher and the returns lower than they should have been.

A scientific approach to tackle this issue would be to understand the goals to be achieved in the entire HR process, the gains and losses, decision choices, and resource constraints, and be able to measure the relevant data, so that we could model the entire process mathematically and solve it to get optimal solutions. Operations Research (OR), an analytical method of decision making, solves management problems by breaking down the problem into elementary components, and solve them in well-defined steps using mathematical analysis.

The application of the following OR techniques in HR functionalities can boost organizational productivity by optimizing resource utilization:

Assignment Problem:

This technique determines what resource (could be anything from human resource, capital, or devices) can be most optimally assigned to departments, jobs, or cost centers for a process to maximize revenues or minimize resource utilization.  For example, consider a case wherein five jobs are to be distributed among five workers, based on the time taken by each worker to finish a particular job. In such scenarios, assignment problem is suitable for to minimize the total task time.

Linear Programming:

This technique optimizes a linear target function, which could be profit or cost, subject to different constraints namely labor, material, time, money etc to give an optimal mix of a solution.  Consider a situation in a company XYZ where the HR must choose the optimal number of employees in two departments to maximize the productivity of each department. But they have constraints like salary budget, workhours, training budget and system availability. Linear programming will be suitable in this scenario, or even for problems such as labor scheduling.

Queuing Theory:

It is a mathematical analysis of every components of a queue or a pipeline, inclusive of number of servers, customers, systems, arrival time, service time and the relevant processes. Queuing theory can improve  ticketing/customer service time and workforce allocation by understanding the behavior of the complaint/ticket arrival and servicing process through simulation using the past data, thereby analyzing the arrival and service time to shorten the queue length (number of tickets per employee) to determine the required number of server employees, and eventually solving the SLA achievement issue, while simultaneously tackling goal setting aspects of minimum service per day per employee, and under/over utilization of human resources.

Goal Programming:

This technique utilizes linear optimization for situations wherein multiple goals are involved. Its primary goal is to reduce the opportunity cost of not achieving a non-prioritized goal over an important goal.  An application of it could be in workforce planning to find the optimal number of hires, considering several goals like productivity maximization and hiring and training budget, each having its own unique importance.


It is a probabilistic technique wherein we mathematically model a real-life situation and run the outcomes of that situation on time- or event-based triggers. Every task in a situation happens according to its probability of occurrence, and its occurrence may trigger the happening of other events based on their probability from the past data. Simulation can be used to model any process in HR, or any other domain, like establishing a recruitment pipeline by modeling the entire lifecycle of an employee from recruitment till separation, while considering hiring rate, attrition rate and other important factors.


Operations Research and Human Resource Management may be different fields running parallelly to each other, but their amalgamation can bring about great results in the scope of decision making for organization leaders with respect to managing their workforce efficiently. Optimization gives them the right sense of direction to allocate resources in the right area, based on a mathematically determined criterion than any random, manual method.

Vimal Mangeshkar is a Business Analyst and a team member with the Innovation and Product Engineering - Analytics (IPE - Analytics) group within TCS Platform Solutions. He possesses proficiency in business analytics, business intelligence and functional knowledge in HRM and operations and supply chain management. As a business analyst, he is responsible for the implementation of several predictive and embedded analytics in the home grown HRMS platform of TCS Platform Solutions. He holds a PGDM degree in Marketing and Operations Management from SDM Institute of Management Development, Mysore, and a bachelor’s degree in Mechanical Engineering from Goa University.