Crystal Ball: Business Resilience in 2030 and the Digital Dexterity Effect
Digital transformation is today’s tested and proven path to business resilience. We help manufacturers digitally transform to strengthen their resilience every day. But our era is defined less by permanence than by alacrity of adaptation. Manufacturers must already evolve their approach to business resilience to remain competitive.
Granted, the need for this evolution is neither intuitive nor obvious. And in fairness, resilient manufacturing enterprises in 2030 will look much like such enterprises do today. But the means for achieving and sustaining resilience by decade’s end will be radically different. Here’s why, and how to prepare for it.
Why business resilience will be different in 2030
Resilience describes a business’s ability to adapt to change effectively and efficiently. For many reasons, data-driven manufacturers are indeed able to adapt well. That’s why businesses that aren’t already data-driven are becoming so.
But digital transformation is a continuum. And few companies believe they have done all they need to do in their transformation. Most recognize that they are in the early or intermediate stages of digital transformation. That will change.
By 2030, businesses that lead their industry will have passed beyond the stages of digital transformation. The businesses will have entered a state of digital dexterity. In that state, business resilience manifests in sharply different ways than it does today.
How business resilience will be different in 2030
Business resilience based on digital dexterity has many facets. Among them, three stand out as being potentially unexpected. First, high-level objectives will remain vital to defining initiatives aimed at strengthening business resilience. But those objectives will not be the exclusive driver. Digitally dexterous manufacturers will employ a fluid governance model. This reflects the future reality wherein company-wide resilience initiatives will emerge from diverse organizational levels, and not always from the top.
A second difference in the way business resilience will manifest lies with the workforce. Digital dexterity means making sure that the workforce has access to, and understands how to use, your business’s data and technologies. Employees will have the information they need to understand how changing their own workflows could have wide-reaching benefits. They will be able to see ways to boost resilience from their specific position. These will be ways not obvious from a top-down view.
In fact, the top-down view will itself be different, which is the third key facet we are considering. Digital dexterity will mean that all executives – not just those in IT – feel at home with technology. In the same way that an executive today can assess how well one staff member is performing, they will be able to assess how well a technology is performing. For example, is a particular AI algorithm doing what it should, and is it enabling the desired business resilience outcomes? That level of digital literacy will be a basic requirement for leaders in digitally dexterous companies.
What you can do now to prepare for the future of business resilience
We’ve called out ways of enabling business resilience in the future that will be strikingly different. Preparing for those differences will set you on the right path. A cultural awareness and acceptance of shared responsibility for business resilience takes time to develop. But it also follows naturally as your workforce evolves on the path to digital dexterity. The key here is to ensure that everyone in your organization has the opportunity to participate in the digitization of their roles. Organizational change management will be instrumental in this workforce development.
Establishing a higher baseline of digital literacy among your leadership team takes time as well, and you’ll need to begin soon. Leaders must be able to evaluate technologies for their effectiveness as intuitively as they do employees, but that is predicated on educating and training leaders with the requisite skills. Ideally, upskilling and reskilling should always remain slightly ahead of technological change.
Business resilience in 2030 will be decentralized and deeply integrated in the manufacturing enterprises’ people, processes and technologies. It will be the inevitable outcome of your organization’s digital dexterity.
To learn more, please visit NTT DATA’s manufacturing page.
About the Author
Siva Gurupackiam is Senior Vice President of Manufacturing Industry Solutions at NTT DATA.
Crystal Ball: Quo vadis, Industry 4.0 and digital transformation?
In the latest in our Manufacturing in 2030 Crystal Ball series, we look at where Industry 4.0, transformation and digital value creation are headed
Global economic disruptions include supply chain bottlenecks, trade restrictions and rising prices for raw materials, all of which give manufacturers a big headache. Producers are also under increasing pressure to readjust their manufacturing footprint and reduce consumption to meet sustainability targets. Most manufacturers are still stuck with a geographic footprint set down in the 1980s, driven by ongoing labor arbitrage around the globe. Large factories are still predominant in low-cost regions like Asia, Eastern Europe, and South America. Accelerating trends toward automation, upskilling and high demand for customized products, combined with increasing regulatory requirements for sustainable manufacturing, are pressuring companies to rebalance this equation. To increase efficiency and respond to threats and trends, many producers have implemented Industry 4.0 technologies.
But quo vadis? Where are these trends going?
The Industry 4.0 vision – smart, networked factories, closer to customers
Industry 4.0 technologies offer no less than the ability to redesign the global manufacturing footprint, to position factories closer to markets, reduce logistical nightmares and increase the visibility of suppliers and customers.
The digital transformation offered by Industry 4.0 technology has four central goals:
1. Customization, insight-driven manufacturing and lot sizes of one
With customers increasingly demanding highly personalized products, mass customization has become a competitive playground. Western manufacturers need to differentiate their offering (via customization) from cheaper offshore competition to retain their markets. Industry 4.0 includes a wide range of technology innovations like 5G, artificial intelligence (AI), machine learning (ML), PLM and digital twins, MES, IoT platforms, and additive manufacturing (3D printing). These can support the agility, flexibility, and efficiency to offer customers such highly personalized products and services, thereby enhancing their brand experience, increasing loyalty, satisfaction, and ultimately, profitability.
Infor customers in manufacturing have switched from series production to highly customized operations in segments as diverse as the fabrication of individual sports rifles for biathlon athletes, medical science equipment, commercial laser pointing products, the fenestration industry (producers of windows and doors) and industrial cranes. Product individualization based on unique combinations of modular components and very late customer change requests have become essential ways to improve the customer experience. The vision is that consumers can switch their product configuration on the fly.
With an increasing shift of focus toward software-driven experience-based services, there will be pre-purchase changes for specific experiences. Today, some cars already come with three driving modes: sports, comfort and eco. In the future, cars may be chosen for events instead of a car’s lifetime – a camper van just for the summer holidays, an SUV for the winter holidays, a limousine for the business trip. All these will be equipped with event-based options, possibly an extra seat row for the children and special cooling options for the summer holidays, heated steering wheel and a snow-drive option for the winter holidays, and additional horsepower for a long-distance business trip.
2. Incremental innovation and transformation to product-as-a-service
Product lifecycles are becoming shorter as customer attachment to one-size-fits-all physical products decreases. Companies no longer compete exclusively on product features, instead offering value-added services, and manufacturers are adapting their operations to optimize business efficiency. In automotive, software-enabled service platforms are a new ground for differentiation, replacing hardware-related factors such as engine power and precision-engineered door handles. Success in this realm requires more knowledge about customers, software-based services and the ability to innovate and respond quickly to changing customer preferences.
3. Digital innovation reducing market distance
To reduce delays and transportation costs, leading manufacturers build smart factories closer to the customer. Redesigning the manufacturing footprint is especially imperative for the manufacturing industry, with its notoriously complex, fragile, and logistically challenging global operations and supply chain. New business models and technologies help reduce the distance to the end consumer. With digitalization, automation and robots, factories need fewer workers but more skilled ones. Thus, locations in Western countries become attractive despite higher labor costs.
With the digital revolution unfolding, there will be more ways to overcome global footprint constraints, such as these two concepts.
First, a widespread network of high-tech automated micro-factories. The micro-factory was invented in 1990 by the Mechanical Engineering Laboratory of Japan, referring to cell-based manufacturing in a multitude of small and highly automated plants, to deliver customizable designs locally. But it has not yet become widespread. Today, we observe much interest, but few operational implementations of micro-factories. Despite the appeal of quick deployment, higher flexibility, lower capital investment, and reduced environmental impact, the implementation of micro-factories is limited to start-ups and new business models. Even the discussion about micro-factories has quieted down. The assembly line still plays a major role, and the efficiency of a modern automated volume production site with just-in-sequence operations are hard to beat with a more distributed approach to production. Gartner® places Mobile Factories just before reaching the tipping point of “Peak of Inflated Expectations” on the just released Hype Cycle™ for Advanced Technologies for Manufacturers, 2023 and classifies them as: “5-10 years to mainstream adoption.”1
So, the micro-factory concept could become a strong factor to increase the value of customization and higher flexibility, fueled by zero distance and innovation in computing, robotics and AI. But it is not yet a top priority for operational executives.
Second, a relocation to high-cost countries for access to resources and customers. In contrast to the slow development of micro-factories, a counter-trend of companies setting up rather large factories in Western countries is burgeoning. First, there is a plethora of gigafactories. Introduced by Elon Musk, the term refers to giant plants to produce power units generating gigawatts of power via EV batteries. Gigafactories are set up in places like Canada, Austria, the United States, and Norway, by companies including Volkswagen, Northvolt, Miba and Frey. In addition, Infineon and Intel launched large factories in Austria and Germany to produce their core microchips, while Rheinmetall created a factory for producing electronic components for the F-35 fighter plane. This trend is driven by access to excellent logistics and highly qualified resources rather than by cheap labor.
4. Agility to deliver business and technology requirements
An effective platform enabling swift reaction to change, adjustment of the global footprint and further efficiencies via automation requires a comprehensive digital transformation. But for many organizations, challenges to connect separate technologies, true digitalization of processes and the complexity of existing systems continue to be roadblocks to executing a digital vision.
McKinsey has outlined critical success factors, including these four:
– A truly scalable operating model
– Distributed technology allowing local and independent innovation
– Access to reliable and current data when needed
– Strong adoption and change management
Cloud ERP at the core of Industry 4.0
Historically, manufacturers have architected their organizations and IT portfolios with a traditional monolithic enterprise-centric and on-premises ERP. But this inside-out model, with static, scheduled interfaces to the “outside world,” is unfit for tomorrow’s intelligent factories in response to an even more dynamic, constantly changing and truly real-time business environment. Legacy ERP systems focus on structured data from ERP transactions, not unstructured data that has become business relevant (e.g., IoT, clickstream, social media). This siloed, intra-organizational and structured-data focus does not provide the agility to adapt to and get ahead of current and anticipated business conditions.
The demands of the modern economy and the forces discussed above have led to replacement of monolithic ERP with a more modular, standardized cloud-based “hub and spoke” network approach, which can adapt and scale in line with business priorities. The capabilities to integrate, manage data, secure data and applications, and identify and provide optimal user experience have become the focus of overall enterprise strategy. The modular ERP approach is built around a core, vertical-specific ERP solution as a strategic integration platform to connect to supplemental applications, potentially from multiple vendors. This core ERP becomes the foundational platform for agility and innovation at scale. Additionally, embedded and advanced capabilities enable more intelligent automation by leveraging AI- and ML-driven insights and actions to improve the manufacturing process.
Digital cloud platform impact on time-to-value
The Infor digital ERP platform is designed to shorten time-to-value and improve customer experience to ensure success and improved business outcomes.
It is an industry-specific solution with preconfigured processes built in, and incorporates learnings from the sales process into the provisioned product. This is coupled with a heavy investment in tooling and content to shorten the initial implementation effort, cost and risk, and have the catalogue of predefined industry processes to accelerate deployment. The cloud simplifies updates and eliminates the need for major upgrades and for a large technical team to implement and maintain business applications.
It is set to become the central platform for dynamically driven innovation. Every company that joins increases the industry cloud scale in sectors such as automotive, industrial manufacturing and A&D, and accelerates the need and pace for relevant industry-specific additions. In 2030, industry communities will drive standardized cloud-based functionality based on their needs for manufacturing functionality, compliance with regulation, and process optimization through deep strategic interaction to set priorities per industry. Upgrades will be deployed immediately as standard additions with transparent and global KPIs throughout their entire manufacturing footprint. Over time, there will be a rich library of relevant tested use cases for process optimization based on AI per industry, available to all participants.
And the winner is … the smart, agile factory
Success belongs to the prepared. In today’s digitally transforming and connected discrete manufacturing industry, being prepared requires a re-evaluation of traditional business processes, technology capabilities and the main traditional asset, the global manufacturing footprint. Critical across each of these dimensions is effectively connecting systems and locations in an open and standardized structure and harnessing the power of data.
Manufacturing companies started to invest in new digital technologies such as AI to improve operational efficiency in areas such as demand forecasting, fault finding in production and warehousing. But today, most organizations struggle to roll out new technologies to the global universe. Those who will seriously invest in their digital platform to build a dynamic, intelligent and connected factory network put themselves into a favorable position to deploy consistent innovation steadily, quickly and globally. This change will not happen overnight. The move toward digital workflows and transparent open standards requires the whole organization to shift. Those who commit to that journey will be able to use the digital innovation benefits immediately to their business advantage in 2030. They will gain the ability to instantly respond to market disruptions, customer requirements and regulation, and create new products and services on the fly.
In an increasingly uncertain market environment, success will reward manufacturers that most effectively align new data management skills with modern information technology platforms to render their factories truly smart and insight driven. This includes the enablement of closeness to customers and their need for experiences rather than traditional products, decision-making adapted to swift reaction to change, and real-time, dynamic, sustainable and market-relevant manufacturing.
About the Author:
Dr. Henning Dransfeld is Director Industry & Solution Strategy at Infor. He is responsible for the development and implementation of Infor’s growth strategy in the discrete manufacturing in segments including A&D and automotive manufacturing in EMEA. Dransfeld has years of experience as an industry analyst. At Information Services Group (ISG), he was responsible for manufacturing, data analytics and IoT across Europe. He also led numerous consulting projects in these and related specialties. Prior to that, Dransfeld held positions at Ovum (now Omedia), T-Systems, Forrester and Experton.
1Gartner Hype Cycle for Advanced Technologies for Manufacturers, 2023 Published 25 July 2023 – ID G00789107 By Marc Halpern, Sudip Pattanayak, Christian Hestermann, Alexander Hoeppe
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Extrapolating from the results of NTT DATA’s recent research report, Innovation Index: Shifting from Disruption to Growth, John Petrusick, Managing Director, Manufacturing — Data and Analytics Practice, offers insights into the future of data in the latest in MLC’s Manufacturing in 2030: Crystal Ball series.
Data-Driven Transformation and the Future of Human-Centric Innovation by 2030
By 2030, I envision a significant change where data flows freely from sources including the shop floor, enterprise resource planning systems and third-party external providers. Manufacturers will centralize, harmonize and analyze data to enable real-time action. This encompasses all actions, including automated actions, to maximize business efficiency. Doing so will impact aspects of the business ranging from daily production operations on the plant floor, to engaging customers in selling activities, to the development and testing of new products.
With respect to sales, products and services, manufacturers will create seamless customer experiences in which customers can purchase goods using their preferred interface. Infusing data into a design thinking-led approach will also improve overall experiences once the product is in use. From an operational data perspective, employees on the shop floor or within the business will see performance data in real time and act instantly.
Highly commoditized or repetitious manufacturing environments might function as a largely ‘headless’ or ‘dark’ operating environment. Such environments rely on processes that are highly routine, reproducible and repeatable. They will, therefore, be able to leverage physical robots in the ecosystem to take the place of today’s manual interventions. These environments will have a significant advantage in productivity and efficiency while improving safety. They may use technologies including robotic process automation for monitoring and control, generative AI for requests from outside observers, and physical robots to take actions when physical interventions are required. Nevertheless, I do not foresee a near-term scenario in which manufacturing is 100% automated with no human involvement. Through 2030 there will continue to be entire plants, production lines and core processes where the need for creativity will keep humans integral.
Empowering Manufacturing through Data-Driven Insights
Data’s role will evolve naturally, and part of the role will entail solving problems. But the primary function data will serve is to enable insights that guide the actions of individuals, software or machinery so that they deliver increasing business value.
Currently, the data sphere focuses on providing high-quality, reliable data to manufacturing business actors — humans, software, physical robots and so on. These actors need specific analytics or insights in forms such as real-time or near-real-time Key Performance Indicators from a production line on quality to make value-adding decisions. When such data indicates deteriorating quality, we may need to halt the line and perform a root cause analysis to remediate the issue to re-gain performance with thresholds.
In a preventive maintenance scenario, telemetry data from machinery might show that a machine will fail within two hours. A worker would then receive an alert prompting them to reallocate capacity from that line to others, if possible, to allow for necessary maintenance. Alternatively, in a product development scenario, we might be able to design and test a product virtually. This will help manufacturers create the right product to the correct specifications the first time around, thereby reducing time to market.
As the right analytics become available in 2030 and beyond, manufacturers will gain the real-time insights to deploy end-to-end automation using autonomous tooling. Automation may extend even to the point of delivery, potentially with no human intervention. This data-driven automation will create additional value through improved efficiency and shorter time to revenue generation.
Unlocking Future Manufacturing Success for Enhanced Plant Operations
Currently, we are observing advantages in various aspects of plant operations such as preventive maintenance and quality improvement. For example, vision robotics is already providing assessment data at all stages of manufacturing, from raw materials to finished products. Moving toward 2030, we see manufacturers that start or continue to follow data and analytics best practices gaining significant benefits. These benefits will be in part due to the advantages of an expanded role for AI and the benefits it can provide, enabled by continuously improving data quality. In combination with the Internet of Things (IoT), AI will have the potential to help manufacturers build factories that are more agile, transform their workforce, make significant progress against sustainability objectives, improve security and compliance, and more. Again, this is contingent on establishing the right data standards and practices.
Building Technology Ecosystems that Unleash Data’s Power
Realizing the potential of data as we are discussing it here will not require any singular technology, but rather an ecosystem of solutions working in concert.
Even today, when we consider the data stack, we emphasize cloud-native data solutions that shift away from monolithic platforms towards an ecosystem of best-in-breed, loosely coupled and high-use technologies and tools.
Data in 2030 will similarly require an array of elements. These range from IoT sensors on machines — many of our clients already have such systems in place or are testing them — to the ability to centralize this data and provide global visibility to it. To cite just one example, manufacturers will increasingly implement machine vision, which involves converting video into data for analysis across a wide variety of use cases from quality inspection to safety compliance.
Finding Future Success by Navigating Data’s Complexities to Overcoming Storage, Reliability, and Analytics Challenges
Data storage and reliability challenges I alluded earlier to the need for data centralization. Many of our clients have already conducted pilot projects or have implemented some of the technologies previously discussed in various plants. But most have yet to successfully centralize their data for global visibility. At the heart of this issue will be the need for investment as, historically speaking, manufacturers have not made the significant investments in their data ecosystem needed to resolve this key dependency.
On the flip side, despite the importance of data centralization, there are cases where manufacturers will need to balance their need for centralization versus a need for rapid analysis and action. In such cases, the challenge will be how to embed intelligence into a local system and thereby enable machine-learning-based decision-making at the edge.
Both challenges will be pervasive. Consider a global manufacturer or a multi-site operator aiming to achieve visibility across all their operations. They may have plants that are almost entirely automated with minimal human intervention and others that have a larger human presence. Both environments present the need for clear visibility into each plant’s operations via data, so the different plants might all require a sort of “control room” view. The first step to enabling that view is to store the data on which to base the view. The data may reside in a centralized location or at defined edge locations.
The next step is to ensure the data’s reliability, whether the data is in storage or in transit. This phase involves a data management component that underpins everything before proceeding to the analytical stage.
Data analytics challenges
After overcoming data storage and reliability challenges, manufacturers can shift to the challenge of data analytics. For most manufacturers, the data management in place today is not at a level where they can trust the data without question.
At the same time, there are potential solutions to these challenges from which two separate elements come to mind. First, from a data management perspective, manufacturers could use machine learning algorithms to conduct quality checks and make recommendations on how to master the data. This process is crucial to effective analytics.
The second element pertains to both the techniques of analysis and the human factor involved. Often, achieving success with analytics depends on clearly defining the question we are trying to answer and the action we aim to take. When discussing data and analytics, our shop floor personnel may say they lack the needed technical knowledge. But the reality is, they do not need to understand all the complexities of data. Instead, as data professionals or leaders, we should focus on the actions they take and the decisions they must make in their roles, and develop analytics capabilities accordingly.
Traditional data analytics versus decision-based analytics
Anticipating the evolved and central role the workforce will play in manufacturing in 2030, decision-driven analytics is about determining the necessary actions the workforce must take to create value in a business. First, we need to ask the right questions to guide these actions. We then identify metrics or measures to establish decision points and gauge results. Lastly, we must determine the type of data needed to create those metrics and measures.
The decision-driven approach to analytics can be simplified to four elements: actions, questions, metrics and data. The goal is to understand the actions that create value in a business, then enable these with data strategies and analytics that also yield value.
Fostering Data Literacy and Collaboration to Bridge the Gap Between IT and Business for Action-Oriented Analytics
From a technologist’s perspective, it is essential to understand what is happening in the business and what actions or decisions we are informing or enabling with the technologies we are deploying. As such, IT organizations must enhance their engagement with the business. Simultaneously, business actors might need to upskill their data comprehension.
This does not mean that all business actors must learn to write SQL queries and Python scripts or be up to speed on the latest data science modeling techniques. Rather, it means they must have the requisite level of data literacy to understand the actions they take in their business and how data could help them make better decisions. Approaching it from this angle will help manufacturers find success, in part because business teams and IT organizations are thinking similarly and working towards achieving specific business outcomes.
Infographic. Aspirent, an NTT DATA Company.
To bridge the gap between IT and business teams, we have found success using a structured Decision Architecture framework focused on the following critical topics:
- What actions do we have at our disposal to drive value for the business?
- What questions do we need to answer in order to decide which actions to take?
- What metrics will help us answer those questions?
- What data do we need to construct those metrics?
This decision-driven framework offers manufacturers a more targeted, action-oriented approach to data and analytics that enables the creation of specific business cases and a clear path to unlocking their data’s potential.
Empowering Manufacturing Excellence by Navigating Data-Driven Insights for Collaborative Success and Outcomes
Ultimately, data analysis and subsequent actions depend on the workforce. It is essential to ensure that we provide data in a format that the workforce can use effectively. Similarly, the speed and effectiveness with which currently top-of-mind technologies can grow is constrained by the need for data that is accurate, complete, reliable, relevant and timely. Data and analytics reside at the core of high-value business outcomes — whether driven by the workforce, AI or combinations thereof. Potential outcomes range from creating more resilient supply chains to accelerating innovation and delivering new services to engaging customers in new ways.
In short, although we have covered a lot of ground here, my single most important piece of advice is to create and maintain a focus on driving collaboration at the intersection of IT and business teams with a focus on the business context and outcomes of data usage. By doing so, manufacturers will be able to increase the business value that data solutions create and avoid complications preventing the realization of that value.
If this data discussion has sparked your interest, we invite you to check out our infographic “Innovate Confidently with Data-Driven Decisions.” It features relevant data from the 2023 NTT DATA Innovation Index.
About the Author
John Petrusick is a Data and Analytics Managing Director at NTT DATA, serving as the Manufacturing Industry Lead. In his role he advises manufacturers on how best to leverage their data to create business value-driven, actionable, and analytic insights to increase efficiency, sustainability, and competitiveness. He is passionate about helping clients solve their data challenges, specifically in data governance, architecture, engineering and integration, advanced analytics and business intelligence, and intelligent automation, as well as helping them implement strategies to drive digital transformation.
Crystal Ball: A Futuristic Workforce Odyssey
For humans and machines to work together by 2030, the workforce evolution must start now
Extrapolating from the results of NTT DATA’s recent research report, Innovation Index: Shifting from Disruption to Growth, Kim Curley, NTT DATA’s Digital Evolution Leader and Vice President of People & Organization, shares her insights about the future of the manufacturing workforce in the first of our Manufacturing in 2030: Crystal Ball series.
Picture this: the year is 2030. Across the U.S., the manufacturing workforce has evolved into a symphony of humans and machines working in harmony. This dramatic transformation has made the workforce a technology-enabled powerhouse. Artificial intelligence, robotics, and virtual reality lead the charge, transforming the factory floor into a buzzing and orchestrated hub of activity.
Workers use advanced technologies to perform their tasks more efficiently and safely, collaborating seamlessly with their robotic counterparts. The shop floor thrums with the smooth whirr of precise machinery and the hum of advanced systems. Augmented reality displays guide workers through complex tasks. And to make this possible, the workforce has become a melting pot of diverse talents and skills that is continuously learning and developing
It is a rosy view of the future, but realistic based on current data and trends that illuminate a path to this future state.
Changing Skillsets for 2030
The 2030 manufacturing workforce will be doing things quite differently than they do today. Four in 10 manufacturers have already
begun leveraging augmented or virtual reality devices, and that will only increase. That usage will expand to support workforce instruction and training. One example of this could be teaching workers to switch over a production line. Providing that training in a virtual reality environment would mean the company doesn’t need to shut down real-world production.
As the usage of advanced technologies rapidly expands, the skillsets of the manufacturing workforce must expand, too. In addition to physical technical skills, workers will need to strengthen their understanding of technologies as well as problem solving, critical thinking and leadership skills. To build that workforce over time, you must start by expanding the skillsets of managers and leaders now. Starting today is critical to being successful in 2030, indeed, 39% of manufacturers report that they have already increased their investment in leadership development.
A More Resilient Enterprise
The evolved workforce will enable significant benefits. The big one is resilience. An augmented workforce better enables the company
to respond to changes in the marketplace. With such a workforce, companies can train faster, reset the lines, and change worker skillsets more rapidly. Paving the way for this evolution, upskilling and reskilling programs for new and/or existing workers are underway today at 54% of manufacturers. More broadly, making your workforce future-ready entails steps such as improving safety, productivity and talent; incentivizing workforce agility; teaching new leadership skills; engaging the workforce; and maintaining a future-focused mindset. We’ll look at each in turn.
Improving Safety, Productivity, and Talent
The NTT DATA 2023 Innovation Index survey showed that businesses are increasingly addressing potential disruptions in strategic plans because these events present opportunities for those businesses that can act with resilience and speed.
So, it is not surprising that manufacturers are placing increased importance on their workforce and the workforce’s ability to adapt and move quickly. The more connected and engaged the workforce is, the better it can shift and flex when the unexpected happens.
Three vital areas come immediately to mind when considering how these coming changes address the problems facing today’s manufacturing industry. The first is safety. As noted earlier, one of the best commercial applications of virtual technologies is training. Training workers to complete physically demanding or potentially hazardous tasks in a completely safe environment — without the cost of physically constructing such simulated environments — is a game changer. Fully 77% of manufacturers report plans to use these technologies within the next two years. That’s well ahead of the cross-industry average of 64%. Using these tools for ongoing training programs helps ensure that workers know exactly what they need to do and how to do it. And it does that while protecting their safety and the safety of the products they manufacture.
A second area of lift is in productivity — specifically, decreasing production errors and downtime. The proliferation of sensors enabled by 5G, advanced computing and machine learning technologies gives us better insights than ever before. These insights enable us to better plan and use scarce resources, another game changer.
The third area is talent. Emerging technologies in the factory call for a new-collar workforce that understands both manufacturing and Manufacturing 4.0. As the industry increases its use of and innovation with advanced technologies, more visionaries will be attracted to manufacturing. At the same time, advocacy initiatives like the Manufacturing Institute’s Creators Wanted program help spread information about this shift in manufacturing work so the next generation of young talent is aware of these opportunities to work with technology. This shift in the reputation of the industry overall can help bring the best and the brightest talent into manufacturing. The workforce will transform from the outside in as it changes from the inside as well.
Incentivizing Workforce Agility
For people and technology to align more closely, it will be important that the workforce sees the benefits of change. Many additional technologies come into play with a technology-enabled workforce. Remote collaboration tools and digital twins can allow manufacturers to assess and optimize processes virtually. These tools may require their users to have specific skills, such as a familiarity with the way data is used to create digital twins, to realize their full value. An agile and augmented workforce will be prepared with those skills or will be readily able to develop them.
The heightened importance of the workforce will call for momentous changes. Historically, manufacturing has considered the human as a component of the manufacturing line. The concept of human capital itself came from the manufacturing industry. Shifting our thinking changes how we develop people and give them desirable career progressions. It also impacts how we pay and incentivize people.
Adopting practices for compensation, rewards, and training from other industries to attract and keep the best and brightest can be beneficial. In high tech, for example, companies such as Google, Apple, and Facebook prioritize employee development and learning opportunities. They offer training programs, mentorships, and career advancement pathways. Some 84% of manufacturers have at least piloted investments in training for employees in areas such as factoring customer behavior into decision-making. To date, though, just three in 10 manufacturers report adjusting total rewards packages to increase their competitiveness when searching for new talent or retaining existing workers.
New Leadership Skills
The path to a future-ready workforce will include overcoming hurdles. One of the biggest is the need for a radically new style of leadership. Leaders in-house today often have massive institutional knowledge and a long history of viewing the workforce in a particular way. Having such leadership has often led to success. But going forward, that will need to change. That is a challenge. You have to teach the most senior leaders to see the workforce — both its potential and its needs—differently. But even incremental change comes slowly. Presently, for example, only 13% of manufacturers report being highly effective at delivering on worker-centric policies such as flexible working options and schedules.
Certainly, you cannot change a whole company’s culture unless you’re working from the top down as well as the bottom up. But once you do, you are positioned to develop and sustain a culture of innovation, experimentation, and resilience.
Engaging the Workforce
The workforce can and should be an active part of its own evolution. First, engage with your workforce – truly engage. Understand who your people are and what is important to them. Find ways to connect them to your company’s purpose and the impact you want to have on the world around you. The stronger those connections are, the more powerful a force your workforce can be in driving the transformation you’ll need over the coming years.
Second, expose more of the workforce to technologies and how these tools can help them and the business. Manufacturers are slightly behind the cross-industry average at the use of updated tools and technologies, with only 29% considering it important to employee satisfaction and engagement. Our Innovation Index shows that the best ideas to advance the business most often come from the individuals who already know the most — your workforce.
A Future-Focused Mindset
Mindset is everything. Without a growth mindset at every level of your organization, you will struggle to accomplish a transformation that will drive success between now and 2030. This mindset must be pervasive, encompassing everything from policies to hiring practices, investment decisions and the employee value proposition you put into the marketplace. When you embrace a mindset that says we are going to try new things and grow, and your workforce embodies it every day, you will create a resilient organization that is comfortable pushing boundaries to create the future.
About the author
Kim Curley is Digital Evolution Leader and Vice President of People & Organization at NTT DATA.
Will AI Enable Autonomous Plants and Factories?
Let’s Talk About AI event speaker, Dr. Hiroaki Kanokogi, shares how AI allowed Yokogawa to autonomously run a chemical plant for 35 consecutive days
When toddlers learn to stack blocks, they learn by trial and error — often with immediate feedback from a parent or other adult. It is a model-free learning process, or reinforcement learning, that does not require them to learn Newton’s equation to figure out how to stack the blocks.
For Dr. Hiroaki Kanokogi, Yokogawa Digital Corporation’s President and CEO, reinforcement learning (RL) in artificial intelligence (AI) has direct uses in a manufacturing environment. In fact, as Kanokogi shared during the Manufacturing Leadership Council’s Manufacturing in 2030 Project: Let’s Talk About AI event, RL was a building block when Yokogawa used AI to autonomously control a Japanese chemical plant for 35 days earlier this year.
In his presentation at Let’s Talk About AI, Kanokogi shared that there are serious challenges to applying RL to real world manufacturing. First, he said, traditional RL takes 1 million to 1 billion trials to go beyond human learning, and second, manufacturers must include safety assurances.
To overcome the first of these challenges, Yokogawa and the Nara Institute of Science and Technology developed scalable reinforcement learning called Factorial Kernel Dynamic Policy Programming (FKDPP) specifically for plant control. FKDPP allows for faster learning (typically in about 30 trials) and robust protection against disturbances. Yokogawa was able to demonstrate that FKDPP can autonomously stabilize water levels in a fundamental three tank level control experiment significantly quicker than traditional proportional-integral-derivative (PID) control.
At Let’s Talk About AI, Kanokogi shared four videos that chronicled FKDPP’s iterative attempts to stabilize the water. In the first iteration, AI does not know anything yet, so when the valve is opened the water level goes all the way up. In the 20th iteration, AI can control the water in a somewhat stable manner, but it varies and resembles a human’s performance on the task. For the 25th iteration, AI learns how to regulate the variation. By the 30th iteration, the FKDPP perfects the process. Kanokogi pointed out that this final iteration demonstrated that once AI finds a good way, optimization of this process is AI’s strength.
For the second challenge around safety assurance, Yokogawa was able to prove AI can satisfy this need during this year’s 35-day autonomous factory operation. The company first built a good simulation model by using domain knowledge in a digital twin so the AI could learn. Step two called for simulation and evaluation using both past and live data. Finally, the company ensured safety and control in the actual plant using Yokogawa’s integrated process control system, CENTUM™ VP DCS.
For Yokogawa and its autonomous operation, Kanokogi reported that he and his team continue to look at problems in the factory where AI can be applied. While the first-of-its-kind, 35-day automation demonstration is truly impressive, he sees manufacturing working in an autonomous plan-do-check-act (PDCA) loop by 2030. This loop will run continuously, and AI will help the plant improve itself. While there is no need for human intervention during this loop, Kanokogi pointed out that AI cannot add new sensors or integrate new technologies, so human experts will maintain a defined role in manufacturing.
Like a toddler with blocks, autonomous factory operation might be in its nascent years, but with the help of AI and Yokogawa’s FKDPP technology, maturation by 2030 is possible.
Manufacturing in 2030 Project: Let’s Talk About AI was held Dec. 7-8, 2022 in Nashville, Tenn. The event was part of MLC’s Manufacturing in 2030 Project.
The Need to Accelerate Industrial AI Adoption By 2030
AI will become a key player in driving manufacturing competitiveness in the years ahead.
David R. Brousell, Co-founder of the NAM’s Manufacturing Leadership Council, called on all manufacturers to accelerate their understanding and use of new Artificial Intelligence (AI) technologies in his opening speech at the MLC’s latest Manufacturing in 2030 event, Let’s Talk About AI, in Nashville, Tenn., this morning.
“The stakes for our industry and our country couldn’t be greater as our economy becomes increasingly digital,” asserted Brousell. “Global competition for dominance in AI is underway, with manufacturing as a key player in the race. Our competitiveness as an industry at home and abroad will increasingly be defined by AI expertise, application, and experience – and in a trusted and responsible way.”
Technologically, he noted, AI is finally coming into its own after a long development period and researchers now estimate that the value of the global AI industry will rise rapidly from $93.5 billion in 2021, to a substantial $1.8 trillion by 2030.
AI is also a pervasive technology, he continued, meaning that it will be incorporated into many other technologies including semiconductors, software applications and platforms, and communications equipment. It will increasingly power the operation of front-office applications, ERP, PLM, MES, CRM and other key operational applications. Robotic systems, too, will increasingly be guided by AI.
And although only 9% of respondents to a recent MLC study said that they saw AI and ML as a game-changer for the industry today, by 2030, 53% said that they believed it would indeed become a game-changing transformative force.
Citing a U.S. Patent Office report from October 2020 that stated, “AI is poised to revolutionize the world on the scale of the steam engine and electricity”, Brousell stressed that’s why manufacturers now need to better understand how AI may shape how they run their factories and plants, how it will influence their workforce strategies, what business benefits may attend AI use, and what challenges the industry must overcome to realize its potential in the years ahead.
However, “as with any important technology,” he added, “let alone one as unique as AI, there will be a learning curve replete with twists and turns.”
One of those twists is the fact that AI remains a controversial technology. Some see it as an existential threat to humanity; others see it unleashing a new wave of productivity and efficiency and enabling people to have better and more rewarding business lives.
Brousell also believes that AI’s unique ability to learn, and what that ability implies for predicting machine and operational patterns and behavior, qualifies it to be “in a special place among all of the technologies associated with digital transformation.”
“I can’t think of another technology that we have employed in our factories and plants,” he added, “that requires us to ask the question: do we need a code of ethics for AI use?” According to recent MLC research, he noted, 75% of manufacturing executives already believe a code of ethics will be needed in the years ahead.
Nevertheless, predicted Brousell “AI is here to stay”, and that its influence will only grow in operations, in the workforce, in the interactions across supply chains, and with customers and partners in the years ahead.
“AI”, he concluded, “is truly a force to be reckoned with” for the future of manufacturing, and manufacturers now need to act with urgency to accelerate its adoption to drive competitiveness in the years ahead.
Ten Takeaways from M in 2030 Project A Lens on the Future Panel
With the recent release of the Manufacturing in 2030 Project survey A Lens on the Future, MLC assembled an expert panel of speakers to discuss the results and bring context to the survey findings.
Moderated by David R. Brousell, the MLC’s Co-Founder, Vice President and Executive Director, the panel included Dennis McRae of West Monroe, Greg Wagner of EY, Chirag Rathi of Infor, and Joe Zakutney of NTT DATA.
Here are ten key takeaways from the discussion:
1. 84% of survey respondents expect an increased pace of digital adoption in the next decade
Dennis McRae, West Monroe’s Senior Partner and Practice Leader, Consumer & Industrial Products, noted that the acceleration rate depends on the size of the company, but he said the biggest thing is if you haven’t started your Manufacturing 4.0 journey in a meaningful way, now is the time to do it to maintain competitiveness.
“One of the first things you can do is really establish a team focused on digital with the right leaders, the right innovators, and also the right disruptors who can really challenge and do what leadership needs to get done,” McRae said.
2. 58% of survey respondents have autonomous factory operations on their radar by 2030
Chirag Rathi, Senior Director, Industry and Solution Strategy at Infor, pointed out that the desire for autonomous operations is not new and that General Motors was talking about this in the 1980s. Citing deep-learning and self-learning algorithms in machine learning, digital twins, blockchain, and autonomous transportation, he sees autonomous operation as a game changer, but he cautioned that full autonomy is unlikely in many circumstances.
“The cost of doing full autonomy in most industrial manufacturing processes might be too high,” he said. “So you will have part autonomy in several manufacturing arenas where the business case makes sense, but it will be a decision made on a case-by-case basis.”
3. 76% believe manufacturing should adopt an AI code of ethics
For Joseph Zakutney, NTT DATA’s Vice President, Manufacturing Industry Consulting and Digital Transformation, thinking about AI’s future means protecting against biases and cyberattacks, while accounting for safety.
“Procedures will need to be put in place to make sure that we’re complying to [a code],” he said. “We need to make sure that the software that we are releasing is fair, reliable, explainable, takes data protection and government regulations into consideration, and is focused on the well-being of society.”
4. Almost half of respondents indicated they expect workforce shortages to continue through 2030
Traditionally, manufacturing doesn’t have the best image according to Greg Wagner, EY’s Data Driven Manufacturing Leader. It is considered dirty, loud, and can be physically intense at times, but Wagner pointed out that those seeking purpose-based work should be attracted to manufacturing. The old adage of “being a cathedral maker and not a bricklayer” fits here, according to Wagner.
“If we change the paradigm and what we’re looking for, the types of job experiences we can give people, and use automation to get rid of some of those menial tasks that people don’t enjoy and free up their capacity to focus on bigger problem solving, it will mean more impactful types of roles,” Wagner said. “That’s going to really start to attract people and start to soften some of that gap we see right now in hiring.”
5. 81% of manufacturers are looking for greater speed and flexibility
When we think about speed and flexibility, what people really want is responsiveness, according to Wagner.
“If we really want to be able to respond quicker, we need to know what’s going on and we need to invest in better end-to-end visibility of what’s happening within our factories and what’s happening across our network so that we can be more adaptive and have the right insights to drive that change,” he said.
McRae added, “There’s a big opportunity for manufacturers in terms of connecting with their customers, building that client experience, and really monetizing a lot of the data that’s already in the business.”
6. By 2030, 50% of respondents believe digital adoption will be a game changer.
For Rathi, we’ll be closer to Industry 5.0 by 2030 with hyper-customization, responsive and distributed supply chains, and business model innovations. In fact, Rathi said we already have the building blocks to make this a reality.
“We have certainly got a lot of the raw materials to make that transformation happen,” he said. “So we will have a lot of transformative changes by that time period.”
7. Digital acumen is important across functions and at various leadership levels
One challenge McRae sees is getting everyone on the same page using the same language so that data assets are understood across company levels by all who manage data and products in the business.
“It’s not just around different levels, but also across functions,” added Wagner, noting that there is a wide array of digital understanding and how those technologies can be applied.
8. Hiring for a digital background versus a subject matter expert depends on the project
“I think we’re seeing the rise of data scientists and citizen data scientists at some organizations. At the same time a lot of data DIY products are becoming available, and they will become more prevalent by 2030, meaning that people with subject matter expertise will be able to basically design and develop their own data science projects,” Rathi said. Because of that, he believes subject matter experts will be in higher demand in most cases vs. data scientists.
9. For companies slowing down their digital projects due to the economy, focusing on specific things can help prevent losing ground
“Digital adoption is really a people play right now,” said Zakutney. “I’d stay focused on people and process, because ultimately, that’s what you’re going to end up automating [when funding comes back].”
“You can’t quit your digital investments,” added McRae. “If you don’t become digital, you’re going to be left behind. At the same time, prioritizing those digital investments specifically around areas that are going to improve your customer experience and take costs out over the next few years are going to help you win.”
10. Beyond the panel discussion, the survey report provides insightful data about the future of manufacturing
The MLC’s Manufacturing in 2030: A Lens on the Future research survey includes front-line insights from over 260 senior manufacturing industry executives, spanning multiple functional roles, and representing large-, medium-, and small-sized manufacturing companies from multiple industry sectors. Armed with this rich combination of real-world predictions and forward-thinking understandings, the MLC hopes that manufacturers can better plan their longer-term future and find ways to enhance their value, competitiveness, and contribution to society. Download the complete survey data and report.
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