The semiconductor industry has been growing at a rapid pace for the past few years. Market research firm IDC estimates that the semiconductor industry grew at a rate of 10.8% in 2020 and will grow at 12.5% this year, resulting in a $522 billion market sector. IDC attributes much of this growth to the impact of COVID-19.
The increased demand for semiconductor chips is due to new generations of smartphones, tabs, laptops, and desktop computers used in industries such as healthcare for telehealth services; in the education sector for online teaching and instruction; and as more people worked remotely. At the same time, the automotive industry, a heavy user of semiconductors, is packing more and more chips into vehicles as it attempts to offer all the creature comforts consumers want as they embrace the connected car experience.
In the manufacturing sector, too, the pandemic has driven home the values and virtues of setting up connected factories that enable contactless manufacturing and uninterrupted operations in the face of a crisis. All these trends indicate that the demand for semiconductor chips will rise steadily in the future. Despite the rosy growth projections, the semiconductor industry still faces challenges, chief among which is continuing to innovate even as it delivers expected price/performance improvements.
Therefore, it is imperative that the industry invest more in research and development to drive innovation while at the same time optimizing costs by leveraging technology such as cloud computing.
If one examines the key attributes and requirements of the semiconductor industry – skilled resources, high competition, complex automation tools, data and IP, differences in industry supply chains, and the brief shelf-life of designed chips, it is apparent that these factors are highly expensive and difficult to manage. Given the level of investments and expertise required, there are very few players in this industry. The race for excellence is fierce, and a considerable effort and investment is dedicated to driving R&D to identify areas and avenues for innovation.
Faster time to market through the acceleration of design cycles, performance enhancements of chips through upgrades and updates, and IP protection through foolproof and flawless security systems are the top three business priorities of this industry. The chip companies invest most of their time, energy, and capital in fulfilling these priorities. However, operational priorities are equally important, such as driving efficiencies in the manufacturing process through data analytics; optimizing operations, processes, and costs; and driving productivity through collaboration.
Cloud computing provides a reliable and seamless infrastructure to address both the business and operational priorities of the semiconductor industry.
The ever-increasing demand from consumers for products with higher compute powers and processing abilities has resulted in shorter product lifecycles, requiring semiconductor manufacturing companies to bring products to market faster.
To this end, applying cloud computing in the semiconductor industry offers scalable storage, big data analytics capabilities, and enhanced productivity with collaboration tools for reviews and feedback that enable quick product launches.
Cloud also provides a flexible, scalable, elastic, and secure infrastructure for chip designing by providing on-demand compute for EDA tools. It enables semiconductor manufacturers to set up and access high-performance computing (HPC) power with virtual machines (VM) images, enabling quicker design and development cycles.
Cloud offers a data lake or repository that enables storing, processing, analyzing, and inferring the foundry’s generated data. Manufacturers can use data insights for predictive performance and analytics as well as the management of resources in their supply chains, thereby improving production uptime and yield. It also allows for specific artificial intelligence and machine leaning use cases for fault detection in the production line using imaging techniques and smart analytics tools.
Chip designs evolve with each release, and the chip design companies have families of chips in incremental progression/evolution cycles. The chip lifecycle data must be logged, analyzed, and processed for value generation. Cloud Service Providers (CSPs) like Amazon Web Services offer storage and analytics capabilities to chip design companies to apply AI and ML models for systematic data processing. They also provide the necessary infrastructure to integrate IoT and implement Industry 4.0 solutions for smart and connected manufacturing .
The semiconductor manufacturing industry is highly competitive, and the success or failure of a chip manufacturer entirely depends on the ability of the manufacturer to collaborate effectively with an eco-system that includes suppliers, OEMs, and internal teams for design reviews, feedback, and testing. Cloud infrastructure provides a centralized system to track the productivity of the different stakeholders, enabling transparency and boosting efficiency, especially in the current times of COVID 19 using collaboration tools such as MS Teams, Google Workspace, and Google Meet.
Unlike on-premise data centers managed by internal IT teams with constraints on skill, availability, and resources, cloud infrastructure is managed by specialists such as GCP, AWS, and Microsoft. These service providers have made huge investments in R&D, infrastructure, and resources, and provide service-level agreements which ensure uninterrupted operations for semiconductor foundries.
One of the primary reasons for the semiconductor industry to not adopt or scale cloud has been the business criticality of its operations. However, modern-day CSPs provide SLAs that comply with industry requirements and, in some cases, go beyond to ensure reliability. For example, GCP provides a robust architecture with high-bandwidth connectivity across 25 regions and 76 availability zones to deliver global services.
The use cases for sensors, chips, computing, IoT, and Industry 4.0 are ever-increasing. It is thus imperative for the semiconductor industry to be extremely agile and offer unmatched on-demand scalability and flexibility to ramp up/down its compute infrastructure to accommodate R&D, design, testing, and validation of GTM activities. Analytical capabilities to draw insights and make quick decisions must also be in place in order for the industry to deliver on its reputation of being agile. Cloud offers all these capabilities to the industry and at the same time drives home the cost benefits, security, and efficiency.
There are several aspects of cloud infrastructure that can drive innovation for the semiconductor industry. To begin with, it can provide a leeway for the industry to squeeze in cost efficiency to a perceived rigid cost structure. The possibilities of leveraging IoT, AI, ML, big data analytics for gaining visibility, and driving efficiencies throughout the chip manufacturing value chain are tremendous. It can provide EDA support, high-performance design, HPC, and High Volume Manufacturing (HVM) capabilities that will enable better outcomes at lower costs.
Cloud offers instant scale and capabilities to perform and execute operations across the semiconductor value chain from design to yield without investing in physical on-premise data centers, reducing infrastructure development costs. It provides a collaborative infrastructure for value chain stakeholders to review and test the designs and offer feedback irrespective of the location of the stakeholders. Chip manufacturers can also drive the cost efficiencies on account of improved uptime owing to predictive maintenance capabilities and the security that cloud infrastructure offers.
The semiconductor industry powers a host of other industries, and several of these industries manage data categorized as highly sensitive, IP, business-critical, or compliance-driven. The dedicated investments by the CSPs in ensuring the security of their cloud infrastructure is an added advantage for the semiconductor industry to ensure data and IP protection for its clients. These CSPs provide more secure and reliable infrastructure at lower costs than the on-premise setup. For example, Google’s global-scale infrastructure protects billions of users with world-class security.
The semiconductor industry has been a pioneer in enabling digitalization across industries. With Industry 4.0 and IoT gaining prominence, the use-cases of semiconductor chips have evolved rapidly from device-specific applications to sensorization, integration, and communication areas.
However, the irony of this industry is that despite being the transformation catalyst for all the other sectors to adopt digitalization, the industry on its own has been lagging when it comes to the adoption of technologies such as cloud computing for cost optimization, innovation, and streamlining operations. According to KPMG, even when most other technology industries have been adopting digital transformation at a rapid pace of 89%, the adoption rate of the semiconductor industry remains at a paltry 50%.
Considering the outlook for the semiconductor industry, utilizing the cloud for digital transformation is the only way the industry can scale and position itself to meet consumer demands for speed, accountability, security, innovation, and reliability.
Nilo Caravaca, Hologic’s vice president of operations for Costa Rica and Latin America, says the company has an “important purpose”: to improve and save women’s lives around the world. At the Costa Rica facility that he manages, the company manufactures diagnostic and imaging equipment that protects women’s health, such as mammography systems and bone density scanners.
In pursuit of their goal, Caravaca and his team have embraced innovative technologies as well as best practices in talent management. For their achievements in attracting, upskilling and retaining a world-class workforce, the NAM’s Manufacturing Leadership Council awarded Hologic the 2021 Manufacturer of the Year award in the small and medium enterprise category. But the company is not stopping there. Caravaca anticipates further innovations, as Hologic keeps prioritizing efficiency, safety and growth.
Here is a snapshot of Hologic’s two award-winning projects and a look at things to come.
Supply chain innovation: Almost every product made by Hologic’s Costa Rica facility serves a patient with an urgent medical issue. That means its supply chain must be incredibly resilient and reliable.
- To meet these critical needs, Hologic launched a project called “Impacting Lives Every Day,” which employed robots for moving materials and bots for automating processes, while improving operations using real-time data and analytics.
- The project has resulted in a more reliable supply chain that gets products to patients faster while improving quality and safety.
Talent management: Caravaca believes companies need to focus on people in addition to technology to make the transition to Manufacturing 4.0, the next wave of technological progress.
- To that end, his team developed a new set of talent management processes that helps attract and recruit the best employees on the market, as well as ensure they have the opportunity to perform at their highest level.
The last word: An engineer by trade, Caravaca has a simple “formula for the future” of manufacturing: “Find the right talent, fit that talent in the right position, engage it and add tenure over time.” That will allow people to grow into their roles and perform at their peaks—the best result for both the company and the employees themselves.
To learn more about the innovative technologies and processes at Hologic’s Costa Rica facility, read “Hologic’s Winning Formula” in the August 2021 issue of the Manufacturing Leadership Journal.
When it comes to data management, most manufacturers are basically teenagers. They’ve gotten past the early stages but have yet to reach full maturity and mastery in their approach. In fact, it is often unclear what the data strategy is, who is responsible for it or even what the data is worth in the first place.
A new survey from the NAM’s Manufacturing Leadership Council shows us how manufacturers are progressing in their quest to harness the power of data—a capability that could have transformative power for many manufacturers throughout their operations. Below are some highlights.
Data collection: Most manufacturers rate their organizational data skills as just average, saying they struggle to collect the right data and interpret it.
- Fifty-eight percent of respondents said their company had just a moderate ability to collect data that is meaningful for their business needs.
Data analysis: If gathering data is a challenge, gaining insights from that data is an even bigger one.
- Seventy-five percent of respondents ranked their organization as only somewhat capable in their ability to analyze their manufacturing operations data.
- Even more worrisome, 11% of respondents said their organization was not at all capable of this type of analysis.
Applying insights: The practical application of data to create value is also a challenge for many manufacturers.
- Almost one-third said they expend greater than 80% of their efforts on gathering and organizing data—as opposed to analyzing and applying insights from it.
Other stumbling blocks: The survey revealed additional impediments to using data:
- The lack of systems available to capture the data (46%)
- Data inaccessibility (43%)
- The lack of skills to analyze data effectively (39%)
Opportunities: The good news is that even with these imperfect efforts, organizations are largely leveraging the data they do have to make informed decisions.
- Forty-eight percent said their organization makes data-driven decisions frequently, while 18% said they make data-driven decisions constantly.
The bottom line: Seventy-five percent of respondents said data mastery will be essential for future competitiveness. Indeed, data mastery is crucial to the industry’s transition into Manufacturing 4.0—the next big wave of industrial innovation—and the MLC will be tracking the industry’s progress closely.
To see more insights from the latest MLC M4.0 Data Mastery Survey, read “Growing Pains” in the August 2021 issue of the Manufacturing Leadership Journal.
Nexteer Automotive is on a mission to make driving a car safer, more fuel-efficient, and future-focused through its production of steering and driveline safety-critical car and truck electronic and hydraulic power products. In addition to electric and hydraulic power steering systems, steering columns, and driveline systems, the company manufactures advanced driver assistance systems (ADAS) and automated driving-enabling technologies for more than 60 customers in every major region of the world, including BMW, Ford, GM, Toyota, and Volkswagen. Nexteer’s products complete the connection from the steering wheel to the wheels on the road.
With software being a key component of most of Nexteer’s products, it’s no surprise that this multi-billion-dollar global business used a data-driven, holistic, and integrative approach to manage its complex global operations. Called the Digital Trace ManufacturingTM (DTM) System, it was created by Nexteer to provide a global architecture that connects and standardizes the thousands of data-producing components generated by its 27 manufacturing plants around the world.
In a virtual factory tour held on August 11, Manufacturing Leadership Council members got to see DTM in action. On the first stop on the tour, MLC members saw the production processes and traceability system at work at the company’s Plant 3 in its Saginaw, Mich., site, near the company’s headquarters in Auburn, Mich. The Saginaw facility, Nexteer’s largest, includes six manufacturing plants comprising 3.1 million square feet of manufacturing floor space where all the company’s core products are made. The site also houses a powerhouse and water treatment site, a global technical center, a test and validation center, and a test track. MLC members learned about the complexities involved in running a large-scale automotive component manufacturing plant, as well as how Nexteer has improved its manufacturing processes using the DTM system to connect data and maximize efficiency across the 150 operations required to manufacture its rack-and-pinion EPS products.
At the next stop, participants learned about how Nexteer uses data-acquisition tools to manage its business. Every time a new program is launched, a detailed process flow map is created for each step. This information is then shared with the equipment builders, so the machines are designed and programmed appropriately for the needed data processing, including what data will be sent to traceability and which processes use barcode scanners or other methods to track part serial numbers, such as RFID tags.
It’s one thing to have a large system collecting data — and it’s another to be able to use that data effectively. Nexteer uses intelligent manufacturing, big data, and local technology to collect, move, store, notify and summarize information for its global traceability system. The system, which is used in all the company’s plants to track information from thousands of machines daily, collects cycle time information. If there’s a fault, it collects and stores information including operation error codes and a description of all the pertinent information.
The data displays summary information for current station status directly on the floor on an hour-by-hour basis. It also makes historical information available for problem-solving purposes and provides automatically generated daily and weekly reports on all facets of the operation that are sent to cross-functional groups for awareness and problem-solving. Flawless materials control and delivery also is critical for production line efficiency, and Nexteer’s system can track material from receiving and shipping through the production line with single-box precision.
Not only does the system allow them to eliminate discrepancies by tracking the movement of each piece of material with high precision, it also eliminates the need for physical inventory processes. The ability to understand the manufacturing process outputs, and how these outputs affect your business goal, is extremely powerful. Nexteer uses its Center of Analysis (COFA) to communicate and correct any issues that arise.
Nexteer’s innovative approach to integrating design and manufacturing systems, from DTM to COFA, enables the company to deliver a dynamic, comprehensive view of its global manufacturing operations on a minute-by-minute basis — and benefits the company’s employees, customers, and shareholders.
For almost two decades, the NAM’s Manufacturing Leadership Council has been showcasing the best-performing, most innovative and most influential manufacturers in the field. Its yearly Manufacturing Leadership Awards recognize organizations of all sizes and from all sectors, along with the individual leaders who are spearheading their transformations. Now, your company or leader could be among the next cohort of winners: nominations for the 2022 season opened on Aug. 16.
What’s involved: Since 2005, the ML Awards have recognized more than 1,000 outstanding leaders and projects that have sped the transition to Manufacturing 4.0, the next wave of industrial progress created by digitization.
- Nominations are judged by a group of seasoned industry executives with expert knowledge of digital transformation. Past judges have come from companies such as Lockheed Martin, GM, Merck and 3M.
- Any manufacturing organization is eligible, and all may apply through the MLC’s online application process. Project nominations include a timeline and written overview of a project’s business and operational impact, while individual nominations ask for details about a leader’s achievements and influence on his or her organization and the manufacturing industry at large.
Highlights of the 2022 season: This year, the awards will feature 11 categories, nine for projects and two for individuals.
- Digital Transformation Leadership: This category is for accomplished operations leaders who have transformed their companies through technology adoption, performance and process improvements or business culture changes. Leaders at any level of the organization may apply.
- Next-Generation Leadership: This category honors remarkable manufacturing professionals aged 30 or younger who demonstrate the leadership needed in the digital manufacturing era. If you have a young, inspiring leader on your team who acts as a role model within and outside your organization, nominate him or her today.
- Project categories: This year’s awards recognize excellence in artificial intelligence/machine learning, supply chains, business culture transformations, organizational collaboration and more. The complete list is here.
Why it matters: The COVID-19 pandemic only reinforced how much manufacturing matters to our entire society, at every level and in every household. The 2022 ML Awards will recognize many of its most remarkable accomplishments, showcasing an industry that remains unceasingly dynamic even in the midst of crisis.
Don’t wait: Nominations are due Dec. 20. They can be submitted directly by manufacturing organizations or by their consulting partners or PR and marketing firms. You can complete your application here.
Hundreds of manufacturing leaders came together this summer to discuss the industry’s next century of technological dominance. Augmented reality, artificial intelligence, robotics and more were all on the schedule, with companies unveiling their cutting-edge techniques and exchanging invaluable knowledge.
This premier gathering of talent is called Rethink, and it is the Manufacturing Leadership Council’s yearly conference on Manufacturing 4.0—the next wave of industrial progress created by digitization. It offers manufacturers a range of ways to engage with leaders and experts, including interactive case studies, collaborative think tank sessions and keynotes.
This year’s Rethink showcased a number of innovative technologies that are already transforming companies around the world. Here are some highlights.
Augmented reality is the new reality: PTC President and CEO Jim Heppelmann explained the benefits of augmented reality, which can give much more information to frontline workers and help manufacturers bridge the skills gap—the lack of sufficient skilled workers to fill available jobs.
- For example, augmented reality allows companies to record the expertise of workers who may soon retire, thus improving the training programs for new workers, Heppelmann pointed out.
Read more of Heppelmann’s expert advice here.
Robotics will support workers: In a keynote address, MIT’s Dr. Daniela Rus explained the coming evolution in human-machine relationships. She predicted that robots will enable workers to control production lines more precisely and configure them for rapid, customized production.
Read more about Dr. Rus’s predictions here.
Intelligent platforms are key: Intelligent platforms help manufacturers capture and understand data—the key to success in manufacturing’s digital era, according to Sid Verma of Hitachi Vantara and Mike Lashbrook of JR Automation.
- One of the biggest challenges is learning how to collect data strategically—because a plant floor can generate tons of it. “Just collecting data on the [operational] side does not work for us,” said Verma. “We have seen horror stories where people spent their entire IT budget just collecting data because they didn’t know where to start.”
Read more of Verma and Lashbrook’s insights here.
The bottom line: No matter where you are in your digital transformation, Rethink can help you move forward. It is the perfect place to discover new technologies and learn best practices for implementation.
For more information about the MLC, including Rethink 2022, email [email protected].
“Water, water, everywhere, nor any drop to drink.”
Samuel Coleridge used those words to describe an ill-fated seaman’s predicament in The Rime of the Ancient Mariner. But when it comes to data – or more accurately, a seemingly endless sea of data – the sentiment might also describe how many manufacturers feel about their efforts to make it useful.
Most manufacturers have discovered that collecting data is merely a starting point. Nearly every segment of the enterprise generates some level of data, from back-office functions like human resources and sales and marketing, to operational functions like production and supply chain. Taking it beyond collection, however, and into segmentation, management, and analysis is like the difference between filling a bucket with water vs. trying to count and sort the drops.
But those that dedicate themselves to the effort find it to be a wise and worthwhile pursuit. Results from the MLC’s M4.0 Data Mastery survey in this month’s edition of the Manufacturing Leadership Journal show overwhelmingly that data enables higher quality decisions. The majority of respondents also said the increase in manufacturing data has boosted productivity and lowered costs.
Given the massive disruptions manufacturers have experienced as a result of the COVID-19 pandemic, it’s no surprise that many are keenly interested in moving toward the predictive insights that are so much a part of data’s promise. This type of foresight can help manufacturers activate contingencies for the next natural disaster or supply shortage, or prevent the next equipment failure or production defect.
Having access to a lot of data might make an organization smart, but extracting insights from that data and applying it to business decisions will make an organization truly intelligent. Manufacturers are still finding their sea legs in the realm of data, but it’s likely that many will find that these rough and largely uncharted waters are creating skilled sailors. – Penelope Brown
Manufacturers see data’s massive potential to improve operations, predict disruptions, and bring about new revenue streams, but realizing that promise continues to be a work in progress.
Data could be described as the lifeblood that enables the digital enterprise. In the 18-plus months that the COVID-19 pandemic has been roiling supply chains, forcing once-live interactions to go virtual, and necessitating remote work and collaboration, manufacturers have seen the immense value of using data to keep operations going and make better-informed decisions. It isn’t hard to imagine a far worse scenario if this once-in-a-generation disruption had happened in a time of less (or no) digital maturity.
But while former IBM CEO Ginni Rometty once said that big data is the new oil, a more apt comparison might be that data is like sunlight – in infinite supply, unyielding, quite often blinding. But it can also be illuminating, shedding light on former dark spots by improving transparency and visibility, enabling businesses to grow and thrive.
Manufacturing data mastery is in its tween years for most enterprises – certainly past its infancy, but still awkward and gawky and not quite fully formed. It is often unclear who is responsible for data strategy, what the data strategy is, or what data is actually worth to an organization. But manufacturers also say data has helped them to grow their productivity, lower their costs, boost efficiency, and improve quality.
These findings and others from the MLC’s new M4.0 Data Mastery Survey provide insights on where manufacturers are on their journey to harness the power of data and its revolutionary promise.
Where Improvement is Needed
Most manufacturers rate themselves as just average at their organizational data skills, and they struggle not only to collect the right data but also to interpret it. Fifty-eight percent said that their company had just a moderate ability to collect data that is meaningful and impactful for their business needs (Chart 11). More than a quarter ranked themselves as low in this area.
It is often unclear who is responsible for data strategy, what that strategy is, or what organizational data is actually worth.
But data collection is not the greatest struggle. Even more respondents said they had room for improvement in terms of finding insights from that data, with 75% ranking their organizations as only somewhat capable in their ability to analyze their manufacturing operations data (Chart 12). In this area, 11% of respondents said their organizations were not capable of this type of analysis.
Furthermore, a gap remains between the effort to collect and sort data and the effort to apply insights and create value from that data. Almost a third said they expend greater than 80% effort in gathering and organizing data vs. the effort expended to analyze and apply insights from the data (Chart 8).
Other stumbling blocks to utilizing data to a greater extent speak to the unwieldy tangle that manufacturers often find when undertaking data projects. This includes a lack of systems to capture the data (46%), followed by data inaccessibility (43%) and a lack of skills to effectively analyze data (39%) (Chart 16).
However, for what they are able to collect and analyze, more often than not organizations are leveraging that data to make informed decisions. Forty-eight percent say that their organization makes data-driven decisions frequently, while 18% say that they make data-driven decisions constantly (Chart 13).
Tools of Collection and Analysis
Digging a bit deeper into organizational data tactics, 79% said their shop floor systems are the primary source of manufacturing data, followed closely by ERP systems at 77% (Chart 6). This may not be surprising given the near-ubiquitous nature of those technologies within many manufacturing facilities.
What could be a trend to watch, though, is the growing use of edge computing systems (18%) and even embedded systems in products (12%). The former shows that manufacturers are taking advantage of faster networking technologies to process and store data closer to where it is produced and consumed, while the latter could hold promise for monitoring product lifecycle and performance, in addition to sustainability by making products more efficient and reducing materials usage and waste.
“Manufacturing organizations have serious governance issues to address, such as having a formal plan and somebody ultimately in charge of data management.
But among those emerging technologies lie some old faithful ones. CPA favorite Microsoft Excel is still the leader as the manufacturing data analysis tool of choice 34 years after its initial release, with 71% of respondents saying they use it (Chart 9). Meanwhile, AI is making inroads in its use for analysis, with nearly a third saying they are using in-house AI systems (28%), and others using cloud AI systems (12%) or an external AI partner (8%).
The Fundamental Flaws
Assigning value to data is an elusive undertaking for many manufacturers. Of those who do, most measure it by impact on operational performance (44%), as it is likely the easiest way to see the results of manufacturing data projects (Chart 1). But nearly as many admitted that their organization has no measure for data value (42%), a somewhat troublesome finding given the time, resources, and effort that go into data collection and analysis.
It’s difficult to determine if this is merely an oversight, or if reliable models just haven’t yet been developed to assess ROI, but this will be an important and necessary undertaking for manufacturers to make impactful data-led decisions. The good news is, though, that executives appear motivated to take on this pursuit, as 40% of respondents said data collection, management, and analysis were included as part of annual objectives for company executives (Chart 3).
But manufacturing organizations have other serious governance issues that also must be addressed, such as having no formal plan for data management or having nobody ultimately in charge. Four out of 10 respondents said that their company had no corporate plan, strategy, or formal guidelines for how data is collected and organized across the enterprise (Chart 2), an almost-astonishing number at a time when manufacturers are making significant investments in digital technologies to build connected operations.
“In today’s algorithm-driven world, manufacturers must pay heed to data accuracy, quality, and fidelity.
Manufacturers continue to take a scattered approach to governance and control over data, though many have a head of IT or combined IT/OT team ultimately in charge of data governance and strategy (Chart 4). However, 12% said that no one has data governance responsibility at their organization, a glaring issue that must be addressed at any company that wants to stay competitive in the long run.
Trusting the Data
One of the oldest idioms in computer science lexicon is garbage in, garbage out, meaning that flawed input data will result in flawed output data. In today’s algorithm-driven world, manufacturers must pay heed to the accuracy, quality, and fidelity of the data they are using for all-important business decisions. But it’s nearly an even split between manufacturers who check for that accuracy – 49% saying they do vs. 46% saying they complete no such checks (Chart 10).
Whatever leads those manufacturers to believe they can trust their data, there is no question that data is driving much-improved decisions – 94% said the use of data has helped their company make better decisions, though just 35% say it has helped them to make faster decisions (Chart 15).
To underscore the views that manufacturers have on data’s value for competitiveness, there is little room for debate that most see it as a requirement. Seventy-five percent said that data mastery will be essential for future competitiveness, with 25% saying it will be supportive for competitiveness – and not one single respondent saying that it will have no impact at all (Chart 17).
There is little question that manufacturers see the immense value that data can bring to their businesses. As organizations grow their data competency, they will seek to move past simply monitoring and collecting data to unlocking the insights and predictive ability that will be essential to future competitiveness. But until those organizations address the fundamentals of data mastery, they are likely to feel more growing pains along the road to that promising tomorrow. M
Part 1: CORPORATE DATA GOVERNANCE & ORGANIZATION
1. Many Organizations Have No Measure for Data Value
Q: How do you measure the value of the data in your organization? (Select one)
2. Many Organizations Lack Formal Data Collection Guidelines
Q: Does your company have a corporate-wide plan, strategy, or formal guidelines for how data is collected and organized across the enterprise, including manufacturing operations? (Select one)
3. For Many, Data Mastery is an Executive Objective
Q: Is data collection / management / analysis included in some way as part of the annual objectives for company executives? (Select one)
4. CIOs, IT Teams Largely Responsible for Data Governance
Q: Who is responsible for data governance and strategy in your organization? (Select one)
5. Cost Savings, Quality Top List of Business Objectives
Q: What are your key business outcome objectives for embarking on manufacturing data projects today and what do you expect your primary objectives to be in 2 years’ time? (Check top three for Now and top three in 2 years)
Part 2: DATA COLLECTION & ANALYSIS TACTICS
6. Shop Floor Systems, ERPs are Primary Data Sources
Q: What are the primary sources of your manufacturing data today? (Check all that apply)
7. Pandemic Makes Supply Chain Analytics a Priority
Q: Has capturing and analyzing certain types of data become more important to your organization in the wake of the COVID-19 pandemic? (Check all that apply)
8. Gap Lies Between Data Collection and Application
Q: What is your estimate of percent effort to gather and organize data relative to the percent effort to analyze, derive insights, and apply those insights to creating value from that data? (Select one)
9. MS Excel Still Leads as Data Analysis Tool
Q: What systems do you use to analyze the manufacturing data you collect? (Check all that apply)
10. Checking Up on Data Accuracy, Quality
Q: Does your company have a process to verify the
accuracy and/or quality of the raw data before decisions are made on it? (Select one)
Part 3: ORGANIZATIONAL DATA MASTERY
11. Matching Data Collection to Business Needs
Q: How would you rank your company’s ability to collect the right data the business needs from your manufacturing operations?
12. Data Analysis Sees Room for Improvement
Q: How would you rank your company’s ability to analyze the data from your manufacturing operations?
13. Data Leads More Decisions, More Often
Q:How often would you say your organization makes data-driven decisions? (Select one)
Part 2: DATA-DRIVEN OUTCOMES & CHALLENGES
14. Data Boosts Productivity, Lowers Costs
Q: How has the increase in manufacturing data
helped you to improve your manufacturing organization? (Check all that apply)
15. Quality, Speed of Decision-Making Improves
Q: How has the use of data affected your
company’s decision-making? (Check all that apply)
16. Data Capture, AccessRemains an Obstacle
Q: What are the most important challenges or obstacles hindering your organization from making more data driven decisions? (Check top three)
17. Most See Data Mastery as Essential to Competitiveness
Q: Looking forward, how important do you think mastering manufacturing data will become to your competitiveness as a future business? (Select one)
Survey development was led by Penelope Brown, with input from the MLC editorial team and the MLC’s Board of Governors.
Most manufacturers tend to view the value of their data in terms of its use in optimizing operations. But by managing data as a strategic business asset, its value could be much greater.
“Data is the new oil.” It’s a phrase we hear with increasing frequency in many business contexts. Companies across multiple industries now view data as increasingly essential to their success and market advantage. Data today, like oil over the last hundred years, is increasingly a source of wealth, power, and success, and a driver of the emerging digital economy.
But there are differences. When oil is gone, it is gone. Yet data generates more of itself and can be used multiple times for multiple purposes. Data is also cheaper to store and easier to transport. But it’s also easier to be stolen and is impossible to clean up if you spill it.
Data is also potentially more valuable. Companies with certain data characteristics that behave in data-driven ways generate economic benefits and have value beyond others. Investors now tend to favor data-centric companies. Research shows data savvy companies, such as those with a chief data officer, a data science team, and an enterprise data governance function, have twice the market-to-book value as their peers.1 Data-product companies, for which data or digital products are the primary offering, have three times the market-to-book value. Further, data for some organizations might actually be worth more than the value of the company itself.2
Maximizing Data Potential
Manufacturers have access to and use data from many sources: customers, equipment, processes, transactions, quality, IoT or streaming devices, and more. They’re also generating and acquiring more of it by the day. According to the Manufacturing Leadership Council’s (MLC) first survey on M4.0 data, conducted in March 20203, more than a quarter of manufacturing companies surveyed said they their manufacturing data volumes doubled or tripled in size over the previous two years. Looking ahead, over a quarter of companies expect data volumes to surge by more than 500% over the next two years.
But what are they doing with it, and to what extent are they using it to drive enterprise value? Manufacturers most commonly associate the value of their data with its use in optimizing operations. The MLC study validates this: 54% of participants said they measure the value of data in terms of the impact on operational performance, driving value by managing cost of goods sold; maximizing on-time in-full (OTIF); increasing capacity and labor productivity in light of labor and material shortages; or improving performance in productivity, efficiency, or quality.
Lockheed Martin, for example, recognized it could use data to proactively predict program health and apply course correction measures before problems arose. The company correlated and analyzed hundreds of structured and unstructured metrics for thousands of programs to identify a concise set of leading indicators of program performance. The analysis even uncovered specific words from a program manager’s comments that are predictors of a program downgrade. This increased program foresight by three times, facilitating earlier program assessments. Ultimately, the company was able to avoid hundreds of millions of dollars in losses due to program delays4.
Optimizing operations, of course, is one way to create value from data. But this is only a steppingstone in the journey toward maximizing its potential value. That requires treating data as an enterprise asset—a real mindset shift for many organizations, particularly in manufacturing.
In fact, this mindset is particularly important given the new realities brought on by pandemic-accelerated changes to the manufacturing workplace and workforce, as well as shifts in customer demand that require manufacturers to be able to change up operations faster than ever. Manufacturers that are only using descriptive analytics to fine-tune operations will quickly fall behind those able to use prescriptive or predictive insights to adapt their operations to the velocity of the market and drive profitability.
Accountants may not recognize data as an asset on the balance sheet, but that doesn’t mean companies can’t begin managing it as one. Manufacturers are already adept at managing physical assets, so treating data with the same care shouldn’t be too much of a stretch. Infonomics, the emerging discipline of managing and accounting for information with the same or similar rigor and formality as other traditional assets—offers a useful framework for driving greater value from data. It consists of three key elements: monetizing, managing, and measuring data value.
1. Monetize Data
Maximizing the value of data begins with looking at it in terms of its economic benefits. There are many directions this can take. Direct monetization includes bartering or trading with data, selling raw data through brokers or data markets, or selling insights or analysis. But monetization is about more than selling data assets. It instead comprises any and all ways that available data can generate new value streams for an organization, both internally and externally. Indirect methods of monetization include improving process performance and effectiveness (as in the Lockheed example), developing new products or markets, enhancing/digitalizing products and services with data, and forging and streamlining partner relationships. Mastering indirect monetization can, in fact, lead to greater direct monetization.
The best way to illustrate potential applications to manufacturing is through stories and examples, and there are plenty of them.
Creating new revenue streams: Sometimes selling information is a preferable alternative to no revenue at all. When a mid-sized U.S. manufacturer of sonic buoys and other inertial sensors recognized it was losing business to lower-cost manufacturers in Mexico and elsewhere, it licensed its expertise in the form of detailed manufacturing and testing processes to those who would otherwise undercut them. Competitors became partners, and a new revenue stream materialized5.
Transforming the business model: Rolls-Royce was an early pioneer of this concept with its Power-by-the-Hour offering, which it has continued to build upon. The company’s CorporateCare® program, originally launched in 2012 and enhanced in 2018, uses onboard sensors to track on-wing performance and facilitate maintenance6. More manufacturers are moving to a product-as-a-service (PaaS) model, which is dependent on data. For example, Michelin’s EFFIFUEL™ is a PaaS offering targeting commercial vehicles, particularly trucks, using IoT data to improve performance7. The offering uses sensors inside vehicles to collect data about fuel consumption, tire pressure, temperature, speed, and location. A Michelin team then analyzes the data to provide recommendations for fuel-efficient driving. This has led to higher customer satisfaction, loyalty and retention, and increased profits.
Driving value from mergers and acquisitions: When Stratasys purchased MakerBot, a startup manufacturer of desktop 3D printers, in 2013, it also acquired MakerBot’s established 3D printing ecosystem, which continuously develops new applications for 3D printing. This effectively enabled Stratasys to crowdsource research and development data from the community and reduce its own in-house R&D costs8.
Responding rapidly to change: With a $2 billion orange juice business, The Coca-Cola Company must be able to minimize product inconsistencies due to variations in orange crops, sourcing, and seasonality. The company’s Black Book model algorithm, developed by Revenue Analytics, crunches data from up to one quintillion data points, including satellite images, weather, expected crop yields, cost pressures, regional preferences, and detailed data about the 600 flavors that comprise an orange, plus variables such as acidity and sweetness. The result is a precise formula for how to blend orange juice for consistent taste, including pulp content. After a hurricane or freeze that affects crops, the company can replan in 5 to 10 minutes9.
Monetizing data to achieve these types of impacts requires structure and discipline, but also sufficient space for exploration on the front end. It’s helpful to start with workshops designed to conceive and refine ideas for innovating with information to drive new value streams. For the broadest thinking, try to get business leaders, data architects, subject matter experts, and ideally representatives of key customer, supplier, or partner segments into a room together. Inspire them with other data monetization examples from inside and outside the organization and industry. Then allow them to explore available data sources and potential insights and/or external value within or at the intersection of those data sources. Ask questions like: What could we accomplish if we had additional data? What types of external data sources would enable that? Where could we add new sensors or OT sources to generate additional data that may provide valuable insights?
Then assess the ideas generated based on feasibility in order to prioritize those to be developed. This assessment should include a range of include impact factors such as economic benefit, practicality, marketability, societal benefit, or ecological benefit. The feasibility assessment should also consider the complexity involved, including manageability, technology, scalability, and ethicality. It’s important to make sure you vet the financial and systemic impact, as well as scalability, from a true operational perspective in an applied setting.
2. Manage Data as an Asset
Realizing value from data requires proven asset management principles and practices. In this context, that includes data science capabilities as well as enterprise data governance function and principles. But none of that will matter without the right leadership.
Our research has found that organizations with a chief data officer with the right level of influence, authority, and resources, reporting to the CEO or at least with a spot on the executive team, are four times more likely to be using data to transform business processes, products, or services10. They are also three times more likely to generate non-monetary commercial value and seven times more likely to generate monetary value from data externally.
By contrast, organizations where the CIO still maintains ultimate responsibility for the company’s data assets are only half as likely to be employing advanced analytics. In organizations without empowered CDOs, data quality and availability continue to be significant impediments to analytics.
Manufacturers should take note. From the results of the MLC’s M4.0 data study, it appears they have some significant catching up to do. Only 7% of companies participating in that survey reported having a CDO who is responsible for data governance and strategy. Rather, most place data responsibility with an information technology (IT) head (the CIO or IT VP) or a joint IT/OT team. Further, 18% have no one with data governance responsibility and more than half of the participants in the study said they do not have any corporate strategy, guidelines, or plan for the way data is collected or organized across their companies. Only 18% believe their company is “very capable” of analyzing the data it has.
Establishing true responsibility and accountability for all things data is the essential place to start. Once that is in place, you can then begin developing plans for maturing data and analytics capabilities across various areas, including strategy, technology and architecture, organization and skills, literacy, and culture.
3. Measure and Improve Data’s Potential
You can’t manage what you don’t measure. Organizations tend to manage data volumes and speed, but most are missing the bigger picture. Few measure data quality characteristics such as potential value, business relevancy, cost, impact on business performance, market value, and impact on the organization. For example, research shows that only 11% of organizations know the cost of their data, 12% calculate the financial value of their data assets, and 21% measure the business impact of data quality improvements11. Only 4% have developed ways of measuring data value in monetary terms with an assigned dollar value, and only 7% are now beginning to measure data value against data-driven services. Thirty percent do not have measures in place to value the increasing volumes of data that digital technologies create. On the other hand, companies with executive-level CDOs are three to four times more likely to formally measure the value of the company’s data assets.
To help gauge and improve data’s economic characteristics, companies should start by recognizing the three degrees of data value: realized, probable, and potential. The latter reflects the value that could be derived by applying data to all relevant business processes. Proper information valuation should include both foundational measures that can help improve information management discipline: the intrinsic value of information (how correct, complete, and scarce is this data?), business value of information (how good and relevant is this data for specific purposes?), and performance value of information (how does this data affect key business drivers?). Information valuation should also consider financial measures that can help manage and improve information’s economic benefits: cost value (what did it cost to collect this data, or if we were to lose it?), market value (what could we get from selling or trading this data?), and economic value (how does this data contribute to revenue/expense savings?).
Manufacturers now have significant opportunity to understand and take advantage of data’s unique economic characteristics. The good news is that they recognize this. All respondents to the MLC study said they believe data is either essential or supportive to their future competitive success.
This represents a substantial transformation for many manufacturers, but it is possible, and positive examples are out there. In fact, in all of our research, it was a manufacturing company, Textron, the parent company of Bell Helicopter and Beechcraft, Cessna and maker of other specialized technology products, that stood out as embracing the possibilities and potential of value of data so well12.
“We no longer differentiate ourselves primarily via the performance of our products. Rather, we gain advantage from our ability to monitor enormous amounts of data from inside and outside our business, find insight in that data and act on it more quickly than our competitors,” said the company’s director of global ERP and analytics at the time. “Our finance people used to chuckle about the idea of information as an asset, the reality is that for most employees, our business is data: 70 percent of our employees don’t touch aircraft, but everyone touches data.” M
1 Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage, page 211, Douglas B.Laney, 2017
3 “The Key to Future Competitiveness: M4.0 Data Mastery,” Manufacturing Leadership Journal, Paul Tate, April 2020; https://www.manufacturingleadershipcouncil.com/m4-0-data-mastery-the-key-to-future-competitiveness-11805/
4 Infonomics, pg. 42
5 Infonomics, page 32
7 Emad, “Michelin: Tires-as-a-Service,” digital.hbs.edu, November 17, 2016
9 Infonomics: pages 83-84
11 “The Business Case for the Chief Data Officer,” Douglas B. Laney, MIT Chief Data Officer and Information Quality Symposium, 2001
12 Infonomics: page 292
Manufacturers willing to change how they approach workforce development have an opportunity to stand apart from the competition.
For manufacturing companies, the challenges and uncertainty posed by the COVID-19 pandemic have been layered on top of persistent workforce trends that will remain unresolved even after social distancing fades from memory, and that can undermine their ability to innovate and thrive into the future.
Consider the aerospace and defense (A&D) sector, for example. While the commercial aerospace market has taken a hit amid the pandemic’s economic fallout, defense spending remains robust. A&D still has plenty of growth potential with the evolving drone market, which offers wide commercial applications. Plans are also on the table for air taxis and hydrogen-powered passenger aircraft, not to mention the nascent global space economy.
But seizing these innovative opportunities requires tech-savvy workers who are being increasingly drawn to other sectors with greater reputations for innovation, including technology, health care, and even gaming. In its 2020 World’s Most Attractive Employers report,¹ Universum found that A&D ranked below computer software and technology as a preferred industry to work in among new IT and engineering graduates. No A&D employers ranked among the report’s top 50 most attractive companies to work for.
Recruiting top talent in this and other advanced manufacturing sectors is only going to become more critical in the future. To remain competitive, industry leaders must continually develop their companies’ digital acumen and evaluate new technologies as they come into play. The ability to attract, develop, engage, retain, and inspire both the current workforce and the next generation of employees is essential. Learning programs and systems also need to be in place for upskilling and reskilling employees to help those companies succeed.
In addition, these same leaders must change their own ways, embracing new behaviors and value systems and getting comfortable with shifting corporate structures and collaborative cultures. In short, the manufacturing industry must revisit its overall personnel strategy to attract the best and brightest thinking among demographics whose career mindsets and preferences differ from previous generations.
Transformation is not a singular proposition in today’s environment, if it ever was.² For organizations to truly reimagine their working worlds and reshape their futures, transformation programs need to live, breathe, and continuously evolve. This is particularly true of an organization’s workforce. If, as is expected, the global economy begins recovering in 2021, even greater competition in labor markets will be a given.
With that prospect ahead, there are five ways that manufacturers can help make their workforce approach stand apart from the rest.
1. Define and promote a purpose and culture
Younger employees appreciate a strongly felt and inspiring purpose or mission, beyond a profit motive. The pandemic has reset and raised the bar. Fifty one percent of manufacturing employees in 2021 EY Work Reimagined Employee survey3 felt that culture actually improved during the pandemic with a focus on people first and intense communications. They want to play a role in realizing a future vision. Industrial companies need to play the culture card by creating a more purposeful, flexible, and agile work environment to attract millennials. Many technology companies have announced work from anywhere opportunities and are actively recruiting talent from manufacturers that historically had been safe, if not head-to-head, with one of the tech hubs. That a lot of emerging technologies are targeting the industrial sector is also building appeal, providing opportunities for millennials to work with cutting-edge tech and apply it in unprecedented ways. This isn’t just about attracting talent. It’s also about keeping it.
“A majority of workers want to maintain a similar level of flexibility in their work when the pandemic is over.”
The convergence of new technologies can fundamentally shift the manufacturing enterprise, supporting the movement to M4.0. Manufacturers should be embedding artificial intelligence, blockchain, and robotics in their operations today and finding talent anywhere they can that can help them do it effectively. A new global workforce with the ability to augment these technologies is beginning to emerge. Manufacturing companies should be more direct in highlighting these opportunities to potential recruits and delivering on them to their current workers.
Yet, in another EY survey,4 more than one-third of employees (35%) reported a disconnect between their organization’s stated purpose and its day-to-day actions. Manufacturers need to take steps to bridge this gap by defining the day-to-day attributes that define culture in both the work the company does and the behaviors of all colleagues.
If the company stepped up to play a role in the fight against COVID-19, be proud of that effort and make sure employees feel that pride as well. If the company is helping to advance autonomous technology or developing new ways to decarbonize the business, or simply coming up with new processes that are more efficient and more productive, make sure it matters to the team as much as it does to the company’s customers and key stakeholders. If the company is supporting employee wellbeing and work-life balance, make sure that leaders are promoting flexibility for the jobs and roles where it is available. When people have a purpose and feel connected to the culture, they’ll be much more likely to take ownership of it.
For A&D, there is a strong focus on culture and, within that, the purpose behind the sector’s nonfinancial objectives. In commercial aerospace, it’s about passenger safety, not profit. Teams are at their best when they are fundamentally thinking about how to get people where they need to be safely. It’s the same concept with the defense industry. The opportunity to support national security and provide the military with the best technology and tools it needs to do fulfill its mission and applying the latest tech to help it do so, gives deep meaning to a team’s effort and sense of purpose.
Employees want to be part of a team, and they want that team to be engaged in doing meaningful work. Finding the right balance in a world where remote working is becoming more common, is important. In the 2021 EY Work Reimagined Employee survey, 54% of US manufacturing respondents indicated they value the ability to meet and interact with colleagues in their workplace and 62% reported being concerned about the ability to meet with remote team members. At the same time, 40%+ consider themselves to be remote ready, and value the flexibility of the option to work virtually in many jobs and roles.
“The manufacturing industry must revisit its overall personnel strategy to attract the best and brightest thinking.”
2. Recognize that workforce expectations are changing
In spite of the challenges posed by the pandemic, employees remain positive about their work. In the 2021 EY Work Reimagined Employee survey, 62% of US manufacturing respondents said it’s “very likely” they will stay in their current job for the next 12 months. A majority of these workers, however, want to maintain a similar level of flexibility in their work when the pandemic is over, including the 69% who indicated they would like to choose when they start and finish their work each day.
If preferences for when and where they work aren’t met, 35% of respondents said it’s “likely” they would quit, and 19% said it’s “very likely” they would leave their job.
The key takeaway is that all employers, including those in the manufacturing sector, need to rethink how they approach the future of workforce development and working cultures. The pandemic has proved across most industries that a high level of productivity can be achieved outside of the traditional construct of employees working eight hours a day, five days a week, in the office or on the plant floor.
It’s not a millennial issue, or an us-versus-them conflict between labor and management. Everyone needs to work together to build a model that maximizes potential and gives a company the best chance to succeed. In some cases, the best structure is to have people in a certain spot at a certain time each day. Certainly, manufacturing is one of those sectors that can’t be done entirely by remote work. But those companies that are willing to have an open dialogue with their teams and discuss the best way to move forward will be the businesses that put themselves in a better position to attract, and retain, the best talent.
3. Be more diverse and inclusive
Considering the continuing disruption and challenges on the horizon, the most successful companies need to recruit for diversity of thought. Manufacturing companies owe it to themselves to look at candidates from outside of their traditional recruitment venues. It’s not always easy. In the A&D sector, government contractors are typically constrained by citizenship and security clearance barriers. Other segments of manufacturing have their own barriers that come into play. On the one hand, they want to pull from a wider pool of talent. But companies often have specific needs or skills, so it becomes easier to stick to what they know.
As with many aspects of both life and business, you get out of a particular effort what you put into it. If companies really want to find the best people to hire, they are most likely going to have to work at it. That means casting a broad net and proactively finding ways to develop talent where they haven’t found it before. One way is to hire for skills instead of specific credentials and be willing to mold talent. Companies need to examine their internal culture and practices to ensure they can champion and coach high-caliber individuals to drive the next generation of leadership. They may find people who always had the skills to be a force in their industry but never thought it was a viable option for their career path.
4. Thoughtfully consider politically charged topics
Companies in every sector have begun to very visibly take stands on broader public issues where in the past they may have opted to remain more neutral. However, employees in the newer generation of workers expect their employers to be more purpose driven and vocal, especially against perceived injustices and inequities.
Companies need to determine where they stand on these issues publicly and be willing to back their positions up with action. That can also tie in to how they present their company’s purpose to recruits and the types of recruits they appeal to. For instance, during US protests about policing practices, one large aerospace company announced that it was issuing millions in grants for groups that serve minority and underserved students pursuing science, technology, engineering, and math (STEM) education.
Many manufacturing companies are understandably focused on the short-term day-to-day challenges of keeping their employees safe amid the pandemic, balanced against the need to continue being productive. But they can’t let the troubles of today obscure the potential of the future: broader workforce and talent initiatives will be required to strengthen the business over the long term. They need to take steps to embed a clear and meaningful purpose throughout the organization and to create an environment in which it is defined and lived, authentically, at all levels.
“The key takeaway is that all employers, including those in the manufacturing sector, need to rethink how they approach the future of workforce development and working cultures.”
5. Redefine the role of HR
The future of work means HR has to evolve to drive the people experience, serve as an innovation hub, and deliver value outside its traditional role. A recent episode of the EY Future of HR podcast series5, for example, addressed the concept of a “people value chain” as a way to help redefine the HR role for the years ahead.
In the past, 80% of HR’s time and budget was dedicated to traditional vertical services that were largely administrative and operational. The remaining 20% of HR’s time and budget was then focused on more horizontal people services which, ultimately, are most important to developing employee experience and longer-term value to the company. The people value chain concept shifts the more traditional HR activities, such as administrative and procedural services, to a digital people team and virtual global business services, enabling the company’s people consultants to transition into more strategic work that provides long-term value.
To deliver experience at scale, the people function of the future must work horizontally across the enterprise. The people consultant role is fueled by key collaborations with cross-functional teams, executive leadership, virtual business services teams, and the gig workforce, using a mix of human and digital interactions, and allowing high-value people consultants to expand their reach within and beyond the traditional HR function in a number of ways.
- Cross-functional teams: functional leaders serve both as beneficiaries of HR services, and as cross-functional partners with input and shared accountability for people contributions and experiences.
- Architect people solutions for business opportunities: serve as the primary people advisor to the executive leadership team and bring the right combination of people capabilities to the boldest ambitions and most difficult business challenges.
- Foster program and service execution through virtual Global Business Services (GBS): prepare virtual GBS teams to execute programs and deliver services at scale; understand feedback and partner to innovate and drive continuous improvement.
- Human and digital and the gig economy: digitize HR work, augment people contributions with intelligent automation and enable confident business decisions through rich people insights. Enable gig people consultants to accelerate through the company learning curve and add value to in-flight initiatives.
In this new model, people consultants will be able to focus on the high value-add areas, where typically only 20% of time and budget is currently focused. This new model allows the people consultants to dedicate most of their time on these horizontal people services that drive long-term value.
Reset for the Future
Culture and HR management are often overlooked in the world of manufacturing, where the primary focus has often been on the machines, the products, and the processes that keep the company in business. With the pandemic, however, HR has been at the center of a reset of enterprise focus, from rethinking the use of office space, to redefining health and safety, and the introduction of new workplace technologies for enhanced collaboration and communication.
Today’s successful manufacturers are now coming to understand that they need to devote just as much time to talent acquisition, retention, and experience as they do to every other function in their organization if they want to successfully reimagine the way work is performed and managed in the future to maintain competitiveness.
The views reflected in this article are those of the authors and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.