Automate These 7 Operations to Cut Down Your Manufacturing Costs

By 2025, as Industry 5.0 evolves, AI will be more than just task automation—it will be about working smarter by automating repetitive tasks and focusing on strategic business initiatives. This approach is more sustainable, adaptable, and solution-oriented. Yet, manufacturing remains one of the least automated industries, with AI-powered automation systems still underutilized.

So, what's holding manufacturers back from fully adopting AI automation? What are the top 7 automation strategies you can implement to reduce manufacturing costs and gain a competitive edge? Let’s dive in.

What’s Giving Manufacturers a Hard Time?

Post-COVID, there’s great pressure from rising inflation, supply chain disruptions, and skyrocketing costs. Completing projects on time and within budget has become increasingly challenging. Inflation and fluctuating labor and material costs might seem out of your control, but what if you could future-proof your organization against these challenges?

Most manufacturers like you face the challenge of processing complex data from engineering drawings, as traditional tools like Optical Character Recognition (OCR) simply won’t cut it. The large volume, varied diagram formats, and intricate scales often force manual data extraction, which is time-consuming and inefficient, and relying on outdated methods is driving up costs and leaving you behind your competitors.

According to a 2015 report by McKinsey, an estimated 478 billion of the 749 billion hours (64%) spent on manufacturing-related tasks globally were identified as automatable.¹

This is where AI-driven intelligent data extraction steps in. It’s not just about keeping up, but getting the best out of your team with faster and more efficient workflows. Studies have shown that companies that have automated these critical processes are experiencing a 30% higher growth rate compared to those using manual methods. That’s not just improvement—it’s a complete transformation.

Rising costs and supply chain disruptions have been at the top of everyone’s mind, and  AI transformation is the key to solving these challenges.

7 Key Operations to Automate

Let’s face it: keeping up with automation trends can get confusing. With AI everywhere, deciding what’s worth automating in your manufacturing process—and what isn’t—can be overwhelming. Plus, let's be real: implementing AI is a significant investment. But if done right, it can be the key to cutting costs and boosting efficiency across the board.

So, where should you focus to get the best return on automation in 2025? Here are 7 key areas to help you keep manufacturing costs in check while staying ahead of the curve:

1. Employee Hiring and Training

Staffing, hiring, and retaining skilled workers in manufacturing is a growing challenge, exacerbated by labor shortages and the prolonged learning curve for many roles, which can take years to master. The time and cost associated with training and retaining skilled workers can be significant. When experienced employees leave, they take valuable knowledge with them, disrupting workflows. 

“On average, companies in the U.S. dedicate around 62 hours per year to train each employee, which amounts to over a week of full-time work for training alone. Large companies typically spend more—about $1,689 per employee—while smaller organizations allocate around $826​.”(HR, Daily Advisor)³

You can significantly cut training costs with AI, as it stores critical workflow knowledge permanently unless manually deleted. Automated data extraction models like IDP not only retain this knowledge but also make it easily accessible for onboarding new hires, effectively reducing the learning curve. AI-driven knowledge bases allow technicians to receive instant, specific guidance on complex tasks, freeing up senior staff to focus on strategic responsibilities. This not only accelerates problem-solving but also eliminates operational bottlenecks, resulting in more efficient workflows and lower training expenses.

2. Data Extraction and Analytics

According to a report from Gartner, organizations lose an average of $12.9 million annually due to poor data quality.² Beyond the immediate revenue impact, this issue complicates data ecosystems and hinders effective decision-making. Manufacturing companies are particularly affected, ranking first on this list due to the intricate formats and complexities of the documents they handle.

Automated data extraction and analytics form the foundation of any manufacturing unit, where precision and speed are paramount. Each one of the thousands of engineering diagrams that circulate through your business is packed with vital information needed by teams across R&D, production, quality assurance, design, and maintenance. The faster and more accurately that data can be extracted and shared, the more efficiently each department can work, cutting down costs and reducing delays.

Therefore in manufacturing, the key to cost efficiency lies in the precision and speed at which data from these diagrams is processed. Whether it’s ensuring that production specs are followed to the letter or that quality control is up to standard, having accessible, accurate data available to your engineers is critical. The Solution?

AI-driven intelligent document processing (IDP) can handle complex engineering drawings. Advanced AI algorithms can not only identify and extract value pairs from diagrams but also interpret those values in context with other elements, similar to how the human brain deciphers meaning. 

The extracted data can be customized to fit the exact format your business needs, even when dealing with scale variations or other complexities. For example, consider an engineering drawing where the piping system is measured in inches, while the machinery layout is marked in millimeters. Traditionally, this would require manual recalculations to ensure everything aligns correctly. With IDP, both units are automatically detected and processed, allowing the system to calculate the differences and convert the machinery layout into inches or the piping system into millimeters. The final data is delivered in the specific unit and format your business needs, eliminating the need for manual intervention and reducing the risk of errors. 

‍3. Quality Control and Early Defect Detection 

Manually extracting data from engineering drawings and managing detailed documentation often result in gaps and outdated information, making it harder to stay compliant with industry standards. This increases the risk of non-compliance and potential penalties. The integration of AI-driven IDP for automated quality control (QC) systems allows for continuous monitoring of discrepancies between engineering designs and final outputs. This real-time quality assurance helps detect flaws early in production, avoiding the need for expensive fixes later. 

Consistently maintaining high quality in manufactured products is crucial, and data analytics plays a pivotal role in this effort. With predictive analytics, you can foresee potential defects by analyzing data from sensors and production logs and identifying patterns that may indicate issues before they arise. Should a defect occur, AI accelerates your root cause analysis by pinpointing the exact origin of the problem. It examines multiple variables and correlations, giving you precise insights that facilitate rapid resolution. This proactive and detailed approach not only enables you to implement quick fixes but also helps you develop strategies to prevent future occurrences. 

4. CAD & ERP Integration

One of the biggest hurdles for manufacturers is managing disconnected systems—like Computer Aided Design (CAD) and Enterprise Resource ERP software—that don’t communicate with each other. This disjointed approach can lead to inefficiencies and lost productivity despite investing a lot in these technologies. The right AI tool will help you integrate these systems, enabling seamless data flow from design to production, and boosting your team’s productivity to the fullest.

For example an engineer updates a product design in CAD, IDP can automatically extract changes such as updated part dimensions, and feed this information into the ERP system, ensuring accurate production scheduling and resource allocation.CAD designs often include the Bill of Materials (BOM), which lists all components required for manufacturing. IDP can automatically extract the BOM from CAD documents and integrate it with the ERP system to update inventory levels, check stock availability, and trigger procurement actions. Advanced IDP systems.

Incorporating design revisions after the initial BOM creation can often become a logistical nightmare, with research showing that nearly 50% of manufacturing delays are due to outdated or incorrect BOM information. However, by leveraging AI-driven data extraction and integrating your existing systems, you can eliminate these bottlenecks and ensure more accurate, efficient production processes.

But AI-driven IDP systems automatically extract the Bill of Materials (BOM) and update the ERP system, as soon as a new product design is finalized,  triggering the procurement of necessary parts and materials without any manual effort. Any design changes made in CAD—such as adjustments to part dimensions or production timelines—are instantly captured by IDP and reflected in the ERP system. This enables real-time updates to task scheduling and notifies production teams immediately, enhancing communication across departments and minimizing delays and costs.

Additionally, IDP can extract cost-related data from CAD drawings, such as material usage and part complexity, and combine it with ERP data like labor and overhead costs. This enables manufacturers to generate more accurate cost estimations for production runs, ensuring that resources are optimized and aligned with the latest design specifications.

The automated data transfer between CAD and ERP eliminates the need for manual input, significantly reducing lead times and improving the overall efficiency of the manufacturing workflow. With real-time updates and seamless data flow, manufacturers can transition from design to production faster, minimizing downtime between phases and enhancing operational efficiency.

Even if your organization doesn’t have CAD or ERP systems in place, IDP can still function independently and offer significant advantages mentioned in the previous and upcoming sections. By automating the extraction and processing of critical data from engineering drawings, invoices, or production documents, IDP streamlines workflows and eliminates manual data entry errors. It can bridge the gap between disconnected departments, ensuring that key information—such as dimensions, materials, and costs—flows seamlessly into your planning and production processes. This leads to increased efficiency, reduced operational bottlenecks, and optimized productivity, regardless of the systems you currently use.

5. Data Management and Storage

AI can also improve how your IT and data teams manage information. By automating metadata processing and cataloging, AI frees up data stewards from manual tasks, speeding up governance projects and reducing costs.  Using (Generative Artificial Intelligence) GAI to streamline metadata management significantly improves the quality and accessibility of data for projects throughout an organization. With GAI, data teams can automate tasks such as data crawling, tagging, and definition creation to eliminate most of the manual work involved, leaving experts to validate the results. It can also streamline the process and substantially reduce the time and costs associated with governance and data cataloging projects. This allows teams to focus on validating results rather than managing tedious data entry.

6. SLA adherence and Team performance 

Intelligent Document Processing (IDP) not only streamlines data capture but also enhances operational efficiency by incorporating Service Level Agreement (SLA) awareness into the extraction process. By prioritizing tasks based on urgency and compliance deadlines, IDP systems can automatically push the most critical documents to the front of the queue. This ensures that time-sensitive tasks are addressed promptly, significantly reducing the risk of SLA breaches. Moreover, by leveraging real-time analytics, IDP can identify patterns in document processing that align with SLAs, enabling organizations to proactively manage workloads and allocate resources where they are most needed.

Additionally, IDP plays a vital role in evaluating team performance by providing insights into individual and team-based efficiencies. By analyzing data extraction times, error rates, and SLA compliance, organizations can identify areas for improvement and implement targeted training or process enhancements. This data-driven approach not only highlights top performers but also uncovers systemic bottlenecks that may hinder productivity. Ultimately, incorporating SLA-aware data extraction through IDP fosters a culture of accountability and continuous improvement, empowering teams to optimize their workflows and contribute to overall business success.

7. Quotation Estimation 

As an industrial manufacturer, you know that quoting for custom products can be a significant challenge. Unlike sectors where standard quotes can be easily reused, each Request for Quotation (RFQ) you receive often requires a unique and detailed response. This process can be time-consuming, especially when it demands substantial input from your engineering teams. Reviewing each document independently can drive up your costs and slow down your operations.

AI can help you instantly estimate quotations by automating data extraction from RFQ files, enabling you to generate precise quotations in mere seconds. This technology not only considers complex specifications, material costs, and labor requirements but also enhances both speed and accuracy.

One significant advantage of AI-powered quote estimations is its ability to analyze historical data to uncover pricing trends. By leveraging these insights, you can optimize your quotes in real-time, ensuring they are competitive and reflective of current market conditions. This means you’re not just responding faster; you’re also making more informed pricing decisions that can lead to increased profitability and a stronger position in the marketplace.

That’s a Wrap

Manufacturing in 2025 will center on making smart automation choices. AI has the potential to transform your business by Intelligent Document Extraction reducing material waste, enhancing quality control, streamlining processes, and accelerating training and quoting. The key is not just to automate, but to automate wisely. Identify these 7 specific operations where automation will have the most impact to keep your manufacturing costs under control and ensure your business thrives. 

References 

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1. Chui, Michael, Katy George, James Manyika, and Mehdi Miremadi. "Human + Machine: A New Era of Automation in Manufacturing." McKinsey & Company, September 7, 2017. https://www.mckinsey.com/capabilities/operations/our-insights/human-plus-machine-a-new-era-of-automation-in-manufacturing.

2. Sakpal, Manasi. "How to Improve Your Data Quality." Gartner. July 14, 2021. https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality

3. Grensing-Pophal, Lin. "How Much Are Companies Spending on Employee Training in the US?" HR Daily Advisor. August 21, 2024. https://hrdailyadvisor.blr.com/2024/08/21/how-much-are-companies-spending-on-employee-training-in-the-us/

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