case study operating environment

•   Home
• Student Scholarship
• Honors Theses

An Analysis of Operating Environment & Strategy: A Case Study of Tata Motor

Collections

entitlement

Export search results

The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.

Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine)

• Technical Article---Peer-Reviewed
• Published: 22 November 2016
• Volume 17 , pages 56–67, ( 2017 )

Cite this article

• Ali Nouri Qarahasanlou 1 ,
• Reza Khalokakaie 1 ,
• Mohammad Ataei 1 &
• Behzad Ghodrati 2  

358 Accesses

7 Citations

Explore all metrics

When a system’s performance is inadequate, the concept of availability importance can be used to improve it. The availability of an item depends on the combined aspects of its reliability and maintainability. In a system consisting of many subsystems, the availability of some subsystems is more important to system performance than others. The availability measure determines the priority of availability across subsystems. Most researchers only consider operation time and ignore the influence of the operating environment; therefore, their estimations are not accurate enough. In contrast to previous research, we focus on the influence of the operating environment on the system/subsystem’s characteristics with a view to prioritizing them based on the importance of availability. The paper considers part of the mining fleet system of Sungun copper mine, including the wagon drill, loader, bulldozer, and dump truck subsystems. We identify an ordered list of possibilities for availability improvement and suggest changes or remedial actions for each item to either reduce its failure rate or reduce the time required to repair it.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Availability Importance Measure for Various Operation Condition

Expected Impact Quantification Based on Reliability Assessment

Application of reliability-centered maintenance for productivity improvement of open pit mining equipment: Case study of Sungun Copper Mine

Amin Moniri Morad, Mohammad Pourgol-Mohammad & Javad Sattarvand

J. Barabady, U. Kumar, Reliability analysis of mining equipment: a case study of a crushing plant at Jajarm Bauxite Mine in Iran. Reliab. Eng. Syst. Saf. 93 , 647–653 (2008). doi: 10.1016/j.ress.2007.10.006

Article   Google Scholar  

B. Ghodrati, Reliability and operating environment based spare parts planning. Luleå University of Technology, 2005

J. Barabady, U. Kumar, Availability allocation through importance measures. Int. J. Qual. Reliab. Manag. 24 , 643–657 (2007)

S. Beeson, J.D. Andrews, Importance measures for noncoherent-system analysis. IEEE Trans. Reliab. 52 , 301–310 (2003). doi: 10.1109/TR.2003.816397

Z.W. Birnbaum, On the importance of different components in a multicomponent system. DTIC Document, 1968

H.-W. Chang, R.-J. Chen, F.K. Hwang, The structural Birnbaum importance of consecutive-k systems. J. Comb. Optim. 6 , 183–197 (2002)

W. Wang, J. Loman, P. Vassiliou, Reliability importance of components in a complex system, IEEE, 2004, pp. 6–11

E. Borgonovo, The reliability importance of components and prime implicants in coherent and non-coherent systems including total-order interactions. Eur. J. Oper. Res. 204 , 485–495 (2010)

W. Kuo, X. Zhu, Some recent advances on importance measures in reliability. IEEE Trans. Reliab. 61 , 344–360 (2012)

H. Ascher, Regression Analysis of Repairable Systems Reliability. Electronic Systems Effectiveness and Life Cycle Costing (Springer, Berlin, 1983), pp. 119–133

Book   Google Scholar  

S. Kumar, Performance analysis and optimization of some operating systems of a fertilizer plant. Ph.D. Thesis. Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, 2010

S.H. Hoseinie, M. Ataei, R. Khalokakaie, U. Kumar, Reliability modeling of water system of longwall shearer machine. Arch. Min. Sci. 56 , 291–302 (2011)

Google Scholar  

S.H. Hoseinie, M. Ataei, R. Khalokakaie, B. Ghodrati, U. Kumar, Reliability analysis of drum shearer machine at mechanized longwall mines. J. Qual. Maint. Eng. 18 , 98–119 (2012). doi: 10.1108/13552511211226210

U. Kumar, B. Klefsjö, S. Granholm, Reliability investigation for a fleet of load haul dump machines in a Swedish mine. Reliab. Eng. Syst. Saf. 26 , 341–361 (1989). doi: 10.1016/0951-8320(89)90004-5

B.S. Dhillon, Bibliography of literature on mining equipment reliability. Microelectron. Reliab. 26 , 1131–1138 (1986)

B. Dhillon, O. Anude, Mining equipment reliability: a review. Microelectron. Reliab. 32 , 1137–1156 (1992)

N. Gorjian, L. Ma, M. Mittinty, P. Yarlagadda, Y. Sun, The explicit hazard model-part 1: theoretical development, IEEE; 2010, pp. 1–10

B. Ghodrati, U. Kumar, Reliability and operating environment-based spare parts estimation approach: a case study in Kiruna Mine, Sweden. J. Qual. Maint. Eng. 11 , 169–184 (2005)

B. Ghodrati, U. Kumar, D. Kumar, Product support logistics based on product design characteristics and operating environment (Society of Logistics Engineers, Huntsville, 2003), p. 21

X. Gao, T. Markeset, Design for production assurance considering influence factors, 2007

X. Gao, J. Barabady, T. Markeset, Criticality analysis of a production facility using cost importance measures. Int. J. Syst. Assur. Eng. Manag. 1 , 17–23 (2010)

A. Barabadi, J. Barabady, T. Markeset, Maintainability analysis considering time-dependent and time-independent covariates. Reliab. Eng. Syst. Saf. 96 , 210–217 (2011)

D.G. Kleinbaum, Survival Analysis (Springer, New York, 2011)

A. Barabadi, T. Markeset, Reliability and maintainability performance under Arctic conditions. Int. J. Syst. Assur. Eng. Manag. 2 , 205–217 (2011)

M. Rausand, A. Høyland, System Reliability Theory: Models, Statistical Methods, and Applications , vol. 396 (Wiley, Hoboken, 2003)

A. Saltelli, S. Tarantola, F. Campolongo, Sensitivity analysis as an ingredient of modeling. Stat. Sci. 15 , 377–395 (2000)

A. Saltelli, M. Ratto, S. Tarantola, F. Campolongo, E. Commission, Sensitivity analysis practices: strategies for model-based inference. Reliab. Eng. Syst. Saf. 91 , 1109–1125 (2006)

S. Kim, D.M. Frangopol, Optimal planning of structural performance monitoring based on reliability importance assessment. Probab. Eng. Mech. 25 , 86–98 (2010)

Minitab n.d. http://www.minitab.com/en-us/ . Accessed 10 Nov 2015

Reliability Software, Training, Consulting and Related Reliability Engineering Analysis Services from ReliaSoft Corporation n.d. http://www.reliasoft.com/ . Accessed 10 Nov 2015

IBM—United States n.d. http://www.ibm.com/us-en/ . Accessed 10 Nov 2015

Wolfram|Alpha: Computational Knowledge Engine n.d. http://www.wolframalpha.com/ . Accessed 10 Nov 2015

Download references

Author information

Authors and affiliations.

Faculty of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran

Ali Nouri Qarahasanlou, Reza Khalokakaie & Mohammad Ataei

Luleå University of Technology, Luleå, Sweden

Behzad Ghodrati

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ali Nouri Qarahasanlou .

Rights and permissions

Reprints and permissions

About this article

Nouri Qarahasanlou, A., Khalokakaie, R., Ataei, M. et al. Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine). J Fail. Anal. and Preven. 17 , 56–67 (2017). https://doi.org/10.1007/s11668-016-0205-z

Download citation

Received : 29 January 2016

Revised : 05 June 2016

Published : 22 November 2016

Issue Date : February 2017

DOI : https://doi.org/10.1007/s11668-016-0205-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Availability importance measure
• Operating environment
• Reliability
• Maintainability
• Mining fleet
• Find a journal
• Publish with us
• Track your research

About the Journal

Case Studies in the Environment  is the only journal exclusively devoted to publishing peer-reviewed environmental case study articles, as well as articles that focus on the pedagogy of using studies for in-class instruction or other purposes. The journal’s overarching objective is to publish case studies that provide insights on critical environmental issues to students, educators, researchers, and environmental professionals and policymakers.

ISSN: 2473-9510

Frequency: Continuous online-only article publication

Impact Factor: 0.8

• Special Collections

Read topically-themed collections of Case Studies in the Environment content

• Prize Competition

Case Studies in the Environment Prize Competition

Twitter: @CaseStudEnv

Affiliations.

• Recent Content
• All Content
• Info for Authors
• Info for Reviewers
• Info for Librarians
• Editorial Team
• Online ISSN 2473-9510
• Copyright © 2024

Stay Informed

Disciplines.

• Ancient World
• Anthropology
• Communication
• Criminology & Criminal Justice
• Film & Media Studies
• Food & Wine
• Browse All Disciplines
• Browse All Courses
• Book Authors
• Booksellers
• Instructions
• Journal Authors
• Journal Editors
• Media & Journalists
• Planned Giving

About UC Press

• Press Releases
• Seasonal Catalog
• Acquisitions Editors
• Customer Service
• Exam/Desk Requests
• Media Inquiries
• Print-Disability
• Rights & Permissions
• UC Press Foundation
• © Copyright 2024 by the Regents of the University of California. All rights reserved. Privacy policy    Accessibility

This Feature Is Available To Subscribers Only

Sign In or Create an Account

HR’s new operating model

The way in which organizations manage people used to be relatively straightforward. For more than two decades, multinational companies generally adopted a combination of HR business partners, centers of excellence, and shared service centers, adjusting these three elements to fit each organization’s unique nature and needs.

Today, this approach—introduced by Dave Ulrich in 1996 1 David Ulrich, Human Resources Champions: The Next Agenda for Adding Value and Delivering Results , first edition, Boston, MA: Harvard Business Review Press, 1996. —is rapidly evolving. In interviews with more than 100 chief human resources officers (CHROs) and senior people leaders from global multinational businesses, we identified five HR operating-model archetypes that are emerging in response to dramatic changes in business and in the world—including heightened geopolitical risks, hybrid working models, and the rise of majority-millennial workforces.

These emerging operating models have been facilitated by eight innovation shifts, with each archetype typically based on one major innovation shift and supported by a few minor ones. The key for leaders is to consciously select the most relevant of these innovation shifts to help them transition gradually toward their desired operating model.

Eight innovation shifts driving HR’s new operating models

Today’s increasingly volatile, uncertain, complex, and often ambiguous business environment is forcing companies to transform at an unprecedented pace. The global COVID-19 pandemic and rapid evolution of workplace technology have accelerated the adoption of various alternative, hybrid working models—as well as new challenges in monitoring employee conduct and performance. The emergence of majority-millennial workforces has led to a profound shift in employee preferences. And the “Great Attrition” of workers , 2 Aaron De Smet, Bonnie Dowling, Marino Mugayar-Baldocchi, and Bill Schaninger, “‘ Great Attrition’ or ‘Great Attraction’? The choice is yours ,” McKinsey Quarterly , September 8, 2021. exacerbated by demographic developments in many parts of the world, has intensified existing talent shortages.

HR plays a central role in navigating this upheaval, creating a need for the function to rise to a new level of adaptability and responsibility . 3 Laura Blumenfeld, Neel Gandhi, Asmus Komm, and Florian Pollner, “ Reimagining HR: Insights from people leaders ,” McKinsey, March 1, 2022. While every organization has its own trajectory and HR operating model, our interviews with senior leaders revealed that organizations are innovating in ways that are collectively changing the HR function from the “classic Ulrich model”:

• Adopt agile principles to ensure both strict prioritization of HR’s existing capacity and swift reallocation of resources when needed, enabling a fundamentally faster rate of change in the business and with people and how they work.
• Excel along the employee experience (EX) journey to win the race for talent in the time of the Great Attrition , 4 Aaron De Smet, Bonnie Dowling, Marino Mugayar-Baldocchi, and Bill Schaninger, “‘ Great Attrition’ or ‘Great Attraction’? The choice is yours ,” McKinsey Quarterly , September 8, 2021. enabling both employee health and resilience.
• Re-empower frontline leaders in the business to create human-centric interactions, reduce complexity, and put decision rights (back) where they belong.
• Offer individualized HR services to address increasingly varied expectations of personalization.
• ‘Productize’ HR services to build fit-for-purpose offerings with the needs of the business in mind, and to enable end-to-end responsibility for those services through cross-functional product owner teams in HR.
• Integrate design and delivery with end-to-end accountability to effectively address strategic HR priorities, reduce back-and-forth, and clarify ownership.
• Move from process excellence to data excellence to tap into novel sources of decision making using artificial intelligence and machine learning.
• Automate HR solutions to drive efficiency and capitalize on the power of digitalization in HR.

These innovation shifts are driving the emergence of new HR operating models, albeit with different degrees of influence depending on the nature of individual organizations (Exhibit 1). In analyzing the drivers, we identified five HR operating archetypes.

Five emerging HR operating models

These eight innovation shifts have enabled companies to rethink how they manage their people and the best way to do so. Exhibit 2 shows the five emerging HR operating models we identified, which are all enabled by two core elements: a strong, consistent data backbone and a user-friendly, highly reliable service backbone. When asked which two archetypes best fit their HR operating model, 48 percent of people leaders attending a recent webinar selected Ulrich+, 47 percent EX-driven, 36 percent leader-led, 31 percent agile, and 6 percent machine-powered. 5 Reimagining HR Webinar Survey, McKinsey, November 2022, n = 140 senior people leaders. Figures do not sum to 100%, because of the possible selection of multiple answers.

This model is an adaptation of the classic Ulrich model, with HR business partners developing functional spikes and taking over execution responsibilities from centers of excellence (CoEs). In turn, CoEs are scaled down to become teams of experts and selected HR business partners. They are supported by global business services and have a digital operations backbone. Many CHROs believe the classic Ulrich model is not up to solving today’s HR challenges, with HR business partners lacking the skills and time to keep up with the latest HR developments. Inflexible CoEs limit agile reactions, while other organizational boundaries have steadily become more permeable. Multinational businesses with mature and stable business models are often the ones that experience these pain points.

An agile transformation

A global financial institution underwent an agile transformation with a focus on IT delivery, supported by an agile HR operating model with 2,000 staff members. It first structured its HR function along the employee life cycle, aligning resources to the employee experience (EX) journey: when they join, work, develop, perform, and exit. The evolution to an agile model was supported by three HR innovation shifts:

• reducing the number of handovers by integrating run (servicing and operations) and change (product delivery) activities into “workstreams”
• setting up workstreams with end-to-end service responsibility (for example, design and delivery of recruiting), common goals, and steering
• allocating resources to agile pods with product crews for each workstream and agile ways of working

Projects that cut across multiple product crews were supported with a center-of-excellence initiative manager at the divisional level, and the stream-by-stream transition plan was phased over two years.

This model calls for a smaller number of HR business partners, with an emphasis on counseling top management, while CoE professionals focus on topics such as data and analytics, strategic workforce planning, and diversity and inclusion. The freed-up resources are pooled to implement cross-functional projects. CHROs who favor this operating model believe that HR needs to accelerate to keep up with the increased focus on execution exhibited on the business side and to prevent HR from hindering rapid transformation. Companies are applying this and other agile methodologies when experiencing rapid growth or discontinuity. (For an example of this model, see sidebar “An agile transformation.”)

Optimizing the employee experience

A global software company adopted a new business strategy to maximize the customer and employee experience, committing to a two-year transformation journey. Its first step was to mirror the customer experience for employees by identifying and revamping “moments that matter” along the employee life cycle. Three HR innovation shifts facilitated this: persona-driven HR services began following a customized approach; product owners took on end-to-end responsibility over HR concept, design, and delivery to deliver moments that matter; and HR, IT, and business operations combined into a comprehensive data function.

This model is meant to help CHROs gain a competitive advantage by creating a world-class EX journey. Putting EX first means allocating disproportionate resources toward “moments that matter.” For example, HR, IT, and operations experts could be granted full responsibility to jointly plan, develop, and roll out a critical onboarding process. By creating a world-class EX, HR becomes the driving force in bridging cross-functional silos and in overcoming the patchwork of fragmented data and processes that many organizations suffer from today. The companies employing this model are highly dependent on their top talent, with a small set of clearly defined competencies. (For more on this model, see sidebar “Optimizing the employee experience.”)

In this model, CHROs transition HR accountability to the business side, including for hiring, onboarding, and development budgets, thereby enabling line managers with HR tools and back-office support. This archetype also requires difficult choices about rigorously discontinuing HR policies that are not legally required. Too much oversight, slow response times, and a lack of business acumen in HR have led some companies to give line managers more autonomy in people decisions. Companies exploring this choice typically have a high share of white-collar workers, with a strong focus on research and development.

Machine-powered

With this model, algorithms are used to select talent, assess individual development needs, and analyze the root causes of absenteeism and attrition—leaving HR professionals free to provide employees with counsel and advice. As digitalization redefines every facet of business, including HR, CHROs are looking for ways to harness the power of deep analytics, AI, and machine learning for better decision outcomes. Organizations that are experimenting with this are primarily those employing a large population of digital natives, but HR functions at all companies are challenged to build analytics expertise and reskill their workforce.

Innovation shifts shaping HR model archetypes

While innovation shifts have shaped the traditional HR operating model and led to the emergence of new archetypes, not all innovation shifts are equal. Each archetype is typically based on one major innovation shift and supported by a few minor ones (Exhibit 3).

For example, a leader-led archetype is mainly shaped by the shift of empowering the leaders and the front line. At the same time, it gives more flexibility to the needs of the individual (the “cafeteria approach”) because leaders have more freedom; it also builds on digital support so leaders are optimally equipped to play their HR role. Alternatively, an agile archetype is strongly focused on adapting agile principles in HR, but it typically also aims to move toward a productized HR service offering and strives for end-to-end accountability.

The critical decision for senior people leaders is to consciously select the most relevant of these innovation shifts to transition gradually toward their desired operating-model archetype. For example, the leader-led model puts business leaders, rather than HR, in the driver’s seat, allowing line managers to choose the right HR offerings for their individual teams. And for companies that decide to deploy machine-powered HR, the key is building and relying on deep analytics skills. This model uses integrated people data to make targeted, automated HR decisions.

In large, diversified organizations, CHROs may find that different archetypes fit the differentiated needs of specific businesses better and may adopt a combination of HR operating models.

Transitioning to a target operating model

Transitioning to a future-oriented archetype is typically a three-step journey. First, CHROs and their leadership teams align on the right operating-model archetype for their organization based on the most pressing business needs, expectations of the workforce, the wider organizational context, and the company’s dominant core operating model. In large, diversified organizations, CHROs may find that different archetypes fit the differentiated needs of specific businesses better and may adopt a combination of HR operating models.

Second, HR leadership teams prioritize the three or four most relevant innovation shifts that will move their function toward their chosen operating-model archetype. When doing this, people leaders need to reflect on strategic HR priorities and, even more important, the shifts required to establish the operating model given its feasibility, the potential limits to the speed of implementation, and the magnitude of change. (Today, we find that the capacity to change the HR information system is often the most limiting factor.) For example, if a company is operating in a traditional hierarchical “command and control” way, the sole shift of HR into an agile archetype requires profound and demanding changes to ways of working, likely beyond only HR. Similarly, a business accustomed to a “high touch, concierge service” HR approach will find that a shift to a leader-led archetype is challenging and requires significant effort to implement.

Finally, teams think comprehensively about the transition journey, working toward core milestones for each of the prioritized innovation shifts individually and ensuring a systemic, integrated transformation perspective at the same time. This requires mobilizing for selected shifts, building new capabilities, and acting on an integrated change agenda in concert across business and HR.

Sandra Durth is a senior expert and associate partner in McKinsey’s Cologne office, Neel Gandhi is a partner in the New York office, Asmus Komm is a partner in the Hamburg office, and Florian Pollner is a partner in the Zurich office.

The authors wish to thank Fabian Schmid-Grosse and Christian Winnewisser for their contributions to this article.

Explore a career with us

Related articles.

Reimagining HR: Insights from people leaders

Agile talent: How to revamp your people model to enable value through agility

This time it’s personal: Shaping the ‘new possible’ through employee experience

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Sensors (Basel)

A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study

Paweł stączek.

1 Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, 20-618 Lublin, Poland; [email protected]

Jakub Pizoń

2 Department of Organization of Enterprise, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland; [email protected]

Wojciech Danilczuk

3 Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, 20-618 Lublin, Poland; [email protected]

Arkadiusz Gola

Associated data.

All the data is available within the manuscript.

The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company’s production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs.

1. Introduction

Increasingly sophisticated IT system solutions open up new horizons for the development of technical measures applicable in diverse areas of the economy [ 1 , 2 ]. Advances in both computer hardware and computational technology allow the collection and analysis of large amounts of data [ 3 ]. This infrastructural-and-analytical perspective paves the way to mass digitization, which is a fundamental component of Industry 4.0 [ 4 ]. The term Industry 4.0, which alludes to the past industrial revolutions, stands for the newest revolution, which essentially involves the digitization of processes and their components [ 5 ]. Each of the past revolutions introduced a new technical solution that had a critical impact on the development of industry in a given era. The first three industrial revolutions were brought about by mechanization, electrification, and the use of information systems [ 6 ]. Currently, the introduction of technologies and solutions associated with the Internet of Things (IoT), Cloud Computing, and Cyber-Physical Systems drives the Fourth Industrial Revolution [ 7 ]. These technologies have particular applications in the digitization of manufacturing and logistics processes and current mapping, diagnosis, and analysis of those processes [ 8 ]. The important fact that nowadays, thanks to these technologies, analyses are available in near real-time, that is, directly at the time when the changes are taking place, allowing the right decision to be taken at the right time.

The more and more advanced digitization technology also affords a completely different approach to the problems of designing and improving production systems and processes, as it eliminates the costly stage of testing the design assumptions in real-life conditions [ 9 ]. On the other hand, digitalization requires the implementation of appropriate protection mechanisms as well as investments in IT infrastructure. Furthermore, it should be remembered that the quality of the results obtained through digitization depends on the quality of the data obtained—which should be the focus of all researchers. A special role in this respect is played by the Digital Twin technology, which permits us to represent a real-world object in a digital world and to approximate its past and present states and forecast its future states in order to control the decision-making process [ 10 ]. The aim of this study was to show how the Digital Twin technology could be applied in testing the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to test the design assumptions in an early phase of the implementation of an AMR vehicle in a production hall. This was carried out by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes.

This paper is divided into three main parts. The first part ( Section 2 ) presents the concept of the Digital Twin technology and its main assumptions. It describes how digital twins work and how they can be deployed as well as the benefits of their application. Section 3 is devoted to the issues of the use of AMR vehicles in the industrial environment and the main problems related to testing the design assumptions for the operation of this type of vehicle in the difficult conditions created by the robot’s surroundings. Section 4 provides an example of a practical application of the Digital Twin technology in testing the assumptions for the implementation of an AMR on a shop floor of a company producing power drive components. The article ends with a Conclusions section, which summarizes the most important points of the present study and outlines the directions of future research in the area discussed in this paper.

2. The Fundamentals and Main Assumptions of the Digital Twins Technology

The development of Industry 4.0 systems is based mainly on IT solutions dedicated to production and in-house logistics [ 11 ]. These technologies constitute adaptations of native information technologies to industrial requirements and combine, in an interdisciplinary fashion, production engineering, IT systems, mechatronics, communication technologies, and data analytics [ 12 ]. The assumptions of Industry 4.0 are a set of functionalities of these information technologies, selected and combined as required by a production company’s business needs ( Figure 1 ). As they are implemented in Industry 4.0, these technologies are given specialist names, such as Industrial Internet of Things, Cloud Manufacturing, or Digital Twin. Coupled together, they create Industry 4.0 solutions [ 13 ].

General scheme of Industry 4.0 solutions.

One way in which the diverse information technologies can be combined in an interdisciplinary manner is to create a digital twin solution. This issue is a new topic that has, in recent years, attracted the attention of numerous scientists. Importantly, it is also becoming popular among managers and practitioners, which shows that it is one of the most fertile and profitable areas of engineering and IT research [ 14 ]. The very term “digital twin” is defined in the literature in a multifarious manner. According to some authors, a digital twin is a virtual representation/model that interacts with a physical system throughout its life cycle [ 15 ]. Other widely known definitions relate to information exchange between two spaces that include sensors, data, and models [ 16 ]. Still others define the digital twin as the cyber part of a Cyber-Physical System (CPS) [ 17 ]. According to the definition proposed by the National Institute of Standards and Technology (NIST), “a digital twin is the electronic representation—the digital representation—of a real-world entity, concept or notion, either physical or perceived” [ 18 ]. Digital twins are deployed in areas such as optimization, monitoring, diagnosis, and prediction [ 19 ].

Digital twins consist of a physical part and a digital part ( Figure 2 ). The physical part is the object/device/physical counterpart and the sensors with the embedded technologies are responsible for obtaining, aggregating, and transferring data. The digital part, on the other hand, involves technologies (hardware infrastructure, software) that allow for the creation of a digital module. The two parts exchange information collected via computer network links, with all calculations supported, for example, by a cloud computing model. Importantly, such systems have the benefit of allowing the user to operate at the level of the digital model rather than directly at the level of the physical device.

Digital twin data flow.

Depending on a specific need, the real-world object may be represented either very accurately or only approximately. How faithful a representation of the physical object a digital twin is depends both on how much information can be obtained about the object’s structure, its parameters, and cause, state, and effect variables, as well as on the business need (the purpose of using the model). In other words, Digital Twins can be simple or highly complex. When the second case happens, which often happens within the construction industry, for example, DT is often assumed as an SoS—System of Systems [ 2 , 20 ].

In engineering applications, models are mainly based on mathematical description of the laws of physics. However, for complex systems, “black-box” models are also used. Data-driven models are more and more commonly deployed to identify and describe the relationships between various variables [ 10 ].

The most important thing in the modeling process is to determine which of the model’s variables will be the key ones. Often, these variables are defined on the basis of the states that a given object is in. States refer to aspects of a system which evolve in time relative to their previous values and causal variables (for example, ones imposed externally by the system’s interactions with its environment). System state monitoring thus allows to trace the history of a system’s behavior over time. System parameters, in turn, describe how the inputs to the system affect states and how different states relate to one another in time. In many cases, system parameters remain constant or change slowly. When they do change, the variability makes testing the physical object difficult. The situation is completely different for a digital representation of the object, which can be tested multiple times for different values of parameters and causal variables.

It is worth noting that states and parameters for a particular digital twin representation are defined at a specific level of abstraction selected for the idealized representation. The representation includes information about the system itself, as well as its environment and the related processes. However, the system’s states and the parameters stored in the virtual representation of an object are not easily exchanged between physical reality and the virtual representation. This is because not all system states can be directly measured with the available measurement methods due to technological or economic constraints.

Therefore, often, digital twins have different representation levels, which specify how faithful the digital copy is to its physical counterpart. Many definitions indicate that the digital twin represents the highest possible level of model fidelity. However, the level of model fidelity relates directly to the level of abstraction selected for the virtual representation. It can be assumed that models with a higher fidelity ensure a better fit between the physical system and the virtual representation. At the same time, this assumption may not be feasible if the information (variables) that can be obtained from the system are not appropriate for the given level of model fidelity. Uncertainties or observation and modeling errors resulting from insufficient data can belie the potential benefits arising from the use of a higher fidelity model. Faulty data can lead to wrong decisions. Hence, one should make sure at the stage of creating a digital twin that the variables and parameters of the model have been properly validated and tested. Additionally, even if the required data could be collected, there still remain additional challenges associated with the high-fidelity model approach, which include data storage management and constraints on data transfer, computing power, and decision support time. For these reasons, a digital twin is assumed to be a virtual representation of a specific physical system. The fidelity of the representation depends mainly on the specific case of use, which is determined by a company’s particular business need [ 21 ]. It is worth mentioning that the digital twin can be built incrementally. That is, at first the basic model of the object’s operation is mapped, then new characteristics are added, and finally the full mapping in both operation and 3D form of the object is completed.

The basic need which motivates the use of a digital twin is to monitor and test a system that changes over time. Digital twins are used to improve the efficiency of machines, conveyors, and other devices. By tracking and modeling changes that take place over time in the virtual representation as a function of input variables to the system, one can approximate the past, present, and future states in order to control the decision process [ 10 ]. In this context, digital twins are also used to model systems and to collect and analyses information about processes and people, and can thus be employed to solve complex problems. This approach is used for many applications for both AMR and other engineering problems [ 20 , 22 , 23 ].

Simulations using digital twins can be performed alongside physical experiments. When conducted in combination, virtual and physical tests permit to verify both the models and the quality of the digital twin representation. This enables experts to create and calibrate models used in optimization applications, as discussed later in this article [ 24 ].

3. The Application of AMR in the Industrial Environment of Industry 4.0

One of the trends of the Third Industrial Revolution (Industry 3.0), naturally pursued in Industry 4.0, is the robotization of production processes. Industry 3.0 assumed that production processes would be subject to automation involving, among others, the use of industrial robots. The intended task of industrial robots is to take over some of the repetitive operations so far performed manually by humans [ 25 ]. In Industry 4.0, this idea has been taken further to the point where robots’ actions will no longer have to be programmed, but will be performed autonomously. The operator’s only responsibility will be to give commands to the robot, and the machine will decide on its own how to execute those commands [ 26 ].

The concept of automated robotic operations is realized by Automated Guided Vehicles (AGV) and AMR [ 27 ]. AMR are the latest solution in the sector of unmanned mobile robots for moving materials [ 28 ]. Most often, AMR are used in large warehouses or inside production plants to perform shop floor transport operations and handle materials, semi-finished products, finished products, or goods [ 29 , 30 ]. Statistics show that as early as 2000, about 20,000 autonomous vehicles were used for shop floor transport operations in industrial conditions [ 31 ]. In recent years, solutions of this type have become the focus of extensive research, and the number of scientific publications in this field is growing ( Figure 3 ).

Number of publications regarding digital twins, AMR, and AGV according to the Scopus database.

Based on practical experience related to the development and implementation of projects involving AMR solutions, we outline here the potential areas of application of the Digital Twins technology. We discuss an example of a practical use of a digital twin developed and deployed in an R&D project aimed at implementing an autonomous shop floor transport system.

One area in which a digital twin can be constructed to support the implementation of an AMR is the development and testing of the robot’s navigation and localization algorithms. To create, improve, and optimize these algorithms, usually numerous tests of the real-world object have to be performed. In navigation and localization processes, the robot’s operating environment is also of key importance, since it determines how the robot’s localization algorithms and the autonomous AMR path planning algorithms operate [ 32 ]. In the case of mobile robots, the real-world objects to be considered include both the vehicle and the space in which it will be moving. Research and experiments on localization and navigation algorithms then require access to both the vehicle and its operating environment (or a laboratory emulating the operating environment). When prototyping such solutions, often, a research team has only one robot (prototype) to experiment with and a limited access to its target operating environment. In cases like that, building a digital twin seems to be the most beneficial solution. Vehicle control algorithms can be transferred to a virtual environment to allow research teams to work independently of whether or not they have access to the physical object. If a digital twin is used, several teams can work simultaneously on a solution, even if they only have one copy of the prototype. By recreating the robot’s operating environment as part of the digital twin, researchers can test the localization and navigation algorithms in the virtual operating environment when the real shop floor cannot be used (e.g., because the experiments would disrupt the production processes taking place in the factory). It may also be easier to introduce changes and test new variants of control algorithms in the environment of the digital twin than in the real-world setting. For example, if one wants to test the operation of a navigation algorithm under disruptions (e.g., illegible navigation markers, sensor damage), one can conduct an experiment in the digital environment and test the object’s behavior before any changes are implemented in the physical object.

Another issue that should be considered in projects that use AMR or AGV is safety. When AMR or AGV are deployed in industrial or commercial settings, certain machine safety requirements must be met. In the European Union, all machines offered on the market must comply with the Machinery Directive [ 33 ] and are subject to risk assessment pursuant to EN ISO 12100: 2021 [ 34 ]. Any vehicle which is designed to operate in an industrial setting is subject to ISO 3691-4: 2020, depending on its intended use [ 35 ]. For robots working in commercial, non-industrial environments (e.g., an office building), the requirements are set out in ISO 13482: 2014 [ 36 ]. In geographic areas outside Europe, the safety requirements for machinery are provided for by separate national regulations, e.g., the American Occupational Safety and Health Administration (OSHA) regulation.

Both ISO 3691-4: 2020 and ISO 13482: 2014 set a number of requirements that an AMR must meet. These regard

• the maximum speeds at which an AMR can travel,
• the AMR’s ability to identify the operating zone in which it is located (and adjust its behavior to this operating zone, e.g., by measuring its own speed or changing the direction of travel),
• the vehicle’s behavior when a human or another safety-related object is detected), e.g., sending a visual warning signal, changing direction of travel,
• maximum braking distances and the maximum pressure forces that the robot can exert on a human or another safety-related object,
• control of the driving direction, and
• the vehicle’s behavior during steering in manual mode and in service mode.

In addition, these standards specify a number of requirements for the robot’s operating space and how it must be adjusted to ensure the safety of operators, including:

• designation and marking of permanent communication paths,
• designation and marking of operating zones, areas which must not be entered by operators (e.g., fencing off the robot’s operating zone), and areas which must not be entered by vehicles.

Algorithms related to the safe operation of an autonomous vehicle can be implemented and tested in a digital twin environment. Similarly to navigation and localization algorithms, safety functions can also be assessed in a virtual environment. One advantage of this approach is that a hazardous situation (e.g., a man falling down in front of a moving robot) can be simulated and assessed in a virtual environment before the appropriate algorithms are transferred to the real object. Some cases of use, like the example given above, could be dangerous or impossible to test in a real-life setting. Additionally, problems related to the robot’s operating environment are worth modeling using the digital twin. By doing so, one can check different variants of the process and various configurations of the operating environment (e.g., warehouse layout options, new communication routes) before they are implemented in the real-life object, which significantly reduces the costs and the number of changes made to the physical object.

Once the project has reached full maturity and has entered the operational phase, after the prototyping and implementation phases, the digital twin can still be used as a very helpful tool. The purposes for which it will be used may, of course, change. An important issue is the operational strategy adopted for AGV or AMR [ 37 ]. Most commonly, these vehicles are equipped with diagnostic functions that monitor the operation of individual subassemblies. By monitoring motor temperature and currents, one can determine whether the power drive systems are working properly and whether dangerous anomalies may occur in them (e.g., as a result of overheating or overloading). Data collected from the robot’s internal sensors can also be used to predict potential failures or wear, which can be prevented by timely maintenance. Similarly, it is possible to monitor the operation of power supply systems or communication modules [ 38 ].

From the operator’s point of view, the greatest advantage of using a digital twin is the possibility of observing the vehicle’s operation in a virtual environment. This allows the operator to control the robot’s actions, send commands to the robot to perform logistics tasks, and monitor the work progress on these tasks. The benefits of using a digital environment scale up with the increase in the size of the vehicle fleet, especially when it is supervised by one operator only. When managing a fleet of robots, managerial staff can use the data collected by real objects and stored in the digital twin environment to produce statistics and calculate KPIs for the operation of the intralogistics system [ 39 , 40 , 41 ]. The archiving of data from real-world objects allows managers to obtain information on.

• the average number of logistics tasks one vehicle/robot performed per day,
• the average time a vehicle/robot performed one logistics task (and how that time compares to the planned operation time),
• how many of the tasks assigned to the vehicles required the operator’s manual intervention (e.g., the robot incorrectly estimated its position in the operating environment or was stopped due to a failure),
• the efficiency of using the robot,
• whether there have been any security incidents (e.g., the operator stepping in front of the moving vehicle, which necessitated a manual restart of the robot’s safety system, which is required in selected operating zones).

In addition, when the space in which the vehicle moves is represented in the digital twin, the operator can send driving commands to the robot. If a robot is coupled to an automatic logistics system that manages the operation of the entire fleet of vehicles, the operator may not know what instructions have been given to the individual robots. Representation of vehicles and their operating environment as digital twins and the integration of the twins with the automatic logistics system allows the operator to remote-control the operation of the entire fleet and take appropriate measures (whenever necessary). When the physical environment is represented in the digital space, there is no need for the operator to be physically present near the vehicles’ workspace.

The concept of the digital twin can be applied at various phases of a project to provide a number of options and benefits (as listed in Table 1 ). At the stage of research and prototyping, a digital twin allows to prototype and develop a product without access to the physical object; during operation in the target physical environment, it allows for supervision of operations, the sending of commands, and controlling of the operating parameters; from the point of view of managerial staff responsible for logistics processes, a digital twin provides access to collected data, which can be used for efficiency analysis and reliability prediction. Considering all these aspects, it seems reasonable to use the methods and capabilities offered by digital twins in projects making use of AMR robots. The benefits of using a digital environment scale up as the complexity of the overall system and the size of the vehicle fleet increase. In the next section, we present an example of the application of a digital twin in an R&D project involving an AMR vehicle designed to execute internal logistics operations in a production company.

Benefits of using digital twins of AMR/AGV and their operating environments.

4. Case Study

For AGV or AMR to be implemented in a company, they need to meet a number of requirements. In the previous section, we listed problems to which the use of digital twins could provide some promising solutions. One of these problems, namely “Testing and validation of navigation algorithms in different vehicle operating conditions”, seems to be particularly interesting from the point of view of implementation This problem is of interest to both suppliers and end users of digital twins. However, it is particularly important to companies whose shop floors have not been designed to accommodate mobile robots. Production halls often contain infrastructure elements (poles, ramps, stairs) which may make it difficult or impossible for a robot to move around, be it due to its shape or the type of its drive system kinematics (e.g., a non-holonomic drive). Modifications to shop floor infrastructure create additional implementation costs and sometimes may be infeasible (e.g., structural elements of a factory building cannot be removed). Therefore, before a company purchases autonomous vehicles, or even when it is already implementing an autonomous vehicle technology, an appropriate trial/test environment is needed, which will allow the technology to be tested from the perspective of the company’s specific conditions.

A traditional solution to such a problem would be to conduct a series of tests in which different, but also repeatable, operating conditions would have to be recreated or set up using the real object. However, it is not always possible to perform tests in real-world settings. Additionally, such tests require the engagement of the company and its employees. In extreme cases, tests can lead to difficulties that may even halt production.

For these reasons, solutions alternative to real-life testing are sought, and this is where the digital twin is useful. Digital twins of AMR and their target operating environments can be constructed to test, in virtual reality, the robots’ control and navigation algorithms and to check whether they are performing the planned intralogistics tasks correctly. Simulations and analyses conducted using a digital twin allow the evaluation of a system’s behavior in various scenarios, especially atypical ones. This approach makes it possible to meticulously configure the robots, adjusting them and their workspace to the specific shop floor conditions and the intralogistics tasks the robots will be performing. In this way, the implementation time is reduced as the solution does not have to be tested, or is only partly tested, in real-life conditions. The digital twin technology is thus a standard solution that can be tailored to the needs of a specific company. Of course, final testing should be carried out in the actual production environment, but it should be kept to a minimum.

In this paper, we conducted a case study to check whether the use of a digital twin does bring the expected benefits. The findings are discussed in the sections below.

The concept of a digital twin was used to verify the design assumptions at an early phase of implementing an autonomous AMR vehicle in a company’s production hall. Before trials with the actual robot were conducted on the shop floor, digital models (digital twins) of the vehicle and the shop floor along with its equipment were created. Then, numerous computer simulations were carried out to test, assess, and improve the robot’s localization and navigation algorithms and travel paths, execution times for the planned intralogistics tasks, etc.

The following sections provide short descriptions of the vehicle, its target operating environment, the digital twins created for them, and the simulation software used. Then, a case of verification and improvement of a selected intralogistics task executed in a potentially “problematic” part of the shop floor is described.

4.2. The Autonomous Vehicle and Its Operating Environment

The main component of the automated transport system being implemented was the prototype of the AMR type transport vehicle shown in Figure 4 . The robot towed overhead a rack (trolley) with a load placed on an EUR-pallet or in a box-pallet. The vehicle had two independently powered wheels which moved in a single axis (a differential system with (2.0) kinematics) and self-aligning castor wheels.

The prototype of autonomous mobile robot under the transport trolley ( left ). Main sensors of the robot ( right ): A—safety laser scanner, B—stereo vision 3D camera, C—high definition 2D camera, D—motor incremental encoders.

The robot was equipped with ambient sensors and an on-board control system, which allowed it to

• determine its position and orientation in the global reference system,
• autonomously plan the travel path to a given destination point,
• follow the planned path, and to detect and safely avoid obstacles along its way.

The robot’s task was to automatically execute the intralogistics tasks assigned to it on the shop floor, a part of whose digital model is shown in Figure 5 . The AMR could only move through safe corridors between the areas marked with the yellow line on the ground (storage fields).

View of the digital twin of the real factory hall (model created in Gazebo simulator).

The Adaptive Monte Carlo Localization (AMCL) algorithm [ 26 ] was used to allow the vehicle to localize on the basis of information from its safety laser scanners. A stereoscopic camera had been installed at the front end to detect objects below and above the detection plane of the laser scanners. The vehicle’s motion path was planned using Search-based Planning with Motion Primitives, as described in [ 42 ] and implemented in the software package Search-Based Planning Library.

4.3. Digital Twins

Computer simulations with the digital twins of the AMR vehicle and the production hall were carried out using Gazebo software [ 43 ]. Gazebo is an environment dedicated to the simulation and high-quality visualization of models of robotic systems and their surroundings (workspace). Gazebo has a computing engine (numerical procedures) that simulates the physical phenomena occurring in mechanical systems, including the kinematics and dynamics of rigid bodies (also in movable joints), interactions via contact, friction, and gravity, and even the influence of wind. Importantly, it permits the simulation of the operation of virtual counterparts of sensors used in robotics, such as accelerometers and gyroscopes (IMU), distance scanners, and digital (2D) and stereoscopic (3D) cameras.

Figure 5 shows a visualization of a fragment of the digital twin of the shop floor created in the Gazebo simulation environment. The model incorporates fixed elements (walls, columns, racks, roller conveyors), box-pallets in storage areas, and the figures of employees. These components are important from the point of view of the operation of the simulated vehicle’s sensors and the robot’s localization and navigation algorithms.

Figure 6 a shows the digital twin model of the AMR (without the transport trolley) positioned on the floor of the virtual factory hall. The blue rays parallel to the floor around the vehicle visualize the operation of two simulated laser distance scanners (installed at the front and at the back of the robot). The red rectangle underneath the vehicle marks the protected area monitored by its security scanners.

View of digital twin of real AMR in Gazebo simulator ( a ). Visualization of estimated AMR pose on the occupancy grid of production hall ( b ).

Figure 6 b shows a window of the RViz program designed for visualization and monitoring of the operation of robotic systems and their control algorithms. RViz is part of the Robot Operating System ( ROS ) middleware suite [ 44 ]. The window shows the model of the AMR against the background of an occupancy grid—the areas in the plane of the shop floor shown as a grayscale image. The black pixels on the occupancy grid indicate the forbidden zones, which the vehicle cannot enter. Grid pixels with a shade closer to white will be preferred by the navigation algorithms during path planning.

Yellow dots in Figure 6 b visualize the results of measurements of the distance from objects detected by the robot’s digital twin’s virtual laser scanners. The digital model of the laser distance meters takes into account the random nature of the variability of the measurement results (characterized by a given normal distribution). It should be emphasized that the vehicle’s position and orientation on the occupancy grid (i.e., in the global reference frame of the shop floor— Figure 6 b) are estimated by the AMCL algorithm and may therefore be burdened with error. In the bottom left corner of the RViz window, one can also see the image of the vehicle’s operating environment from a simulated camera installed on the front end of the robot’s digital twin.

The vehicle control, localization, and navigation algorithms exchange information with the real robot’s sensors and drive controllers via communication mechanisms implemented in ROS . The Gazebo simulator, in which the digital twins of the robot and the shop floor are represented, is also compatible with these mechanisms, which means the real robot and its sensors can be very quickly replaced with digital twins simulated in Gazebo .

Figure 7 shows the results of the operation of the algorithm planning the robot’s path from the current pose to the goal pose (at the top of the window). The blue arrows represent the poses set for the vehicle in the successive stages of its journey. All the algorithms of the robot’s on-board control system worked with digital twins (image from a simulated camera is shown) in the same way as they would have worked with the real vehicle in the real factory hall.

Visualization of calculated path for AMR to the dock position (at the top), blue arrows show subsequent desired poses of AMR.

4.4. Improvement of the Intralogistics Task

The digital twins of the AMR and its operating environment described in this paper were used extensively during the development, improvement and testing of the robot’s on-board localization and navigation systems. Figure 7 shows the path, planned by the navigation algorithm, leading to the docking station located at the end of the roller conveyor (at the top of the figure). The path runs along a safe corridor between prohibited areas which the robot is not allowed to move in. The simulations demonstrated that, despite the fact that the corridor in which the robot was allowed to move was relatively narrow relative to the size of the protected zone around the vehicle, the robot did reach the given docking position. However, the next simulations showed that the robot could only complete the return path by driving backwards— Figure 8 a (here, the vehicle is in its initial pose) even though the planning algorithm preferred driving in the forward direction. Unfortunately, the corridor was too narrow for the robot to turn around on the spot.

Calculated paths for AMR starting from the dock position (at the top) to destination pose (at the bottom) on the original occupancy grid ( a ) and on modified occupancy grid ( b , c ).

In addition to the front and rear safety laser scanners, the prototype robot was equipped with a stereo vision (3D) camera and a high-resolution (2D) camera, which were mounted only at the front of the vehicle— Figure 4 . The 3D camera allowed the robot to detect objects in front of it, also those “invisible” to the front security scanner (which operated only in one plane, parallel to the ground, Figure 6 a). On the other hand, the 2D camera and the automatic image analysis algorithm helped the robot to find the passive navigation markers placed in the factory hall. The fact that the robot could “see” better the surroundings in front of it was precisely why the robot’s control system preferred for the robot to move forward. Notice that the AMR’s maximum forward speed was 0.7 m/s, while its maximum backward speed was only 0.3 m/s.

We performed a series of simulations which showed that the vehicle completed the path presented in Figure 8 a, driving in reverse only, over the average time t A = 44 s. In view of the above, we proposed two variants of the robot’s safe corridor in which the forbidden zone was modified slightly by adding a 2 × 0.4 m rectangular recess visible in the upper part of Figure 8 b,c. The vehicle could use this recess to turn around on the spot and then cover a large part of the return path at a higher speed driving forward ( Figure 8 b,c).

The average travel times in the series of simulations for the modified versions of the robot’s safe corridor ( Figure 8 b,c) were t B = 22.5 s and t C = 23.3 s, respectively, i.e., they were almost 50% shorter than for the initial version of the corridor ( Figure 8 a). Given that additional sensors were only installed on the robot’s front end, the risk of collision with obstacles was reduced since the vehicle completed most of its path driving forward. The results of the simulations are summarized in Table 2 .

Simulated AMR travel times for three different shapes of the robot’s safe corridor.

5. Conclusions

In this paper we showed how the Digital Twin technology could be used in the design and implementation of an AMR in a production hall that offered limited possibilities of planning the robot’s paths. Our investigations led to the following conclusions:

• The use of the Digital Twin technology accelerated the development of AMR localization and navigation algorithms under various operating conditions, at the same time reducing the costs of testing and validating them.
• The computer simulations, conducted with the use of a digital twin, allowed us to improve the shape of the AMR’s communication route. As a result, the time which the robot needed to complete its intralogistics task was reduced by half.
• It should be underlined that the scenarios we considered were specific for the given company. A solution was presented which required only a slight modification of the robot’s safe corridor.
• Not only the robot’s travel time, but also the time of system implementation was reduced. Testing was carried out remotely, without the need to perform on-site tests.
• The case study confirmed that the use of a digital twin brought the expected benefits.

The positive results we obtained provide motivation for further research into the potential benefits of using digital twins in other areas of technology and industry.

The significant problem that increased during the provided research on the digital twin and AMR vehicle modeling was the lack of uniform standard of design patterns. Moreover, there are no universal IT tolls, software, or frameworks that help to quickly and efficiently develop digital twins. In our opinion, this constraints can cause real difficulties when apply digital twins in real industrial conditions. The low level of universalism and lack of proven design patterns will provide the necessity of individual treatment of each implementation of digital twin in industry. Moreover, the design teams can be forced to develop their own IT tools and programming libraries, which can cause the increasing cost of digital twin implementation. What is more, the time of spreading such solutions in the commercial environment will be much longer. To summarize, it can be noticed that the conception of digital twins has been already well described in the literature and is well-known in many research and industrial centers. Unfortunately, there are still no necessary design patterns and universal IT tools that effectively support this area of Industry 4.0 in real industrial conditions and there is a strong necessity to carry out further works in this area.

Author Contributions

Conceptualization, J.P. and W.D.; methodology, P.S.; software, P.S.; validation, A.G.; formal analysis, P.S.; investigation, W.D.; resources, P.S.; data curation, P.S.; writing—original draft preparation J.P. and P.S.; writing—review and editing, A.G.; visualization, W.D.; supervision, A.G.; project administration, W.D.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.

The project/research was financed in the framework of the project Lublin University of Technology-Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR's) Operating Environment-A Case Study

Affiliations.

• 1 Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, 20-618 Lublin, Poland.
• 2 Department of Organization of Enterprise, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland.
• 3 Department of Production Computerisation and Robotisation, Faculty of Mechanical Engineering, Lublin University of Technology, 20-618 Lublin, Poland.
• PMID: 34883834
• PMCID: PMC8659435
• DOI: 10.3390/s21237830

The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company's production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially "problematic" part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs.

Keywords: AGV (automated guided vehicles); AMR (autonomous mobile robots); Industry 4.0; IoT; computer simulation; digital twin.

• Diagnostic Tests, Routine

About Stanford GSB

• The Leadership
• Dean’s Updates
• School News & History
• Commencement
• Business, Government & Society
• Centers & Institutes
• Center for Entrepreneurial Studies
• Center for Social Innovation
• Stanford Seed

About the Experience

• Learning at Stanford GSB
• Experiential Learning
• Guest Speakers
• Entrepreneurship
• Social Innovation
• Communication
• Life at Stanford GSB
• Collaborative Environment
• Activities & Organizations
• Student Services
• Housing Options
• International Students

Full-Time Degree Programs

• Why Stanford MBA
• Academic Experience
• Financial Aid
• Why Stanford MSx
• Research Fellows Program
• See All Programs

Non-Degree & Certificate Programs

• Executive Education
• Stanford Executive Program
• Programs for Organizations
• The Difference
• Online Programs
• Stanford LEAD
• Seed Transformation Program
• Aspire Program
• Seed Spark Program
• Faculty Profiles
• Academic Areas
• Awards & Honors
• Conferences

Faculty Research

• Publications
• Working Papers
• Case Studies

Research Hub

• Research Labs & Initiatives
• Business Library
• Data, Analytics & Research Computing
• Behavioral Lab

Research Labs

• Cities, Housing & Society Lab
• Golub Capital Social Impact Lab

Research Initiatives

• Corporate Governance Research Initiative
• Corporations and Society Initiative
• Policy and Innovation Initiative
• Rapid Decarbonization Initiative
• Stanford Latino Entrepreneurship Initiative
• Value Chain Innovation Initiative
• Venture Capital Initiative
• Career & Success
• Climate & Sustainability
• Corporate Governance
• Culture & Society
• Finance & Investing
• Government & Politics
• Leadership & Management
• Markets & Trade
• Operations & Logistics
• Opportunity & Access
• Organizational Behavior
• Political Economy
• Social Impact
• Technology & AI
• Opinion & Analysis
• Email Newsletter

Welcome, Alumni

• Communities
• Digital Communities & Tools
• Regional Chapters
• Women’s Programs
• Identity Chapters
• Find Your Reunion
• Career Resources
• Job Search Resources
• Career & Life Transitions
• Programs & Services
• Career Video Library
• Alumni Education
• Research Resources
• Volunteering
• Alumni News
• Class Notes
• Alumni Voices
• Contact Alumni Relations
• Upcoming Events

Admission Events & Information Sessions

• MBA Program
• MSx Program
• PhD Program
• Alumni Events
• All Other Events
• Operations, Information & Technology
• Classical Liberalism
• The Eddie Lunch
• Accounting Summer Camp
• Videos, Code & Data
• California Econometrics Conference
• California Quantitative Marketing PhD Conference
• California School Conference
• China India Insights Conference
• Homo economicus, Evolving
• Political Economics (2023–24)
• Scaling Geologic Storage of CO2 (2023–24)
• A Resilient Pacific: Building Connections, Envisioning Solutions
• Adaptation and Innovation
• Changing Climate
• Civil Society
• Climate Impact Summit
• Climate Science
• Corporate Carbon Disclosures
• Earth’s Seafloor
• Environmental Justice
• Operations and Information Technology
• Organizations
• Sustainability Reporting and Control
• Taking the Pulse of the Planet
• Urban Infrastructure
• Watershed Restoration
• Junior Faculty Workshop on Financial Regulation and Banking
• Ken Singleton Celebration
• Marketing Camp
• Quantitative Marketing PhD Alumni Conference
• Presentations
• Theory and Inference in Accounting Research
• Stanford Closer Look Series
• Quick Guides
• Core Concepts
• Journal Articles
• Glossary of Terms
• Faculty & Staff
• Researchers & Students
• Research Approach
• Charitable Giving
• Financial Health
• Government Services
• Workers & Careers
• Short Course
• Adaptive & Iterative Experimentation
• Incentive Design
• Social Sciences & Behavioral Nudges
• Bandit Experiment Application
• Conferences & Events
• Get Involved
• Reading Materials
• Teaching & Curriculum
• Energy Entrepreneurship
• Faculty & Affiliates
• SOLE Report
• Responsible Supply Chains
• Current Study Usage
• Pre-Registration Information
• Participate in a Study

Tesla: Business & Operating Model Evolution

Learning objective.

• Priorities for the GSB's Future
• See the Current DEI Report
• Supporting Data
• Research & Insights
• Share Your Thoughts
• Search Fund Primer
• Affiliated Faculty
• Faculty Advisors
• Louis W. Foster Resource Center
• Defining Social Innovation
• Impact Compass
• Global Health Innovation Insights
• Faculty Affiliates
• Student Awards & Certificates
• Changemakers
• Dean Jonathan Levin
• Dean Garth Saloner
• Dean Robert Joss
• Dean Michael Spence
• Dean Robert Jaedicke
• Dean Rene McPherson
• Dean Arjay Miller
• Dean Ernest Arbuckle
• Dean Jacob Hugh Jackson
• Dean Willard Hotchkiss
• Faculty in Memoriam
• Stanford GSB Firsts
• Certificate & Award Recipients
• Teaching Approach
• Analysis and Measurement of Impact
• The Corporate Entrepreneur: Startup in a Grown-Up Enterprise
• Data-Driven Impact
• Designing Experiments for Impact
• Digital Business Transformation
• The Founder’s Right Hand
• Marketing for Measurable Change
• Product Management
• Public Policy Lab: Financial Challenges Facing US Cities
• Public Policy Lab: Homelessness in California
• Lab Features
• Curricular Integration
• View From The Top
• Formation of New Ventures
• Managing Growing Enterprises
• Startup Garage
• Explore Beyond the Classroom
• Stanford Venture Studio
• Summer Program
• Workshops & Events
• The Five Lenses of Entrepreneurship
• Leadership Labs
• Executive Challenge
• Arbuckle Leadership Fellows Program
• Selection Process
• Training Schedule
• Time Commitment
• Learning Expectations
• Post-Training Opportunities
• Who Should Apply
• Introductory T-Groups
• Leadership for Society Program
• Certificate
• 2023 Awardees
• 2022 Awardees
• 2021 Awardees
• 2020 Awardees
• 2019 Awardees
• 2018 Awardees
• Social Management Immersion Fund
• Stanford Impact Founder Fellowships and Prizes
• Stanford Impact Leader Prizes
• Social Entrepreneurship
• Stanford GSB Impact Fund
• Economic Development
• Energy & Environment
• Stanford GSB Residences
• Environmental Leadership
• Stanford GSB Artwork
• A Closer Look
• California & the Bay Area
• Voices of Stanford GSB
• Business & Beneficial Technology
• Business & Sustainability
• Business & Free Markets
• Business, Government, and Society Forum
• Second Year
• Global Experiences
• JD/MBA Joint Degree
• MA Education/MBA Joint Degree
• MD/MBA Dual Degree
• MPP/MBA Joint Degree
• MS Computer Science/MBA Joint Degree
• MS Electrical Engineering/MBA Joint Degree
• MS Environment and Resources (E-IPER)/MBA Joint Degree
• Academic Calendar
• Clubs & Activities
• LGBTQ+ Students
• Military Veterans
• Minorities & People of Color
• Partners & Families
• Students with Disabilities
• Student Support
• Residential Life
• Student Voices
• MBA Alumni Voices
• A Week in the Life
• Career Support
• Employment Outcomes
• Cost of Attendance
• Knight-Hennessy Scholars Program
• Yellow Ribbon Program
• BOLD Fellows Fund
• Application Process
• Loan Forgiveness
• Contact the Financial Aid Office
• Evaluation Criteria
• GMAT & GRE
• English Language Proficiency
• Personal Information, Activities & Awards
• Professional Experience
• Letters of Recommendation
• Optional Short Answer Questions
• Application Fee
• Reapplication
• Deferred Enrollment
• Joint & Dual Degrees
• Entering Class Profile
• Event Schedule
• Ambassadors
• New & Noteworthy
• Ask a Question
• See Why Stanford MSx
• Is MSx Right for You?
• MSx Stories
• Leadership Development
• Career Advancement
• Career Change
• How You Will Learn
• Admission Events
• Personal Information
• Information for Recommenders
• GMAT, GRE & EA
• English Proficiency Tests
• After You’re Admitted
• Daycare, Schools & Camps
• U.S. Citizens and Permanent Residents
• Requirements
• Requirements: Behavioral
• Requirements: Quantitative
• Requirements: Macro
• Requirements: Micro
• Annual Evaluations
• Field Examination
• Research Activities
• Research Papers
• Dissertation
• Oral Examination
• Current Students
• Education & CV
• International Applicants
• Statement of Purpose
• Reapplicants
• Application Fee Waiver
• Deadline & Decisions
• Job Market Candidates
• Academic Placements
• Stay in Touch
• Faculty Mentors
• Current Fellows
• Standard Track
• Fellowship & Benefits
• Group Enrollment
• Program Formats
• Developing a Program
• Diversity & Inclusion
• Strategic Transformation
• Program Experience
• Contact Client Services
• Campus Experience
• Live Online Experience
• Silicon Valley & Bay Area
• Digital Credentials
• Faculty Spotlights
• Participant Spotlights
• Eligibility
• International Participants
• Stanford Ignite
• Frequently Asked Questions
• Founding Donors
• Location Information
• Participant Profile
• Network Membership
• Program Impact
• Collaborators
• Entrepreneur Profiles
• Company Spotlights
• Seed Transformation Network
• Responsibilities
• Current Coaches
• How to Apply
• Meet the Consultants
• Meet the Interns
• Intern Profiles
• Collaborate
• Research Library
• News & Insights
• Program Contacts
• Databases & Datasets
• Research Guides
• Consultations
• Research Workshops
• Career Research
• Research Data Services
• Course Reserves
• Course Research Guides
• Material Loan Periods
• Fines & Other Charges
• Document Delivery
• Interlibrary Loan
• Equipment Checkout
• Print & Scan
• MBA & MSx Students
• PhD Students
• Other Stanford Students
• Faculty Assistants
• Research Assistants
• Stanford GSB Alumni
• Telling Our Story
• Staff Directory
• Site Registration
• Alumni Directory
• Alumni Email
• Privacy Settings & My Profile
• Success Stories
• The Story of Circles
• Support Women’s Circles
• Stanford Women on Boards Initiative
• Alumnae Spotlights
• Insights & Research
• Industry & Professional
• Entrepreneurial Commitment Group
• Recent Alumni
• Half-Century Club
• Fall Reunions
• Spring Reunions
• MBA 25th Reunion
• Half-Century Club Reunion
• Faculty Lectures
• Ernest C. Arbuckle Award
• Alison Elliott Exceptional Achievement Award
• ENCORE Award
• Excellence in Leadership Award
• John W. Gardner Volunteer Leadership Award
• Robert K. Jaedicke Faculty Award
• Jack McDonald Military Service Appreciation Award
• Jerry I. Porras Latino Leadership Award
• Tapestry Award
• Student & Alumni Events
• Executive Recruiters
• Interviewing
• Land the Perfect Job with LinkedIn
• Negotiating
• Elevator Pitch
• Email Best Practices
• Resumes & Cover Letters
• Self-Assessment
• Whitney Birdwell Ball
• Margaret Brooks
• Bryn Panee Burkhart
• Margaret Chan
• Ricki Frankel
• Peter Gandolfo
• Cindy W. Greig
• Natalie Guillen
• Carly Janson
• Sloan Klein
• Sherri Appel Lassila
• Stuart Meyer
• Tanisha Parrish
• Virginia Roberson
• Philippe Taieb
• Michael Takagawa
• Terra Winston
• Johanna Wise
• Debbie Wolter
• Rebecca Zucker
• Complimentary Coaching
• Changing Careers
• Work-Life Integration
• Career Breaks
• Flexible Work
• Encore Careers
• D&B Hoovers
• Data Axle (ReferenceUSA)
• EBSCO Business Source
• Global Newsstream
• Market Share Reporter
• ProQuest One Business
• Student Clubs
• Entrepreneurial Students
• Stanford GSB Trust
• Alumni Community
• How to Volunteer
• Springboard Sessions
• Consulting Projects
• 2020 – 2029
• 2010 – 2019
• 2000 – 2009
• 1990 – 1999
• 1980 – 1989
• 1970 – 1979
• 1960 – 1969
• 1950 – 1959
• 1940 – 1949
• Service Areas
• ACT History
• ACT Awards Celebration
• ACT Governance Structure
• Building Leadership for ACT
• Individual Leadership Positions
• Leadership Role Overview
• Purpose of the ACT Management Board
• Contact ACT
• Business & Nonprofit Communities
• Reunion Volunteers
• Ways to Give
• Fiscal Year Report
• Business School Fund Leadership Council
• Planned Giving Options
• Planned Giving Benefits
• Planned Gifts and Reunions
• Legacy Partners
• Giving News & Stories
• Giving Deadlines
• Development Staff
• Submit Class Notes
• Class Secretaries
• Board of Directors
• Health Care
• Sustainability
• Class Takeaways
• All Else Equal: Making Better Decisions
• If/Then: Business, Leadership, Society
• Grit & Growth
• Think Fast, Talk Smart
• Spring 2022
• Spring 2021
• Autumn 2020
• Summer 2020
• Winter 2020
• In the Media
• For Journalists
• DCI Fellows
• Other Auditors
• Academic Calendar & Deadlines
• Course Materials
• Entrepreneurial Resources
• Campus Drive Grove
• Campus Drive Lawn
• CEMEX Auditorium
• King Community Court
• Seawell Family Boardroom
• Stanford GSB Bowl
• Stanford Investors Common
• Town Square
• Vidalakis Courtyard
• Vidalakis Dining Hall
• Catering Services
• Policies & Guidelines
• Reservations
• Contact Faculty Recruiting
• Lecturer Positions
• Postdoctoral Positions
• Accommodations
• CMC-Managed Interviews
• Recruiter-Managed Interviews
• Virtual Interviews
• Campus & Virtual
• Search for Candidates
• Think Globally
• Recruiting Calendar
• Recruiting Policies
• Full-Time Employment
• Summer Employment
• Entrepreneurial Summer Program
• Global Management Immersion Experience
• Social-Purpose Summer Internships
• Process Overview
• Project Types
• Client Eligibility Criteria
• Client Screening
• ACT Leadership
• Social Innovation & Nonprofit Management Resources
• Develop Your Organization’s Talent
• Centers & Initiatives
• Student Fellowships

What caused Dubai floods? Experts cite climate change, not cloud seeding

• Medium Text

DID CLOUD SEEDING CAUSE THE STORM?

CAN'T CREATE CLOUDS FROM NOTHING

Coming soon: Get the latest news and expert analysis about the state of the global economy with Reuters Econ World. Sign up here.

Reporting by Alexander Cornwell; editing by Maha El Dahan and Alexandra Hudson

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

World Chevron

Pro-Palestinian protesters arrested at Yale, Columbia cancels in-person classes

Police arrested dozens of people at a pro-Palestinian demonstration at Yale University on Monday, hours after Columbia University canceled in-person classes in response to protesters setting up tent encampments at its New York City campus last week.

• International edition
• Australia edition
• Europe edition

‘Children won’t be able to survive’: inter-American court to hear from climate victims

Historic hearing will receive submissions from people whose human rights have been affected by climate change

Julian Medina comes from a long line of fishers in the north of Colombia’s Gulf of Morrosquillo who use small-scale and often traditional methods to catch species such as mackerel, tuna and cojinúa.

Medina went into business as a young man but was drawn back to his roots, and ended up leading a fishing organisation. For years he has campaigned against the encroachment of fossil fuel companies, pollution and overfishing, which are destroying the gulf’s delicate ecosystem and people’s livelihoods.

He says there have been huge declines in the amount of fish he and others can catch – 70% in the past decade – leading to widespread hunger in an already poor region. “We are now getting fish below the minimum size, which are the ones that could have provided us with security in the future.”

Medina is angry at the fossil fuel companies that are taking over part of the coast and have caused oil spills, and angry at the authorities that license them and undermine community attempts to restore mangrove forests. He is also deeply concerned about how warming water is bleaching the coral reefs through which his prey swims.

“We see how industrial activity is affecting our entire ecosystem,” he says. “But we also know that climate change is affecting our environment. It is a struggle and we are trying to make it visible in order to be heard.”

Medina will be telling his story this week to a panel of judges in Barbados during the first part of a historic hearing on climate change by the inter-American court of human rights.

The inquiry was instigated by Colombia and Chile, which together asked the court to set out what legal responsibilities states have to tackle climate change and to stop it breaching people’s human rights.

The detailed request seeks clarity on many issues, including children’s and women’s rights, environmental defenders, and common but differentiated responsibilities – the idea that all countries have a role to play in tackling climate change but some should bear a bigger burden. As well as mitigating and adapting to climate change, it asks how states should tackle the inevitable loss and damage .

Although climate change affects the whole world, the two countries told the court that its effects are not experienced uniformly or fairly. Their request letter warns that people in Chile and Colombia already deal with the daily consequences of the climate emergency, including droughts, floods, landslides and fires.

“These phenomena highlight the need to respond urgently and based on the principles of equity, justice, cooperation and sustainability, with a focus on human rights,” they said.

Courts around the world are increasingly making the link between climate justice and human rights. This month, the European court of human rights ruled for the first time that weak government climate policies violated fundamental human rights.

But the global south is leading the way. The Costa Rica-based court was set up in 1979 to interpret and apply the US convention on human rights, a treaty ratified by members of the Organization of American States . Twenty states have accepted its jurisdiction, including most Latin American countries and several Caribbean islands. Neither the US nor Canada have done so.

It is the third international court tasked with providing an advisory opinion on climate change, alongside the international court of justice and the international tribunal for the law of the aea . Such opinions are highly influential and set the framework for future legal action.

However, the inter-American court is the only one focusing on human rights. In a previous opinion it recognised the right to a healthy environment and affirmed that states must protect human rights affected by environmental harm, even if it happens outside their borders.

That recognition was enforced in March, when it ruled that Peru had violated the right to a healthy environment of people living in the country’s “most contaminated town” .

“The inter-American court is generally known and sees itself as a court that is much more willing to innovate with the law and to draw on sources from around the world,” said Sophie Marjanac, the accountable corporations lead at environmental law charity ClientEarth who will be speaking at the Barbados hearing.

Unlike the other courts, the inter-American court accepts written submissions from organisations and individuals, and has invited many of these to its oral hearings.

The hearing will begin with statements from the governments of Chile, Colombia and Barbados, followed by Mexico and Vanuatu. The court will then hear from UN bodies, legal experts from the Americas and further afield, local and national campaign groups, trade unions and refugee organisations. The eclectic mix of speakers includes Grupo Energía Bogotá, a large regional gas company.

One key part of the opinion tackles intergenerational equity, and the court will hear directly from youth people.

Jovana Hoschitalek, 18, a teacher and Grenadian climate campaigner, has seen significant changes in her home island.

“The sea is rising, quite a few of our plants are dying and water is becoming more scarce,” she said. “Sooner or later the things that I have grown up with, my younger sisters aren’t going to be able to experience.”

Hoschitalek is preparing to tell the court about her experiences. “I want to try to tell them how important it is that the future generations can be seen because … children won’t be able to survive the harsh climate that will come if things don’t take a drastic change.”

Trina Chiemi, the founder of youth network Fast Action on Climate to Ensure Intergenerational Justice, hopes the hearing will be an empowering process. “With the inter-American court we’re able to share our voices directly, and they’re able to look and see the faces of the people that are affected.”

The court’s subsequent hearings in the Brazilian cities of Brasília and Manaus in May will include many more frontline stories from the climate crisis including people living in “sacrifice zones” in Chile, Bolivian women fighting to protect their local water supplies and Indigenous communities.

Medina and others are speaking at the hearing with the support of Asociación Interamericana para la Defensa del Ambiente (Aida), an environmental law organisation that works in Latin America.

“A lot of issues that are going to be raised may seem disconnected,” said Marcella Ribeiro, a senior human rights and environment attorney for Aida. “But what I think is really beautiful [about] hearing from environmental defenders and communities directly is that they can pinpoint where or how these environmental issues connect with climate change. For example, environmental degradation and their ability to adapt.”

Once the opinion is published, it will have direct influence on the countries that accept the court’s jurisdiction. Legal experts say it will be an authoritative source on the obligations of states to respond to climate change, potentially boosting action to cut greenhouse gas emissions, supporting adaptation measures and helping establish mechanisms to address loss and damage.

Campaigners also hope it will invigorate existing climate lawsuits and petitions, such as those currently stalled at the inter-American commission on human rights (the court’s sister organisation), and say it could form the basis for future domestic or regional climate litigation.

It could even be used by countries during arbitration claims in investor-state dispute settlements , many of which are brought by companies in extractive industries.

The opinion is expected to have an impact outside the Americas too, including on the ICJ’s pending advisory opinion.

“We in the territories know something about our environment, we know what is happening,” said Medina. “Many scientists come and study what is happening and they can give context. But we who have experienced the changes … it is very important that our voices are heard.”

• Climate crisis
• Human rights

More on this story

Far right’s rise in Portugal could threaten ambitious climate action

Weatherwatch: how reducing air pollution adds to climate crisis

Today in Focus How Swiss women won a landmark climate case for Europe

Copernicus online portal offers terrifying view of climate emergency

What’s really happening with emissions and the climate crisis in Australia

Weatherwatch: study makes green case for electric planes

The Audio Long Read Rage, waste and corruption: how Covid changed politics – podcast

Taxing the wealth of UK’s richest would raise billions for public services

Most viewed.