Session: 02-05-01: IoT and Digital Twins

Paper Number: 164117

Investment Analysis of Digital Twins 

A digital twin is a computer model of a physical system, such as a machine or building, that has the potential for high accuracy, precision, and flexibility to model various aspects of a system. Digital twins primarily function to make predictions or as a status indicator for the system being modeled. This paper discusses the potential savings to U.S. manufacturing if digital twins are adopted throughout the industry, as estimated in NIST Advanced Manufacturing Series report 100-61. The total potential impact of the adoption of digital twins in the manufacturing industry is estimated to be $37.9 billion annually. A Monte Carlo analysis puts the estimate between $16.1 billion and $38.6 billion with a median of $27.2 billion annually. The estimate is made by first approximating the benefit of the broader category of data tracking and analytics using survey data and industry data. Digital twin’s share of this savings is estimated by analyzing the trends in investment recommendations from the Department of Energy’s Industrial Assessment Center program and applying those estimates to the approximated savings from advancing data tracking and analytics.

This paper further proposes a method for analysis of investments that have many options, such as digital twins. In order for the benefits of data tracking and analytics investments to be realized, including those from digital twins, the right level of modeling (e.g., digital twin vs. tracking a selection of data items) must be selected based on factors such as the system complexity and cost consequences of having non-optimal system design/settings. If real-world systems are matched with the wrong modeling solutions, the total industry impact will not be realized. Thus, robust methods and practices for investment analysis in digital twins is needed in order for their impact to be achieved. As the number of options increases, it becomes necessary to be more systematic in how items are organized. A naïve approach might be to compare every combination of options for an investment, resulting in many combinations being compared. For instance, consider an example of a digital twin with two options with one of the options having three sub-options, totaling five options altogether. Comparing every combination would amount to comparing 17 calculations or combinations. This can be simplified by ranking all the options using one of two proposed metrics: 1) marginal net present value or 2) marginal internal rate of return. Using one or both of these metrics, a decision maker can identify the highest performing combination through identifying the highest performing individual options.

Presenting Author: Douglas Thomas NIST

Presenting Author Biography: Douglas S Thomas is an economist in the Applied Economics Office at the National Institute of Standards and Technology. He serves in multiple leadership roles, including being the founding chair of the ASME Investment Analysis subcommittee, project leader for multiple NIST projects, and leading ASTM workgroups. Thomas' work focuses on manufacturing industry costs, risks, and economic decision making. This work utilizes input-output analysis, econometrics, and survey techniques to measure costs and risks faced by manufacturing establishments. The results guide both manufacturers and research organizations in identifying high cost/loss areas of manufacturing that might have a high return when investing in research. This work also includes developing methods and/or standards for economic decision making used by manufacturers and manufacturing industry researchers.

Authors:

Douglas Thomas NIST