Manufacturing Operations Management Talk

IT-OT Convergence Is a Requirement for Smart Manufacturing

Operational Technology (OT) and Information Technology (IT) have had a long history of isolation from each other in many industrial organizations. However, they have been slowly converging on the scene as companies make advances towards Smart Manufacturing methods. Companies that master this convergence ahead of their peers will have an advantage towards realizing the Smart Manufacturing vision.

IT, OT, and IT-OT convergence

OT has traditionally been associated with industrial environments and includes the hardware and software systems that control and execute processes on the shop floor including data acquisition, supervisory control systems (SCADA), programmable logic controllers (PLC), and computerized numerical control (CNC) machining systems.

IT has traditionally been associated with the office environment and includes the information systems and communication infrastructure used to run the business functions. IT resources include computers, data storage, networking devices, and processes to create, process, store, secure and exchange all forms of electronic data.

Each one of these technology areas have been traditionally managed by separate departments in many manufacturing companies and staffed by personnel with a different set of skills, training, and career path.

However, Smart Manufacturing has a highly connected vision for the factory of the future with information flowing in near real-time between production and enterprise systems to achieve highly orchestrated business, physical and digital processes within plants, factories and across the entire value chain. [1] For this future to become a reality, the convergence of IT and OT systems is a must-have requirement, even with the technical, cultural and security challenges that comes with it.

IT-OT convergence is the end state sought by manufacturing organizations whereas instead of separation between IT and OT as different technical areas of authority and responsibility, there is an integrated approach to process optimization and information flow between production automation and enterprise information systems.         

The evolution of IT-OT convergence

In the last two decades, the distance between OT and IT has become thinner. In part, this can be attributed to the ubiquitous use of internet and wireless connectivity for PCs and IIoT devices at the production floor.

While IT inherently covers communications as a part of its scope, OT has traditionally not been a networked technology, and especially not to the internet. Many industrial devices for monitoring or control didn’t have embedded computing capabilities. Those few devices with computing resources generally used closed proprietary protocols and PLCs rather than technologies that provide control through IT software on IT servers. The production control systems often relied on “air gapping” for security.

However, advances such as machine-to-machine communication, as well as the introduction of IIoT devices fitted to legacy industrial equipment are requiring that OT and IT departments start working closer together.

IIoT devices include a wide assortment of sensors for gathering conditions such as temperature, pressure, vibration, and chemical compositions. IIoT devices also include actuators that translate digital commands and instructions into physical actions, such as controlling valves and moving mechanisms.

IIoT devices can employ wireless communication over standardized networking protocols to communicate the relevant data from each physical system back to IT systems for monitoring and analysis purposes–IT systems that include applications, servers and storage which might be running on-premises or on the cloud. The results of that analysis can then be passed back to the physical system to allow more autonomous operation, enhance accuracy, benefit maintenance, and improve uptime.

Figure 1. IT-OT convergence has been evolving over the last few decades

The diagram in Figure 1 shows a timeline of how OT and IT systems have been evolving independently over the last few decades with a converging trajectory. OT systems like SCADA and PLCs have been adopting support for IT networks, data exchange standards and web interfaces. IT systems have been adopting a more modular approach to apps and support for APIs that help connect IIoT devices, machines and production systems.

Back in the 1970’s, the ISA95 standard (as depicted on the left side of Figure 1) prescribed a four-layer approach to the design of connected production and business systems. In the last few decades, we have seen that the advent of Smart Manufacturing/IIoT platforms, IIoT devices, APIs, big data analytics and data lakes has challenged the layered approach in favor of a flatter approach with more connectivity options (as depicted on the right side of Figure 1). [2]

Integrated IT-OT systems enable new levels of process integration across production and business functions. For example, an IIoT sensor can collect operational data on a machine at the factory and send it over a wireless network to an IT application that performs predictive maintenance analysis. That application can trigger a maintenance order in the maintenance system to dispatch a mechanic and perform maintenance on the machine to avoid the potential of downtime due to a machine malfunction. 

Consider an IIoT-enabled wind turbine as another example of IT-OT systems working together. By itself, a wind turbine would be classified as industrial equipment along with all the equipment and electronics necessary to generate power and connect that power to the grid. However, IIoT sensors can be added to detect wind direction and intensity while communicating its output, condition (temperature, vibration, pressure), and status to a centralized controlling location. The central system analyzes the data, provides commands needed to autonomously configure the wind turbine for optimum performance under current weather, and can trigger workers to take action when unusual conditions are sensed.

Leveraging cloud and edge computing

The addition of edge computing capabilities to IIoT devices enables real-time data processing closer to the source. Instead of sending the data over a network to a centralized location for processing, the IIoT devices can analyze time-sensitive production process data and return insights quickly for direct monitoring of industrial conditions before the data becomes obsolete.

Edge capabilities are important because IIoT and OT devices are often part of a distributed network architecture, making transmission to a central processing location difficult or impossible in some cases. Edge devices can also maintain critical industrial systems running when a connection is down or interrupted, which would otherwise incur costly consequences.

Figure 2. An example leveraging cloud and edge computing for data analytics and storage

Figure 2 depicts an example of how IT and OT systems could be integrated leveraging cloud services and edge computing for enhanced analytical and data storage capabilities. Concerns about connection latency and bandwidth are some of the reasons for deploying workloads to the edge. However, moving processing and storage closer to users and machines can also address concerns about data protection while enabling decentralized autonomous decisions for machine and process adjustments on routine situations and expected changes of conditions. Gartner discusses more edge computing use cases in their “2021 Strategic Roadmap for Edge Computing”. [3]

Figure 3. An example of gradually transitioning in IIoT and big data platforms into the IT-OT landscape

The reality for many industrial organizations is that they have implemented multiple generations of equipment and systems that need to somehow be part of the convergence story. Figure 3 shows an example of how a company can transition gradually to a new architecture by implementing cloud services and an IIoT backbone. In this example, the company first moved their ERP system to the cloud. They also implemented an IIoT backbone in parallel to their SCADA and MES system and started connecting new equipment to it. The IIoT backbone is publishing data to a data lake in the cloud where predictive analytical models are analyzing the data.  Similar transition scenarios have been documented by LNS Research in the article “The Holy Grail and the Puzzle in Discrete and Batch Manufacturing Applications”. [4]

Culture and security challenges

IT-OT convergence is not just about technology, it is also an organizational convergence dealing with the structure of the internal business. IT and OT departments must reform their processes to accommodate each other, changes must be well communicated throughout the process, and employees need to be cross-trained. For example, a business might follow specific processes for storing and protecting IT data, but this process might have to be adapted or extended for converging OT systems.

With this new level of integrated systems, workers can be empowered with more insights to help them in their daily jobs. However, workers will need to be educated on new technology, methods, and insights for the organization to fully leverage the benefits of the new capabilities. 

As IT reaches more OT systems, air gaps can't provide adequate security for network communication and OT data. Organizations driving IT-OT convergence must educate and train staff to understand and implement adequate security. When implemented properly, IT-OT convergence can merge business processes, insights and controls through secure systems and a uniform governance model.

As an example, Georgia-Pacific implemented several procedural and organizational enhancements when implementing centralized operations data and integrating IT and OT departments including a central collaboration support center to share data and best practices. [5]

Overcoming culture and governance issues with a wider set of stakeholders across the business is not a trivial task and it could make the difference between a smooth or rough ride along the convergence journey. A recommended strategy is to use multi-disciplinary teams to help guide the effort and establish common terminology, drivers and goals among IT and OT teams. Cross-discipline collaboration is key to a successful initiative. [6] Early convergence on security practices will also be essential.

Benefits of IT-OT convergence

The following are some of the benefits of IT-OT convergence over separate IT and OT:

• Improved automation and visibility because integrated OT systems can transmit real-time production data to enterprise systems
• Faster time to implement Smart Manufacturing solutions into an IT-OT integrated environment
• More decentralized autonomous decisions at the edge near the work cell for routine situations and semi-autonomous triggering of alerts for non-routine situations
• Improved IT and OT governance, compliance, and security methods with shared auditing staff
• Effective device management because all IT and OT systems are seen and managed through a common methodology
• Efficient energy and resource usage, as OT systems can be integrated to IT analytics and AI for better data for performance optimization
• Readiness to integrate production data directly into the supply chain, both up and downstream from the manufacturing plant. Intelligent, automated processes based on supply chain and logistics integration can help minimize inventory and fine tune time-to-market delivery

Successful IT-OT integration is an essential step in the journey towards creating a fully connected, dynamic and flexible Smart Manufacturing enterprise. It’s not a small step to take, but without it, the ability of a manufacturer to compete and participate in a highly connected manufacturing ecosystem will be limited. The manufacturers that make early progress on IT-OT convergence will gain significant operational and market advantage.

References

[1] A Refined Smart Manufacturing Definition for 2021, C. Leiva, AutomationWorld, 2021

https://www.automationworld.com/process/iiot/article/21232436/a-refined-smart-manufacturing-definition-for-2021

[2] The Impact of the Internet of Things on MOM Solutions, A. Hughes, LNS Research, 2016

https://www.lnsresearch.com/research-library/research-articles/-ebook-the-impact-of-the-internet-of-things-on-mom-solutions

[3] 2021 Strategic Roadmap for Edge Computing, B. Gill, Gartner, 2020

https://www.equinix.com/resources/analyst-reports/edge-computing-strategies-gartner-2021

[4] The “Holy Grail” and the “Puzzle” in Discrete and Batch Manufacturing Applications, T. Comstock, LNS Research, 2021

https://blog.lnsresearch.com/the-holy-grail-and-the-puzzle-in-discrete-and-batch-manufacturing-applications

[5] Georgia-Pacific’s Approach to IT/OT Convergence, L. Rodriguez, AutomationWorld, 2020

https://www.automationworld.com/process/workforce/article/21137682/georgiapacifics-approach-to-itot-convergence

[6] Improving Production – How IT, OT and Quality Can Collaborate, W. Goetz/R. Rossbach, Pharmaceutical Technology Europe, 2018

https://www.pharmtech.com/view/improving-production-how-it-ot-and-quality-can-collaborate

 

A Brief History of Smart Manufacturing

Around 2005, connectivity, data, and computing power were advancing at Moore’s Law pace along with the Internet, eCommerce, social media, and smartphone platforms. The concept of cyberinfrastructure entered the vocabulary around that time.

The term Smart Manufacturing was coined in 2006 at a National Science Foundation workshop on Cyberinfrastructure [1]. It was called Smart Process Manufacturing at that time but was quickly shorten to Smart Manufacturing as the work evolved around the initial concepts.

At that time, the term cyberinfrastructure was being used in the context of implementing new applications that combined the power of data exchanges through networks that aggregate information about different facilities and locations with advances in data modeling and computational power. The NSF workshop outlined strategies for multi-scale dynamic modeling and simulation, large-scale optimization, sensor networks, data interoperability, requirements-driven security, and coined the term “Smart Plant”.

“The ‘Smart Plant’ is composed of ‘smart assets’ that not only provide their basic process function but provide proactive feedback on the economic, environment, health and safety performance of that asset in aggregation with the other assets and in the moment. Smart plants operate to tighter specifications and involve a much greater understanding of the processes, greater automation and decision support, expanded use of automation, data and data interpretation, and a new-generation workforce that is trained and oriented toward a knowledge and information mindshare.” [1]

In parallel, Germany was working on a similar initiative completely independently called Smart Factory, and a couple years after that, they renamed it Industrie 4.0. Both Smart Manufacturing and Industrie 4.0 have evolved in parallel. Industrie 4.0 had a focus on cyber-physical systems while Smart Manufacturing has focused on highly connected information-driven manufacturing. There is a big overlap on both agendas, and we will continue to see parallel and joint efforts going forward.

In 2010, the Smart Manufacturing Leadership Coalition (SMLC) gathered a group of over 50 industry leaders in a workshop to advance the development of the infrastructure and capabilities needed to deliver the full potential of Smart Manufacturing. The group documented goals for Smart Manufacturing in the report “Implementing 21^st Century Smart Manufacturing” [2] along with challenges like affordability, usability, interoperability, customer integration, protection of proprietary data, and cyber security.

In 2014, the DKE/DIN Industrie 4.0 German Standardization Roadmap Version 1.0 [3] was published. The Germans stressed standardization as key to the success of the Industrie 4.0 initiative. The roadmap noted the importance of:

• Integration of technical processes and business processes
• Digital mapping and virtualization of the real world
• The integration of data-enabled “smart” products with production systems
• Extensive use of the internet

The roadmap defined cyber-physical systems in the plant as seamlessly integrating digital data from the physical production process and “smart” products into synchronized information systems that optimize the production workflow through simulation and analytical tools.  

The German initiative is soon followed by similar industrial initiatives in other countries that took notice of the importance of advancing manufacturing in a global economy competition.

Between 2010 and 2016, early adopting manufacturers in the U.S. continued to advance the implementation of Smart Manufacturing techniques. Organizations including the Manufacturing Enterprise Systems Association (MESA), the Industrial Internet Consortium (IIC), and the Smart Manufacturing Leadership Coalition (SMLC), brought together manufacturers, consultants, technology vendors, and academia to accelerate the implementation and document the practices and progress in Smart Manufacturing. [4]

Manufacturers implementing Smart Manufacturing are not just reducing cost, they are implementing technology-enabled business models and turning traditional factories from cost centers into profitable innovation centers through the integration of technologies including:

• Industrial Internet of Things (IIoT) 
• Smart machines and collaborative robotics 
• Cloud and edge computing  
• Enterprise integration and API management platforms
• A2A and B2B standards for multi-vendor interoperability
• Big data processing and predictive analytics capabilities 

In 2016, MESA International published the report “Smart Manufacturing Landscape Explained” [5] and NIST published the paper “Standards Landscape for Smart Manufacturing” [6].

In 2016, CESMII—the U.S. Smart Manufacturing Institute—was formed as one of multiple Manufacturing USA institutes focused on bringing together industry, academia, and federal partners to increase U.S. manufacturing competitiveness and promote a robust and sustainable national manufacturing R&D infrastructure. CESMII was established with a mission to radically accelerate Smart Manufacturing technologies adoption including advanced sensors, controls, platforms, and optimization models. The CESMII Roadmap for Smart Manufacturing was published in 2017 [7].

By 2017, Smart Manufacturing has gained wider adoption. Trade organizations and consulting firms were documenting success stories and practices as in the report by Deloitte titled “The Smart Factory” [8]. Consulting organizations also started publishing guidance like the Singapore Smart Industry Readiness Index [9] to help manufacturers assess their business practices and establish roadmaps towards higher levels of Smart Manufacturing adoption.

Smart Manufacturing was recognized as including vertical and horizontal integration of connectivity, intelligence, workforce, and automation across multiple dimensions of business processes including product lifecycle, operations, and supply chain.

Today, Smart Manufacturing technologies and practices have matured but the adoption has not crossed the chasm and moved beyond the early adopters into the early majority for wide adoption in the ecosystem. It is necessary to move to the next stage of adoption—the democratization of Smart Manufacturing.

References

[1] Workshop on Cyberinfrastructure in Chemical and Biological Process Systems: Impact and Directions, National Science Foundation, Davis, 2006

[2] Implementing 21^st Century Smart Manufacturing, SM Leadership Coalition, 2011

https://www.controlglobal.com/assets/11WPpdf/110621_SMLC-smart-manufacturing.pdf

[3] The German Standardization Roadmap for Industrie 4,0 Version 1.0, DKE German Commission for Electrical, Electronic & Information Technologies of DIN and VDE, 2014

https://www.din.de/resource/blob/65354/1bed7e8d800cd4712d7d1786584a7a3a/roadmap-i4-0-e-data.pdf

[4] On the Journey to a Smart Manufacturing Revolution, IndustryWeek, Leiva, 2015

https://www.industryweek.com/technology-and-iiot/systems-integration/article/21967056/on-the-journey-to-a-smart-manufacturing-revolution

[5] Smart Manufacturing Landscape Explained, MESA International, 2016

https://www.pathlms.com/mesa/courses/14866

[6] Standards Landscape for Smart Manufacturing, NIST, 2016

https://nvlpubs.nist.gov/nistpubs/ir/2016/NIST.IR.8107.pdf

[7] Smart Manufacturing-Leveraging the Democratization of Innovation, CESMII, 2017

https://www.compete.org/storage/EMCP_SmartManu_Program_FINAL.pdf

[8] The Smart Factory, Deloitte, 2017

https://www2.deloitte.com/content/dam/insights/us/articles/4051_The-smart-factory/DUP_The-smart-factory.pdf

[9] The Singapore Smart Industry Readiness Index, Singapore Economic Development Board, 2017

https://www.edb.gov.sg/en/about-edb/media-releases-publications/advanced-manufacturing-release.html

 

Smart Manufacturing Benefits Beyond Performance Improvement

Smart Manufacturing is transformational, radically impacting the performance of the manufacturing ecosystem through measurable improvements in areas such as: speed, agility, innovation, quality, costs/profitability, safety, asset reliability and energy productivity. ​

The decision on how to embark on the smart manufacturing journey should align around a strategic vision for the future of the organization. The steps on the journey vary for different organizations with different types of processes and different market drivers so they cannot be easily generalized. But regardless of the strategic business drivers, demonstrable value along the journey is essential to successful adoption and incremental investment required to sustain the smart manufacturing journey.

Undertaking a smart manufacturing initiative and upgrading the manufacturing operations management strategy generally addresses the following broad categories of benefits with the ultimate result of improving profitability which in turn accelerates investments in more innovation.

Productivity and Cost Reduction  

The increased automation tied to many Smart Manufacturing projects can achieve great productivity improvements. Improvements that might give your company a competitive edge by raising the bar for price and quality in your market. Automation can reduce cycle time, labor time, and quality errors.

Data coming from monitored machines and processes feed into AI-driven insights to alert of pattern changes and suggest improvement for more efficient use of resources. For example, more predictable inventory requirements can lead to reduced safety buffers, and correlation of process and environmental data can lead to energy cost optimization.

Utilization and Reliability

Information coming out of connected high value machines can reveal asset performance issues and lead to higher levels of utilization and lower levels of production downtime. Predictive maintenance analysis helps prevent unplanned downtime by flagging equipment for proactive maintenance based on usage and performance data. These improvements not only lead to higher business continuity, but they can also increase machine availability for additional production output.

When a key industrial machine goes down, it is usually a whole line that goes down. On top of the repair cost, employees might end up idle, and production schedule might be affected, costing loads of money and unhappy customers. The brand reputation can be damaged, and orders can end up being cancelled.

Quality

It might have been okay to have a few defects in years past, but today’s customer has more access to information, more vendor choices, and is really looking for zero defects.

Smart Manufacturing technologies can be used to monitor quality aspects of the product and process in real-time to reduce process variability, eliminate undetected errors and catch issues as early as possible in the process to minimize scrap and rework costs.

Data with the right context and relation to process and resources allows analysis to dig past the symptoms to understand what is really happening and why it is happening. The additional metrics and insights can help identify human, machine, or environmental causes of poor quality for quicker resolution.

The ultimate benefits of higher quality go directly to improved customer relations with higher brand equity, lower warranty cost, and reduced risk of product recalls.

Beyond Continuous Improvement

The above areas of cost reduction, quality, and utilization improvement through automation and integration might be enough to justify many Smart Manufacturing technology investments, but it would be short sighted for the organization to miss the opportunity for a more strategic look into Smart Manufacturing for digital transformation of the business and ecosystem that yields the following additional strategic benefits.

Transparency, Speed and Collaboration

In Smart Manufacturing, data moves from machine-to-system and system-to-system without human intervention and is available for AI-driven insights and human analysis in near real-time across multiple production lines located anywhere in the world. However, Smart Manufacturing is not just about the data. In fact, many companies are inundated with data from new sensors and enhanced machine monitoring yet not realizing the full benefit from that data.

Transparency, speed, and collaboration are all linked together in the Smart Manufacturing vision.  Information flow must be designed for raw data to get contextualized into information and analyzed for insights that are provided back into multiple systems in the manufacturing ecosystem. Insights that drive event-driven autonomous actions for routine situations and enhanced human decisions for non-routine situations.

If the processes in the plant are still bound to paper-based forms, we cannot achieve the desired level of speed and semi-automated processes for Smart Manufacturing. If information sits around on desktops and takes hours or days to get in front of the right person, we are not achieving the desired benefits. The insights need to get to the right people at the right time to make the right decisions. Decisions that will prevent errors, prevent delayed actions, optimize outcomes, and get disseminated quicker into the whole value chain.

Traditional manufacturing plants operate in silos with minimal collaboration or knowledge sharing. Smart Manufacturing gives production lines, business processes and departments improved capabilities to communicate, share data, collaborate, and make improvements regardless of their systems, location, or time zone. These enhancements make it realistic to manage manufacturing operations with more precision and better collaboration among employees, suppliers and partners.

Smart Manufacturing creates an open atmosphere of information-based decisions where decision makers will have the trusted data when it is needed, where it is needed and in the most useful form. Problem solving will not be limited to localized decisions. Instead, problems can be prioritized and tackled based on a total enterprise and ecosystem picture.

Innovation, Agility and Resiliency  

Smart Manufacturing systems are integrated with open interoperable APIs allowing manufacturers to quickly change equipment, process flow, product configuration, labels, and packaging. A smart factory is equipped with modular solutions and systems that can easily be reconfigured to scale up or down production, introduce new products, create one off production runs, or create high-mix manufacturing opportunities. This agility makes the organization adaptable to changing demand and more resilient to handle market disruptions.

However, the biggest reward in a Smart Manufacturing strategy comes from leveraging the higher levels of connectivity and information to (a) enhance operating models, (b) provide more personalized product and service offerings, and (c) innovate partner ecosystems to drive higher revenue and customer value.

The increased speed of digital communications and the ability to quickly change product configuration means that we can have increased speed to market for new products and higher ability to capture market share.

Consumers increasingly want direct interaction with a brand and its manufacturing capability. The desire to co-create and customize products applies not only to consumers but also to B2B customers even if it is just a custom label, added feature, or additional product data. For example, Smart Manufacturing solutions can automate track and trace functionality and provide the customers with higher levels of digital data to go along with the product.

Consumers also expect higher levels of customer service and faster response to request for customizations or service issues. A highly connected ecosystem can disrupt traditional supply chains with highly orchestrated processes. Smart Manufacturing can put companies in a better position for partnering and exchanging data in such ecosystems to meet these market demands.

Transformation at a National Level

Smart Manufacturing opens new areas of innovation that will optimize the entire manufacturing industry to create higher quality products, improve productivity, increase energy efficiency, and sustain safer plant floors. 

Smart Manufacturing can make US manufacturers more competitive in the global landscape, help towards onshoring more production, and offer the opportunity to boost employment in the US. As Smart Manufacturing is adopted, new technology-based high skill manufacturing jobs will become available in addition to related non-manufacturing positions.

In addition to raising US productivity, Smart Manufacturing is good for the environment by reducing waste of resources and energy consumption. Energy is directly saved as processes are optimized based on energy usage insights, and indirectly saved as waste of resources is reduced by reducing defects, scrap, and overproduction of inventory in a more efficient supply chain.

Even though some energy is created from renewable sources, much is still created from fossil fuels (coal, natural gas, and petroleum). Reducing energy consumption reduces carbon emissions for a healthier planet and improved quality of life.

Smart Manufacturing Champions Must Address Organizational Culture Upheaval

Photo by JESHOOTS.COM on Unsplash

There is very good technology available today that helps manufacturers solve real problems, but that is not what Smart Manufacturing is about. Smart Manufacturing is a transformational opportunity that requires comprehensive cultural change to truly leverage technology in a future state where teams are working together to achieve an optimized value creation process for internal stakeholders, through to customers and shareholders.

New technologies should be explored with a clear understanding of how they support the desired future mix of products, services and business models for the enterprise.

A transformed digital manufacturing enterprise is enabled by technology and, more importantly, a shared mindset of enterprise-level transparency, optimization and enhanced decision-making.

There are several natural forces challenging transformation in mature organizations.

The first challenge is the existing ingrained organizational culture—the legacy patterns and shared assumptions that have worked for years and are passed down to new team members as best practices.

Legacy thinking can make it difficult to institutionalize new processes encouraging transparency, collaboration and viewing external resources as partners instead of suppliers.

The second challenge might be counterintuitive, but many successful organizations are moved by the relentless pursuit of incremental improvements. They have ingrained a culture of cost reduction over the last two decades. Organizations will need to focus on rewarding progress toward the future vision versus rewarding solely based on performance improvement.

A third challenge is the emotional side of business transformation—emotions fueled by a lack of understanding of differing perspectives, motivations and concerns among subcultures within the organization—is often underestimated.

The transformation champion acts as group therapist at times, facilitating convergence and recognizing that each subculture sees the initiative through different lenses.

Some managers might be wary of the initiative if they have had a bad prior experience with automation or IT projects. Other managers might try to push for results too quickly, underestimating the effort in scaling solutions to the entire enterprise. It is important to pace the transformation and make time to evaluate the needs for different types of products and production processes along the way.

The engineering and IT team can help with that evaluation. Engineers consider themselves craftsmen and experts in building elegant solutions. They want an efficient overall system and will resist initiatives that feel half-baked in a rush to rewire everything. The initiative will be better accepted by this subculture if they feel recognition and ownership in new processes.

Production technicians might be wary for different reasons, such as a lack of new skills or a reduction of the workforce. It is not enough for the initiative champion or coach to spend all of his or her time on training technical skills; it is just as important to facilitate open forums to allow employees to express their concerns.

The Smart Manufacturing transformation is as much of a cultural and emotional upheaval as it is a reengineering of processes and systems. Concerns about organizational changes and upskilling must be managed throughout. The new organizational culture must embrace continuous change of the business and ecosystem considering the new reality of a continuously changing marketplace.

 

Article was published as: Digital champions must address cultural upheaval, Conrad Leiva, SME Smart Manufacturing, May 2020

Digital Supply Chain Resilience Tactics

The implementation of modern manufacturing operations management practices including manufacturing systems, lean manufacturing and six-sigma have improved effectiveness and operating costs within many manufacturing companies. However, it takes an entire supply chain to bring manufactured products to the marketplace and supply chains are more vulnerable than ever due to a range of risks including small inventory buffers, reduced slack in lead times, higher product complexity, and global interdependence.

We have learned that events like the COVID-19 pandemic, 9/11 and Hurricane Katrina can cause big and sudden disruptions in supply chains. Disruptions so big that regular inventory buffers of materials and parts cannot diminish the impact. Customers are left waiting for product deliveries and market shelves cannot be replenished on time.

During these times, it behooves companies in the same supply chain to innovate as a collaborative team to enable new levels of efficiency and resiliency in producing manufactured goods through higher levels of transparency, agility and orchestration. A general supply chain resiliency strategy strives to maintain performance measures during potential disruptions including fulfillment of orders, delivery reliability, and customer satisfaction.

Supply Chain Resiliency Definition

Supply chain resiliency is the ability of a supply chain to both resist disruptions and recover operational capability after disruptions occur. Where operational capability is the continuity of operations at the desired level of connectedness, control, and delivery of products and services to end customers.

Resistance capacity is the ability of a system to minimize the impact of a disruption through tactics including inventory buffers and supplier redundancy. Recovery capacity is the ability of a system to minimize the time between disruption onset and the time when the supply chain returns to regular performance levels of delivery to end customers. The recovery time includes (a) the time to detect the problem and (b) the time to orchestrate the response and recovery. However, it is important to note that, after big disruptions, the demands from the market might have changed and might not support or require the prior levels of performance.

Supply Chain Resiliency Tactics

• Minimize the time it takes to detect a disruption in the supply chain. Review practices and systems for supply chain monitoring, communication and orchestration. Establish a digital supply chain. Implement an early warning system that quickly bubbles up concerns from lower tiers in the supply chain.
• Increase the capability to switch to a different product mix to adapt to shifts in demands due to a market disruption. For example, in response to a weather or health related crisis, the demand for essential products increases as the demand for nonessential products decreases.
• Increase supplier loyalty through partnership programs and incentives for key suppliers to deliver during tough times. 
• Assess sensitivity to failure in supplier nodes in the supply chain for each product line. For each node, determine the possible impact of deliveries stopped for a few weeks. The impact to end-customer deliveries based on the expected time it takes to recover from the failure of the node.
• Review inventory buffers. It is tempting to increase buffers and hoard critical materials and parts to avoid any disruption but there are inventory costs associated with this tactic. 
• Design products for configuration flexibility to allow for the use of alternate materials and parts. If possible, design to use off-the-shelf parts  
• Establish supplier redundancy. Wherever possible, establish more than one supplier for critical material and parts. For off-the-shelf parts, buy from distributors that pool supplies from several suppliers to minimize the impact of losing one supplier during a disruption period.
• Work with suppliers ready to engage in a digital supply chain and flexible to converting production lines, switching processes, increasing capacity, and supplying alternate parts when needed. 

Major disruptions will always have an impact, but we can be better prepared for major and minor supply disruptions through risk mitigation strategies. 

 

Additional articles on the topic:

Supply Chain Resilience for an Era of Turbulence, The Economist, 2020

Building Supply-Chain Resilience for Turbulent Times, Supply Chain Brain, 2020

The Resilient Supply Chain Benchmark: Ready for Anything? Turbulence and the Resilience Imperative, The Economist, 2020 

Top Seven Manufacturing Challenges in 2020

We have been hearing about the promised value of Industry 4.0 for several years. 85% of businesses agree on this potential. However, manufacturing productivity has been flat for the last decade and many industry digital initiatives are stalled. Many manufacturers have not started initiatives and many others are in assessment mode with a few pilot projects.

Some of the challenges for manufacturers are new:

#1. Uncertain demand rebound for some goods

Although manufacturing is not on the top five on the list of impacted industry jobs during this pandemic, it is still showing a big impact at number six on the list. However, it is not a question of whether it will bounce back or not, it is more a question of when and how fast. We hope manufacturing will be one of the sectors to bounce back quickly from the impact of the pandemic.

#2. Higher productivity is needed to reshore

One of the encouraging trends is reshoring. There are several reasons for the shift from offshoring to reshoring. Economies in many go-to offshoring countries have grown stronger, resulting in increased wages for their residents. In the locations where labor is still inexpensive, the infrastructures often cannot handle complex manufacturing operations. Industry is also looking to reshore suppliers in critical medical and food supply chains where reliance on imports surfaced big issues during this pandemic.

To sustain the reshoring trend, manufacturers must continue achieving increases in productivity using new technologies like smart manufacturing and robotics to automate many of the processes that used to require intense human labor. All in all, this resurgence in reshoring will lead to an increase in made in the U.S.A products in the future.

But prior challenges for manufacturers are still there:

 #3. Global competitive pressures

The U.S. is no longer on the top of the list of manufacturing most competitive countries when you consider innovation capability, health, skills and business environment. What will it take for U.S. companies to reclaim and hold the top spot? Manufacturers will have to shift their focus to advanced technologies and smarter talent for greater competitiveness.

Shifts in sourcing and shipping logistics are constantly evolving throughout the manufacturing industry, especially as a result of the COVID-19 pandemic. The ongoing trade war between China and the U.S. rages on, causing the short-term outlook for domestic companies to be uncertain. Manufacturing supply chains will need to be more resilient in adapting to potential future disruptions.

Manufacturers should focus on delivering the best digital customer service experience possible and increase their efforts toward digital projects that build agility and scalability to help to manage risk in supply chains during disrupted and uncertain times.

#4. Exponential pace of technology innovation   

From sensors, to automation, artificial intelligence, robotics and cloud computing, the challenge for many manufacturing companies is keeping up with the rapid pace of technology innovation. Not only to use it in their products but also to leverage it in their processes and customer services.

Newer industrial automation equipment incorporates sensors, controllers, computers, and network connectors. The use of robotics including robots working alongside humans is expected to triple or quadruple the next five years. Yet, integration of equipment to manufacturing systems is still too expensive for many small and medium manufacturers.

Many manufacturers understand the important role technology plays, yet many also feel they are not providing their team the best tools for bottom-line success. Thus, there is often a disconnect between understanding the need for new technology and actual implementation and adoption.

#5. Increasing complexity in products and supply chain

Over the last years most manufacturers have seen their products become more complex. Not only are products increasing in complexity, but many organizations are not equipped with the right tools to manage the intricacies of complex product development and manufacturing.

Product designs are getting more intricate with new materials, more parts and an increasing number of electronic components, microprocessors and embedded software. Not only are the number of components increasing, parts are sourced from multiple vendors and are now much smaller and more technologically advanced, adding another layer of complexity to the product and its manufacturing.

Organizations need information systems to help with these complexities in the product lifecycle starting with design, through supply chain and manufacturing operations. Modern supply chains are dynamic with a lot of change taking place, a lot of stakeholders involved, and increasing requirements for data exchange for higher collaboration, transparency and risk management.  

#6. Talent pool shrinking for manufacturers

During the past several years, the skills gap has been top of mind for U.S. manufacturers. We can forecast that the demand for talent in manufacturing will come back in the next few years, due to retiring baby boomers and reshoring trends. Many manufacturing jobs were going unfilled over the past few years and that trend will probably pick up again once manufacturing starts hiring again.

Even in an age of automation and robotics, skilled workers are still needed for their problem-solving capabilities, to fill managerial positions and to perform analysis. Baby Boomers are aging and retiring and there are simply not enough skilled workers to fill the positions they’re leaving. To fill the widening skills gap, manufacturers will need to train existing workers to perform skilled tasks and get creative in their efforts to attract workers.

#7. Bombarded by vendor’s varied propositions

In an ever-accelerating digital era, businesses feel pressured to meet rapidly changing customer demands, reinvent or evolve themselves, and beat competitors to the punch by being the first to provide faster and better solutions. To thrive in this new data-rich era, manufacturers want to adopt new technology at the plant floor and mine their data into smart software systems to continuously monitor, analyze and optimize the use resources in their complex operations.

However, there are many technology options to evaluate and many vendors putting forward great sounding value propositions for big data, artificial intelligence, IoT platforms, workflow tools, PLM, ERP, MES, QMS, EAM, and supply chain management systems. In the quest to reap the benefits of these technologies, organizations may focus too heavily on the hype surrounding specific technologies, as opposed to whether the innovation can help solve their problem or business strategy.

It is essential that organizations put a good plan together that focuses on achieving business goals, adapting to marketplace changes, and taking significant steps towards achieving their long-term Smart Manufacturing vision.

Conquering the barriers holding back adoption of Smart Manufacturing technology could create a competitive advantage, while simultaneously helping to battle competition from overseas and the manufacturing skills gap. In future articles we will discuss strategies for conquering these challenges.

Six Trends Accelerating the Smart Manufacturing Future

The Smart Manufacturing future is upon us with intelligent, real-time orchestration and optimization of business, physical and digital processes within factories and across the entire value chain that delivers the end-product. A value chain wherein resources and processes are automated, integrated, monitored, and continuously evaluated based on high levels of transparency and data exchange as close to real-time as possible.

A vision for Smart Manufacturing within reach in the next two decades thanks to advances in platforms that are making integrated systems and technologies accessible to more manufacturers, and further empowered by consortia focused on making available the knowledge required to run Smart Manufacturing systems and ecosystems.   

Several trends are fueling optimism on achieving the Smart Manufacturing vision:

#1. Product manufacturing and service are converging based on new market demands

For many products, the market is switching from buying a mass-produced product off-the-shelf to buying a custom configured product as-a-service. These new blended manufacturing-service business models require more customer interaction and elevate the value of the digital data that goes along with the product during its service life. These ecosystems are forced to evolve to deliver the required data services.

#2. Manufacturers are redefining their processes and services around new enabling technologies

New technologies are not just enabling optimization of existing processes, companies are rethinking their processes, value chains, and business models. The proliferation of smart phones, Internet of Things (IoT), cloud services, low-code platforms, and DIY machine connectivity are causing manufacturers to empower the customer and internal citizen technologist (engineer, developer and data scientist) with more data and tools in order to accelerate the pace of innovation in the whole ecosystem.

#3. Hybrid human-machine processes are on the rise

About three decades ago, when innovation led to the high use of robotics in factories, many people predicted that within 10 years all factories would be filled with robots, and there would be no human operators. Decades later, more than half of the manufacturing tasks are performed by human operators.

Rather than replace humans, robots will work collaboratively with a balanced distribution of responsibility. Technologies like cobots, exoskeletons, artificial intelligence (AI), and augmented reality (AR) will augment, assist and empower the future worker.

#4. Manufacturing ecosystems are achieving higher levels of connectivity and transparency

There has been much progress in engineering, production automation, and enterprise business systems in the last few decades within their respective islands. Manufacturing operations Management (MOM) systems and paperless operations are the new norm in the factory. Machines are getting smarter with embedded computers, AI, and application program interfaces (APIs) ready to exchange data with MES and cloud platforms. Smart Manufacturing eliminates manual steps and inconsistencies bringing silos of information together and finally links them in a full digital thread of automated data exchanges.  

The smart factory becomes a node in a connected smart ecosystem with API requirements for partner and customer interaction. The required data exchanges go beyond purchase order, shipment and warranty data if the ecosystem is delivering new data services with the product. For example, if a company is including a product digital twin as an additional service, it must be ready to make accessible, to the ecosystem and customer, 3D and simulation models of the product along with each unit’s unique as-built and operational data.  

#5. Manufacturing systems are incorporating more automated intelligence 

The availability of low-cost sensing, pervasive connectivity and cloud computing services has made it practical to access and holistically analyze data across integrated systems. Unstructured datasets such as images, natural language, and even messages in social media have become part of the data available for analysis.  More integrated data and AI capabilities are bringing us closer to systems with automated routine decisions where humans intervene only when necessary.

#6. Manufacturing culture is embracing digital

Manufacturing companies are investing more into their organizational culture. There is a recognition that technology plays a key role in the company image and in attracting talent.  National governments have incentives for companies to adopt Smart Manufacturing and are supporting efforts to educate the workforce with the required new skills.  Staffing services are evolving to fill the demand for highly specialized skills.  

 

 

Smart Manufacturing Feud Verifies MOM and Digital Twin Popularity

MESA had a recent web event focused on Smart Manufacturing with a game show type of format and an opportunity for the audience to participate through polls. The Smart Manufacturing Community Feud was initially inspired on the Family Feud game show, but the idea was modified to an interactive format with the webcast viewers.

Two topics are worth highlighting because they do not conform to some of the industry analyst opinions.

• When asked about Manufacturing Operations Management (MOM/MES), the popular opinion is that MOM is evolving as a central repository of contextualized data. However, many also believe it will also be divided up into smaller modular apps. These are not mutually exclusive options. The MES platform can be designed to provide the data layer separately and allow varied modular apps to interact with the data layers via APIs.
• The most surprising response was the excitement around digital twins. There is a significant percent counting on the results of digital twin initiatives as an essential part of their business model. However, there is concern about the investment required and the effort to align multiple parallel initiatives among different departments working on simulation models and digital twin type of functionality. 

Other key points from the discussion are summarized below. These echoed many of the opinions we have received at workshops and regular calls with the MESA Smart Manufacturing Community members.  

• There is still a big percentage of manufacturers that have not fully embraced the Smart Manufacturing journey, but the industry leaders are starting to share their success stories and initiatives are becoming more prevalent.
• However, over half of the companies polled reported a mismatch between their Smart Manufacturing goals and the vision portrayed at conferences. Many have set their expectations for Smart Manufacturing much lower.  This aligns with previous surveys where there seems to be a 50-50% split on people focused on cost reduction and optimization versus business transformation and new business models.
• The vast majority believe that workers will be part of the factory of the future, but they will be augmented by co-bots, exoskeletons, and AI-driven assistants.
• Most agree that we do not need more data than we are collecting today. Having data does not seem to be the problem. We are either not collecting the right data, or we are not organizing, relating and modeling the existing data in ways that enable better use of analytical tools.
• Many are expecting security and privacy standards to evolve quickly and become the new norm in the industry for the entire supply chain. There is also enthusiasm behind blockchain as a technology that can help with data exchanges among value chain partners.

The link to the full recording is available at:

The Smart Manufacturing Community Feud – Episode 1  

https://services.mesa.org/ResourceLibrary/ShowResource/a65664c2-eeb1-4dcd-9075-dbc7d69d008c

This Industry 4.0 Revolution is Very Different – It’s About Ecosystem Transformation

I experienced the end of the Third Industrial Revolution as I began my career in manufacturing. Closed government and private networks gave way to an open network called the Internet. Numerical Controlled (NC) machines, Programmable Logic Controllers (PLC), and Personal Computers (PCs) were adopted at the factory floor. They were integrated to automation and tracking systems based on spreadsheets and the first wave of commercial manufacturing software, Computer-Integrated Manufacturing (CIM), which became the Manufacturing Execution System (MES).

We are now amid the Fourth Industrial Revolution. It is fueled by the convergence and proliferation of multiple game-changing technologies. including smart phones, cloud computing, 3D simulation, 3D printing, robotics, augmented reality and AI—coupled with advances in speed, capacity and affordability of computers, networks, sensors and integration platforms that enable enhanced platforms for commerce and industrial systems.

Where the last revolution felt focused on productivity gains through mass production and optimization of the factory, the current revolution feels focused on a new era of manufacturing supporting mass customization and products sold as services. It is finally possible to move forward with initiatives that started decades ago, about automation, data acquisition and bridging the IT (information technology like MES) and the OT (operational technology like PLCs) across many vendors and standards. However, many management teams are looking beyond the optimization and continuous-improvement mode of the last few decades.

The story behind this revolution is much bigger, and it is focused on transformation to new business models, orchestration of the value chain and evolving manufacturing ecosystems. Ecosystems with a mix of firms making parts and machines, developing software and providing services along with products. Separately, each ecosystem participant can focus and become very efficient in specialized competencies. Together, ecosystem partners can deliver complex solutions to the customer with a high level of customized service.

It is generally accepted that a great many business executives believe ecosystems are important—and that many are still unable to balance between investing in optimization of the current business and exploring the opportunities of new ecosystems and business models.

To get ready to participate in evolving ecosystems, start by making sure a digital backbone is established within each factory. A manufacturer still running the plant on paper will not be ready for a digital ecosystem. Every machine might not need to be connected to the factory system, but data-intensive stations placing a lot of components, running complex programs and inspecting parts should be providing data directly into a manufacturing system like MES. Every physical job needs a digital footprint in the MES. The MES is managing production and quality-control processes while updating in real time the production and customer orders in integrated enterprise systems like ERP with information that must eventually flow directly into the ecosystem network.

Technologies like data-integration hubs and IIoT platforms are facilitating the infrastructure. But establishing an efficient digital value chain and ecosystem is a complex issue for individual firms to tackle. Organizations must start thinking about the data and APIs needed for bi-directional communications among ecosystem participants and create a roadmap for how and when internal systems will support those types of data exchanges.

The journey to a connected ecosystem will take a few years with work to be done outside the four walls of the company. Industry groups, consortia, and institutes like MESA International, IIC, CESMII, and NIST are helping facilitate the convergence to digital infrastructure and data-exchange standards required to democratize the participation in ecosystems.

But many firms are sitting back and waiting. A far better move is to engage with these organizations now.

 

This article was published as: Ways to get ready to take part in evolving ecosystems, SME.org, C. Leiva, 2019

https://www.sme.org/technologies/articles/2019/october/ways-to-get-ready-to-take-part-in-evolving-ecosystems/

 

Taking Evolutionary Steps Towards Smart Manufacturing

In a recent article we discussed the hidden treasures within manufacturers’ existing systems. If the organization has already taken steps to go paperless and get the most out of existing systems, what do they work on next?

We tend to use the term digital transformation when we talk about Smart Manufacturing, but the term digital evolution might be more appropriate to describe the journey. We do not want to imply that this is a rip-and-replace all systems proposal. Many companies have already begun the transformation and have systems in place like a Manufacturing Operations Management (MOM), Manufacturing Execution System (MES), PLM and ERP that are important foundational linchpins. For these companies an integrate-and-extend strategy is more appropriate.

Leverage existing systems

Organizations can leverage the advances they have made in recent years. Extending and integrating existing systems can help mitigate risk and save money.

A recent survey from Gartner [1] and MESA International reveals that 98% of manufacturers believe there is more value to capture from their current manufacturing system and have identified next steps to extend and further integrate systems in their digital evolution journey.

Manufacturers have identified the following areas as small evolutionary steps towards the Smart Manufacturing goals:

Mine and join data across silos

Manufacturers have more data on-hand than they realize. Existing data might be trapped in silos or not organized to enable joining data across systems. For example, MES data is usually leveraged for operational metrics but could also be made available to join with data in other enterprise systems like ERP and PLM for higher levels of business analysis and optimization.

Industrial IoT (IIoT) platforms are capturing data straight out of sensors and smart machines for predictive analysis tied to equipment maintenance. This data could also be joined to MES for more automated and accurate real-time data collection.

Integrate via APIs

Application Programming Interfaces (APIs) can not only facilitate integration into a system of systems, but also expose data for mining across the enterprise.

API strategies that extend into the supply chain uncover a substantial amount of valuable data. This is because most supply chain visibility applications concentrate on connecting plan, source, make and deliver domains, and rely on ex-post-facto production data. In turn, most manufacturing enterprise applications provide real-time data and analytical tools focused solely on internal plant optimization and asset performance. Integration of these systems would provide better orchestration and optimization opportunities for the plan-to-produce and order-to-cash processes.

Analyze with AI

Artificial intelligence (AI) and robotic processes are helping manufacturers sense, extract, synthesize and analyze data across traditional silos. Not just across internal systems, but also across partners, suppliers and customers. AI can make it practical to organize these varied data sources into meaningful insights to fulfill the higher analytical and optimization goals of the Smart Manufacturing integrated enterprise.

References

[1] Survey Analysis: More MES Value to Be Captured from Supply Chain Collaboration and IIoT, Franzosa and Jacobson, Gartner, 2019