Manufacturing investments that drive operational efficiency

Manufacturers are operating in an environment of labor shortages, rising costs and economic uncertainty. They are also dealing with the rapid emergence of AI and automation. Businesses must invest in technology that improves efficiency to remain competitive. But questions about which investments are worth the cost can be overwhelming.

Keep reading to learn which technology investments manufacturers should prioritize to overcome current business challenges and make long-term gains in improving operational efficiency.

The efficiency challenges facing manufacturers today

The manufacturing industry has made significant progress since the disruptions of the COVID era. But post-pandemic, significant challenges still remain.

Labor shortages remain a long-term issue

Hiring qualified production employees remains difficult across much of the manufacturing sector. Even when positions can be filled, turnover remains high, making it difficult to maintain consistent production schedules. Insufficient staffing can prevent equipment from operating at the levels required to meet customer demand.

Automation has become more expensive

Automated solutions are seen as an answer to labor shortages. But automation projects require greater upfront investment than they did several years ago. Inflation, lingering supply chain issues and tariffs on imported equipment and parts have increased the cost of robotics, machinery and automation technologies.

AI decision paralysis

AI is top of mind for many people in the manufacturing industry right now. Many organizations hesitate to invest in proven automation technologies because they’re waiting to see what new AI tools emerge on the market. While AI will continue to transform manufacturing, delaying practical investments can leave companies operating with outdated processes while competitors continue to improve efficiency.

What automation investments have the highest ROI in manufacturing?

Not every automation investment delivers the same return.

Manufacturers often achieve the strongest results by investing in technologies that eliminate repetitive, non-value-added work while remaining flexible enough to support multiple products or production lines.

Examples include:

Packaging automation

Packaging frequently requires significant labor while adding little customer value.

Automated packaging solutions can reduce repetitive labor requirements and improve consistency and throughput. They can operate across multiple product configurations when properly designed.

Machine monitoring systems

Machine monitoring technologies are becoming increasingly valuable because they provide real-time visibility into:

• Machine uptime
• Downtime causes
• Production rates
• Equipment performance
• Process consistency

This data can be used to respond to issues on the production floor faster and to develop strategies to maximize productivity.

Beyond helping manufacturers identify bottlenecks, machine monitoring systems create the data foundation needed for future AI applications. The organizations that collect and organize operational data today will be better positioned to deploy advanced analytics and AI capabilities tomorrow.

Process control technologies

Investments that improve machine consistency and process repeatability often generate substantial returns. Better process control reduces variability, improves quality and minimizes the need for downstream human inspection and correction.

Reducing overhead in the back office

When manufacturers think about improving efficiency, they often start with the production floor. However, some of the best opportunities to reduce overhead exist in the back office.

SG&A tasks frequently involve manual data entry, repetitive transaction processing and labor-intensive reporting that can be automated.

Here are some examples:

• Accounts payable and accounts receivable processes: AI and automation can streamline invoice processing, payment matching and collections tracking by reducing manual data entry and accelerating transaction workflows. This helps improve accuracy, shorten processing times and free finance staff to focus on higher-value activities.
• Sales order entry and management: Automated order processing tools can capture orders from emails, portals and customer documents, populate ERP systems and flag exceptions for review. This reduces administrative workload, minimizes errors and speeds order fulfillment.
• Financial reporting activities: AI-powered reporting tools can automatically consolidate data from multiple systems, generate standard reports and identify anomalies or trends. This reduces the time spent on manual report preparation while improving reporting consistency and insight generation.
• Customer communication workflows: Automation platforms can handle routine customer inquiries, order status updates, payment reminders and service notifications through email, chat or self-service portals. This improves response times while reducing the labor required for repetitive communications.
• Internal reporting and data analysis: AI can quickly analyze large volumes of operational and financial data, generate dashboards and identify patterns that might otherwise go unnoticed. By automating data collection and analysis, manufacturers can reduce reporting overhead and enable faster, data-driven decision-making.

How data can reduce management overhead

Advanced data can streamline mid-level supervisory functions. Modern dashboards, automated reporting and AI-powered alerts can deliver operational insights directly to managers instead of requiring manual data collection and analysis

For example, data from your production floor can power AI agents that deliver real-time insights into production. Floor supervisors can look at that data and quickly identify which machines or units were producing at expected rates and which weren’t. That data, combined with the supervisor’s knowledge of part or machine history, enables more targeted investigations and faster solutions to production slowdowns.

Let data drive inventory decisions

Supply chain uncertainty continues to make inventory management a difficult balancing act. While some manufacturers respond by increasing inventory levels, simply carrying more stock is rarely the most effective solution. The goal isn’t maximizing inventory. It is maximizing visibility.

To make informed inventory decisions, manufacturers should combine customer forecasts with historical demand patterns, supplier lead times and market intelligence. Analyzing historical data can improve planning accuracy when customer forecasts are imperfect.

Collaboration plays a role in inventory management

Rather than carrying inventory risk alone, manufacturers should work closely with both customers and suppliers to establish realistic expectations regarding demand, lead times, inventory levels and forecast accuracy. Open communication across the supply chain often results in better inventory decisions than independently managing uncertainty with larger inventory buffers.

Don’t start with AI — start with your data

When manufacturers consider new automation or AI investments, it’s natural to focus on the tools or systems. But it’s important to first evaluate the quality of your organization’s data.

Before investing in advanced AI tools, you should answer these questions:

• What data are we currently collecting?
• Which business metrics truly matter?
• What information are we not capturing today?
• How accurate, consistent and accessible is our data?
• Can our systems share information effectively?

The value of any AI or automation system depends largely on the quality of the information available to it. Manufacturers that establish strong data management practices before implementation will be able to extract more value from their technology investments.

How can manufacturers measure the ROI of their technology investments?

AI and automation investments are made to increase productivity. Here are some metrics you can analyze to determine if you’re getting your money’s worth:

Throughput

Throughput measures how much value the business creates relative to its workforce.

This metric evaluates how efficiently the organization converts purchased materials into customer value using its available labor.

Higher throughput generally indicates a leaner, more productive operation.

Earned labor

Investments in automation and AI tools need to increase earned labor metrics.

Earned labor compares the amount of labor that should have been required to produce a given level of output with the labor actually used.

For example, if production standards indicate a certain output should require eight labor hours to manufacture, but actual production required 10 labor hours, the operation underperformed expectations. If that same amount of product is produced with seven labor hours, you outperformed labor expectations, and your earned labor metric increased.

Back-office productivity

Administrative efficiency should also be measured. Evaluate improvements by examining cash collection, sales activity, customer service performance or accounting productivity relative to staffing levels.

AI should increase the amount of work each employee can accomplish rather than simply reducing headcount.

What technology investments should be prioritized in the near-term?

The manufacturers that remain competitive over the next several years will likely focus on investments that are adaptable, scalable, and capable of supporting long-term business goals.

Priority areas include:

Flexible automation

Flexibility allows manufacturers to adapt as customer demand changes. To be ready for fluctuating demands, prioritize scalable technology that can be used for multiple products, processes or production lines.

AI-powered knowledge transfer

As experienced workers retire, manufacturers risk losing decades of operational knowledge. AI tools that capture expertise, troubleshooting methods, best practices and institutional knowledge can help organizations preserve the experience of long-tenured employees. These tools can accelerate the development of new employees and potentially reduce training costs.

Workforce enablement tools

Future investments should account for ongoing labor constraints. Technologies that help employees become more productive, make more informed decisions and manage larger workloads will likely generate significant value. Also, prioritize tools that reduce dependence on difficult-to-fill positions.

Align investments with long-term business strategy

Every business has different technologies needs based on its goals and roadmap. There is no off-the-shelf solution that is right for everyone. Manufacturers should first determine what they want their business to look like three to five years from now and then invest in technologies that support that vision. The goal is not simply to buy new technology but to build a future operating model that remains competitive despite workforce challenges, changing markets and evolving customer expectations.

How Wipfli can help

Wipfli helps manufacturers benchmark operational performance, assess production and back-office processes, identify opportunities for improvement and develop practical roadmaps for automation, AI and digital transformation. Our experienced professionals can help you develop a roadmap that turns operational efficiency initiatives into measurable business results. Start a conversation.

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