Top 2 strategies to secure investment for AI-powered asset optimization in manufacturing

As a manufacturing manager, you likely face the challenge of navigating a crowded field of investment proposals. Often, more tangible projects—such as upgrading a production line—take precedence because their benefits are easier to grasp. So, how can you overcome this challenge and secure funding for asset maintenance improvements? In this blog, I share proven strategies to help you gain approval for your proposals and secure the resources you need.

Asset optimization initiatives across industries

Asset optimization initiatives vary widely depending on the industry, the type of equipment, and the specific goals of the business. These initiatives are designed to improve efficiency, reduce costs and enhance the overall reliability of critical assets. Let's look at examples from different manufacturing sectors to illustrate diverse approaches.

1. Optimizing wear predictions in polyethylene (pe) production: A large polyethylene (PE) producer wants to optimize the wear patterns of knife edges used to cut endless PE strands into small granules. The challenge here is striking the right balance: repair too early, and the costs increase unnecessarily; repair too late, and the product quality suffers due to degraded cutting precision. The use of predictive maintenance techniques can assist with this.
2. Rail switch monitoring for national railway operators: A national railway operator uses an asset monitoring and management system for real-time health status updates for 6,000 rail switches and wants to enhance the system’s capabilities by incorporating predictive features. Specifically, the focus is on predicting Remaining Useful Life (RUL) and failure modes for rail switches. Deeper insights into the performance trends and potential failure scenarios can help plan maintenance more effectively, reduce unexpected repairs, and ultimately extend the Mean Time Between Repairs (MTBR), while optimizing costs and improving safety.
3. Improving product quality and maintenance in foam manufacturing: High-end foam is made in vertical ovens, with heating pads to roll sheets of foam into delicate, thin foam rolls. When one of these pads fails, the issue is not immediately visible. However, the product quality deteriorates, impacting production efficiency and product consistency. Monitoring the heating pads' health in real time requires sensors and asset monitoring technology. Furthermore, incorporating predictive analytics can help estimate the components' RUL, allowing for more timely repairs.

Complexity of decision-making in investment optimization

In the latest CGI Voice of Our Clients research, 38% of manufacturing executives identify optimizing investments and operations as a significant challenge in achieving better outcomes. The complexity of optimizing an investment portfolio can be attributed to several factors, particularly the fast-evolving technology landscape. A major challenge for manufacturers is the sheer volume of new technology providers emerging with solutions that could potentially revolutionize operations. Some examples are:

1. Cloud solutions disrupting traditional systems
   The rise of cloud technology has made solutions from hyperscalers viable alternatives to traditional Manufacturing Execution Systems (MES) and Computerized Maintenance Management Systems (CMMS). These cloud providers now play an increasingly significant role in the Manufacturing Operations Management Systems (MOMS) landscape, offering scalability and flexibility that traditional on-premise solutions struggle to match.
2. Data and analytics platforms are the new norm
   With the surge in data generation, many existing MES providers and new market entrants are developing dedicated platforms for managing production data and providing AI-driven insights. These platforms offer robust functionality and sophisticated self-service capabilities that allow manufacturers to harness data in real time, enhancing decision-making and operational effectiveness performance.
3. Emergence of self-service and low-code solutions
   The increasing use of self-service and low-code platforms—designed for production environments—enables manufacturers to develop tailored workflows and automate processes without requiring extensive technical expertise. This democratization of software development facilitates quicker implementation and more agile responses to evolving operational needs.
4. Evolution of MES/MOMS
   Traditional MES vendors are broadening their offerings to encompass the entire range of MOMS with integrated capabilities that now feature AI tools customized for specific use cases. This evolution allows manufacturers to access more comprehensive solutions that unify various operational aspects, from real-time monitoring to predictive maintenance.
5. AI: from hype to reality
   Manufacturers are moving beyond theoretical applications of AI and even generative AI (GenAI) to prioritize tangible, use-case-driven deployments. The availability of AI-based solutions that are not just aspirational but scalable offers measurable improvements in production efficiency, predictive maintenance, quality control, and more.

What are some of the real-world implementation challenges?

CMMS/EAM investments frequently compete with projects that provide quicker, more tangible returns, such as new machinery or product development. While these options may seem more beneficial in the short term, effective implementation of CMMS/EAM ultimately enhances asset availability, production quality, operational efficiency, and long-term profitability.

1. High initial costs: Smaller manufacturers are often discouraged by the significant upfront investment required to implement a CMMS/EAM system, which includes software licensing, integration, and hardware infrastructure. Additionally, the total cost of ownership can increase notably due to customizations, system upgrades, and the incorporation of hardware such as sensors and IoT devices.
2. Integration complexity: Data inconsistencies, legacy systems, and siloed data across departments can make the integration of a CMMS/EAM system with other enterprise systems such as ERP, inventory, and IoT platforms complex and costly. Additionally, the integration process can lead to operational disruptions that need to be managed carefully.
3. Lack of skilled personnel: Operating modern CMMS/EAM solutions requires expertise in areas like data analytics, IoT, and IT management, which manufacturers currently lack. Added to that, maintenance teams accustomed to traditional, manual methods may resist transitioning to digital CMMS/EAM systems, necessitating investments in change management, training and fostering a culture of digital adoption.
4. Data quality and availability issues: Inconsistent or outdated data on machine conditions, maintenance histories, and asset life cycles can severely limit the effectiveness of a CMMS/EAM system. For a CMMS/EAM system to deliver maximum value, data must be standardized across all machines and assets. However, inconsistent data formats across different departments or sites can complicate data integration and reduce system efficiency, requiring significant effort in data cleansing and standardization.
5. Cybersecurity concerns: Manufacturers worry that CMMS/EAM systems, if not properly secured, could expose them to cyber threats, potentially endangering sensitive operational data and asset integrity. Furthermore, fulfilling cybersecurity and data protection compliance requirements can be complex and resource-intensive.
6. Difficulty in demonstrating ROI: Companies often face a learning curve and need time to fully realize the benefits of these systems, making it harder to quantify ROI quickly. Additionally, measuring value adds further complexity. Quantifying asset availability and performance improvements—such as reduced downtime or increased product quality—requires detailed analysis, which can be difficult to achieve in the short term.

Strategies to move forward with asset performance optimization

Securing your budget for asset maintenance and optimization requires a strategic approach that aligns your efforts with broader organizational goals while emphasizing the full range of benefits that asset optimization can provide.

Strategy 1: Seek synergies with other digital transformation initiatives

When executives prioritize investment proposals, they typically align them with key company drivers or strategic objectives. Asset optimization initiatives are often internally promoted primarily based on their impact on maintenance costs. While this alone is usually sufficient to justify a healthy ROI, securing the necessary budget often requires demonstrating additional, broader benefits.

Try to identify synergies between asset management efforts and other strategic objectives, which typically could range from sustainability and cost reduction to supply chain agility, production quality, product safety, and environmental, health and safety (EHS) compliance. This holistic approach can create efficiencies and drive greater returns on investment, making it easier to secure the necessary budget for future initiatives.

Strategy 2: Demonstrate benefits beyond asset availability

Beyond maintenance savings, asset optimization drives significant reductions in production costs. With improved machine reliability and more predictable availability, manufacturers can schedule production more efficiently and achieve higher overall equipment effectiveness (OEE). Asset optimization also reduces waste. With proactive monitoring, unexpected breakdowns can be anticipated and prevented, minimizing product loss and maximizing yield. Furthermore, asset optimization leads to higher-quality production. By ensuring machines operate at peak performance, closer to their optimal settings, manufacturers can achieve tighter tolerances and more consistent product quality.

Taking a strategic approach to asset maintenance investment

Securing investment for asset maintenance and optimization can be challenging, but it is far from impossible. By integrating your proposal with larger digital initiatives and quantifying the broader business outcomes, you can present a compelling case for your asset maintenance strategy. Such a strategic approach that aligns your goals with the business’ priorities will ensure your plant’s assets are running at peak efficiency while also securing the necessary resources to drive long-term success.