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ML Journal

Revolutionizing EV Battery Manufacturing with Data and Smart Factory Technologies

Manufacturers of electric vehicle batteries need to develop a data-driven operating model that is efficient and resilient with room to scale as EVs grow within the automotive industry.

Manufacturing 4.0 principles are informing the strategic direction of EV battery manufacturers, shaping their approach to personnel, digital transformation and supply chain.
Data will give manufacturers greater understanding of the materials being used, helping them to find ways to innovate and improve efficiency and productivity.
AI and blockchain technology can give battery manufacturers tools to more quickly adapt when setbacks occur, reducing waste and enhancing quality.  

In an era when efficiency and innovation are paramount, the emergence of smart factories has marked a significant shift in manufacturing paradigms. These advanced manufacturing facilities leverage data, automation and technology to create more efficient and adaptable production processes. Central to this evolution is the integration of Manufacturing 4.0 strategies with overarching business objectives.

In the EV battery manufacturing sector, where precision and reliability are critical, leveraging transformative, data-driven technology is pivotal to surmounting business challenges and enhancing performance in battery manufacturing. The M4.0 best practices emerging for battery manufacturers also offer lessons for manufacturing in other sectors and industries.

EV Battery Market Still Evolving

The alignment of M4.0 strategies with business goals is crucial in the battery industry. Battery manufacturers need to explore business model innovation and find ways to drive peak profitability within their core business while simultaneously incubating new revenue streams. They need to not just retain customers, but also capture new ones and deliver an experience that is results-driven, personalized and responsive.

They also need to build resilience in their operating models, leveraging emerging technology to future-proof their supply chains. And EV battery manufacturers must enable talent on multiple levels, making it easier for employees to embrace digitalization and develop new capabilities.

Data-driven approaches underpin this integration, facilitating informed decision-making that streamlines production, improves output, and enhances metrics like yield and ramp-up times. Incorporating deep mineral and component analytics into these strategies allows for a more comprehensive understanding of materials and their impact on EV battery quality, performance and maintenance. This alignment not only boosts operational efficiency, but also fosters innovation in battery design and functionality, crucial for industries like the EV sector, which demands rapid production of high-quality batteries and aims to avoid future liabilities such as recalls.

“Battery manufacturers need to not just retain customers, but also capture new ones and deliver an experience that is results-driven, personalized and responsive.”


Regulation and standardization is another element that will shape the EV battery market. It could bring much-needed consistency and structure to a sector that is still seeking broader acceptance from consumers who need confidence that service for EVs will be as accessible and user-friendly as the structure they’ve used for internal combustion engine vehicles. At the same time, regulation and standardization can feel limiting to those with a more innovative approach to business and market growth.

A November 2023 Automotive News article addressed the benefits of such standardization “As standardization is achieved in areas such as connectors, chargers, regulations, and payment integration, OEMs and ecosystem partners will be better equipped to explore innovations that will further improve the customer experience, like contactless charging and seamless, transparent, automatic electricity billing.”¹

The article added that OEMs need to be ready for rapid changes in required battery capacity and vehicle range as the EV market continues to take shape.

Digital Transformation Requires Persistence

Digital integration is key in modernizing battery manufacturing processes and it can often be challenging. Companies need to determine where to start, how to scale and how to accelerate their efforts. Infrastructure, disparate systems, repeatability and commercial complexity are just a few trends that can impede digital transformation. It can also take time to break down silos and fully convey the value of the transformation. Manufacturers able to maintain focus, work through challenges and continue moving forward should begin to realize the value of their commitment.

Technologies like IoT and AI create a connected manufacturing environment, enabling manufacturers to monitor every aspect of the production line in real time. This extensive data collection and analysis leads to optimized operations, reduced waste and enhanced product quality. It enables manufacturers to create more agile, flexible and resilient processes that can help them better weather disruption – whether they can see it coming or not. Data provides tools to make those real-time assessments, evaluating a company’s current state, as well as its risks and opportunities, to inform decision-making for the next steps.

Specific technology deployment in battery manufacturing addresses challenges such as maintaining consistent quality and maximizing yield. AI algorithms predict equipment failures and identify potential quality issues in battery mixtures, reducing downtime and enhancing energy efficiency. Advanced analytics identify patterns in production data, leading to insights that drive process improvements. These interventions not only solve existing problems, but also enhance performance, leading to higher quality batteries and increased production rates.

“The implementation of autonomous robots and digital twins in manufacturing processes underscores the transformative impact of hybrid human-machine operations.”


A good example of these advances is found in electrode coating. By utilizing advanced sensors for real-time measurements of thickness and density, manufacturers can significantly enhance the quality of battery cells prior to the aging process. This ensures uniformity and optimal material composition, thus reducing the likelihood of defects post-aging. Additionally, in sample cell testing, the use of advanced analytics combined with holistic approaches and traceability — such as blockchain technology — enables manufacturers to identify suboptimal cells early in the process and avoid producing large quantities of suboptimal battery cells.

Moreover, the integration of this sensor data with ERP and MES systems creates a rich, multi-dimensional data landscape. This synergy enhances decision-making and operational agility, showcasing the essence of M4.0 in modern manufacturing.

In practice, battery manufacturers are leveraging these technologies in various ways. For instance, the implementation of autonomous robots and digital twins in manufacturing processes underscores the transformative impact of hybrid human-machine operations. These innovations enhance precision, efficiency, and adaptability, further revolutionizing the battery manufacturing sector.


The integration of M4.0 technologies in battery manufacturing is a gateway to the future of industrial production. As these technologies evolve, their potential to revolutionize battery manufacturing grows. We can anticipate advancements in collaborative robotics, enhanced digital twin capabilities, and sophisticated AI-driven analytics, contributing to smarter, more efficient, and sustainable battery production.

In summary, the integration of data-driven smart factory technologies in battery manufacturing is a game-changer. By embracing these advancements, manufacturers can overcome traditional challenges, improve operational efficiency, and set new benchmarks in quality and innovation. The future of battery manufacturing is bright, and it is undeniably powered by data.

About the author:


Felipe Smolka is Americas Automotive eMobility Leader at EY



The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

ML Journal

How Digital Twin Applications Can Help Manage and Optimize Inventory

This case study shows how one manufacturer used simulations to evaluate processes and potential equipment investments. 

Manufacturers can use digital twins to conduct real-time analyses of processes or operations to predict future performance.
Running simulation scenarios helped an industrial food production company address bottlenecks and avoid spending millions on new tanks.
Models created using digital twins can be easily modified to evaluate new operating scenarios in the future.  

Plenty of manufacturers use scenario modeling to forecast how their inventory needs may ebb and flow as supply and demand shift. But for organizations that want to enhance the precision of such forecasts, digital twin technology can take things a step further.

Digital twins—essentially virtual representations or simulations of physical operations—allow manufacturers to see how a process or operation performs in real time and predict how it may perform in the future. This type of simulation will increasingly become table stakes in the smart factories of the future.

One RSM client, a midsize industrial food production company, used digital twins to optimize its bulk inventory management capabilities and ultimately determine which investments in personnel, storage tanks, and other supporting storage capacity were necessary. Through simulation, the business was able to re-focus investments on areas of the business that would yield more value. We explore the factors at play in putting this transformative technology into action.

Determining Future Capacity

At the outset of the project, the industrial food production company had numerous priorities: making operations as efficient as possible, improving business processes, and reducing operating expenses while also prioritizing capital investment. To achieve those objectives, the company had purchased production line equipment that would enable it to produce a greater volume of product, but it needed to understand the upstream implications before increasing capacity.

“In running a simulation that accounted for more staff, the scenario met the target sales goal, effectively reducing guesswork of how the business might need to augment staff to accommodate growth.”


That’s where a simulation analysis proved helpful. The company fed operational data into a digital twin model and ran various simulations to understand what the appropriate size of production equipment would be needed to meet the production line demands. Solving that question had many variables, including product mixes, batch sizes, upstream processes’ capacity, new and existing equipment, and plans for future growth.

The main challenge was to determine whether the future-state production system could meet the company’s sales goal of producing an additional 9.5 million pounds of product per year, and how to make sure the organization understood the broader change and impact that growth would have on operations.

RSM’s engagement with the company involved a three-phase approach using digital twins to address that challenge:

1.     Discovery phase

  • Document simulation questions, assumptions, and model scope
  • Collect existing process performance data and/or plan operations studies
  • Document, review, and approve on-paper model of in-scope processes and future-state scenarios

2.     Analysis phase

  • Conduct process studies and finalize model inputs
  • Build simulation model of existing system
  • Validate simulation model against current performance
  • Finalize future-state scenarios

3.     Scenarios and impact phase

  • Modify model to reflect future-state scenarios and answer simulation questions
  • Summarize scenario findings into a final deliverable concluding with a readout for executives and other key stakeholders

Through the simulation process, the company determined that it would need to implement additional production shifts beyond its existing staffing plan to meet anticipated demand. In running a simulation that accounted for more staff, the scenario met the target sales goal, effectively reducing guesswork of how the business might need to augment staff to accommodate growth. The company was also able to determine that additional storage tanks effectively provided no additional production capacity.

With these insights, the company decided to focus on adding equipment at the upstream process before purchasing any new tanks. Running simulation scenarios prevented spending millions on new tanks and focused the company on addressing the bottleneck process.

“The foundational importance of digital twin simulations lies in the fact that they can help businesses transform data-driven insights into real-world decisions.”

Key Benefits

Understanding specific inventory and staff levels is just one aspect of the use case for digital twin simulations. This technology also helps organizations understand the design and layout needs of their warehouse and production facilities and improve stakeholder understanding of impending changes by using 3D representations to explain the change and supporting rationale.

The simulation’s flexible model and dynamic scenario levers enabled the company to experiment with real-world variability that showed the range of expected performance across multiple simulation runs. Models are an enduring asset, and easily modified to evaluate new operating scenarios in the future.

Other key benefits included

  • Apples-to-apples comparisons of key performance measures across alternative scenarios versus current state
  • Simulations that are easily modified for future projects
  • Mitigation of uncertainty and risk of initial plan allowing the client to pivot before missing customer commitments
  • Identification of additional process improvement opportunities with upside of an additional 1 million pounds in annual production

From Insights to Decisions

The foundational importance of digital twin simulations lies in the fact that they can help businesses transform data-driven insights into real-world decisions. For many manufacturers, this capability will be paramount as economic and margin pressures require many companies to shift from a “grow-at-all-costs” strategy to focusing on profitability.

Businesses should investigate how digital twins might help them optimize operations through evaluation of multiple potential scenarios, including1

  • Diversifying and optimizing product offerings with a focus on high-margin products
  • Focusing on production efficiencies across the entire value chain, with continued investment in automation and productivity-boosting technologies
  • Renegotiating contracts with suppliers and working with customers to negotiate cost reimbursements
  • Streamlining processes and upskilling employees to improve efficiencies

All of this can ultimately help organizations better understand where to focus their investments and adapt operations as needed.  M

About the authors:


Casey Chapman is a principal at RSM US LLP.





Joe Krause is a supervisor at RSM US LLP.



1  This paragraph originally appeared in the RSM US article “Margin pressure requires shift from grow-at-all-costs to profitability.”



The MLC Elects New Chair, Vice Chair and Two Leading Industry Figures to its Board of Governors

The Manufacturing Leadership Council (MLC), the digital transformation arm of the National Association of Manufacturers (NAM), has announced the election of new leadership for the MLC Board of Governors for 2024. Additionally, two leading figures from industry have been elected to join the Board. The MLC is the nation’s foremost executive leadership organization dedicated to helping manufacturing companies transition to the digital model of manufacturing by focusing on the technological, organizational, and leadership dimensions of change.

Two long-serving board members have assumed new leadership roles. Dan Dwight has been elected as the MLC Board of Governors Chair. Dwight is President and CEO of Cooley Group and a member of the NAM Executive Committee and Board of Directors. Meanwhile, the new Vice Chair is Ron Castro, Vice President and Chief Supply Chain Officer at IBM Corporation.

Joining as new members of the Board of Governors are Dan Dralle, Executive Director US Government Affairs and Global CIP at Nexteer Automotive, and Larry Megan, Head of Digital at Baldwin Richardson Foods.

“We are very fortunate to have Dan Dralle and Larry Megan join the MLC Board of Governors,” said David R. Brousell, MLC’s Founder, Vice President and Executive Director. “Under Dan Dwight and Ron Castro’s leadership, the Board will vigorously pursue its mission of keeping MLC in the forefront of Manufacturing 4.0 leadership with renewed commitment and energy.”

As an advisory body, the MLC Board of Governors provides guidance to the MLC on its Critical Issues agenda, research studies, and its programs and services for the MLC membership.

“The MLC is leading the manufacturing digital transformation charge,” said Dan Dwight, new Chair. “The Board of Governors keeps its finger on the pulse on what’s happening in factories and connects the dots to technology partners and academia so MLC members can forge a path to a more resilient future for manufacturing.”

With these appointments, the MLC Board of Governors now consists of 18 industry thought leaders who represent the full spectrum of industries and functional roles including large global enterprises, small and medium-sized manufacturers, leading academic institutions and a full array of areas of expertise.

Board members whose terms expired or who have retired include immediate past Board Chair Mike Packer, Eric Fidoten, Brad Heath, Krishna Srinivasan, and Bart Talloen.


Crystal Ball: Business Resilience in 2030 and the Digital Dexterity Effect

Manufacturing Leadership Council Crystal Ball Series Manufacturing in 2030 Project

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.

ML Journal

Creating an M2030-Ready Organization by Embracing Generational Divides

Leveraging the strengths and understand the needs of each generation in the workforce can promote organizational resilience.  

Learn how to evolve your leadership, organization, culture, and people to lead your organization to be future-fit.
Learn how to turn the generational gap into an opportunity by leveraging the unique perspectives, skills, experiences, and preferences of each generation and fostering a culture of knowledge sharing.
Build coaching tribes that utilize mentoring and reverse mentoring to speed up the training of your younger generations, who will soon make up the largest portion of your workforce, while leveraging the unique experience of the other generations in your workforce.   

To successfully implement Manufacturing 4.0, industrial organizations must prioritize leadership, organization, culture, and people. This will enable them to build a strong foundation for adopting new technologies and processes to drive long-term growth and success.

Adopting a collaborative, cross-functional approach involving all stakeholders — employees, suppliers, and customers — requires breaking down traditional organizational silos and fostering a culture of openness, transparency, and continuous learning.

One key strategy for implementing collaborative, cross-functional structures is to establish multidisciplinary teams that bring together individuals with diverse skill sets and backgrounds. These teams should identify opportunities for improvement and develop solutions that leverage advanced technologies.

A clear vision for transformation, easy-to-understand communication of the benefits and goals of the initiative, along with training and development programs are critical change management strategies for the successful implementation of Manufacturing 4.0. Employees need the skills and knowledge to work effectively as the environment around them changes.

To ensure that the change is sustained over the long term, it’s important to use metrics to measure the success of the initiative and to continuously monitor and refine processes. Regular feedback and input from employees, customers, and other stakeholders can contribute to resiliency as market conditions change and technology advances.

“One key strategy for implementing collaborative, cross-functional structures is to establish multidisciplinary teams that bring together individuals with diverse skill sets and backgrounds.”


Creating a culture of innovation and continuous improvement can be achieved through incentivizing employees to share ideas and contribute to process improvements. Regular feedback and recognition can also encourage employees to embrace new ways of working and adopt a continuous improvement mindset.

Finally, partnerships with external stakeholders such as suppliers, customers, and industry organizations can drive innovation and collaboration within the manufacturing ecosystem, accelerating the adoption of new technologies and processes and driving overall industry growth.

Here are the lessons we’ve learned on the crucial facets to becoming a future-fit organization:


●  Establish a clear vision and strategy for Manufacturing 4.0.
●  Show top-down commitment to digital transformation and innovation.
●  Communicate effectively and manage stakeholders.
●  Possess strong change management skills and lead through change.
●  Inspire and guide the organization through the transformation process.
●  Embrace change, promote a culture of innovation, and communicate the vision and goals of Manufacturing 4.0 to all stakeholders.


●  Structure the organization to facilitate collaboration, cross-functional integration, and agile decision-making.
●  Form collaborative cross-functional teams that allow for the seamless flow of information and resources.
●  Implement flexible work arrangements and adopt Agile methodologies.
●  Define clear roles and responsibilities to ensure accountability and alignment across teams.
●  Establish robust project management processes to ensure effective execution of initiatives.


●  Cultivate a culture of innovation and continuous improvement that encourages experimentation and risk-taking.
●  Emphasize data-driven decision making.
●  Empower employees to make decisions and take ownership of their work.
●  Promote collaboration, teamwork, and knowledge sharing.
●  Develop a customer-centric culture.
●  Encourage experimentation, provide tools and resources to innovate, and recognize and reward employee contributions.


●  Recruit individuals with the right mindset and attitude to drive innovation and change.
●  Invest in training and development to ensure employees have necessary skills to operate in a digital environment.
●  Promote a culture of learning and development.
●  Emphasize diversity and inclusion to promote a creative and inclusive work environment.
●  Recognize and reward employees who contribute to Manufacturing 4.0 initiatives’ success.

The challenge of workforce generations today

The four generations currently represented in the workforce each have unique values and expectations that shape their attitudes and behaviors in the workplace. Building organizational resilience requires understanding and leveraging the strengths of each generation.

Baby Boomers value stability and security, and many are approaching retirement age. They have a wealth of knowledge and expertise that can be leveraged through mentorship and knowledge transfer programs to build organizational resilience.

Generation X is characterized as being independent, adaptable, and tech-savvy, and they value work-life balance and career growth. To build resilience, organizations can invest in training and development programs, offer flexible work arrangements, and create career advancement opportunities.

“Organizations are shifting toward career paths that prioritize gaining experiences and skills, allowing employees to move between different parts of the organization where they can best apply them.”


Millennials are the largest generation in the workforce and are known for being entrepreneurial, collaborative, and socially conscious. They value purpose-driven work and expect a work environment that aligns with their personal values. To appeal to this group, organizations can create a purpose-driven culture, prioritize diversity and inclusion initiatives, and offer opportunities for collaboration and innovation.

Generation Z is just entering the workforce and is known for being entrepreneurial, tech-savvy, and adaptable. They value flexibility, work-life balance, and are comfortable with remote work arrangements. To build resilience, organizations can offer a flexible and technologically advanced work environment that leverages their digital skills and supports their desire for work-life balance.

To develop a framework that considers the unique characteristics and preferences of each generation, organizations can follow some key steps:

Develop a culture of respect and inclusion: Ensure that all generations are respected and valued, regardless of age or experience. Encourage communication and collaboration between generations, create a culture that embraces diversity and inclusion, and foster transparency. This improves feedback and ensures that everyone feels comfortable sharing their opinions.
Foster knowledge transfer: Develop programs that transfer knowledge and expertise from older generations to younger ones. Encourage intergenerational teams to work together on projects and initiatives through mentorship, reverse mentorship, and job shadowing opportunities.

  • Provide training and development opportunities: Invest in programs that cater to the learning styles of each generation to upskill and reskill workers across all generations, using both e-learning platforms and in-person training. This will ensure that all employees have the skills needed to succeed in their current roles and in the future
  • Offer flexible work arrangements: Older workers may value stability and traditional work arrangements, while younger workers may value work-life balance and flexibility. Offering flexible work arrangements such as remote work or flexible schedules can help meet the needs of all generations.

“To leverage each generation’s preferences, it’s important to identify each employee’s generation, target group within the organization, and level of training expertise.”


Leverage technology to bridge the gap between generations and facilitate communication and collaboration: Utilize social media platforms, video conferencing, and collaboration tools that allow employees to work together regardless of location or generation.
Putting it all together: Building Coaching Tribes

Today, traditional career paths are often focused on specific job titles (i.e., project manager, supply chain planner, logistic execution analyst) within functional silos. However, new organizations are shifting toward career paths that prioritize gaining experiences and skills, allowing employees to move between different parts of the organization where they can best apply them.

To leverage each generation’s preferences, it’s important to identify each employee’s generation, target group within the organization, and level of training expertise. By doing so, organizations can choose the appropriate learning methods and content to effectively train and develop their employees.

Since companies today are made up of individuals from different generations, each with their own unique skills and expectations, this diversity creates a “tribal” culture where different groups require different experiences. Therefore, we propose a reciprocal coaching environment where generations can learn from one another. More experienced members can share their seniority and knowledge, while younger members can contribute their understanding of new digital technologies and their innovative approach to work. This collaborative effort will help create a more productive and connected workplace culture.  M

About the author:


Jose “Pepe” Tam is Digital Transformation Vice President at Softtek.


ML Journal

Dialogue: A Future of Data-Driven Leadership

With expertise in data science, Dow’s Amanda Ahrens has a unique understanding into what the power of data can mean for the future of manufacturing and its role within R&D, sustainability, asset optimization and more. 

Last June, Amanda Ahrens of Dow was honored at the Manufacturing Leadership Awards as the winner of the Next-Generation Leadership category, which is given to a high-performing emerging leader aged 30 or younger. Her background in mathematics and data science is emblematic of the emerging role of data analytics in manufacturing – a true embodiment of the industry’s future.

Ahrens has experience with both corporate IT and R&D during her time at Dow, an unusual and challenging career path but one that gives her unique insight into different areas of the business. She is also a strong advocate for promoting STEM career for women and minorities, serving as a volunteer for the Michigan Council of Women in Technology, computer science coordinator for Dow’s Girls in STEM program, and a coordinator and lecturer for an introductory data science programs for HBCU undergraduates. In this interview, she discusses data’s growing role in manufacturing, how it can provide fuel for ESG initiatives, and what she thinks are the most exciting use cases for AI.  

Q:  Data science has a growing and important role in manufacturing. How do you see that role evolving in the next 3-5 years?

A: It is becoming easier to access data to build models, deploy models, and maintain models.  Data quality, however, has always been and continues to be a barrier to building models that provide useful results.  As manufacturers see a return on investment regarding data science, if reinvested to maintain high-quality data (by sharing the results with data owners, investing in soft sensors and AI enhanced sensors, investing in systems fit for purpose that enable fast and easy data cleansing and analysis), other use cases will be unlocked.  AI and data science is best when people don’t even realize it is there and will need to be embedded into modern processes and tools to realize the full value cases.

Q:  You started at Dow as part of the corporate IT team before making the switch to R&D. What was that transition like for you, and how does data analytics play a role in R&D?

A: My internal clients were completely different in corporate IT, R&D, and Manufacturing regarding what data they regularly worked with, how they used data, and how they wanted to receive data and data analysis.  During my transition, I learned about my new client personas. In R&D, data analysis is used to design experiments, analyze lab test results, identify product portfolio gaps for new product opportunities, predict formulations to deliver desired properties, and many other use cases.  The data in R&D is very wide, meaning that there are fewer rows (or samples) than columns (chemical structure, properties, applications testing results). Wide data requires data analysis techniques that are meant for this type of data.

“AI and data science is best when people don’t even realize it is there and will need to be embedded into modern processes and tools to realize the full value cases.”


Q:  Your role has evolved to include ESG initiatives, working toward the goal of reducing Dow’s carbon emissions. How can manufacturers use data to further their sustainability goals?

A: Having granular, frequent, and accurate data on direct air, waste, and water emissions as well as energy consumption allows for prioritization of capital investment for sustainability initiatives.  Tracking raw materials or feedstock with sustainability attributes through procurement, production, material movements, and product sale in a streamlined way enables tracking for product certification and scale to progress on both circular economy and climate goals. Data from suppliers can help you strategically source to lower Scope 2 and Scope 3 emissions and purchase energy, feedstock, and raw materials with sustainability attributes in alignment to your sustainability goals.  Data from customers can help you understand your downstream Scope 3 emissions, develop products that better meet their needs and their sustainability goals, and progress on your sustainability goals in the process.  The few examples above illustrate that data from your own operations, suppliers, and customers is foundational to decision-making to advance sustainability goals.

Q:  It was noted in your awards nomination that you are one of the leaders for Dow’s annual Data Science Challenge. Can you describe that program?

A: Dow has a biannual Data Science Challenge, and I co-led the 2022 Data Science Challenge.  Each challenge is run in a shark-tank like format where cross-functional and cross-business teams pitch their data science idea to a panel of Dow executives (sharks).  The winning project ideas are then invested in. In 2022, the theme was advancing Dow’s sustainability goals regarding climate, circular materials, and safer materials. The winning idea and other challenge ideas have been incorporated into our digital strategy.

“Girls who are exposed to women in manufacturing will grow up knowing that it is a possibility for them. Women seeing women thriving in manufacturing will attract more to the industry.”


Q:  You also have experience with testing AI use cases for Dow. While a number of manufacturers are using AI for quality and preventative/predictive maintenance, what are some emerging use cases that you think have potential for greater impact?

A: A lot of data is unstructured (text) and stored in various systems, emails, PDFs, Word files, Power Point slides, etc. With large language models, like ChatGPT, we can make better use of this collective knowledge to answer procedure questions faster.  Soft sensors and sensors that are enhanced or augmented with AI will enable us to gather data that used to be more costly to gather with traditional instrumentation and metering.  Having more granular data on emissions, water, and energy from these types of sensors enables optimization of manufacturing processes further regarding energy efficiency, emissions, water conservation, etc., with the use of AI, in addition to optimizing asset utilization. AI could also be used to aid in shut down and start up activities.

Q:  Women tend to be underrepresented in STEM fields, and they make up less than 30% of the manufacturing workforce. What changes need to take place to bring more women into the industry?

A: Girls who are exposed to women in manufacturing will grow up knowing that it is a possibility for them. Women and male allies being in the classrooms sharing the exciting scientific and engineering feats that are accomplished in manufacturing will inspire girls to become women in manufacturing.  Providing employees time to volunteer and sponsoring events to expose girls to STEM is important for developing the pipeline. Then, once the women are in manufacturing, invest in keeping them there. Ensure women have both women and men on their board of sponsors and mentors. Provide an avenue for employees to attain reliable childcare and senior care for their family members.  This would greatly benefit women since they are often primary caretakers, especially for employees that do not have an 8-5 work schedule as finding care outside of the typical work week is even more difficult.  Women seeing women thriving in manufacturing will attract more to the industry.  M

Headquarters: Midland, Michigan
Industry Sector: Chemicals
Annual Revenue (2022): $56.9 billion
Employees (2022): 37,800
Production: 106 sites in 31 countries

Title: Sustainability Data Architect – Enterprise Architecture (Houston, TX), 2021 to present
Education: Michigan State University, B.S., Mathematics; Texas A&M University, M.S., Analytics
Previous Roles:
– Data Visualization Specialist – Advanced Analytics (Midland, MI), Dow
– Research Informatics Analyst – R&D Information Research (Midland, MI), Dow
– Data Scientist – Digital Operations Center (Houston, TX), Dow

About the author:

Penelope Brown

Penelope Brown
is Senior Content Director of the NAM’s Manufacturing Leadership Council.



ML Journal

Placing Innovation at the Heart of Transformation

To build the right foundation for long-term growth, manufacturers must put innovation and digital strategies at the heart of transformation. 

MLC Crystal Ball

Massive geopolitical, technological and cultural changes have prompted unprecedented shifts in industrial manufacturing.
As pressures mount to build smart products, digitize operations and exceed customer expectations, incremental approaches to change are falling short.
Manufacturers need to design a transformation that is innovative, agile, cross-functional, and scalable to realize tangible business value.  

Over the last several years, challenges related to geopolitics, technology, the pandemic and climate change have shaken the foundation of industrial manufacturing – creating tectonic shifts in how manufacturers think, operate and deliver their products and services.

The pressure to make products smart and connected, digitize the factory and operations, create more automated and reliable supply chains, and deliver on rising customer expectations presents significant opportunities to move toward radical growth. Yet many manufacturers’ current approaches – driven by siloed teams, functions rather than strategies, and static views of competitive and operational landscapes – present significant risks on the road to reinvention.

Legacy manufacturers trying to speed ahead into the future of smart products, digital platforms and new service-oriented business models urgently need a better way forward. A hybrid, innovation-led approach that incubates future businesses while simultaneously optimizing today’s products and operations can provide practical, value-driven solutions in both the short and long term.

Disruptive Forces Shift Operational Priorities

The range of disruptive forces manufacturers are experiencing has implications across the entire enterprise. These forces include:

  • sector convergence
  • power shifts within value chains
  • evolving customer expectations
  • volatile macroeconomic environments
  • tougher workforce dynamics
  • sustainability pressures
  • transformative technologies

In response, manufacturers are prioritizing digital transformation and innovation-related investments. According to the EY January 2023 CEO Outlook Pulse survey, virtually all (97%) of industrial manufacturing CEOs indicated that continuing digital and technology transformation to deliver growth and operational advantages is either a very or fairly important near-term priority, despite near-term economic uncertainty.

Through these investments, manufacturers seek to revolutionize processes by implementing innovations such as digital twins, artificial intelligence and machine learning. At the same time, they are advancing product and service offerings across the value chain that are smart and connected.

However, to fully realize the significant opportunities arising from disruption and the value of these investments, manufacturers must tie them to a unifying vision of where their markets are headed.

“Legacy manufacturers trying to speed ahead into the future of smart products, digital platforms and new service-oriented business models urgently need a better way forward”


A Different, Innovation-Focused Approach to Manufacturing Transformation

Forging a new path to the future will require manufacturers to put their growth agenda at the center of transformation. This plan needs to be backed by a detailed strategic roadmap that accounts for both the innovations needed to succeed and the transformation required to launch and scale these breakthroughs in the market. It must also be developed with an enterprise-wide outcome in mind, breaking down functional and geographic silos.

While not every initiative needs extensive cross-organizational coordination and buy-in, many of the most prominent areas for manufacturers’ business reinvention are inherently interdisciplinary. Effective transformations demand engagement with a range of intersecting value drivers, including product and service innovation; customer experience; intelligent and sustainable supply chains; workforce and talent; and business model innovation.

Image 1: The value drivers of tomorrow

Image 1: A chart showing the value drivers of tomorrow that intersect include product and service innovation; customer experience; intelligent and sustainable supply chains; workforce and talent; and business model innovation. Source: EY

Once a decision is made to pursue small- or large-scale reinvention, manufacturers can maximize the speed, agility and long-term value generation by adopting the following best practices.

Start With the End in Mind But Also Know Where to Begin

With business challenges clearly defined, manufacturers should explore solutions using a future-back approach that starts with the end in mind. The critical first step involves rapidly defining and assessing tomorrow’s potential futures from the outside-in, and predicting how these future scenarios may shape customer needs, market conditions and value pools across time horizons. In parallel, organizations must also perform a thorough review of their current capabilities, particularly those that may play an increasingly important role in future competitiveness.

Taken together, assessments of a company’s current state and potential futures serve as the foundation of a new strategy that will act as the transformation’s North Star – guiding it relative to the level of ambition, while optimizing development speed and investment spending. This strategy should also provide a framework for cross-enterprise transparency and engagement.

Image 2: Our approach to transformation

Image 2: A chart showing the continuous innovation process which defines the future growth agenda, designs the transformation plan to deliver results both at the enterprise and functional level. Source: EY

Design the Transformation With a Hands-On Approach to Innovation

Rapid prototyping is an essential step for manufacturers to substantiate their future views and refine perspectives on must-have innovations. Multiple, accelerated iterations of both physical products and digital offerings are important for assessing features and costs in the context of various customer expectations, demand levels and competition scenarios.

Making the future tangible will pay dividends in the design phase of the transformation. Understanding necessary enterprise-wide adaptations – from specific technology or talent enhancements to new business models – will inform an achievable, cross-functional plan. The ability to “show your work” will also help build support across various internal constituencies as they are enlisted in the effort.

Start with the end in mind

Image 3: A chart showing the continuous innovation process which defines the future growth agenda, designs the transformation plan, analyzes existing products and operations, connects new products, embeds digital platforms to redesign As-A-Service (XaaS). Source: EY

Accelerate Results By Incubating New Business Model Concepts

When future growth depends on technologies, customer expectations or new value pools that are not addressed by a manufacturer’s current core competencies, innovation at the business model level may be a solution. For example, what are the implications of collecting, analyzing and monetizing data from a new connected product? Successfully assessing and addressing these impacts could enable manufacturers to leapfrog the competition.

“To fully realize the significant opportunities arising from disruption and the value of these investments, manufacturers must tie them to a unifying vision of where their markets are headed”

Given the enterprise-wide implications of such changes, legacy manufacturers should prioritize small-scale experimentation with a lean, internal start-up style approach. In conjunction with ongoing physical product innovation, manufacturers will want to invest in teams to explore the internal and external infrastructure and capabilities needed to support an operating model for an offering’s full lifecycle. This approach can be particularly beneficial for evaluating the technology infrastructure required for a potential new business model. By using focused, lean teams, manufacturers can leverage agile sprints to further define capabilities and architectural requirements as the transformation progresses.

Identify Ecosystem Partners to Address Critical Capability Gaps

As manufacturers innovate, they will have to decide whether their long-term strategies are better served by seeking ecosystem partners versus internally developing or acquiring new capabilities. Assessing targeted value pools and key differentiators of success can help inform these decisions.

Manufacturers whose future offerings are likely to depend on the secure sharing and analysis of data at scale will want to consider partnering with technology providers as a more efficient path to market. However, the factors to consider when choosing a mission-critical partner can be extensive, particularly given the high cost of potential failure. Legacy manufacturers can benefit from a structured process to weigh the benefits of various collaborators.

Manufacturers exploring business model innovation may also find a need for ecosystem partners outside of the technology sector. For example, future mobility business models may involve close collaboration among vehicle OEMs, energy, infrastructure and insurance firms.

Key Questions For Manufacturers Considering Reinvention

The urgency around transformation has escalated as manufacturers grapple with multi-faceted disruptions. Reactive, short-term focused responses limit the vision for enterprise-wide transformation. When thinking about the right approach, manufacturers should consider the following questions:

  1. Product and service innovation: How do we evolve our product portfolio to create dynamic products and services that address customer demands for smarter and more connected features, customization, and safety/security?
  2. Customer experience: How does a shift from B2B to B2C or D2C impact how we operate today?
  3. Operations:
    a. How can we leverage emerging technologies to future-proof our supply chain?
    b. How do we design our future manufacturing capabilities as a competitive differentiator?
  4. Workforce and talent: How can we advance innovation as a core capability through hiring, developing, training and incentivizing our employees?
  5. Business model innovation: How do we innovate to drive the core business to peak profitability while simultaneously incubating new growth engines?

Answers to these questions may help leaders address strategic or operational gaps. With a clear vision of their organization’s future ambitions in mind, and a strong strategy for innovation-focused transformation, leaders can position their organizations to accelerate growth and leapfrog their competition.  M


About the Authors:

David Takeuchi
is EY’s Global Strategy and Transformation Business Model Innovation Leader



Jerry Gootee
is EY’s Global Advanced Manufacturing Sector Leader



Claudio Knizek
is EY-Parthenon’s Global Advanced Manufacturing and Mobility Leader



ML Journal

Manufacturing and Supply Chains in 2030

AI, digitalization, automation, and proximity to end markets will shape supply chains over the next decade.  

Creating flexible, resilient supply chains brings increased inventory, more suppliers, and higher input costs.
With AI, companies can ideate, create prototypes, improve processes, and analyze problems rapidly.
To be competitive, businesses will need to invest in AI, digitalization, and automation.  

Manufacturers have made significant changes over the last three years as global events exposed structural vulnerabilities in just-in-time shipping and supply chain operations. Now, disruptive artificial intelligence (AI) technologies signal that another market transformation may be upon us alongside those other forces reshaping supply chains.

Over the next decade, companies will continue to prioritize flexible, resilient supply chains. But that flexibility brings a higher cost in the form of increased inventory, more suppliers, and higher input costs. In 2030, we expect AI will play a larger role in helping manufacturers manage supply chain costs strategically. Proximity to end markets and the increased presence of digitalization and automation will also shape supply chains. Global middle market manufacturers will need to invest in their supply chain capabilities with these factors in mind.

AI in Manufacturing

AI will be both disruptive and enabling. AI is neither an unthinkable nor unexpected external market force—its impact will only increase over time. Manufacturers should view AI as an enabler that can improve their business.

According to a recent MIT Technology Review article, the most likely use cases for AI in the industrial space will be software developed for logistics, transportation, civil engineering, construction, energy, and manufacturing. Manufacturers should consider dedicating research and development (R&D) and innovation teams to monitor when software-as-a-service companies incorporate AI into new and existing software and when those technologies are expected to hit the market.

Because AI solutions will largely come in the form of software, companies’ software expenditures—already on a steep incline in recent years—will continue to grow (Figure 1). But AI will also help industrial businesses rapidly ideate, create prototypes, make process improvements, and analyze narrowly defined problems, all of which will help mitigate costs.

Figure 1: U.S. Software Expenditures*

ChatGPT, for example, represents a significant leap in AI capabilities and provides a helpful glimpse of what is possible. The AI tool—classified as generative AI—was released in November 2022. Its output, though imperfect, will get exponentially better in the next few years, as will that of other AI tools such as Claude AI.

Hurdles do remain for widespread AI adoption though. For AI to work well and to be effective, it needs good training data. Raw data will not produce good results on their own; a human must clean and transform large volumes of that information into usable data sets. To address this issue, we anticipate software companies creating synthetic training data representing a problem that an AI model seeks to solve, resulting in a significant improvement in adoption rates.

“By 2030, 60 percent of manufacturers expect more nearshoring or onshoring of their operations to boost resiliency and better meet local customer needs.”


Implementing AI for its own sake will not be beneficial for manufacturers. Any AI project should have a clear path to profitability and pay for itself in efficiencies. A rule of thumb is to start small and aim to scale up later.

Proximity to End Markets

Manufacturers still need to think holistically about how they incorporate AI alongside other changes, such as moving operations closer to their end markets. Recent Manufacturing Leadership Council surveys found that by 2030, 60 percent of manufacturers expect more nearshoring or onshoring of their operations to boost resiliency and better meet local customer needs. Indeed, U.S. foreign direct investment to and from key geographies such as China is showing signs of shifting as geopolitical tensions steer manufacturing away from that country.

In 2021, China saw a 6 percent drop in foreign direct investment (FDI) from the U.S., according to U.S. Bureau of Economic Analysis data. In 2021, India saw U.S. FDI increase by 7.6 percent (Figure 2). During 2020 and 2021, Mexico saw a 9.1 percent and 6.1 percent increase in FDI from the U.S., respectively. Both countries are positioning themselves as friendly alternatives to China.

Figure 2: U.S. Foreign Direct Investment in India*

Figure 3: U.S. Foreign Direct Investment in Mexico*


As 2030 approaches, we anticipate this shift toward nearshoring will accelerate. Companies that are eyeing factories in new locations should assess where there may be simultaneous opportunities to invest in digitalization and automation at those facilities.

Digitalization in Manufacturing

Digitalization will continue to be the driving force behind evolution for manufacturers across their business functions; 83.9 percent of executives surveyed by the Manufacturing Leadership Council expect digital adoption will accelerate in manufacturing throughout the decade. Ninety-one percent of those surveyed agree they will need to spend more on digitalizing their businesses than they currently do. Factors contributing to this trend include baby boomers retiring, lack of access to skilled and unskilled labor, and the need to increase operational productivity and capacity.

Digitalized factories provide automated real-time alerts and analytics for production, and for shop floor and warehouse performance, all of which can help manufacturers pivot in the face of those workforce challenges. Data analytics that feed dashboards with set key performance indicators can enable management to be more flexible. E-learning and learning management systems can help with onboarding, training, and retaining new staff.

Looking Ahead

The complex nature of developing a supply chain fit for 2030 will take time, patience, and thoughtful investment in talent, new business infrastructure, and key business processes. Manufacturers should start assessing which changes they need to make now. M

This article is adapted from the original version published on in April 2023. It has been modified with RSM’s consent.

About the author:


Matt Dollard is an industrials senior analyst at RSM US LLP.


ML Journal

A Digital First Mindset for Manufacturers

Follow these five steps to enable planning, collaboration and investments in talent and technology that are vital to digital transformation.   

Manufacturing companies must invest now in digital infrastructure to collect and analyze data for insights, optimization and quality improvement.
Embracing automation will enhance efficiency and free up employees for more complex tasks.
Collaborate with partners and invest in talent development while prioritizing cybersecurity to protect your organization’s digital assets.  

The Challenge

Digital operations and data analysis are transforming the future of manufacturing. We are already seeing examples of the power of digital and data to inform, automate, and improve manufacturing processes from strategic planning to execution. Companies incorporating digital capabilities have proven efficiency gains (think constraint management, overall equipment effectiveness (OEE), quality, predictive maintenance, compliance), but will also see strategic advantage in becoming more formidable in on-going battles related to the dwindling manufacturing labor pool, demand and supply volatilities, and the overarching pervasive need to find levers to support growth or maintain already tight margins. But for most manufacturing companies, delving into digital is easier said than done with common hurdles including technical debt, reluctance to change and ineffective data management.

Operating in a digital age starts with letting go of legacy systems, processes and ways of working. But even the biggest manufacturing companies are burdened by technical debt, with outmoded systems that are incompatible with modern digital technologies. This can make it difficult to integrate new solutions into existing data, workflows and processes.

Beyond technical debt, employees can be wary of adopting new digital technologies due to a lack of understanding or fear of job loss. This can slow the adoption of digital solutions and hinder organizations’ ability to innovate.

Another complication is data management. Manufacturing companies generate vast amounts of data from machines, products and customers. However, this data is often siloed and not easily accessible or usable. This can make it difficult to gain insights into customer needs and preferences, optimize production processes, and improve product quality.

So, how do manufacturers prepare for the digital future?

Five Steps for Digital Success in Manufacturing

To shed legacy systems, bring employees on board and do more with data, it is important to invest in digital infrastructure supporting digital information systems. This will enable the collection and analysis of data from machines, products and customers to gain insight into customer needs and preferences, optimize production processes and improve product quality.

Alongside this, embracing automation will reduce costs, improve efficiency and increase productivity. By automating repetitive tasks, manufacturers can free up their employees to focus on more complex tasks that require human skills. But doing this requires careful planning, collaboration and targeted investment in talent and technology.

1.  Assess the current state of digital readiness
To achieve digital success, it is vital for manufacturing companies to assess their current state of digital readiness. This all-important gap assessment helps organizations’ leadership teams to identify areas and opportunities for improvement. With this knowledge in mind, leaders can begin to prioritize their efforts and allocate resources more effectively. When selecting the most critical areas for improvement, success will be more likely when these improvement areas align with business goals and customer needs. This will help them focus their efforts on initiatives that have the greatest impact.

“Even the biggest manufacturing companies are burdened by technical debt, with outmoded systems that are incompatible with modern digital technologies”


2.  Develop a roadmap for digital transformation
Having assessed their organization’s current state of digital readiness, manufacturing leaders can develop a roadmap for digital transformation that sets goals, priorities and timelines for deploying digital initiatives. This roadmap, with timebound tasks and responsible owners, focuses on objectives and avoids wasting resources on initiatives that do not align with business goals.

It is often the case that organizations throw themselves into adopting new technology and systems without necessarily considering how it supports the overall business strategy. A shiny new digital system powered by artificial intelligence might seem like a worthwhile investment but is unlikely to deliver a return on investment if it does not address a key operational need or challenge.

3.  Collaborate with partners across the value chain
Customers, suppliers and service providers should be collaborators – looking at what other organizations are doing to become more digital can provide useful insight and experience. Manufacturing companies can collaborate with partners across the value chain to share data, expertise, and best practices to help them identify new opportunities for growth and innovation. They can also look beyond their own industry for inspiration and knowledge.

4.  Invest in talent development
One of the key reasons firms can struggle to adopt digital is because their teams are not ready to do so. Much of this comes down to attitude, and feeling safe experimenting with new systems without worrying that they will be replaced. Helping employees to feel confident about digital systems and emergent technologies is part of the battle. A company that has had great success with digital adoption on the shop floor has worked to shift roles from operators to optimizers. It is essential to ensure employees have the right skills and knowledge to succeed in a digital environment. This starts with leaders who demonstrate the behaviors and skills required, and with digital champions who help their colleagues to build familiarity. Providing training programs, hiring new talent with digital skills, and partnering with educational institutions to develop new curricula can help to drive towards a digitally savvy workforce.

“By automating repetitive tasks, manufacturers can free up their employees to focus on more complex tasks that require human skills”


5.  Prioritize cyber security
More digital assets mean a greater threat of compromise (malicious or unintentional) of IT/OT systems leading to potential to manufacturing disruptions, faulty planning and management decisions, quality issues, compliance violations or damaged business partner relationships. Conducting regular cyber security audits can flag any potential risks before they become issues and inform proactive measures to limit the negative impact. Manufacturing companies can also partner with cyber security experts to develop a comprehensive strategy.

Manufacturing organizations face myriad strategic and operational challenges; however, charting a path to digital is critical to remaining competitive and thriving in the future. Technical debt and resistance to change must be tackled head on to achieve a successful digital future. Additionally, recognizing and addressing the expanded exposure to IT/cyber risk via digital and accelerated dynamics of talent development is critical to a sustainable digital future.  M

About the Authors:


Michael Platz is a Supply Chain and Manufacturing Operations Expert at PA Consulting




Shanton Wilcox is a Partner and America’s Leader in Manufacturing at PA Consulting


ML Journal

Winning the ‘Product Innovation Game’: The 2030 Mandate for Manufacturers

To win at product innovation, manufacturers will need to make their NPD processes more robust by integrating Operations staff earlier and by using AI and other Industry 4.0 technologies. 

To survive and prosper, manufacturers must strive to excel at product innovation.
Manufacturers can use build-and-test iterations to validate new products technically and with customers. Build-and-test iterations can be done early, often, quickly, and cheaply.
● Artificial intelligence can be a game-changer in the new product development process.  

Innovation propels companies to greatness! Consider the transformation in the list of top 10 companies in America over the past three decades. In 1990, all top 10 US firms were in traditional physical products industries (Table 1). Half were in oil and gas or petrochemicals. By 2020, only two of the 1990 top ten—IBM and GE—remain on the list, but further down.

Table 1: Top 10 US companies, 1990 vs. 2020


Two fundamental factors underlie this 30-year shift:

  1. A transition from manufacturing: Only half of the top 10 US firms in 2020 are manufacturers, while the product offerings of the top 10 firms has shifted from older technologies, such as oil and gas or traditional autos, to newer ones, such as IT and electric vehicles (EVs). Even individual companies like IBM, once renown for making computers, now predominantly offer software and services.
  2. An emphasis on successful product innovation: The newer entrants on the 2020 list, such as Apple, Microsoft, and Tesla, surged primarily due to successful product innovation. In contrast, companies that failed to innovate effectively were replaced. For instance, Kodak’s inability to adapt to disruptive innovation—namely, digital cameras—led to its downfall. Innovators like Tesla surged ahead with EVs, while GM fell behind, no longer on the top 10 list.

Innovation will remain the disruptive force moving forward to 2030, even more so than in the past. We now live in the Fourth Industrial Revolution driven by technologies such as artificial intelligence (AI) and machine learning, blockchain, and robotics and automation, which will create huge changes in industry, particularly in manufacturing.

Strategically, manufacturers must adopt some or all of these new technologies, and build them into their products and processes. A less visible but equally vital message is that manufacturers must strive to excel at product innovation in order to grow and prosper. Ironically, Kodak developed the first ever digital camera, but the company’s failure to successfully commercialize it ultimately led to its demise.

“Many firms’ NP processes are cumbersome and bulky—there’s too much bureaucracy and ‘non-value-added work.’”


Many manufacturers have a long way to go when it comes to successfully commercializing new products. New-product project success rates for physical product firms are now at 25–30 percent1,2—that is, less than one-third of new-product projects succeed commercially!

The Role of Operations in New Product Development (NPD)

How can Operations make a difference to the business’s pace of product innovation and its new product success rate?

Here are four ways:

1   Play new-product football*: A best practice—consistently found to be key to success—is that NPD is a cross-functional team effort.3 Operations/Manufacturing have often been left out of the new-product process. If included, they’re not on the field until near the end of the project, just before the final commercialization stage (Figure 1). This flawed process moves the ball down the field department by department—from Marketing to RD&E and finally to Operations and Sales. The term “transfer to the plant” too often typifies the final play from development to commercialization; a disconnect occurs. This process resembles a relay race, with the baton being passed from person to person (or department to department) with many dropped batons. And it is not the way to win at new products.

A truly cross-functional project team includes staff from all key departments—Marketing, RD&E, Operations, and Sales—and the project advances thanks to a team effort, much like a football team moves the ball, play by play, to the goal line. The process is typically a stage-and-gate new-product process like in Figure 1, where stages are the “plays” and gates are the Go/No Go decisions point or “huddles on the field.”4

Although there is often little work for the Operations members of the project team early in the project, they are still key contributors because their input, knowledge, and experience are crucial to success. They provide insights into

  • Manufacturing feasibility (Can it be made?);
  • Existence of core competencies (Can we make it?);
  • Need for capital equipment, costs, and timing; and
  • Likely manufacturing cost, source of supply risks, and resolution.

This information is vital to building a robust business case for a project, for making the right project selection decisions, and for ensuring a smoother, faster production startup in the commercialization stage.

Figure 1: Typical Stage-and-Gate New-Product Process

2    Making the right Go/Kill decisions: One way to improve new product success rates is picking the winners by making the best go/kill decisions. Besides being project team members early on and sharing important information, an important role for Operations senior management is to be at the Go/No Go meetings for projects (the gates). Gates are where NPD investment decisions are made, and where management decides which projects move forward. Project innovation is a business endeavor, not just the domain of Marketing and RD&E. Operations must have a clear voice at the investment decision table.

3    Accelerate the game—remove the waste: Today’s fast-paced world demands accelerated development. Many firms’ NP processes are cumbersome and bulky—there’s too much bureaucracy and “non-value-added work.” As a result, projects move at a crawl. Lean principles (Lean Six Sigma) were widely applied on the factory floor in the early part of this century, and Operations staff know the methodology well.

Operations can lead by example, helping the other business functions streamline the innovation process. Typically, value stream analysis is used to map the entire new-product process, idea to launch.4 Bottlenecks and time wasters get identified, and a root cause analysis is undertaken to determine why. Then time wasters are removed or reduced. It’s an effective method, used often in Operations, but it is not so well known by other functions. Some firms have applied this Lean methodology to NPD with quite dramatic results—up to 40–50 percent savings in time to market.4

“AI can also be used to facilitate portfolio management and project management. Within five years, predictive analytics may be making the Go/No Go decisions at gates.”


4   Be Agile in NPD: Everything changes quickly today: What was true in the early stages of the project is no longer true by the latter stages. A major unexpected challenge in NPD is that things have changed by the time the project reaches commercialization: the customers’ needs have shifted, a competitive product has been introduced, or product requirements are no longer valid. Instead of moving into production quickly, the project grinds to a halt, and it’s back to the drawing board. Late-stage changes in product design create huge costs and time delays.

A key principle of Agile development5 is being flexible and being able to pivot quickly—the product design is allowed to evolve as the project moves forward. Frozen design specs from the beginning of the development stage are a thing of the past. The goal is to get the get the product right pre-production. This agility concept is borrowed from Agile software development.

How? The project team undertakes a series of build-and-test iterations beginning before development, all the way to production startup.6 These build-and-test iterations validate the product both technically and with customers, early, often, fast, and cheaply. Built into each iteration are tests—technical tests as well as a demo to stakeholders (both customers and management). Negative feedback usually calls for a pivot in the project. If these iterations and pivots happen early and often, the cost and time of making changes is far less, and the product will be fully validated by the time it get to commercialization.

Some project teams fail to build these iterations into the process, especially the customer demos. Demos take time and money, and often involve a physical visit. (By contrast, in software development where Agile originated, demos can be done online.) Other teams wait until the product is almost developed before showing it to a customer. Smart teams, however, devise ways to test and demo non-existent products much earlier, and also undertake technical testing. Digital products, virtual products, computer animations, simulations, and AI tools are available to enable teams to show what the product will be, do, and look like long before a real prototype is ready to test.7 Regular build-and-test validations are much more feasible today thanks to new AI technology.

Operations people must be proactive on NPD projects, pushing hard to ensure that these build-and-text iterations are done; otherwise, odds are high that the product won’t be right when it gets to production startup. When that happens and product changes are needed as the project moves into commercialization, it becomes an “Operations problem.”

Build AI into your NPD System

Operations people have more experience with AI than RD&E, Marketing and Sales staff have. AI has impacted manufacturing in huge and very visible ways—for instance, optimizing the production process, process simulations, quality management, or managing the supply chain.8,9 Except for a handful of leading early-adopter firms like Siemens, Nestlé, and GE, AI has been much slower to penetrate the NPD process, however. As an early-AI-adopter, Operations can model the way and require that AI be pushed further upstream in the NPD process from the commercialization stage (Figure 1). Here are some examples:10,11

  • AI in the Validation Stage: Digital twins mimic the product and can be used to monitor the real prototype product operating remotely during customer field trials. This dramatically improves test data quality, as well as reducing the time it takes for trials.12,13
  • In Development: Development times can be cut in half by doing design and optimization iterations of product components digitally, as GE does in turbine design.14
  • In the front end of the NPD process: AI can be used to scan many information sources looking for unmet needs and opportunities for new products, and can even generate new ideas and concepts.15 With simple verbal prompts, AI can create concepts and make concept drawings.16

AI can facilitate portfolio management and project management and thereby improve both on-time performance and success rates. Within five years, predictive analytics may be making the Go/No Go decisions at gates.

The Path Forward

Making product innovation work better and faster in your business this decade is imperative. But this is not a piece-meal effort—rather it should be an organization-wide initiative led by the leadership team. Operations must play a major role as you reshape your innovation process and methods in order to get it right—making it Lean and Agile, building in iterations for getting the product right, making the process truly cross-functional, and driving AI through the entire process. After all, it’s only the future of your company.  M


  1. Knudsen, M. P., von Pedowitz, M., Griffin, A., and Barczak, G. Best practices in new product development and innovation: Results from PDMA’s 2021 global survey. Journal of Product Innovation Management 2023;40;257–275.
  2. Barczak, G., Griffin, A., and Kahn, K. B. Trends and Drivers of success in NPD practices: Results of the 2003 PDMA Best Practices Study. Journal of Product Innovation Management 2009;26:93–23.
  3. Cooper, R. G. New Products: What Separates the Winners from the Losers and What Drives Success. In The PDMA Handbook of Innovation and New Product Development, 4th, edited by Ludwig Bstieler and Charles H. Noble, 3–43. Hoboken, NJ: Wiley. 2023.
  4. Cooper, R. G. The 5-th generation Stage-Gate idea-to-launch process. IEEE Engineering Management Review. 2022; 41(1):43–55.
  5. Beck, K., Beedle, M., van Bennekum, A. et al. Manifesto for Agile Software Development.
  6. Cooper, R. G., and Fürst, P. Agile Development for Manufacturers: The Emergent Gating Model. InnovationManagemenSE, Nov 10, 2020.
  7. Bilgram, V., and Laarmann, F. Generating Innovation with Generative AI: AI Augmented Digital Prototyping and Innovation Methods. IEEE Engineering Management Review. June 2023;51(2):18–25.
  8. Marr, B. The Future Of Manufacturing: Generative AI And Beyond. Forbes, July 25, 2023.
  9. D’Silva, V.,and  Lawler, B. What Makes a Company Successful at Using AI? Harvard Business Review, Feb. 28, 2022.
  10. Cooper, R. G. The Artificial Intelligence Revolution in New-Product Development. [PDF] 2023.
  11. Cooper, R. G. The Artificial Intelligence Revolution in New-Product Development. YouTube video, 18:37.
  12. Huang, S., Wang, G., Lei, D., and Yan, Y. Toward digital validation for rapid product development based on Digital Twins: A framework.The International Journal Advanced Manufacturing Technology 2022;119:2509–2523.
  13. Nieto-Rodriguez, A., and Vargas, R. V. How AI Will Transform Project Management. Harvard Business Review, Feb. 2, 2023.
  14. Bogaisky, J. GE Says It’s Leveraging Artificial Intelligence To Cut Product Design Times In Half. Forbes, March 6, 2019.
  15. Applied Marketing Science. Our Solutions: Your Insights Partner. 2023.
  16. Roch, J. From Hot Wheels to handling content: How brands are using Microsoft AI to be more productive and imaginative. Microsoft News, Oct. 12, 2022.

About the author:

Robert G. Cooper
is ISBM Distinguished Research Fellow at Pennsylvania State University’s Smeal College of Business Administration, Professor Emeritus at McMaster University’s DeGroote School of Business (Canada), and a Crawford Fellow of the Product Development and Management Association.


* North American football.

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