Checking your browser before you access eBay.

However, talent challenges are still salient. The Employment Cost Index for total compensation in manufacturing, which includes employee wages and benefits, has continued to climb in , gaining 3.8% year over year as of September.16 Labor participation rates have been declining in the United States for over two decades, due in part to an aging population, and this may continue through at least .17 Challenges such as workers’ access to child care, reliable transportation, and their desire for flexibility also remain.18 A study conducted by Deloitte and The Manufacturing Institute in showed that 1.9 million manufacturing jobs could go unfilled over the next 10 years if talent challenges are not addressed.19 The study also found that roles that require higher-level skills could grow the fastest between and , and that a combination of technical manufacturing, digital, and soft skills will likely be required.20

Please visit our website for more information on this topic.

Favorable economic conditions in such as lower interest rates and continued investment in US manufacturing may reignite demand in the industry, which could intensify labor shortages. Though wages are likely to continue to rise, manufacturers have a cost lever that they can pull: reducing turnover. According to a survey of more than 300 human resources leaders at US manufacturing companies conducted by the UKG Workforce Institute, 60% of respondents indicate that the average cost to replace one skilled frontline worker ranges from US$10,000 to US$40,000, while 56% say that employee turnover has a moderate to severe impact on their bottom-line finances.21

To help meet workers’ changing expectations, reduce turnover, and plan for demand volatility, companies seem to be increasingly focusing on improving the worker experience,22 taking an ecosystem approach to talent development,23 and leveraging digital tools that offer advanced talent planning and workforce management capabilities. A recent report by Gartner suggests that by over 80% of large businesses that have hourly employees will have invested in advanced workforce management software solutions.24 In addition to supporting broader digital initiatives to improve operational efficiency, a key goal of these investments is to improve the worker experience, including capturing employee sentiment, suggesting adjustments to shift patterns, enabling flexible scheduling, and improving company communication with hourly workers, according to the report.25

The study also indicates that by , AI-based management of employee skills and how people are deployed to meet business needs will be a core capability.26 By tracking and connecting important parameters like employee skills and certifications (that is, using a skills matrix), the number of people and skills required to produce certain products, and accurate demand forecasts, these tools could allow companies to efficiently plan for the specific workforce needed for upcoming production runs. If gaps are identified, companies could offer upskilling opportunities for existing employees, which can increase retention,27 or work within the talent ecosystem to find and develop workers with the requisite skills.28

Taking this approach could also enable tailored upskilling that helps prepare employees for future work, for example, working alongside advanced technology such as gen AI. Advanced talent-planning tools can also support manufacturers taking a skills-based approach, which may be increasingly important for broadening the talent pool.29 These investments and a focus on long-term talent strategies may help manufacturers build and retain a skilled workforce for and beyond.

2. AI and generative AI in manufacturing: Prioritizing targeted, high-ROI investments

As the enthusiasm surrounding gen AI shifts from “…unbridled excitement” to “a more nuanced and critical evaluation of its real impact on business outcomes,”30 manufacturers have already made significant investments in AI and gen AI, and this trend is expected to continue in and beyond. Deloitte’s Future of the Digital Customer Experience survey found that 55% of surveyed industrial product manufacturers are already leveraging gen AI tools in their operations, and over 40% plan to increase investment in AI and machine learning over the next three years.31 However, companies seem to be taking a more measured approach toward gen AI and AI implementation by following their traditional, holistic return on investment processes. A survey of manufacturers by the Manufacturing Leadership Council found that 78% of respondents indicate that their AI initiatives are part of the company’s overall digital transformation strategy.32 And, as is typically the case with technology investments, a primary measure of success for gen AI will be its ability to drive value in the organization.33

A prerequisite for AI adoption is access to quality data,34 and companies seem to be shifting their focus in this direction: Three-quarters of respondents in a recent Deloitte survey indicated that their organization has increased investment around data life cycle management to support their generative AI strategy.35 However, challenges still exist—in another survey, nearly 70% of manufacturers indicated that problems with data, including data quality, contextualization, and validation, are the most significant obstacles to AI implementation.36 To help overcome these challenges and maximize ROI, manufacturers might consider starting with use cases where there is already a strong data foundation in place.

One example is customer service applications, which are often digital and language-based, and offer access to a wealth of data that typically doesn’t require significant data harmonization or modernization, such as call records, technical documents, warranty data, and claims information. In fact, 74% of surveyed manufacturers in Deloitte’s Future of the Digital Customer Experience survey indicated that they plan to use or are already using gen AI to enhance their customer experience.37 Example use cases include gen AI–based virtual chatbots that can allow customers to efficiently evaluate product specifications and features during their buying journey, or gen AI–based service manuals combined with augmented reality that can facilitate rapid and efficient remote assistance for maintenance and repair.38

Another example is product design. For instance, according to data provider IDC, by , the demand for product innovation will drive 50% of large manufacturers to evaluate engineering archives using generative AI to uncover new opportunities for innovation on legacy products.39 Tying the use cases to critical business initiatives or priorities, such as enhancing customer experience or product innovation, can also be important for securing internal funding and support.  

Identifying targeted opportunities to invest in AI, including gen AI, may be key for manufacturers in as elevated costs and uncertainty are expected to continue in the coming year. Improved efficiency, productivity, and cost reduction have been identified as important benefits achieved through generative AI implementation.40 In addition, in a recent survey, manufacturers indicated that AI and machine learning have the largest impact on business outcomes relative to other smart manufacturing technologies, and that gen AI or causal AI offer the largest ROI behind cloud and software-as-a-service technologies.41 To support AI use case implementation in and lay the groundwork for the future, it will be important for manufacturers to focus on building an overall AI and data strategy, including establishing an operating model, setting up governance, and identifying risks. Yet in a survey by the Manufacturing Leadership Council, only 51.6% of manufacturers indicated that they have a corporate AI strategy.42 A dedicated focus on organizing and structuring data will also be important to create the foundation to facilitate long-term investments in AI and gen AI.

3. Supply chain: Tackling disruptions and elevated costs with agility and efficiency

Supply chain challenges have eased since the height of the COVID-19 pandemic, but pressures remain. For instance, average lead times for production materials have shown significant improvement since their peak in but remain stubbornly higher than pre-pandemic levels.43 While global supply chain disruptions persist, such as attacks on container vessels in the Red Sea, costs also remain high. Over 35% of surveyed manufacturers cited transportation and logistics costs as a primary business challenge in the third quarter of .44

In , companies are expected to face continued supply chain risks, disruptions, potential delays, and elevated costs due to several contributing factors:   

If you want to learn more, please visit our website Red Eagle.

• Shipping delays: Geopolitical tensions and additional factors may contribute to ongoing shipping challenges in . For example, route changes in response to Houthi militia attacks on cargo containers in the Red Sea are likely to continue.45 The increased transit time on these routes has impacted shipping capacity across the globe since the attacks began in October , causing significant delays and shipping rates to double by the summer of .46 Starting in , low water levels in the Panama Canal due to drought caused delivery delays and rising costs for goods and raw materials between the United States and Asia, as well as additional global routes.47 The drought has subsided and low-water-level restrictions have eased in , with the average number of daily trips through the canal approaching pre-drought levels as of August.48 However, drought conditions could return.49
• Labor challenges throughout the value chain: Ongoing labor shortages from production to transportation to warehousing could contribute to delays and higher costs throughout the value chain in .50 The shortage of truck drivers in the United States continues and is expected to accelerate in the years ahead.51 In a survey of more than 600 manufacturing professionals, over 80% said that labor turnover had disrupted production,52 which can lead to slower deliveries. Labor turmoil in supply chains is also a growing challenge across the globe. For example, while the October strike by US dockworkers on the East and Gulf Coasts was resolved in just three days, supply chains were impacted and the worker contract expires on January 15, which could potentially lead to additional disruptions in .53
• Rising input costs: According to the NAM third-quarter outlook survey, respondents expect both wages and raw material prices to continue to rise by another 2.7% over the next 12 months.54
• Potential government policy changes following US and global elections: was considered a “super year” for elections with 72 countries going to the polls and approximately 3.7 billion people—nearly half of the world’s population— potentially voting, according to the United Nations.55 Governmental changes and ensuing policy changes can affect global supply chains due to a number of factors, including geopolitical tensions, trade, tariffs, and industrial policy. For instance, changes to government policy could bolster supply chain restructuring efforts to balance cost and resilience, such as the nearshoring activity that has led Mexico to become the leading trade partner for the United States.56 Respondents in the Fortune/Deloitte CEO survey indicated international trade as the third top area where US elections could have the greatest impact, behind regulations and taxes.57 And manufacturers may already be bracing for potential changes: According to the NAM outlook surveys, the proportion of manufacturers citing trade uncertainties (such as actual or proposed tariffs) as a primary business challenge increased sharply to 34.3% in the second quarter from 25.8% in the first quarter, and continued to rise to 36.8% in the third quarter.58

As supply chain pressures have abated since the COVID-19 pandemic, companies have shifted their strategy from a primary focus on resilience to a new emphasis on balancing optimized cost and resilience.59 Techniques such as diversifying sources, pursuing mergers and acquisitions, enhancing partnerships, and building internal capabilities are helping some companies achieve this goal.60 Amid the disruptions and high costs that could characterize supply chains in , these approaches are likely to remain important.

Staying focused on investment in digital tools that enable advanced supply chain planning techniques, better collaboration with suppliers, simulation, and enhanced visibility may provide an additional boost. In a recent study, 78% of manufacturers indicated that they have implemented or are planning to invest in supply chain planning software.61 Respondents also ranked this software fifth out of 10 technologies that drive the most significant ROI.62 According to another report, some of the top trends expected to impact industrial product manufacturers’ supply chains by are big data and advanced analytics, supply chain digitization, and data management.63 The top priority for sourcing and procurement in for all companies surveyed (across industries) was implementing new technologies and capabilities,64 and this trend is likely to continue in .

4. Smart operations: Building the foundation while prioritizing high-value projects

Manufacturers have continued investing in digital technologies over the last several years despite economic uncertainty, rising costs, and a challenging business climate. For instance, Deloitte’s Digital Maturity Index survey found that 98% of 800 surveyed manufacturers in four major global economic regions have started their digital transformation journey, compared with 78% in , and respondents reported cost optimization, operational efficiency, product innovation, and improving customer experience as key drivers for the shift.65 Further analysis showed that technology investments made by manufacturing companies accounted for 30% of their operating budget in , compared with 23% in , with cloud, gen AI, and 5G being the top three technologies with the greatest ROI.66

Given the need to address elevated material and labor costs, an ongoing skills gap, and potential disruptions from geopolitical factors, investments in digital technologies across manufacturing organizations—in other words, the push toward smart operations—is likely to continue in .
Falling interest rates and the potential for growth could even accelerate investment. Manufacturers will likely continue to prioritize investments in their digital core and data foundation that can enable targeted, high-ROI use cases for cutting-edge technologies such as AI, gen AI, and extended reality (XR). Investments in the following technologies and systems are likely:

• Manufacturing operations management and manufacturing execution systems can connect the enterprise to the shop floor and provide visibility into data across the organization.
• The Unified Namespace data architecture strategy can provide a central source of real-time standardized data that can be utilized by a variety of systems across the business. Unified Namespace can eliminate the need for complicated direct connections between disparate systems that often create significant interoperability challenges.67 It may also lay the foundation for software-defined manufacturing, which aims to further simplify how new technologies are integrated into manufacturing environments in the future.68     
• 5G technologies support data collection and communication: According to Deloitte’s Future of the Digital Customer Experience survey, 34% of industrial product manufacturers plan to invest in 5G technology over the next one to three years.69
• The model-based enterprise can support the digital thread: One in five (21%) of industrial product manufacturers plan to invest in model-based enterprise over the next one to three years.70
• XR and AI may help meet ongoing needs like efficient workforce training, retaining the knowledge of retirees, and augmenting human capabilities. Nearly 30% of industrial product manufacturers plan to invest in XR technologies over the next one to three years, while more than 40% plan to invest in AI and machine learning.71     

The use of simulation in the manufacturing industry could also continue to grow, especially given the potential for business disruptions, the need to control costs, and the continued proliferation of AI tools. For example:

• Causal AI can be used to more effectively simulate cause-and-effect relationships, thereby enhancing decision-making capabilities.72
• Production line simulation can help eliminate bottlenecks and optimize the workflow before any physical changes are made.73
• Process simulation is the top use case that surveyed manufacturers implemented using metaverse technologies, according to the Deloitte and Manufacturing Leadership Council Industrial Metaverse Study, while factory simulation was also prevalent.74 Higher throughput and reduced costs were the primary benefits that companies gained from implementation.75
• Business scenario simulation is also being employed by manufacturers.76 Using a model of the enterprise, challenges such as employee absences, raw materials that arrive with quality issues, and supply chain disruptions can be simulated, and potential actions can be identified to optimize the response.

Another trend to watch in is the likely continued evolution of manufacturing toward a software-driven industry—not just within the factory but also for connecting to products in the field—similar to what has occurred in the auto industry.77 According to the Future of the Digital Customer Experience study, industrial manufacturers are increasingly enhancing the digital connection to their products to gather usage and operational performance data that can help improve performance and serviceability.78 As one example, customers can access portals to monitor fleet performance, schedule maintenance, and chat with company representatives to resolve issues.79 Overcoming interoperability challenges between new and legacy systems, prioritizing cybersecurity and data protection, and developing talent with a blend of technical knowledge, digital skills, and soft skills are likely to be important factors for success in these efforts.80 They will also likely be key to supporting the broader evolution toward smart operations in .

According to Deloitte’s analysis of investor reports of several heavy equipment and engine manufacturers, some companies have continued to make cautious, targeted investments in adding lower-carbon options, such as electric and hydrogen power, to their product lines.84 They seem to be moving toward the goal of meeting previously set scope 3 emissions targets even in a challenging business climate. For example, one heavy equipment manufacturer plans to add over 20 electric and hybrid model options to its lineup by .85A diesel engine manufacturer reports that it continues to make progress on its goal for a 25% reduction in product lifetime greenhouse gas emissions (scope 3) from new products.86

Customers of industrial product manufacturing companies also seem to be maintaining commitments to the adoption of clean technologies to meet their scope 1 emissions goals. For example, a strategic alliance has been formed to develop electric underground mining trucks, aiming for net-zero carbon emissions by , and the first prototype was delivered to a mine for testing in October .87

Several suppliers to industrial product manufacturing companies continue to strategically transform their portfolios to align with electrification and reduced emission trends, according to Deloitte analysis of company reports.88 These companies are emphasizing electrification as part of their strategic focus, particularly in clean energy and sustainable solutions. Some companies are also expecting growth driven by electrification and the energy transition.89

Though industrial product manufacturers seem steadfast in meeting company-imposed emissions goals for their products, looking ahead to , there are several factors that could potentially impact further investment in the development and delivery of clean technology products.  

• Government incentives and regulatory policy: With the possibility of policy and regulatory changes following the US elections, companies may employ a “wait and see” approach in . In fact, regulations and taxes were tied as the top factors surrounding the US elections that could impact businesses, according to the Fortune/Deloitte CEO survey.90 The global super elections year91could also impact regulations and climate policy across the globe, which in turn may influence companies’ appetites for investment as well as customer demand for clean technologies.
• Falling interest rates: Further rate cuts expected from the Federal Reserve92 could fuel increased investment and business spending, including on clean technology products.
• Higher costs: The demand for clean technologies is driven in part by a “green premium” some customers are willing to pay, in addition to regulatory requirements for emissions. With costs likely to remain high for industrial product manufacturers in , they may need to pass on these costs to customers, which could make a green premium even more difficult for customers to justify. On the other hand, although costs remain high, they may stabilize in if inflation remains in check. This could provide an opportunity for manufacturers to reduce prices and, consequently, the green premium that customers are asked to pay.     

Given these factors, companies will likely continue making cautious, targeted investments in manufacturing clean technology products that can maximize profitability and help customers meet their net-zero targets. 

Want more information on Water Solube Marker Manufacturer? Feel free to contact us.