Manufacturing Operations Management Talk

Smart Manufacturing Champions Must Address Organizational Culture Upheaval

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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 Resiliency 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. 

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