Follow us on social

Latest Posts

Stay in Touch With Us

For Advertising, media partnerships, sponsorship, associations, and alliances, please connect to us below

Email
info@globaltechoutlook.com

Phone
+91 40 230 552 15

Address
540/6, 3rd Floor, Geetanjali Towers,
KPHB-6, Hyderabad 500072

Follow us on social

Globaltechoutlook

  /  Latest News   /  Digital Twin : A Revolution in Technology and All you Need to Know about it
Digital-Twin

Digital Twin : A Revolution in Technology and All you Need to Know about it

Introduction

Much before today’s onset of the Digital Twin there were supporting simulation technologies which have made the adoption possible. These technologies are used in manufacturing, marketing, and strategy and the generated science will facilitate the widespread usage.

Evolution

There are 3 main types of simulation methodologies that aide the development of Digital Twins.

  • Discrete Event Simulation and Manufacturing:

Discrete manufacturing as against process manufacturing can be modeled as queuing systems with each workstation considered as a subsystem which processes work that is either raw material or work in progress. The methodology comprises of modeling a discrete event and an activity. There is inherent variance in the processing at each workstation and a probability distribution is assumed and simulated for the arrival of material at each of these workstations. The number of finished products produced per hour is the throughput level of the plant. Multiple products can be accommodated. Metrics for work-in-process (the number of semi-finished products waiting in the queue at each of the workstation) and cycle time (time measured from when the raw material enters the production process till the time when the finished product leaves the process) is computed at the plant level. Cycle time is an indicator of the innovation time necessary to get the product to market. Work-in-progress is indicative of the working capital required. The Little’s Law (Little, 1961) describes the relationship between cycle time, work-in-process, and throughput.

  • System Dynamics and Marketing:

The framework involves a system that dynamically evolves over time. Here, the marketing function is modeled as a set of differential equations. On a computer though they are represented as difference equations which are solved using standard Runge-Kutta methods. These difference equations take parameters as values to simulate the marketing process. The variable of interest is usually sales or profits. The model is represented as stocks and flows with external parameters along with other model variables and constants. Units flow into each stock and the stock either gets depleted or replenished. The system accounts for and incorporates positive and negative feedback loops and time delays. Here too multiple products can be accommodated. The methodology began and took root at MIT with Jay Forrester and his colleagues working on it in the 1950s.

  • Agent-Based Modeling and Strategy:

Agent-based modeling can be used to represent companies as agents. Each of these agents interacts with other agents and the environment comprising of customers and competitors. These agents are represented as objects having an organizational form. During each time step, the company changes it form to either adapt, imitate, or innovate depending on the feedback received from the environment. There exists a fitness landscape which provides economic rent. Thus the firms traverse this landscape which is characterized by two parameters N, the size of the organizational form and K, the dependency within the organizational form that determines the ruggedness as modeled in Levinthal (1997). The model illuminates and helps us understand the predictors of firm performance. The methodology has a strong presence in academic strategy research.

Adoption and Future Use

Digital Twins have recently gained wide publicity with the acquisition of Gamma by the Boston Consulting Group. Mckinsey & Company has announced the formation of a Strategy and Analytics Center (STAC) which has the potential to accelerate the adoption of the technology within client organizations. The biggest advantage possibly arises from building a virtual counterpart to the physical entity for experimentation as a lab where the impact of different strategies can be tried out while incorporate advanced analytics.

 

Author Information:

Prashant Prakash Deshpande

(idprashantd@gmail.com)

Prashant is the CEO of a stealth mode Cleantech company. He works in strategy and operations research. He recently presented a poster at The Operational Research Society, UK’s Annual Conference 2021 which tackled a longstanding academic problem pertaining to the corporate strategic decision-making process. He has a PhD 1st year in Strategy from The University of Texas at Dallas and has close to 9 years of experience.