TRANSFORMATIVE SYNERGY:
Exploring
FOREWORD
TOM KELLY EXECUTIVE DIRECTOR & CEO AUTOMATION ALLEY
If Industry 4.0 had to be distilled into one word, this word would be connection. Connections provide the means for all Industry 4.0 technologies to operate. In manufacturing artificial intelligence (AI) would be useless without a connected sensor to pull data from and a data pool in the cloud. A distributed additive manufacturing network would cease to exist if its array of 3D printers were separated.
Connections may not be the flashiest topic in the pantheon of Industry 4.0 technology, but they are the most important. They are the building blocks, the rudimentary proteins that will build the body of our manufacturing base now and in the future.
Big data and the Internet of Things (IoT) are the two sides of the cyber-physical coin of connection. Big data represents the virtual data pool while IoT represents the physical means to collect and connect a system together. Each is worthy of its own roundtable discussion, but our recent roundtable focused on the magic that happens when they work together, and the work the industry must do to facilitate their growth.
Our first topic of discussion will be to identify the baseline of how big data and IoT are implemented across the manufacturing value chain. What are the
MAROUANE KESSENTINI
WINEGARDEN PROFESSOR, ASSOCIATE DEAN FOR RESEARCH AND GRADUATE STUDIES UNIVERSITY OF MICHIGAN - FLINT
innovative methods? What is the decision-making process across industry sectors on the degree of inclusion?
Secondly, it is a known fact with added cyber complexity in an operation comes added cyber threats. Data privacy, cyberattacks and even industrial espionage threaten to derail the gains of implementing big data and IoT. We need to identify these cybersecurity challenges and work together to eliminate them.
Another topic of note is what comes at the endpoints of big data and IoT. How are organizations analyzing their data pools? Are they deploying AI to help them? Are they able to make real-time decisions for operational efficiency with their findings? What is the line between decision making with an algorithm vs. human analysis?
Automation Alley, along with partners we’ve assembled from industry, academia and government, together can address these questions and drive progress. It is an honor and privilege to present this playbook and to work together toward our shared goal of prosperity through technological advancement.
A SPECIAL THANKS TO THOSE WHO CONTRIBUTED TO THIS REPORT
TOM KELLY EXECUTIVE DIRECTOR & CEO AUTOMATION ALLEY
JEFF HALL VICE PRESIDENTGLOBAL STRATEGIC SLAES OMRON
DWIGHT R. LEVENS, JR. CHIEF TECHNOLOGY AND INFORMATION OFFICER OAKLAND SCHOOLS
BRYAN MONTY AUTOMOTIVCE STRATEGIC ACCOUNT MANAGER - AMERICAS OMRON
GAURAV AGRAWAL FOUNDER & CEO SOOTHSAYER
MAROUANE KESSENTINI WINEGARDEN PROFESSOR, ASSOCIATE DEAN FOR RESEARCH AND GRADUATE STUDIES
UNIVERSITY OF MICHIGAN - FLINT
JESSICA BLACK FOUNDER & CEO BERRY CLEAN BRANDS, INC
Unifying Automation on the Factory Floor: A Data-First Approach
To overcome the challenges associated with data aggregation and automation silos, organizations must adopt a mindset that prioritizes unifying automation on the factory floor. By taking a holistic view of data, teams can identify and address these silos, working towards a single connection and single machine control unit. However, it is important to recognize that being data-first does not mean disregarding the potential costs of short-sighted or quick-fix data aggregation. Incompatible technologies can introduce additional risks to meeting production targets, leading to efficiency losses rather than productivity gains. To mitigate these risks and ensure successful real-time monitoring and data analytics, organizations have turned to open industrial protocols.
Leveraging Open Industrial Protocols for Efficiency and Security
Open industrial protocols such as EtherNet/IP™, EtherCAT®, CIP Safety™, Fail Safe over EtherCAT®, IO Link, MQTT, and OPC UA® provide a means to reduce complexity on the factory floor while seamlessly integrating with native automation systems. The standard OPC UA® server functionality on controllers enables open communications with field devices, meeting the communication needs of SCADA software. This embedded OPC UA® server supports connections from multiple clients simultaneously, ensuring efficient and secure data exchange. By leveraging the security features of OPC UA®, such as authentication and encryption, organizations can minimize unauthorized access and address potential cyber vulnerabilities. OPC UA®’s requirement for credentials adds an additional layer of protection against techniques like 'man in the middle' attacks. This becomes even more crucial when considering the power of high-resolution data and the potential consequences of a cyber-attack turning into a cyber-physical incident. The increasing adoption of OPC-UA Foundation Membership reflects the industry's recognition of this strategy as a solution to digitalization challenges.
Bridging the Gap for Enhanced Operational Efficiency
Deploying both data analytics and IoT technologies presents organizations with unprecedented opportunities to achieve real-time monitoring and improve operational efficiency. By leveraging these technologies, organizations can optimize workforce productivity, make informed decisions, and foster innovation throughout their factories. However, ensuring robust data security and privacy within the IoT ecosystem remains a critical challenge that must be addressed through a data-first mindset. By implementing the right strategies and leveraging open protocols, organizations can unlock the full potential of big data analytics and IoT, paving the way for a more efficient and confident future.
TRANSFORMATIVE SYNERGY:
Exploring Big Data and IoT
“When you have a fundamental grasp of the underlying technology, you can problem-solve your way through it. That is the key.”
With the global Industrial IoT market expected to reach $276.79 billion by 2029, there is significant pressure on business leaders to act promptly.
Leveraging Big Data and IoT
Big data and the IIoT promise great opportunities for the commercial manufacturing sector. Big data alone can uncover hidden patterns and insights that transform businesses and industries, from predicting consumer behavior to optimizing supply chain logistics.
Connecting these technologies creates a data exchange loop, where IoT devices collect vast amounts of real-time data and send it to big data analytics platforms for processing. This allows manufacturers to gain valuable insights and make more informed decisions.
Real-Time Monitoring
Combining big data and IIoT systems for real-time monitoring is particularly valuable. Predictive analytics allow manufacturers to prevent equipment failures before they occur. IoT sensors and machines connected to a central data hub provide a comprehensive, real-time view of the factory floor and beyond.
“When you have a fundamental grasp of the underlying technology, you can problemsolve your way through it. That is the key."
-George Cook Tarus
A Free Use Case for All SE Michigan Businesses on Big Data and Energy Consumption
By Grant Pinkos
Data is ubiquitous in the manufacturing industry, but how many manufacturers are taking advantage of existing data sources connected to their business? DTE usage data is a powerful and free tool to chart, evaluate, and act on your business data in Southeast Michigan. Combined with a free data plotting site, this data provides the opportunity for manufacturers to optimize energy usage. Here is a crash course on getting started:
Acquiring Information from DTE
Get your URL unique to your DTE account from DTEenergy.com, which will grab the hourly electricity usage for your DTE account for the past 426 days on a rolling basis.
Step 1: Log into your DTE account (can be Residential or Business)
Step 2: In the left side pane, click “Hourly Usage” or “Energy Usage Data”
Step 3: Click on Download or share usage data
Step 4: In the Generate Link, click Generate and then Copy Link
Step 5: Paste the Link into the URL box at the top of this free dashboarding tool created in Grafana. Using the time selector in the upper right, you can view your usage for any time period over the past 426 days.
You can also sign up for a free Grafana Cloud account (no credit card required) using this URL. If you need more guidance getting started, this 3-minute video is excellent, or if you need help building your DTE dashboard, feel free to email me directly
That's it! View this hourly data to make informed decisions about your business energy usage. See how much more electricity is used during the day vs. night, summer vs. winter, or weekdays vs. weekends. It is often the simplest data solutions that provide the most value.
Criminals are becoming more sophisticated, making robust cybersecurity measures crucial. This includes firewalls, encryption protocols, and frequent system updates.
"We all share the responsibility of safeguarding user data and ensuring its ethical use. This principle must be embedded within our companies,” said Jessica Black, Berry Clean Soaps Founder and CEO.
Storing and accessing large amounts of data also pose challenges. Traditional data storage methods may not handle petabyte-scale data. Cloud storage solutions offer scalability and flexibility but also raise security concerns.
“On the education side, there are many data points that influence, support, and impact our work. We have funding requirements that rely on data, student learning objectives and needs that are influenced by data,” said Dwight Levens Jr., Oakland Schools Chief Technology and Information Officer.
“Schools are in charge of managing the data of our most vulnerable human population—our kids. The applications we use gather data that is essential to the educational process, but there is also a significant amount of data that the IoT devices are able to capture that we want to protect.”
Implementing a secure data management plan is essential for both protecting sensitive information and ensuring efficient access. This involves choosing the right storage solutions and establishing protocols for data transfer, access control, and disaster recovery.
“Data management in education has many parallels to manufacturing,” Hall said. “The fundamentals are identical — vulnerability through internet access to take advantage of data with high risk to security.”
Organizations also need to prioritize employee education and training on cybersecurity best practices. Regular awareness sessions, phishing simulations, and strict password policies can help prevent costly crises.
“It is always a concern when giving any student data to industry through IoT devices or a software provider without the appropriate data privacy agreements in place and an understanding by our vendor partners of the responsibilities they must adhere to under COPPA,” Levens added.
“We need industry leaders to advocate for security over just functionality with IoT devices. Most IOT devices on the market have vulnerabilities that are easily exploited by bad actors. Everyone whether in the private or public sector would benefit from data being governed under aligned data governance standards.”
Big data and the Industrial Internet of Things are inevitable realities for the manufacturing sector. While some are excited by the potential enhancements, others remain skeptical of the associated risks and challenges. The key is to balance the benefits of big data and IoT with privacy and security concerns to ensure a successful Industry 4.0 transformation for all.
“We all share the responsibility of safeguarding user data and ensuring its ethical use. This principle must be embedded within our companies.”
- Jessica Black
Berry Clean Soaps
INDUSTRY PULSE
Automation Alley posted weekly polls in March 2024 for our LinkedIn followers of over 5,000 professionals in the technology and manufacturing ecosystem on the topic of big data in the industry. This is how the industry responded at a glance.
How likely are you to invest in Big Data/ IoT in your operations?
What percentage of equipment on the production floor of your company is connected to an IoT or Big Data device? Is Big Data/ IoT integration a top priority for your operation?
How confident are you in your cybersecurity to handle a fully interconnected facility with Big Data/IoT?
KEY TAKEAWAYS
Integration of Big Data and IIoT in Manufacturing: The combination of big data and the Industrial Internet of Things (IIoT) offers significant opportunities for the manufacturing sector by enabling real-time monitoring, predictive maintenance, and enhanced supply chain management. This integration allows for continuous data collection and analysis, leading to more informed decision-making and improved operational efficiency
Importance of Data Quality: High-quality data is crucial for the effectiveness of Big Data and IIoT systems. Accurate and complete data ensures reliable insights and decisions, while poor data quality can lead to incorrect predictions and inefficiencies. Ensuring data veracity through regular monitoring and quality assessments is essential for maintaining the integrity and usefulness of the collected data.
Cybersecurity and Data Management Challenges: As data becomes increasingly valuable, robust cybersecurity measures are essential to protect against sophisticated threats. Organizations need to implement secure data management plans, including appropriate storage solutions, data transfer protocols, and employee training on cybersecurity best practices. Balancing the benefits of Big Data and IIoT with privacy and security concerns is crucial for a successful transition to Industry 4.0.
Building IoT Ecosystems and Data Proficiency in Education: It is essential for academic institutions to immerse students in IoT-related technologies from an early age, integrating disciplines to provide a comprehensive understanding of IoT ecosystems. This includes teaching the basics of hardware, software, data collection, and interpretation, progressively moving to more complex aspects like sensors, cloud systems, and cybersecurity. Hands-on, trial-and-error learning is emphasized to prepare students for careers in automation and technology.
Enhancing Public Services through IoT and Data: Governments at all levels can significantly improve public services by leveraging IoT to collect, store, and analyze data. This approach enables efficient resource allocation and informed decision-making. For example, local governments can use traffic accident data to deploy police more effectively, state governments can maintain and improve road infrastructure based on traffic data, and federal initiatives like the U.S. Census can use comprehensive data to allocate financial resources accurately
Supporting Long-Term Decision-Making with Predictive Analytics: Data-driven insights are essential for making informed long-term decisions that impact public welfare and resource management. Governments can use predictive analytics to plan and execute projects aimed at reducing carbon emissions, improving air quality, and maintaining infrastructure. Ensuring equity in service delivery, predictive analytics must be free of bias and involve diverse perspectives, facilitating transparent and fair resource allocation to underserved communities.
Building an Effective IoT Ecosystem: Developing a reliable IoT ecosystem is crucial for maximizing decisionmaking and efficiency in manufacturing and technology firms. Key components include a scalable and adaptable cloud, highquality connected devices, secure gateways, and robust cybersecurity measures. The cloud must handle increasing processing power and memory needs, while devices range from smartphones to AI sensors on the production line. Ensuring strong connections reduces lag time and work stoppages. Cybersecurity is vital, requiring regular audits and extending protections to every device to safeguard personal information and trade secrets.
Enhancing Decision-Making and Efficiency with AI: Free-flowing data within the IoT ecosystem enables real-time decisions and boosts efficiency. AI can monitor production, predict staffing and supply chain issues, and automate tasks such as ordering supplies and detecting faulty parts. However, AI complements rather than replaces human decisionmaking. Industry leaders should integrate AI recommendations with their expertise to maintain robust business practices. AI-driven decision-making involves three degrees: decision support (combining human intelligence with data insights), decision augmentation (recommending alternatives using predictive analytics), and decision automation (leveraging AI for consistent and scalable decisions).
Collaborating and Sharing Data with Other Industries: Sharing data between companies is essential for seamless business operations, especially in partnerships or joint ventures. Manufacturers need access to suppliers for uninterrupted production, but sharing more detailed data (e.g., staffing, production, financials) requires careful consideration. Companies must determine necessary data, ensure cybersecurity, respect privacy laws, and ensure technological compatibility. Conducting cybersecurity audits, possibly by third-party firms, is crucial to protect all parties' data. Effective data-sharing enhances insights into demographics, safety, supply chain efficiency, and compliance, benefiting all involved companies. 1 2 3 4 5 6 7 8 9
In today's rapidly evolving manufacturing landscape, the integration of the Internet of Things (IoT) is no longer a luxury but a necessity. A connected business, leveraging IoT, captures and utilizes real-time data to enhance decision-making processes and maintain a competitive edge. By harnessing this data, companies can optimize manufacturing speed, identify and address defective parts, allocate staffing efficiently, manage assets, streamline product delivery, and monitor supply chain status, among other things. Developing a robust IoT ecosystem is crucial for manufacturing firms aiming to maximize efficiency and connect seamlessly with other industries. Embracing Industry 4.0 through IoT not only drives operational excellence but also ensures sustained competitiveness in an increasingly interconnected world. Below are key considerations for manufacturers on their Industry 4.0 adoption journey:
Create an Effective IoT Ecosystem
The IoT ecosystem is paramount for modern manufacturing. This involves developing a robust and efficient network system that connects seamlessly to physical equipment. Key components of such an ecosystem include scalable cloud infrastructure, high-quality device connections, and stringent cybersecurity measures.
Scalable and Adaptive Cloud: The cloud must be scalable and adaptive to meet the growing demands of production systems. By ensuring the cloud can handle increased processing power and memory, companies can save money by avoiding the need for new systems as the business expands. An adaptable cloud also allows realtime adjustments to production demands as workloads fluctuate.
Reliable Device Connections: Devices, ranging from supervisors' smartphones to AI sensors guiding robots on the production line, are crucial for sending and receiving critical data. A high-quality connection within the IoT ecosystem minimizes lag time and potential work stoppages, keeping decision-makers informed about manufacturing output, material usage, and staffing requirements.
Efficient Gateway Communication: The gateway facilitates communication between the cloud, devices, users, AI systems, and data analysis applications. Unlike simple home devices, industrial gateways must be fast, capable of handling large data volumes, and secure.
Robust Cybersecurity: Given the immense amounts of sensitive information transported and housed within the IoT ecosystem, employing the latest cybersecurity software and protocols is imperative. Regular audits of the entire defense system and extending cybersecurity measures to every device are crucial to protect the system from potential threats.
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Leverage Data for Decision-Making and Efficiency
The free flow of data within the IoT ecosystem enables real-time decisionmaking, significantly enhancing operational efficiency.
AI-Driven Insights: AI monitors efficiency, predicts staffing needs, identifies supply chain issues, and can autonomously address some production-related decisions. For example, AI can automatically reorder supplies based on inventory levels and detect faulty parts on production lines.
Human-AI Collaboration: While AI provides valuable recommendations and data analysis, human expertise remains essential. Industry leaders must combine AI insights with their knowledge to maintain robust business practices.
Olivia Barber, in her October 2023 article, discusses the “three degrees of decision-making” with AI:
Decision Support: Predictive, diagnostic, or descriptive analytics aid humans in making accurate decisions by combining data-driven insights with human intelligence and expertise.
Decision Augmentation: Predictive or prescriptive analytics recommend multiple decision alternatives, enabling rapid analysis of large data volumes and reducing complexity.
Decision Automation: Prescriptive or predictive analytics allow for scalable, fast, and consistent decision-making, enhancing operational efficiency.
Prioritize Workforce Training
As manufacturing firms integrate IoT and big data into their operations, workforce training emerges as a critical component for successful implementation and sustained competitiveness. The rapid evolution of technology necessitates a skilled and adaptable workforce capable of leveraging these advancements effectively.
Skill Development: Employees must be trained to understand and operate IoT devices and systems. This includes training on how to use new tools, interpret data, and integrate AI-driven insights into daily operations. Ensuring that staff are proficient with the latest technology reduces errors, improves productivity, and maximizes the potential of the IoT ecosystem.
Continuous Learning: The fast-paced nature of technological advancements means that workforce training cannot be a one-time event. Ongoing education and training programs are essential to keep employees updated on new technologies, cybersecurity practices, and data analytics methods. Regular workshops, certifications, and training sessions help maintain a knowledgeable and agile workforce.
As manufacturing firms integrate IoT and Big Data into their operations, workforce training emerges as a critical component for successful implementation and sustained competitiveness.
Cross-Functional Training: As IoT and big data touch various aspects of manufacturing, cross-functional training ensures that employees from different departments understand how their roles intersect with technology. This fosters collaboration and a holistic understanding of the entire IoT ecosystem, leading to more cohesive and efficient operations.
Change Management: Transitioning to an IoT-driven environment involves significant changes in processes and workflows. Effective change management strategies are necessary to help employees adapt to new ways of working. This includes clear communication, support systems, and involvement of employees in the implementation process to ease the transition and build a culture of innovation.
Safety and Compliance Training: With the increased reliance on connected devices and data, ensuring that employees are trained in safety protocols and compliance standards is crucial. This includes understanding the cybersecurity measures in place, recognizing potential threats, and knowing how to respond to security incidents.
Connect with Other Industries
Sharing data between companies is vital for seamless business operations. Manufacturers need immediate access to suppliers to keep production lines moving, with AI playing a key role in automating parts and material orders.
Controlled Data Sharing: Companies can limit outside vendors' access to their internal systems, ensuring only necessary data is shared. However, deeper partnerships may require sharing more sensitive information, such as staffing numbers, production output, financials, and market entry plans.
Data Sharing in Joint Ventures: In joint business ventures, sharing insights on demographics, safety procedures, supply chain efficiency, and government compliance is essential. This data helps improve production systems and customer services, making the venture successful.
Cybersecurity in Partnerships: Before engaging in a data-sharing partnership, companies must conduct cybersecurity audits to protect all parties' data. Hiring a third-party cybersecurity firm can provide an unbiased report on each company's preparedness, ensuring robust data protection measures are in place.
High school graduates should have a solid foundation in working with and maintaining the IoT. Colleges and universities are institutions that can expand that knowledge to refine previous skills and aid the students in developing creative solutions to improve existing ecosystems while collecting and interpreting varied data sets.
Imagine a collaboration of college students from several academic disciplines using the IoT to collect, store, and analyze massive amounts of data to study the detrimental impact of climate change on underserved communities. The project could examine the statistics on health, crumbling infrastructure, air quality, sustainability efforts, and government-funded green initiatives. After a comprehensive analysis of the data, students from the social sciences, engineering, education, political science, and technology can collaborate to provide solutions to the problem. With the cooperation of local community leaders, new strategies, processes, and regulations could be affected by the results of these kinds of projects conducted by students.
Digging deeper into data
Information fuels the IoT, and accessing quality data is essential for maintaining a robust and efficient ecosystem. Ensuring data is free from bias requires a meticulous approach: identifying sources, defining criteria, applying specific collection methods, and thoroughly reviewing outcomes. The key to unbiased data lies in interpreting and communicating results objectively and accurately, while also acknowledging and addressing its potential limitations, uncertainties, and ethical implications, and actively seeking feedback.
Mastering data acquisition is a vital skill that transcends from higher education to the workforce, creating well-rounded candidates in all industries. Identifying relevant data for a project is crucial, as multiple factors often come into play.
For instance, a company aiming to introduce its products into a new market must consider more than just current availability. They need to analyze age demographics, disposable income, distribution avenues, supply chain logistics, workforce increases, and the impact on the local economy and culture.
Academic institutions can leverage such real-world scenarios to engage students from various disciplines in data analysis projects. By examining data from multiple perspectives, students can uncover critical insights for informed decision-making. These projects encourage robust discussions and collaborative problem-solving.
Additionally, varying the initial data, such as changing location, population demographics, resource availability, or product familiarity, can further enrich learning. Each alteration offers new outcomes and discussions on the potential impacts on company profitability, market influence, and community effects. This dynamic approach to data analysis prepares students to tackle complex, multifaceted challenges in their future careers.
Encouraging imagination and creativity
The IoT offers opportunities for students to unleash their imaginations and creativity. Data collection extends beyond merely purchasing sets from vendors or uploading sensor readings from production lines for AI analysis. It encompasses CAD, 3D imaging, virtual reality, digital twins, graphic design, sound engineering, programming, and virtual world testing.
Students can stretch their creative boundaries by developing virtual simulations of landscapes on other planets, space stations, rockets, missiles, the ocean floor, roller coasters, and video games. Each simulation generates new data to analyze, prompting students to return to the virtual drawing board to enhance structural integrity, lower energy costs, reduce waste, and refine overall design.
These creative projects are not just engaging exercises—they have tangible real-world applications. They offer pathways into various industries, including space exploration, aerospace, shipbuilding, electronics, and engineering.
By educating students on the IoT and data, we equip them with essential skills for a competitive job market, enabling them to excel in diverse fields. Encouraging creativity within the IoT framework fosters innovative thinking and prepares students for dynamic careers where they can make significant contributions.
10 Unique Careers Dependent on the IoT and Data
Smart City Planner
Designs and implements IoT solutions for urban development, enhancing infrastructure, transportation, and public services through data-driven insights.
Healthcare Data Analyst
Utilizes IoT devices to collect and analyze patient data, improving diagnosis, treatment plans, and patient outcomes in healthcare settings.
Agricultural Technologist
Implements IoT solutions in farming to monitor soil conditions, crop health, and equipment, optimizing agricultural productivity and sustainability.
Industrial IoT Engineer
Develops and maintains IoT systems in manufacturing, enhancing efficiency, reducing downtime, and improving safety through data analysis.
Cybersecurity Specialist
Focuses on securing IoT devices and networks, protecting sensitive data from breaches and cyber-attacks through advanced security protocols and data monitoring.
Environmental Data Scientist
Uses IoT sensors to monitor environmental conditions, analyzing data to address climate change, pollution, and natural resource management.
Supply Chain Analyst
Employs IoT technology to track goods in real-time, optimizing supply chain logistics, reducing costs, and improving delivery times.
Connected Vehicle Engineer
Designs and tests IoT systems in autonomous and connected vehicles, enhancing safety, navigation, and user experience through real-time data.
Smart Home Architect
Creates integrated IoT solutions for home automation, improving energy efficiency, security, and convenience in residential environments.
Wearable Technology Developer
Develops IoT-enabled wearable devices that track health metrics, activity levels, and other personal data, providing insights for users and healthcare providers.
Recommendations for Government
Governments should support the development and adoption of industry standards for big data and IoT technologies.
Government can play a crucial role in facilitating the adoption of Industry 4.0 in advanced manufacturing through various strategies and initiatives. Here are some ways the government can help industry and academia, as well as how it can use these technologies internally:
Funding Research and Development (R&D)
Governments should provide grants and funding for R&D projects focused on IoT technologies and big data. This can help develop new applications and improve existing technologies. Governments can also play a leading role by facilitating collaborations between academic institutions and industry through co-funding initiatives. These partnerships can foster innovation and accelerate the commercialization of new technologies.
Driving Standardization and Regulation
Governments should support the development and adoption of industry standards for big data and IoT technologies. This can ensure interoperability, security, and reliability across different platforms and devices. They can create a regulatory environment that encourages innovation while ensuring data security and privacy. Clear regulations can provide certainty and build trust among industry players.
Prioritize Infrastructure Development
A digital infrastructure is critical to fostering digital transformation. Governments must invest in high-speed internet, 5G networks, and other digital infrastructure to support the widespread adoption of IoT and digital twin technologies. Testbeds and Innovation Hubs will be critical for researchers to test and develop their technologies in real-world conditions.
Invest in Education and Workforce Development
Governments should collaborate with educational institutions to develop curricula and training programs that equip students with the necessary skills in smart manufacturing. It’s important to allocate funding for reskilling and upskilling programs to help the current workforce adapt to new technologies. In addition, governments can play a leading role in spreading awareness to high-tech manufacturing jobs through marketing efforts and organizing conferences, workshops, and seminars to raise awareness about the benefits and applications of big data and IoT technologies in manufacturing.
PLAYBOOK SOURCES
Power and Prediction: The Disruptive Economics of Artificial Intelligence https://store.hbr.org/product/power-and-prediction-the-disruptive-economics-of-artificial-intelligence/10580
OPC Foundation - The OPC Foundation Welcomes Procter & Gamble as its 900th Member https://opcfoundation.org/news/press-releases/the-opc-foundation-welcomes-procter-gamble-as-its-900th-member/
McKinsey - Big data: The next frontier for innovation, competition, and productivity https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation
World Economic Forum - How much data is generated each day? https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/
International Data Corporation - IDC Forecasts Worldwide Sovereign Cloud Spending to Reach More Than $250 Billion in 2027 https://www.idc.com/getdoc.jsp?containerId=prEUR251542423
Log-in for persoanl or business DTE account https://dteenergy.com/
Grafana Cloud Account sign-up https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fplay.grafana.org%2fd%2fcdls2gbzayk1sa%2fdte-energy-usage-data%3forgId%3d1&c=E,1,N4lfN xEhcTKLGkhCrHHzuagY6d1c8Djr3Gvrbk3fSO5UoHltmt3xpbV0PJ_
Building your DTE dashboard tutorial https://youtu.be/nVdeKPRYmmQ?si=gy2bm9sPGVXhO9aG
Direct email to Grant PInkos: grant.pinkos@gmail.com
Business Wire - Industrial IoT Market Report 2022: Rising Investments in Industry 4.0 https://www.businesswire.com/news/home/20230131005829/en/Industrial-IoT-Market-Report-2022-Rising-Investments-in-Industry-4.0-TechnologiesBolsters-Growth---ResearchAndMarkets.com
Grand Canyon University - What Is Veracity’s Meaning in Big Data? https://www.gcu.edu/blog/engineering-technology/what-veracitys-meaning-big-data
In Data Labs - How artificial intelligence will change decision making https://indatalabs.com/blog/artificial-intelligence-decision-making
This content is based upon work supported by the Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response (CESER) under Award Number(s) DE-CR0000023.
This content was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
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