Maximizing ROI² — six ingredients for delivering manufacturing innovation from invention

Nearly half (49%) of manufacturing CEOs that participated in a recent EY CEO Survey plan on accelerating or maintaining current levels of innovation investment and portfolio transformation. Just as important, these CEOs indicated that the main source of financing for these investments will come from performance improvements (56%). This feedback further indicates a changed environment — CEOs have shifted from growth at any cost to investments that must show a clear path to profitability or value creation.

Industrial product companies have over $230b in cash tied up in working capital.⁹ Cash is often tied up in physical assets and working capital that does not directly support long-term value creation.  Furthermore, diversified industrial companies have had one of the lengthiest cash conversion cycles (CCC) of any industry because they are capital-intensive and depend on physical inventories and global supply chains. In aggregate, the industry CCC, which measures a company’s efficiency in turning its resource inputs into cash, has consistently exceeded 60 days (other industries typically see 25-30 days).

To manage balance sheets, drive improved financial key performance indicators (KPIs) and liberate trapped cash, leaders can:

• Reduce the amount of cash tied up in working capital
• Purge the fixed asset ledger of “ghost assets”
• Rationalize software applications across the enterprise
• Optimize the real estate footprint
• Strike the right balance between debt vs. equity
• Reduce days inventory outstanding and days sales outstanding

A coordinated, multistep approach should include strategic process adjustments supported by AI and machine learning (ML), enhanced analytics, monitoring and a cash-culture focus. The ideal outcome for manufacturers is an improved return on invested capital (ROIC) and more operating cash available for new investments. In our experience, our clients typically achieve average working capital improvements of $50m-$100m for every $1b in sales and create enduring value through increased cash on hand.

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The commitment to investing in technology may be clear. But how can leaders avoid overbuilding — or underbuilding — smart factory solutions?

Manufacturing throughput and the ability to anticipate and manage production demand/supply are fundamental to every manufacturing company. Equipment downtime, workforce shortages and supply chain disruptions are major impediments to manufacturing throughput. In response, manufacturers are investing in digital manufacturing solutions enabled by Industrial Internet of Things (IIoT), cloud/edge computing, associated software platforms (e.g., ERP, MES, PLM systems), and, more recently, Generative AI (GenAI) solutions to mitigate these risks. However, 70% of investments fail to meet their goals — an estimated $1.6t in failed or stalled projects in 2023 alone. Additionally, while enterprise spending on cloud computing continues to grow, IT budget pressures and digital business complexities will drive 70% of enterprises to become more adept at managing their cloud spending by 2024, according to a global market intelligence firm. Furthermore, while independent software vendors (ISVs) are offering potentially more economical subscription-based and consumption-based business arrangements, being able to forecast cloud usage is critical to achieving these economies. The reality is that many companies are poor at estimating cloud usage and either overspend (underutilize cloud) or pay a premium for capacity that they don’t need.

To identify fit-for-purpose solutions, manufacturers should:

• Keep business and performance improvement objectives and KPIs at the forefront to navigate the hype surrounding the latest technological innovations (e.g., GenAI)
• Make data-driven investments in technological innovations that offer near-term performance improvements while creating a foundation for enduring enterprise value; there are many diagnostic tools and approaches that can be harnessed to identify manufacturing throughput bottlenecks and inefficiencies that can then be precisely targeted
• Identify and rationalize legacy IT and OT software systems and platforms to not only produce cost savings, but also to reduce complexity and improve master data management and cybersecurity
• Define a robust technical and data reference architecture — in addition to use cases — that will serve as the blueprint for investment and implementation; avoid the temptation to embrace a piecemeal approach and point to solutions that may be less costly in the near term but fail to scale to deliver broader enterprise benefits, lower total cost of ownership (TCO) and the expected ROI2.
• Use digital twins/digital threads and model-based engineering approaches to test assumptions and probable outcomes, de-risk implementation and enable continuous improvements

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A data-driven manufacturing approach is essential to enabling the agility required to succeed in a market fraught with economic and geopolitical disruptions, shifting supply chains, rising material costs, labor and skills shortages, and changing customer expectations. The growth of digital manufacturing, IIoT and connected products and services is generating an unprecedented amount of data. The manufacturing industry generates an average of roughly 2 petabytes of data every year, which is nearly double the next highest industry. Most of the data comes from supply chains, strategic sourcing processes, multifaceted operations within factories, and the stringent stages of compliance and quality management.¹¹  Furthermore, more than one-third of manufacturers say that the amount of data created has at least doubled in the past two years.¹² Maximizing value from data is essential to maximizing the effectiveness of technology investments.

Here are some findings that illustrate the growing role of data in the manufacturing industry:

• Of manufacturing leaders, 22.6% now say that they measure the value from data in monetary terms compared to 4% in 2021. Additionally, 12.1% say that they measure data’s value by revenues of data-driven services compared to 3% in 2021.¹³
• According to Forbes (March 2020), the advanced manufacturing industry is one of three industries that place the highest priority on data integration.
• The leading challenges that manufacturers face in getting more value from data are extracting data from legacy systems, a lack of data analytics skills and integration of data from different sources.¹⁴
• A recent study by the University of Texas indicates that, if the median Fortune 1000 business increased the usability of its data by just 10%, it would translate to an increase in $2.01b in total revenue every year.

Data flows and digital threads represent the “vascular system” of organizations. Companies must resist the temptation to collect data “just because it’s possible” or “because it may be needed at some time.” Defining what data is needed, by whom, for what purpose, and to what frequency and precision, is imperative. Moreover, data hygiene is also critical; knowing what data to purge and when is essential to effective master data management. Data integration and veracity are foundational to provide one source of truth and avoid having to spend time reconciling data from multiple sources.

Before making significant investments in new software, sensors and technology platforms that may generate even more data, companies should start by (a) defining clear business requirements and priorities, (b) identifying actionable insights that can improve overall business competencies,
(c) examining existing data sources and data flows linking them to business requirements, (d) defining a master data management and data governance process and (e) creating a functional data fabric (architecture) to avoid “drowning in deeper data lakes.“

This approach will allow companies to identify “clots” in the vascular system, data discrepancies and voids to enable the identification of targeted solutions (e.g., cloud/edge computing, AI/ML) that maximize information assimilation, processing and exchange. The old cliché holds true here — it’s not the quantity, but the quality, of data that matters most. One sure way to paralyze a person or organization is to flood it with loads of spurious information that obscures the signal in the noise. The efficacy of large language models and GenAI will ultimately depend on conquering this challenge — as will securing the integrity of the data that is exchanged. 

Extracting greater value from data is paramount as companies progress from descriptive analytics, to predictive analytics, to prescriptive analytics (to include GenAI). Companies must determine whether to create custom analytic tools or use turnkey applications offered by external vendors to process and extract value from data. A study by a global market intelligence firm showed that adapting off-the-shelf analytic tools yielded a median ROI of 140% compared to custom-developed analytic tools, which resulted in an ROI of 104%. Manufacturers are increasingly outsourcing data analytics, with 24.1% utilizing external analytics partners this year (2023) compared to 14% in 2021.¹⁵ In either case, arming employees with the appropriate training to use these tools and apply the information to make effective decisions will be critical in maximizing value.

Connectivity boosts performance

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The ultimate source of innovation in any organization is the collective insights of its people. For this collective experience to be unleashed, creativity must become an integral part of the organization’s culture. According to the Organization for Economic Co-operation and Development (OECD), 1.1b jobs will be disrupted by technology innovation in the next five years. Employees will require upskilling (learning to improve current work) and reskilling (learning to do new types of work). According to a recent survey by the National Association of Manufacturers (NAM), while concerns abound regarding AI taking people’s jobs, almost one-third of respondents (32%) believe that they will need to hire more people in an AI-enabled world, even if the roles they perform may be different as AI systems automate routine tasks and allow workers to be retrained and reassigned to more value-added and engaging roles.

Here are some findings that illustrate the value of investing in people:

• According to Gallup, companies that invest in employee development increase profitability by 11%.
• Manufacturers that focus on, and invest in, their people are 2.6 times more likely to succeed in their transformation efforts. Organizational change management initiatives must address both the rational (processes, organizational design and technology) and the emotional/experiential (leadership, culture and capabilities) aspects in a balanced and integrated way. [EY/Oxford Report]¹⁷ 
• The World Economic Forum’s Future of Jobs Report states that, due to the growing uptake of technology and automation, half of all employees will need reskilling by 2025. This remains particularly relevant within manufacturing.
• With the nature of work changing so rapidly, it’s no longer enough just to offer employees opportunities for upskilling and reskilling. Companies also need to help workers become “expert learners.”

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For manufacturers, the competitive field is changing rapidly — and the pace of change is accelerating. Manufacturers are facing nontraditional competitors that bring a new mindset, especially when it comes to creating customer experiences and deeper/stickier relationships. For manufacturers, it’s not just about delivering a quality product reliably; it’s about creating a value-centered offering.

The EY CEO Outlook Survey revealed that over two-thirds (~70%) of CEOs plan to increase digital investments in the next six months, either organically or through M&A and partnerships. However, less than half are planning business model changes – which is a bit concerning since technology + business model transformation typically produces maximum value and ROI2. Investing in the latest technology without a commensurate business model evolution is akin to buying an expensive set of golf clubs (new tech) but not improving your swing (business process) or approach to the game (strategy). You might achieve incremental improvements (e.g., distance), but you will not significantly improve your scores (competitive advantage).

For a growing number of industrial products, the software and digital components offer greater value than the hardware and electromechanical aspects. This transformation of products creates opportunity for new business models and revenue streams beyond the point of sale. For example, with the advent of smart connected products and platforms, manufacturers have not only “as-designed,” “as-built” and “as-maintained” data, but also “as-used” data that can be leveraged to improve the customer experience and associated outcomes delivered by the product. EY research¹⁸  indicates that revenue from advanced service models will more than double by 2024 and that predictive maintenance services will be considered a must-have by 2024. Advanced companies will see 60%-80% of profits coming from lifecycle services (vs. initial product sales).

Offering products as-a-service can improve EBIT margins 3x-7x while also producing steadier revenue streams. Customers are also willing to pay up to an 18% premium for connected products and digital services that deliver improved experiences and outcomes¹⁹ .

Pillars for business model innovation include:

• Strengthening relationships with customers by providing omnichannel experiences
• Establishing a presence in the value chain with a strong market position
• Migrating revenue models from delivery of goods (e.g., point-of-sale) to value-driven relationships
• Capitalizing upon consumer usage data to improve customer experience
• Building partner ecosystems to challenge sector boundaries to innovate at scale

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Manufacturers can build innovation from within, acquire innovation via M&A or establish partnerships with other organizations by creating an ecosystem. Ecosystems offer benefits of speed, flexibility and lower capital commitments.

As manufacturers embrace digital, connected technologies, they also become more susceptible to disruption by nontraditional and niche competitors who can move fast. Manufacturing industry CEOs who responded to a recent EY CEO Outlook survey (2022) cited risk of business disruption by nontraditional competitors as one of their top three concerns.

To create advantages and mitigate potential disruption, companies can embrace mutually beneficial ecosystem partnerships that offer increased value to customers beyond what each individual company can bring alone while also accelerating speed-to-value. Companies included in the EY CEO Imperatives Ecosystem study with high-performing ecosystems reported the following R&D benefits that ultimately produced greater ROI2 (see Figure):

R&D assets bar chart:

Our study of more than 800 business leaders leveraging at least one ecosystem business model has revealed that ecosystems make up, on average, 13.7% of their total annual revenues, drive 12.9% in cost reduction and generate 13.3% in incremental earnings. Seventy-one percent of manufacturing CEOs who already have high-performing ecosystems affirm that those ecosystems are providing greater growth opportunities than traditional M&A.

Despite these findings, only 31% of CEOs surveyed indicated that their strategy includes an external ecosystem of business partnerships. This is due, in part, to the need to protect intellectual property, as well as potential complexities defining cooperative financial and operating models. However, EY research also proves that these challenges can be overcome by implementing a clear governance structure, defining distinct roles and responsibilities, maintaining open lines of communications and getting sponsorship from the top. As our results show, the potential payback from these ecosystem partnerships is well worth the effort to define intentional go-to-market playbooks.

Special thanks to Tyler D. Campbell for his significant contributions to this article.

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