OT Asset Management: Securing Maximum Agility
Asset visibility is essential to maintaining a secure operational environment while also providing real-time insights.
Asset visibility plays a vital role in operational technology cybersecurity. When organizations continuously inventory and classify assets in their facilities, it becomes much easier to protect the entire OT environment.
“Asset management is really the foundational layer of cybersecurity, and it’s a critical component for effective communication between executives and operations staff,” said Ben Miller, Dragos Vice President of Services. “When executives hear about new OT threats in the wild that could impact anything from gas crackers to safety instrumented systems, how can they even get their arms around the relevance of these emerging OT risks to their business if they don’t have an inventory of assets identified and classified?”
Effective asset management enables teams to discover latent vulnerabilities, insecure configurations, and rogue assets. With a comprehensive asset inventory, security teams can determine if new threats apply to their environments more quickly, and they can respond faster to security incidents as they unfold. A clear OT asset portfolio gives decision-makers better information for planning their cybersecurity roadmaps and complying with security and safety regulations. Plus, once assets are properly managed, performance and efficiency can be analyzed and improved, maximizing uptime and profitability.
Unfortunately, many organizations struggle to get a clear view of the OT assets running in their industrial facilities. According to the Dragos 2021 Year in Review report, 86% of services customers have extremely limited or no visibility into the assets in their OT.
“With a comprehensive asset inventory, security teams can determine if new threats apply to their environments more quickly, and they can respond faster to security incidents.”
“Some of the visibility challenges are probably technology-related, but there’s also a need to broaden asset owners’ definitions of what should be considered within an asset inventory,” said Miller. “Comprehensive asset inventories need to include all the components and devices that support your operational process, whether those components are physical or virtual, software or hardware; and it’s not just about noting the existence of the asset.
“To make the data actionable, you need to capture and regularly update its version, firmware status, and configuration state,” Miller said. “Beyond each individual asset, it’s also critical to understand the relationships they have with one another and the communication pathways they establish inside and outside of the organization.”
An effective asset management program enables asset owners and cybersecurity teams to efficiently:
- Discover, identify, and classify OT assets correctly
- Create and continuously update an asset inventory
- Operationalize asset visibility by leveraging its benefits to increase uptime and profitability
From a cybersecurity perspective, continuous OT asset visibility capabilities make it possible to discover connectivity and communications channels operators didn’t even know existed; pinpoint active threats operating quietly in the environment; and identify insecure configurations, latent vulnerabilities, and rogue assets.
In a recent whitepaper, Dragos identified 10 ways that asset visibility builds the foundation for effective OT cybersecurity:
- Asset visibility and management facilitates an understanding of what “normal” means in your environment.
- A well-structured program verifies all OT assets, including those belonging to the Industrial Internet of Things.
- You’ll be able to identify and visualize asset relationships and communication pathways.
- Security teams can detect threats with high signal and low noise ratios.
- You’ll spot rogue assets that you didn’t realize were on your networks.
- An asset inventory provides critical information for incident response.
- Managing assets properly enables efficient mitigation of vulnerabilities and threats.
- Configuration detection can help to supplement change management.
- Compliance reporting will be easier and more clear-cut.
- A well-executed program will help you justify security investments and plan cyber roadmaps.
A Successful Path to Asset Management
An OT-specific, methodical approach toward data collection and asset inventory creation is critical for a successful asset management program. Recording important information, such as software version, physical location, asset owner, and priority, enables many cybersecurity and performance optimization activities.
“Operational technology environments are increasingly targeted by adversaries who try to weaponize organizations’ own hardware and software against them to disrupt industrial process controls. Implementing forward-thinking cyber strategies can help deter, detect and mitigate such threats,” said Ramsey Hajj, Global Cyber OT Leader with Deloitte & Touche LLP. “Cyber threat identification, detection, and prevention controls can help address OT security risks with steps to increase device visibility, segment OT networks, monitor security for the OT environment, correlate security information from OT and IT networks, and establish security operations centers to support ongoing, proactive efforts.”
“Organizations that want to develop an OT-specific, methodical approach to asset management will need a structured plan to determine and execute data collection requirements.”
IT has a long history of asset management and asset inventorying, so the tools, frameworks, and practices around gaining asset visibility are very well tuned to IT use cases. However, OT has unique environmental challenges that need to be managed across industrial assets — and IT tools, integrations, and processes are not designed to meet these requirements. A few simple examples of IT asset visibility tools and tactics that don’t translate well to the OT environment include:
- IT might utilize forced reboots of desktop computers for patch installations, but in an industrial environment, rebooting a workstation could result in weeks of unplanned downtime and introduce significant safety risks.
- You cannot put an agent on a PLC, because they often run firmware or operating systems that are not compatible with agents.
- An IT administrator who performs a network scan using NMAP in an industrial environment runs the risk of knocking sensitive devices like older controllers offline, disrupting production.
- In traditional IT environments, it would be perfectly normal to use active scanning tools for asset discovery and monitoring, but in industrial scenarios, passive techniques are often preferred – if not required – because they’re much safer.
Organizations that want to develop an OT-specific, methodical approach to asset management will need a structured plan to determine and execute data collection requirements. One resource that many OT asset owners use to guide their development of an asset management program is the Collection Management Framework for ICS Security Operations and Incident Response.[PB1] It provides a prescriptive, impact-driven reference based on years of customer experience that’s uniquely suited for the realities of the OT environment.
Whether organizations leverage the Collection Management Framework or some other method, having a plan that’s uniquely suited to OT environments is key.
It’s Not Just About Security
Asset management provides the foundation for a more secure facility, and it’s also the first step toward real-time insights, end-to-end visibility, and scalable solutions to manufacturing challenges. Smart manufacturing solutions can create insights and augment human intelligence with artificial intelligence to help overcome complex challenges, address key business objectives, and boost visibility and performance across the digital supply network. Predicting machine downtime by analyzing performance trends and actively managing the workforce to track worker safety and performance data are two proven benefits.
According to a 2019 Deloitte and MAPI study, 86% of manufacturers believe that smart manufacturing solutions will be the main driver of competitiveness in five years, but the transformation of legacy operations can be daunting. Transforming the facility requires collaboration between manufacturing, supply chain, and IT. New technologies should be adopted and the organization should focus on becoming more insight-driven.
“Transforming the facility requires collaboration between manufacturing, supply chain, and IT. New technologies should be adopted and the organization should focus on becoming more insight driven.”
Deloitte, Dragos and an ecosystem of solution providers, technology innovators, and academic researchers are working together to demonstrate how smart manufacturing solutions can transform enterprises. One such endeavor is The Smart Factory @ Wichita, an experiential center with a fully functioning manufacturing production line where asset owners can be immersed in custom simulations to understand business challenges and see first-hand how cybersecurity is integrated.
“The benefits of effective asset management are significant – this isn’t just a conversation about cybersecurity, although that’s certainly one of the most important benefits,” Miller said. “Asset management historically was manual and tedious work, but continuous and automated monitoring enables higher accuracy, increased productivity, and more agility in your operations. That’s a big upside, especially given its critical role in managing risk across cyber and safety domains.” M
About the author:
Jennifer Halsey is Senior Industry Marketing Manager at Dragos, Inc.
Smart Factories: Finding Greater Value in the Ecosystem
To ensure adaptability, a smart factory should be part of a broader digital transformation strategy for a manufacturer.
The smart factory, with its digital production model, is gaining momentum, driven by a combination of forces that includes global disruptions and instabilities, supply chain disruptions, and heightened customer demands for digital-first experiences. Usually, when organizations think of their objectives for digital transformation, they tend to lean toward production optimization or cost reductions. However, the success of real factory transformation comes from transforming the way companies capture and provide value to their customers.
Organizations embarked on a smart factory journey that has scaled beyond the pilot phase have experienced unprecedented increases in operational efficiency as a result of greater agility in their operations. However, most companies appear to be stuck in “pilot purgatory.” There may be several reasons for this, ranging from lack of leadership, cultural aspects, workforce readiness, and legacy and disparate systems, to a lack of process standardization as well as cybersecurity risks.
Many manufacturers are already leveraging components of a smart factory in areas such as gaining visibility in production operations using Manufacturing Execution Systems (MES), planning and scheduling using real-time production and inventory data, and leveraging augmented reality for maintenance. But a true smart factory is a more holistic endeavor, moving beyond the shop floor toward influencing the enterprise and the broader ecosystem. The smart factory should be an integral part of a broader digital transformation strategy for an organization. That strategy should have multiple elements that manufacturers can leverage to adapt to the changing marketplace more effectively.
The strategic importance of the smart factory is undeniable as early adopters have reported greater agility in their operations, serving their customers better, and driving more to the bottom line. NTT DATA and Oxford Economics surveyed 528 business and IT executives recently. We found that manufacturing organizations of all kinds are prioritizing revenue growth, cost reduction, increased resiliency, and innovation for the three years ahead. In this article, we will discuss the following aspects of the smart factory:
- Key considerations for smart factories and enablers
- Maturity models, assessments, and framework; how to start, scale, and transform
- Use cases
Key Considerations for Smart Factories and Enablers
1. Data Mastery and Analytics – A foundational pivot for smart factories is data mastery. Data mastery involves the use of data and analytics to find insights that help organizations become more efficient and pursue new business opportunities effectively. Data mastery is about more than building data lakes or empowering senior leaders to make better decisions. It is also about structured and unstructured data flowing through organizational processes to enable decisions at the edges of an enterprise.
In today’s manufacturing environment, accessing, aggregating, and analyzing data remain primary challenges. Manufacturing data come in many different formats (both structured and unstructured) and from many different sources: smart sensors, PLCs, DCSs, HMIs, MESs, motion control systems, vision systems, historians, completed work records, operator and maintenance logs, quality records, automation protocols, batch reports, energy meters, spreadsheets, databases, and a host of other sources. To meet these challenges, a well-orchestrated common data framework that is designed to ingest, aggregate, and condition the entire variety of data from the factory floor and then channel it to the company’s AI Data Pipeline for analysis is critical.
What’s essential to meet this critical need is a mechanism to manage data coming from the manufacturing shop floor. First, the data must be contextualized automatically using artificial intelligence, including an expert system, machine learning (ML) classifiers, and highly sophisticated ML algorithms. Next, a factory/plant digital twin must be generated and configured to mirror the actual manufacturing production lines. The digital twin, a dynamic, empirical mirror of the factory production systems, machines, equipment, and processes, produces a real-time semantic context for all product, production, and process data. Analytics can be performed on real-time data streaming from sensors, machines, and production systems to determine state condition and support best practices based on quality reporting, engineering diagnostics, and KPIs for asset performance Using analytics, users can design new reports and dashboards that address predictive maintenance, process optimization, root cause analysis, statistical process control, KPIs, and process deviation, among other data points. The analyzed data can be visualized as real-time, actionable information for every machine, line, and plant across an enterprise in contextualized dashboards that are designed to present the right information to the right people.
Cybersecurity – As more IoT devices are connected to networks, potential attack surfaces can increase, along with risk from compromised devices. These attack surfaces may not be exploitable or vulnerable today but may be easily exploited in months or years to come. Thus, leaving devices unpatched and connected to the network is not likely feasible. The responsibility of securing these devices should not lie solely with those who deploy the connected device; instead, the responsibility should be shared with the device manufacturers, who may be best positioned to implement the most effective security. Historically, OT networks have typically been implemented quickly without proper precautions being taken to electronically safeguard the industrial control assets they contain. Consequently, they are inherently full of risks, which are often not assessed, categorized, or even known and understood. Gartner reported that typical cybersecurity incident losses range from under $50K to over $100M with an average cost of roughly $3M and reported payments for ransomware incidents ranged from under $100K to more than $2M, with an average cost of roughly $520K. Additionally, organizations took an average of 170 days to detect an incident and an average of 66 days to investigate an incident. The time to remediate was on average about 80 days.
To mitigate the risk of cybersecurity, organizations must adopt a holistic approach across lifecycle assessment, implementation, and management.
Figure 1. Cybersecurity methodology
2 Digital Technologies – Smart factories typically integrate functions beyond the factory floor. While the Smart Factory focus mostly spans across operations, quality, sustainability, safety compliance and technology risk mitigation, it also integrates with the ERP, CRM, PLM, and other value chain applications. Ecosystem and sustainability are beginning to play a very prominent role in the evolving smart manufacturing priorities.
Figure 2. A variety of digital technologies are being leveraged to enable transformation in these functions:
Some of the technology considerations and capabilities to address smart factories from an IT and digital perspective are listed below.
Figure 3. Typical stack and technology considerations to keep in mind.
3 Leadership, Organization, Culture and People – Factory Initiatives need a higher level of engagement to manage change. When a factory becomes smart, and front line and management decisions are more data driven, product lines are getting smarter, the traditional corporate silos are being broken down and replaced with flatter, more collaborative organizational structures and monolithic entities are being replaced by more federalized, distributed, and agile value-chain ecosystems, it requires both a top down and bottom-up approach to manage change, culture, training, and communication across the enterprise.
Figure 4. Gartner found that the top three challenges for Chief Data Officers all had to do with concerns about the required culture and skills to achieve their goals.
Prioritize support at a leadership level as well as on the ground to gain organizational buy-in and ensure adoption. It also requires diverse teams with a broad variety of skill sets. The leadership or the chief digital officer must identify and deploy skill sets required to support the initiative such as engineering, master data management, analytics, edge, and data science to ensure the value extends beyond the four walls of an enterprise. Another key point to consider is upskilling resources, reskilling of employees, and ongoing support and learning. It requires development and assessment of skills required for the long term to ensure ongoing success as the smart factory evolves.
Building skills can pay major dividends such as driving acceptance and adoption of solutions and providing support for employees as they adapt and creating a culture of continuous learning. As smart factories leverage advanced digital technologies, roles within the factory will call for new and different skills than had been needed previously, making it challenging to upskill and train. Organizations should also look at some of the non-conventional models to deploy alternate talent models, collaborate with universities to build a pipeline of talent, and leverage the ecosystem of partners. This will also help attract and motivate young people starting their career in manufacturing.
Figure 5. A six-step approach to managing change.
Maturity Models, Assessments and Frameworks:
A Think Big, Start Small, and Scale Fast Approach
Smart factory investments often start with a focus on specific opportunities. It will be effective to start small, test out concepts in a manageable environment, and then scale once lessons have been learned. Once a “win” is achieved, the solution can scale to additional assets, production lines, and factories, thus creating a potentially exponential value creation opportunity. Moreover, achieving the ultimate true “Lights Out” operation requires a crawl, walk, and run approach as illustrated below. A company’s manufacturing strategy and environment will determine which specific issues to address and the way to unlock value through smart factory solutions. Customizing the approach to each scenario and situation can help ensure the resulting smart factory meets the needs of the organization.
Figure 6. A three-horizon approach to achieving smart factories.
Execution Approach – Pilot Site
Plant Maturity Assessment and Transformation Framework
Before an organization embarks on a transformation, it is important to assess and understand the maturity levels of people, process, and technology. While there is no globally accepted maturity assessment framework for smart factories, the World Economic Forum has taken a leadership role in shaping the future of global manufacturing. A vital component of this initiative is the Smart Industry Readiness Index (SIRI). SIRI is a suite of frameworks and tools that help manufacturers start, scale, and sustain their manufacturing transformation journeys. The assessment findings depend heavily on sharing relevant organizational practices and self-reflection on areas of improvement.
Figure 7. Smart Factory Maturity Assessment
The report should have a comprehensive roadmap that depicts current maturity across process groups and people; a readiness index and levers to improve; capabilities required to implement; the next logical step; and recommendations to start the transformation.
Smart factories encompass operations, quality, sustainability, safety compliance and technology risk mitigation. Use cases and process groups vary depending on specific customer and factory needs. Typically, smart factory use case benefits are focused on productivity increases, cost reductions, improved energy efficiency, inventory reduction, lead time reduction, and improved throughput.
The World Economic Forum has reported some of the top use cases implemented by the Global Lighthouse network companies.
Figure 8. Typical process groups and representative use cases in smart factories.
Key Value Realized by Organizations Implementing Smart Factories
Lighthouses are demonstrating how digitally infused operations go beyond productivity improvements to create sustainable, profitable growth. To be sure, the productivity gains are there, resulting from digital machines and management applications driving output increases at the factory level. Looking past productivity, two ways to drive growth stand out: by adopting new business models and unlocking capacity in the people and production processes. The results are encouraging. These measures optimize resources and infrastructure while enabling workers to realize their potential equipped with powerful digital applications – all without massive capital investment or negative environmental impact.
Figure. 9 Value realized
Though there are many different approaches to smart factory deployment, lessons can be learned from each approach that can lead to significant value creation. These range from people-centric experiences such as change management, putting humans at the center of capabilities, and managing skill diversity to broader operational and technological considerations. If leaders take just one lesson away, however, it is the importance of moving forward. All our recent surveys and research show that organizations broadly agree that the future of manufacturing is smart. The empirical results in global lighthouse factories reported by WEF demonstrate the value of the smart model. While it seems daunting, by starting small with specific program objectives that can produce tangible value, companies can get themselves started on driving sustainable value. For those who are in the journey, it is about accelerating and scaling the benefits. For those just contemplating how to start, now is the moment to begin — or risk being left behind. M
About the author:
Baskar Radhakrishnan is Strategic Advisor, Manufacturing Industry Solutions at NTT DATA
How Will AI Impact the Manufacturing Workforce?
AI is changing the way manufacturers do business—from the production line to the back office and across the supply chain. At the Manufacturing Leadership Council’s Manufacturing in 2030 Project: Let’s Talk about AI event last month in Nashville, Tennessee, panelists discussed how those sweeping changes would alter, and enhance, the manufacturing workforce.
A collaboration between the MLC (the NAM’s digital transformation arm) and the MI (the NAM’s 501(c)3 workforce development and education partner), the event provided key insights for manufacturers into how technology and workforce trends interact with each other. Here are a few key takeaways.
Net positive: “The history of technology adoption is about improving the job quality of individuals on the shop floor. AI helps them to do the job better, provide them with better tools, gives them greater authority and ultimately increases the value-add of their jobs. All of that is a net positive for those individuals,” said MI Vice President of Workforce Solutions Gardner Carrick.
- By leveraging data and enabling greater efficiency, AI will improve communication, increase collaboration across disciplines and stimulate innovation, according to the panel.
- In addition, “AI can even inform the workforce’s creativity by working with it to design a new product or system,” said Jacey Heuer, lead, data science and advanced analytics, Pella Corporation.
Skills needed: While you might expect that implementing AI requires workers skilled in programming, data science and machine learning, manufacturers will also need to expand their bench of critical thinkers and problem-solvers. The panelists had a few tips to help companies along.
- Invest in upskilling programs to make the AI integration process at your company smoother and develop the talent you already have.
- Update job descriptions to reflect the skill sets the company will need in the next five to seven years.
- Consider recruiting for and teaching adaptive skills—skills that enable individuals to adapt easily to changing demands and environments—which can increase the flexibility of your workforce.
- Build partnerships with local schools, community colleges and technical and vocational schools to develop talent pipelines that will meet your needs.
The human-AI collaboration: While AI will take over monotonous, repetitive tasks, the panelists predicted that the industry will continue to center around human labor.
- “You can teach AI to do X. You can teach AI to do Y. [However,] combining the two may be really difficult for AI, while a human can do it better. You’re going to continue to see humans in roles that center on making decisions and telling stories,” said Asi Klein, managing director, industrial products and organization transformation, Deloitte Consulting.
- Meanwhile, AI adoption will likely lead to an increase in available jobs, as more skilled workers will be needed to guide and inform these new processes.
The last word: “Over the last 12 years, we’ve seen a lot of technology adoption, but we have not seen a lot of job loss. In fact, we’ve seen job gains,” said Carrick. “There is a lot of opportunity to reimagine jobs to add value that AI will help to illuminate.”
A Guide to Digital Transformation in Manufacturing
Digital Transformation (DX) is a broad business strategy to solve traditional business challenges and create new disruptive opportunities using digital technology – such as maximize revenue, reduce cost, improve quality, and increase flexibility. Use cases range from asset optimization to workforce productivity to industrialization.
Why Manufacturers Need Digital Transformation
To remain competitive, DX for manufacturers is a necessity. Global market intelligence firm IDC predicts that in 2025 global DX spend among manufacturing industry companies will total more than $816 billion. Forrester Consulting found that more than 90% of manufacturing leaders believe that DX is important for their success. Clearly there is a lot at stake and developing a DX strategy is critical to capturing the most value.
The range of opportunities for DX in manufacturing is both a positive and negative. On the one hand, for whatever challenges facing an organization, there is likely a solution out there to address it. But manufacturers are faced with dozens of challenges. Such initiatives are usually driven by a scattergun, technology-driven approach. Ultimately, this results in resources being misdirected and just a small set of initiatives driving true transformation. Instead, companies must employ a laser-focused approach, that emphasizes impact, speed, and scale.
Manufacturers that have successfully adopted DX strategies are more efficient than their competition. That efficiency may be generated by greater worker productivity, asset uptime, better cross-organizational collaboration, or other DX opportunities. Ultimately, regardless of how efficiency is gained, it can be leveraged to increase revenue and/or reduce costs.
Achieving Transformation with Impact, Speed, and Scale
Regardless of the many different challenges an organization faces and the many different solutions on the market, there are fundamentally four objectives for manufacturers that have not changed: maximize revenue, reduce cost, improve quality, and increase flexibility. Impact, speed, and scale are the three key success factors for delivering transformational outcomes, and each must be tied to at least one of the objectives. Here’s how:
Impact – Focus resources on the most important constraints to drive P&L. Based on the dynamics of an industry and strategic roadmap, manufacturers should determine which of the fundamental goals are most important to improve upon and which are most likely to impact these goals in order of their criticality to the business.
Speed – With impactful opportunities identified, attention should turn to speed and scale. Quick wins can make or break a good initiative by building early, positive momentum. A quick win will generate team buy-in and can be leveraged for greater executive support. This rapid time to initial value is best achieved with proven off-the-shelf solutions.
Scale – Scalability must be considered early. No initiative should take place without a comprehensive and actionable scaling plan. If a project is slow to scale it is more likely to lose support and fold. If it cannot scale, then it is not delivering transformational value. The most reliable way to build a scalable plan is to model it after approaches that have already proven to be successful. The key here is finding repeatable use cases, that check off the high impact requirement and can be deployed with off-the-shelf solutions.
A recent PTC survey found that there is a stark difference between the companies that succeed in DX and those that do not. Companies that meet ROI goals expectations beat them by an average of 50%; those that fail miss expectations by an average of 30%.
This insight underscores the requirement for strategic DX for manufacturing organizations, and the PTC Impact, Speed, and Value Framework described here creates a foundation for DX success.
Will Hastings is Director of Product Marketing, PLM for PTC.
Accelerating M4.0 with Legacy System Condition Monitoring
Aligning the solution framework, intended business value, and smart factory expertise is key to success.
Factories striving to get farther down the Manufacturing 4.0 path know they have so much to gain. The question is how efficiently and effectively they can get there. Smart, digital factory solutions such as machine condition and process monitoring sensors, machine learning (ML), and intelligent analytics are available to streamline and optimize legacy manufacturing processes — from operations and maintenance to the supply chain and facilities management. Smart maintenance initiatives are a prime example.
By identifying and capturing unleveraged Big Data and honing it into meaningful, actionable insights, plants gain greater situational awareness and the ability to make more intelligent, proactive decisions. The newfound real-time, future-focused insights accelerate actions that improve performance, productivity, cost efficiencies, and reliability, in addition to increasing safety, cybersecurity, and regulatory compliance. Protecting legacy equipment investments with this approach provides significant cost advantages over sweeping factory modernization.
“Protecting legacy equipment investments with this approach provides significant cost advantages over sweeping factory modernization.”
There is much to consider when taking this approach. Technology and resource challenges must be overcome, risks and opportunity costs must be weighed, priorities must be set, and a framework for success is needed. Achieving all this while still meeting current operational goals is a lot to take on at once. It is why factories are increasingly turning to trusted expert partners for assistance.
Barriers and opportunity costs
Effective data acquisition, storage, analytics, and application are central to any smart factory initiative, whether upgrading legacy equipment with integrated condition monitoring solutions, or more generally trying to make better, more timely decisions. But common factory challenges impeding the journey must be overcome, such as:
- They are insufficiently networked to support new technology at scale
- They have legacy equipment that could be a cybersecurity risk if connected to the Internet
- They do not realize the prevalence of valuable yet hidden data
- They do not really have a way to manage and use the data effectively
Beyond issues like these, there are opportunity costs to weigh for any upgrade. For example, replacing or upgrading a machine or line within a plant requires stopping manufacturing in that area for some amount of time. Hence, strategically augmenting and improving what is already in place is often more valuable than doing an upgrade, considering the longer an asset or line keeps running, the higher the return on its investment.
Additionally, tackling any transformation or smart initiative requires a well-planned, structured activity sequence. For instance, it is necessary to address the core challenges around connectivity and security before going after the needed data.
“The challenge is knowing what data is needed and why, and how it will help to do something not currently possible.”
The pursuit of hidden data within the factory also needs structure. Much like finding important messages in an overloaded email inbox, having excessive sensors and alerts makes it difficult to identify which issues are most important to address. The challenge is knowing what data is needed and why, and how it will help to do something not currently possible. Providing context and aligning that with other data will surface the issues that most require attention, so that the right equipment is monitored, and appropriate actions are taken.
Framework for success
The optimal approach to minimize project risk is with an Industrial Internet of Things (IIoT) solution framework focused on evolution rather than revolution. Most plants cannot afford the costs or disruption of jumping from very old technology to something very new. Likewise, just because an asset can be monitored and generate alerts does not mean it should.
Take, for example, a goal to improve asset reliability with intelligence to reduce equipment failures and costly unplanned downtime. In its report Accelerating Industry 4.0 Through Remote Monitoring and Diagnostics, IDC, a global marketing intelligence firm, observes that the average cost per hour of unscheduled downtime in manufacturing is over $110,000. Conventional equipment maintenance is time- or usage-based, regardless of actual operating conditions, and some equipment is allowed to run to failure. Smart maintenance is proactive and predictive based on condition monitoring data, which improves asset reliability and uptime with measurable ROI and increases productivity and plant performance.
The smart maintenance journey leverages digital sensor technologies that capture legacy equipment condition data for remote machine health monitoring. Even the smallest variations in vibration, temperature, oil quality, or motor function can indicate a failure is emerging or imminent. Secure, rugged, integrated sensors allow for integrated asset monitoring, data collection, processing, and storage — bringing previously hidden machine data to light from which useful intelligence can be gleaned.
Processing this data with predictive analytics and ML allows for real-time alerts and prescriptive actions to avoid asset failure and unplanned downtime. Meanwhile, failure trends can be leveraged to shape purchasing decisions, depreciation rates, and replacement forecasts.
“The optimal approach to minimize project risk is with an Industrial Internet of Things (IIoT) solution framework focused on evolution rather than revolution.”
The framework defines the required functional components of the solution, ranging from how the data is acquired, transported, processed, consumed, and secured to deliver business value. Framing a solution that is cost effective, capable of achieving goals, and increases business value involves:
- Defining the journey, including what problem must be solved
- Making the business case for pursuing it
- Determining what steps will have the most impact
- Building the foundation for the next steps
This process acknowledges the real difficulty of having a large and growing choice of smart solutions on the market. Selecting a functional framework that aligns with today’s problems but can also accommodate future problems will help an organization avoid dealing with multiple competing solutions. In essence, doing something similar but separately is less valuable than having an integrated smart solution that can be expanded and extended over time.
Ultimately, an effective framework will deliver a solution that is feasible, creates business value, and will support long-term strategy with maximum impact. It could even provide a catalyst for new business models, new customer engagement models, and new or expanded markets.
Due to chronic industrial skills gaps and time and resource constraints, many manufacturers are engaging partners to help them meet smart factory goals, rather than going it alone. A recent Forrester research study, Data-Driven Decision Making Drives the Need for IIoT/Remote Sensors, completed in partnership with Advanced Technology Services (ATS), found that 55% of the respondents expected to use a combination of outsourcing to an external partner and in-house resources for data collection and analysis within the last few years. Another 27% planned to fully outsource the data collection, analysis, and gathering of insights to a partner.
“The most capable partners are those with deep technical and analytical expertise, bolstered by experience working across industries”
Choosing a Manufacturing 4.0 partner and their responsibilities depends on what skills and experience will best meet the needs. In the case of smart maintenance, any or all of the following roles may be desired:
- Defining condition monitoring priorities and strategy
- Implementing the right technology and communications
- Storing and managing the data
- Understanding how best to analyze and use the data
The most capable partners are those with deep technical and analytical expertise, bolstered by experience working across industries with many different types of manufacturers and equipment. They may offer a pool of experts operating from a central hub for data-driven remote monitoring and diagnostic support, in addition to regional teams of multi-craft field technicians for on-site maintenance expertise. This distinctive value proposition accelerates the time to value for smart factory initiatives.
Consider sharing the load
Manufacturers have every reason to pursue the high-value advantages of Manufacturing 4.0, such as lowering the costs of operation, increasing asset availability, and reducing unplanned asset downtime. But with the manufacturing environment and available technology solutions continuing to rapidly evolve, the complexities of implementing and expanding smart factory initiatives are growing. Factories struggling to prioritize and achieve their goals would do well to consider engaging a highly qualified partner for assistance. M
About the author:
Chris LeBeau is the Global Director of IT at Advanced Technology Services, Inc., working with industrial maintenance experts and technology leaders to enable Manufacturing 4.0 strategies and maximize the value of technology to manufacturers. Chris previously held positions at Cisco Systems, AT&T, IBM and began his career in satellite communications with the U.S. Army Space Command.
A Digital Transformation Framework to Enable M4.0 Factories
To get to where they’re going, manufacturers must first make an honest assessment of where they are.
Investing in smart factory technologies will become a key area of opportunity for middle market manufacturers looking to differentiate themselves, improve operating costs and stay ahead of the competition. Before ramping up their investment in and implementation of those technologies, though, companies need to have a digital transformation framework and roadmap in place to ensure a thoughtful and prioritized plan. This will allow those advanced tools and processes to work to their full potential.
Having such a roadmap will also help chief financial officers, chief technology officers and other executives identify key priorities amid a high price environment and looming recession, ultimately helping to improve efficiency and save money.
Key elements of a digital transformation framework include an understanding of where the company is in its digital maturity journey, as well as an understanding of M4.0 factories’ implications on the workforce of the future.
Building a Foundation
For many manufacturers, upgrading legacy systems is a central step to becoming a truly digital organization. This may involve upgrading enterprise resource planning systems and inventory management systems, which can enable more precise supply chain visibility. A broader shift to cloud-based IT systems can also allow manufacturers to realize lower risk and lower cost of ownership of the data that lives there.
Overall network architecture is another crucial part of the foundation. Companies will need to integrate historically siloed IT and OT infrastructures, as we wrote recently in this article. This will enable “more circular connectivity throughout operations, better security, continuous improvement in efficiency and, ultimately, growth.”
“Key elements of a framework include an understanding of where the company is in its digital maturity journey, as well as an understanding of implications on the workforce of the future.”
There are significant opportunities for middle market manufacturers to evaluate whether their current digital foundation will suffice in the increasingly connected future. Here are what the stages of a digital maturity model might look like, as RSM lays out in its digital strategy guide:
- Non-existent: “There’s little to no use of technology to facilitate daily operations.” Many processes and systems are manual and production is run largely by paper work orders. The use of automation is not widespread throughout the organization, and very little production data is available for relevant analysis.
- Basic: “There’s a limited use of digital solutions to perform specific tasks.” Some aspects of operations have been automated and/or digitized, and teams are using some operational data to inform decisions. The production floor is still highly manual. The foundational ERP system hasn’t been optimized for all processes but provides basic production data that can be used for retroactive production volume analysis.
- Market contender: “Most day-to-day tasks are facilitated through technology and managed through current systems.” Teams use technology to streamline and improve operations. A foundational ERP system and/or other advanced solutions allow teams to manage supply chain and operations more efficiently. The company can extract relevant data from multiple systems for analysis, but that analysis is still manually intensive and mostly retroactive. Predictive capabilities are still somewhat limited.
- Industry leader: “Competitors and similar organizations view your organization as the benchmark for digital maturity.” Teams leverage advanced software and solutions for shop floor automation and data gathering. Data is automatically integrated across systems and platforms and fed to the business via predictive capabilities, which steer operational performance and inform decision making.
- True innovator: “Your organization is on the cutting edge of technology and recognized as a digital pioneer.” The organization has implemented and trained its workforce on the use of predictive data analytics, machine learning and automation capabilities that allow for continual improvement throughout the operation. The company also has advanced supply chain visibility that allows precise supply and demand synchronization.
A third party can help in assessing where teams across the organization and across business functions fall along this digital maturity spectrum.
Security and Controls
Cybersecurity is another crucial foundational area for industrial companies looking to further their digital capabilities; organizations adopting advanced technologies need to raise the bar for how they protect their information. In the context of digital production and M4.0 factories, where more devices and operations are connected via one or multiple networks, the importance of cybersecurity is even higher.
“Today’s manufacturers need ‘new-collar’ workers who have more technical, advanced skills than traditional white-collar office workers or those in blue-collar jobs.”
When it comes to building cybersecurity for industrial control systems, companies should understand where there might be gaps in the four key areas of oversight, people, processes and technology.
- Oversight: Organizations need to have clear governance and strategies in place around the security of their industrial control systems and ensure that processes incorporate board and executive oversight on everything from understanding cyberthreats to navigating cyber insurance to coordinating with law enforcement in the event of a breach.
- People: People should also have a thorough understanding of the company’s protocols and security organization structure at the enterprise level.
- Process: Companies need to have consistent cybersecurity considerations built into all their processes to ensure not just physical security but also business continuity in the event of breaches.
- Technology: This includes security monitoring, threat modelling, intrusion detection and protection, endpoint security, data loss prevention and security architecture and design.
Labor Impact: ‘New-Collar’ Workers
There may be concerns from employees at all levels of the organization about how advanced technologies will change what day-to-day operations look like. But the main issue here isn’t technology replacing humans throughout the organization. Rather, it’s about how manufacturers should adjust the digital skills they seek out in potential employees, and how best to train existing employees to ensure they keep up with those skills, especially in the areas of analytics and automation.
“For true success, businesses need to evolve constantly and regularly question where there may be opportunities for improvement.”
“Today’s manufacturers need ‘new-collar’ workers who have more technical, advanced skills than traditional white-collar office workers or those in blue-collar jobs, especially in the areas of automation, analytics, robotics and the Internet of Things,” we wrote in early 2022. “While proficiency in machinery is essential, dual knowledge of analytics or advanced robotics is in high demand.”
Organizations will need to understand how this affects their recruitment and training strategies, as well as their investment priorities.
Questions to Frame the Path Forward
There are a few key questions that can help manufacturers chart a path toward ensuring they are M4.0 ready and will be able to use smart factory technologies to their full potential:
- What value do you believe these technologies could drive for your organizations?
- Where does your organization currently fit in the digital maturity journey laid out above? What are the reasons for its current position along that journey?
- What investments or plans will be needed to make this progress?
- What will the changing workforce landscape—and the increasing importance of digital skills—mean for your organization?
In many ways, endeavoring to become a truly digital organization is a never-ending project. For true success, businesses need to evolve constantly and regularly question where there may be opportunities for improvement. M
About the authors:
Jason Alexander is partner at RSM US LLP.
Daniel Wheadon is partner at RSM US LLP.
Jake Winquist is director at RSM US LLP.
Achieving Measurable Value through Data-Driven Manufacturing
“Data collection doesn’t equal success” and other observations from a powerful visit to EY’s Digital Operations Hub @ MxD
The Manufacturing Leadership Council recently partnered with EY for a two-day event focused on preparing for the future of manufacturing. Hosted at MxD in Chicago, Ill., the event focused on data-driven manufacturing and included a tour of EY’s Digital Operations Hub, several discussion panels and presentations, and a collaborative workshop.
Diving deep into manufacturing at EY’s Digital Operations Hub: Day one featured a round-robin visit to a selection of experience modules within the EY Digital Operations Hub. Participants had the option to visit five of the hub’s 31 modules where they heard about topics including workforce upskilling, intelligent demand forecasting and planning, digital performance management, digital worker enablement, edge computing, and more.
As participants made their way around the Digital Operations Hub, led by EY’s Mark Heidenreich, who leads the Digital Operations Hub, EY’s expert team and partners shared a deep dive into each topic, demonstrating the latest technologies and thinking on this important array of manufacturing topics.
Focusing on the future: The second day of programming kicked off with a conversation between David Brousell, MLC’s Co-Founder and Executive Director, and Scott Dixon, EY’s Managing Director – Advanced Manufacturing Technology Leader. The topic at hand was MLC’s latest white paper, The Next Phase of Digital Evolution and what it tells us about the future of manufacturing.
The two focused on data and its important role in manufacturing. While data may be difficult to get to – particularly on-demand – it is an important driver of decisions and value. However, they cautioned that data collection doesn’t equal success. Instead, Brousell and Dixon urged organizations to balance resilience while adding complexity. Brousell recommended that organizations not focus on data’s ability to “knock down silos.” That phrase, he warned, can be scary for subject matter experts. Instead, he recommended weaving silos together so that systems are integrated and domain expertise can be maintained.
Becoming data-driven: Next up, the event covered the top challenges for data-driven manufacturing with a presentation by EY’s Sachin Lulla and Amy Burke, the Americas Consulting Sector Leader – Advanced Manufacturing & Mobility and Advanced Manufacturing & Mobility Markets Leader, respectively. With a survey of 400 manufacturing companies as its basis, the presenters shared how only 10% were experimenting with digital, while only six percent were tackling digital at scale.
Throughout the conversation Lulla and Burke emphasized the need to put humans at the center of any transformation, building digitization and operational excellence around that core. For Lulla, the purpose of technology is to augment human intelligence. The pair agreed that starting with an end goal in mind is important when formulating a data strategy. The organization and employees need to know “the why” behind the data collection and use.
Further, Burke and Lulla recommended that organizations should not just look at gaps in their current workforce, but at what employee skills exist on the team and how upskilling and a learning environment can cultivate a fertile ground for data to be used successfully.
Driving digital with data: Pfizer’s Vice President of Digital Manufacturing, Mike Tomasco, was on hand to share how the pharmaceutical and biotechnology company uses data-driven decision making to create value. Tomasco shared how the company’s initial failures with capturing and using data led to significant successes and allowed Pfizer to move beyond pilot purgatory to large-scale transformation.
Moving beyond: The idea of moving beyond pilot purgatory was explored further to start the final panel discussion moderated by Brousell. Panelist Jim Fledderjohn, Dell’s Manufacturing Vertical Field Director, advised organizations to align pilots to the bigger strategic vision and fail fast. According to fellow panelist Terry Davenport, Rheem’s Executive Vice President, Global Operations, leaders should use the scientific method to learn from pilot projects and prove the value before scaling. From a collaboration standpoint, Microsoft’s Americas Regional Business Lead – Manufacturing, David Breaugh suggests that cross-functional teams help keep an eye on the big picture and unlock insights faster. Meanwhile, James Zhan, PTC’s Vice President, Market Development, IoT Solutions cautioned the audience not to focus solely on pilot purgatory and to be sure to keep an eye on workforce skills purgatory.
The panel also tackled the topic of data measurement, with Fledderjohn urging organizations to be selective about what data they collect – a proactive strategy that will help ensure the data is used and useful. Any process should have a metric that makes things faster, safer, eases worker burden, and offers higher quality and cheaper outputs, added Davenport. To that end, panelist Steve Pavlosky, GE Digital’s Vice President of Product Management, shared that GE shifted its technology roadmap to help customers move data into a single system so operators could make decisions quicker.
Capping it off with idea sharing: The event was capped off by a series of collaborative breakout sessions during which participants brainstormed go-forward ideas and feedback around the topics covered throughout the course of the entire event. Beyond the content that participants absorbed throughout the event, the breakouts gave them a chance to add their own two cents to the discussion, share their own experiences, and take away new perspectives that can be applied to their organizations.
Visit https://www.mxdusa.org/partners/ey/ to learn more.
All photos courtesy of EY.
Found: The Hidden Value in Manufacturing Operations
Once armed with data, the hidden value is often in plain sight.
The October 2022 Manufacturing Leadership Journal article, How to Find Value Hiding in Your Operations, introduced the value stream (Figure 1), which illustrates the various objectives and challenges manufacturers face to produce “as planned” while delivering products to customers on-time, in-full, without quality defects, and at a competitive price. Looking deeper at value streams, here are seven key areas where FORCAM customers are finding tangible value.
Downtimes / outages
There is no better place to start than reducing downtimes and outages, typically one of the first places to look for hidden value and an area with great potential. The key is collecting and seeing the detailed reasons for downtime, and then analyzing those reasons over time and by various criteria, such as in comparison to plan and across lines and plants. While any one incident is easily observable, seeing data in aggregate provides the visibility to see root causes and trends.
In one project, a manufacturer was surprised to learn that true production utilization of an expensive press was actually lower than being reported by the ERP system. This was due to the ERP not having the more refined level of detail of machine status and reporting truly non-productive time as productive. With this greater insight, they were able to pinpoint that a significant amount of downtime was due to waiting for specialists from the tool department to perform routine maintenance on forms for the press. They determined that basic maintenance could be done by the machine operator, reducing the downtime and – as an additional benefit – freeing up the specialist for other tasks. This manufacturer was able to increase availability from 53% to 82%.
Shorten cycle time / setup
Once downtimes are reduced, a next logical area of opportunity is the reduction of cycle times and set up times. Accurate cycle time measurement and monitoring can reveal differences in lines, plants, operators, and machines, as well as trends over time and deviations from standard. This can lead to training opportunities, equipment improvements, process changes, and establishment of more accurate standards.
Setup time reduction is a major area of focus for another FORCAM customer. As an example, they found that a pallet-changing system for a machine was being underutilized, so that the machine was unnecessarily idled for setup. By adjusting the staging of work and through operator training, they were able to increase machine utilization by 20% by performing setup work on an off-machine pallet. They were also able to make greater use of after-shift production during lights-out operations. Further setup time reductions were realized through the automated downloading of CNC programs from a central repository and grouping of orders to reduce changeovers.
Less scrap (Quality)
Continuing on the ring around “as planned” on the value stream, the next stop, less scrap, is all about quality. Bad quality diminishes value not only in wasted material and personnel and machine time, but if bad products leave the factory the company’s reputation and, ultimately, business are at risk.
Timeliness of collecting, analyzing, and reporting quality data held the key to finding value in another FORCAM customer project. Previously, the data was manually collected from each machine at the end of the shift. It was then manually entered and often not analyzed and available for days or longer. Now the information in available in near real-time, enabling action to be taken immediately to prevent substandard products from being produced. The data has also yielded longer term insight into patterns of quality issues that led to process improvements in material handling and changeover, as well as reducing material waste.
In a project that focused on improving Overall Equipment Effectiveness (OEE), the company was under continual pressure to reduce costs and ramp up performance. Its present OEE was insufficient. Once enabled with data, it was able to see a large variance in the time taken to perform a tool change on a key manufacturing process. Further, the times experienced were substantially higher than when the process was observed. The company determined that installing a countdown clock on the machine would allow the operator to anticipate the need for a tool change and to be ready and at the machine when needed. In addition, monitoring the time to change the tools against a standard reduced the time to actually perform the change. The company was able to increase OEE by 20 percentage points.
“While any one incident is easily observable, seeing data in aggregate provides the visibility to see root causes and trends.”
Accurate OEE can also help with investment decisions. In another example of a surprising insight that an asset is running below its specification, a company had to deal with a large order. At first glance this new business made investment in new machinery appear necessary. In this case, the company was lucky because a data-driven OEE project was just started and, as a side effect, showed that the existing equipment was, in fact, able to handle the new order. The tricky part was that from the viewpoint of the shopfloor, the machines appeared to be running at capacity, and therefore the additional machinery met the payback criteria. In this case data-driven versus experience-driven did have a clear winner.
Reduce emissions & energy costs
As highlighted in the Manufacturing Leadership Council’s Critical Issues Agenda, sustainability is an increasingly important topic for manufacturers. And with rising energy and other resource costs, it has become a financial imperative as well. Manufacturing 4.0 and associated digital tools and approaches can serve as an enabler.
One project in this area began with enabling shop floor machines with the ability to collect detailed data on resource usage, including electricity, gas and water. But the hidden value became revealed when this data was related to what the machines were doing, that is, were they in production, setup, warm up, idle, or another state, and what order or operation the machine was performing. This identified opportunities to improve scheduling, grouping, and sizing of production orders with regard to resource consumption, prioritizing use of the most energy efficient machines, and data to support decisions as to when to shut down and when to power up machines. The result was a 20% reduction in energy usage while maintaining the same processes and increasing production volumes.
Improve delivery performance, OPE & scheduling
Improvement opportunities build on each other while working through the value stream, returning to the overall goal of delivering on-time and in-full to the customer, while maximizing Overall Production Effectiveness (OPE).
“Just as the tools for collecting shop floor data keep getting better, so do the analysis tools that lead to discovering hidden value.”
Understanding true cycle times and establishing better standard target times based on real experience data led one company to better schedule and synchronize activities across the plant. They were able to reduce wait and idle times, and make sure materials and personnel were in place. This was especially valuable in a low-volume, high-mix, high-precision environment. With a variable takt time like in this case, only data-driven analysis can produce insights.
In a different project, better understanding cycle times, downtimes, and other information enabled better scheduling, resulting in elimination of escalations and the associated disruptions and cost to prioritize certain customer orders.
Getting from data to value
Just as the tools for collecting shop floor data keep getting better, so do the analysis tools that lead to discovering hidden value. Traditional techniques such as observation, daily or weekly production review meetings, root cause analysis, and continuous improvement programs – enabled with robust, accurate, and timely data – reveal improvement opportunities that are often hiding in plain sight. And emerging analytical techniques such as artificial intelligence and machine learning provide promise to reveal even more value opportunities that are more deeply hidden but waiting to be found in areas such as predictive maintenance, scheduling, and inventory optimization. M
About the authors:
Christian Nagel is Country Lead – U.S. with FORCAM
Four Ways Connectivity is Transforming Manufacturing
Digital transformation relies on connectivity in order to access data, improve processes, and empower people.
Connectivity is at the core of today’s digital transformation initiatives in manufacturing – affecting processes, productivity, and people. The right technology can lead to more empowered decisions, cross-functional collaboration, better talent attraction and retention, and improved workplace safety and employee satisfaction.
However, these productivity, process, and people improvements are not easy to carry out – especially across a network of individual manufacturing sites, each with its own distinct site leadership, IT infrastructure, and culture.
Automating today’s factory takes fast, scalable, reliable, and secure connectivity. Use of advanced technologies such as dedicated fiber, 5G or Wi-Fi, can maximize flexibility in connecting disparate systems in the factory. Robust connectivity can take what is being processed via artificial intelligence (AI) and machine learning (ML) to create fast, better, and more accurate insights.
The connected factory requires big data to help manufacturers make intelligent decisions about how to respond to market changes. Using technologies like Internet of Things (IoT), autonomous-guided vehicles, robots, or video intelligence, manufacturers should consider the amount of data that will be generated. This data can help power predictive analytics, asset tracking, increased responsiveness, and supply chain visibility
The connected factory requires big data to help manufacturers make intelligent decisions about how to respond to market changes.
As manufacturers embark on their digital transformation journey, they need to assess how the transformation will look for their organization and what goals they want to achieve. Understanding their current manufacturing infrastructure and assessing where improvements can be made to meet the identified goals will help with creating a roadmap.
Here are four exciting ways that manufacturers can take advantage of today’s Manufacturing 4.0 technologies and improve manufacturing operations:
Rise of the connected worker
Connected devices have created smarter experiences in our day-to-day lives. This connectivity has even transformed how we work. Connected workers are integrated into their environment and connected to other workers. They’re empowered with real-time information and supported by working systems.
In factories and on the plant floor, connected workers are performing their jobs with the help of digital technologies and devices – including smartphones, laptops, tablets, and smart glasses. Together, the connectivity and the data produced are helping improve employee safety and unlocking new efficiencies.
For example, IoT, AI, AR/VR, and smart devices are empowering employees with information about equipment, processes, and work instructions. Data analytics helps drive more informed decisions and delivers actionable, real-time insights for manufacturers. This helps businesses respond quickly to emerging market trends and gain a competitive edge.
5G can help improve the employee experience. On a production line, employees using tablet computers could send and receive information about equipment status or material supply. Smartphones could be used to scan specific components of a vehicle for accuracy.
Industrial IoT solutions in manufacturing
Industrial IoT (IIoT) is becoming more widespread and has enabled manufacturers to make better informed, strategic decisions using real-time data back to achieve a diverse range of goals, including cost reduction, enhanced efficiency, improved safety, and product innovation.
By moving away from manual processes and calculations towards machine learning, AI-based analytics can enhance IIoT capabilities such as video intelligence. IIoT enables more data to be collected to perfect manufacturing production, find areas of improvement, and possibly predict and offset supply chain challenges.
IIoT can enable better shipment tracking via GPS to let all participants know where the contents are at any time. It can also empower better planning and accountability, helping to close gaps that may otherwise be out of the participant’s control.
IIoT enables more data to be collected to perfect manufacturing production, find areas of improvement, and possibly predict and offset supply chain challenges.
Video intelligence can be used to check critical elements of production and help improve asset protection and risk mitigation. Maintaining visibility and capturing insights within high-risk areas like conveyor belt speed, production accuracy, temperature, or vibration monitoring of older equipment helps manufacturers gain operational efficiencies and better manage their facilities for cost savings.
IIoT is being used to make the manufacturing floor safer and more efficient, which helps increase employee productivity and satisfaction. From ensuring employees maintain the proper distance from equipment to alerting them of required safety equipment, video intelligence helps reduce safety risks with better situational awareness for faster response times to incidents.
Asset tracking and monitoring
Indoor and outdoor IoT tracking supports improved inventory management, while IoT-based vehicle telematics supply enhanced governance of vehicle condition. IoT also supports sustainability initiatives like reducing energy consumption, emissions, and waste, and improving resource efficiency.
Manufacturers are adopting IoT-powered devices to check production facilities and equipment. From conveyors to forklifts, IoT helps monitor and control any mix of equipment. By capturing and analyzing data and diagnostic information, manufacturers can increase the operating efficiency of their business. IoT technology can give manufacturers visibility into where their assets are, how they’re performing, and how well they’re being used. The data generated can be analyzed to generate real-time, actionable insights for manufacturers.
IoT is also beneficial for tracking inventory and fulfilling orders. IoT for warehouse management lets manufacturers monitor the stock availability and their location in the warehouse. The data collected from products and devices helps companies improve warehouse operations and manage their inventories based on market demand.
Autonomous-guided vehicles (AGVs) are examples of how manufacturers are enhancing production in manufacturing. AGVs are a part of many lights-out factory environment strategies.
AGVs that move through a factory floor, yard, or warehouse can generate massive amounts of data to process instructions and make smart decisions to navigate safely. With such heavy data consumption, Wi-Fi alone may not be able to support fast and reliable connections. 5G in the factory can fulfill the volume gap. The lower latency of 5G helps automated robotics and machinery make faster and more reliable decisions.
AGVs can generate massive amounts of data to process instructions and make smart decisions to navigate safely.
Multi-access Edge Computing (MEC), a form of private 5G, provides the foundation for data to flow from the edge of the network where it’s more easily accessible within the factory for prioritized processes. Coupled with macro 5G, it can handle massive volumes of data from sensors, endpoints, and other sources to allow AGVs to avoid collisions, mistakes, and carry out their assignments.
As manufacturers continue to digitally transform to achieve efficiencies and gain a competitive advantage, they must find a balance between having a secure network in place and scaling their digital capabilities.
These four areas of connectivity are playing a significant role in modernizing manufacturing ecosystems. Creating a factory of the future – one that incorporates automation and other technologies – is worth it. M
About the authors:
Andrea (Ande) Hazard is Vice President, Manufacturing & Transportation Solutions for AT&T Global Business where she leads a team of sales professionals, technical architects and engineers delivering AT&T products, services and global solutions to enterprise clients. Her organization focuses on the manufacturing, transportation, logistics and consumer packaged goods industries, ensuring an integrated, enterprise-wide customer experience for their clients.
How Digital Manufacturing Creates Business Opportunities
It’s time to think way outside the proverbial box, according to the Manufacturing Leadership Council, the NAM’s digital transformation arm. In fact, as we get closer to 2030, manufacturers are creating entirely new boxes—including new digital business models, products and services, revenue streams, ways to serve customers and opportunities to increase competitiveness.
Collaborative innovation: By 2030, metaverse technologies will provide rich virtual environments for the collaborative development of new ideas. These shared virtual spaces will enable contributors from multiple remote locations to collaborate in real time.
- These collaborations may include manufacturers, partners, academic institutions and research institutes.
- New concepts can be tested in a virtual world before moving to physical prototyping or production.
Outcome-based products and services: As digital platforms mature and products become increasingly smart and connected, the decade ahead may see a boom in more outcome-based services. This is where the customer doesn’t buy a physical product, but instead signs up to pay for the guaranteed outcomes that product or system delivers.
- This shift will require manufacturers to establish new infrastructure rich in predictive analytics, remote communications and consumption monitoring.
- It also requires a mindset change for traditional manufacturing, from a focus on units and costs to product lifecycles, performance levels and usage.
Blockchain networks: By 2030, blockchain could be leveraged for most world trade, helping to provide the secure traceability and provenance needed to prevent physical product counterfeiting, grey markets in medicines and even the adulteration of the global food supply chain.
- A blockchain is an electronically distributed ledger accessible to multiple users. Blockchains record, process and verify every transaction, making them safe, trusted, permanent and transparent.
- Blockchain technologies promise to be a viable solution to manufacturers’ need to automate, secure and accelerate the processing of key transactions across industrial ecosystems.
E-manufacturing marketplaces: Digitally empowered production-line adaptability, such as the kind that emerged during the pandemic, will provide a foundation for companies to offer spare production capacity to other companies in different sectors.
- This maximizes the return on a company’s production-line investments and can generate new revenue streams for the future.
- Combined with e-commerce, e-manufacturing will enable designers, engineers and/or smaller companies to more easily connect with a large pool of qualified producers to deliver and scale a final product.
Manufacturing in 2030 Project: New Boxes is just one of the industry trends and themes identified by the Manufacturing in 2030 Project, a future-focused initiative of the MLC. For more details on megatrends, industry trends and key themes for Manufacturing in 2030, download the MLC’s new white paper “The Next Phase of Digital Evolution.”