ML Journal

 

Unlock the full potential of your manufacturing operations by building a data strategy that drives visibility, innovation, and competitive advantage. 

 

TAKEAWAYS:
● Creating a comprehensive manufacturing data strategy enables real-time decision-making, improves supply chain resilience, and enhances cross-functional collaboration.
● To build an effective data strategy, teams require tools including AI frameworks, analytics systems, and IIoT sensors.
● Both technological and cultural changes––including leadership commitment, organizational alignment, and iterative learning––are necessary to deliver data-driven manufacturing. 

 

Big data empowers manufacturers to make smarter decisions and take more impactful actions. With improved access to information and advanced analytical tools, companies are unlocking new levels of efficiency and insight across their operations.

However, realizing these benefits is not automatic. Many manufacturers still face significant data-related challenges that hinder collaboration and productivity. To overcome these obstacles, organizations need a clear and effective data strategy—one that enables them to capture, organize, and apply information at scale.

The Urgency of Data in Modern Manufacturing

Modern manufacturing processes are driven by connection. This falls under the broader umbrella of Industry 4.0, which prioritizes the creation of interconnected, intelligent systems capable of self-optimization.

These systems are essential for companies to navigate increasingly complex processes. For example, in global supply chains, manufacturers must manage multiple supply chains and logistics operations simultaneously to ensure that materials and components arrive on time and within budget.

Digital transformations also present both opportunities and challenges. The emergence of artificial intelligence (AI) frameworks powered by Industrial Internet of Things (IIoT) networks lays the foundation for improved productivity. Data underpins all these processes, making it the new currency for operational efficiency, agility, and innovation. To reliably collect this currency, however, companies must have comprehensive data strategies.

The Core Elements of a Manufacturing Data Strategy

Effective manufacturing data analytics strategies share four core elements: infrastructure, interoperability, governance, and security.

• Infrastructure: Infrastructure includes the integration of legacy tools (where possible), along with enterprise resource planning (ERP) solutions, IoT devices, and cloud platforms.
• Interoperability: Interoperability speaks to the standardization of data formats, data collection practices, and decision-making across plants, regions, and business partners.
• Governance: Governance refers to clear data ownership, quality standards, and manufacturing process integrity.
• Security: Data security involves the protection of intellectual property (IP) and ensuring compliance with local, state, and federal laws.

Together, these elements create a data environment that is accessible yet secure, providing the framework necessary for companies to find actionable insights.

Common Pitfalls in Creating a Data Strategy

Creating a data strategy also comes with potential pitfalls. The first is prioritizing quantity over quality. As companies recognize the critical role of data in delivering value, the temptation to collect as much data as possible grows. The problem? Not all data is relevant to every situation. Targeted efforts tend to produce better results.

The second common pitfall is failing to connect data collection with meaningful action. Data is a resource, not an operation. Without solutions that allow staff to access and apply data at scale, strategies have limited impact.

Driving Better Decisions with End-to-End Visibility

Visibility is essential for manufacturing firms to take effective action, but it is only possible if companies have access to accurate, real-time data. For instance, with real-time performance data from production equipment, businesses can anticipate maintenance needs and conduct proactive repairs to prevent downtime.

To capture and leverage real-time data, businesses need a combination of AI tools and data analytics technologies. Intelligent solutions excel at finding and capturing relevant data, while analytics applications provide context.

Using AI and analytics to drive visibility offers three key benefits:

• Proactive response: Predictive analytics can help companies improve inventory planning, reduce machine downtime, and anticipate market demand, thereby minimizing waste.
• Elimination of data silos: An AI-powered data strategy helps eliminate data silos across departments and partner networks, promoting interoperability.
• Ongoing ROI: Visibility gains from AI and big data analytics enhance operations such as inventory management and enable businesses to respond faster when disruptions occur.

Accelerating Innovation Through Actionable Insights

AI-enabled analysis can help manufacturers move beyond reactive operations. According to recent data in an article from Georgetown University, adopters of AI supply chain tools have enhanced service levels by 65 percent, and 70 percent of those using AI say that it delivers strong return on investment. This is because AI can now take on effort-intensive manual tasks, freeing up staff to explore new and innovative approaches to traditional manufacturing processes.

Key data sources for AI include

• Machine data
• Customer feedback
• R&D information

Common use cases for this data in manufacturing include digital twins, machine learning, and quality control automation. Digital twins are digital versions of physical products or components often used to simulate the behavior of physical objects, allowing companies to monitor performance and make informed decisions. Machine learning algorithms improve over time as they are exposed to more data. Finally, quality control automation leverages AI to streamline output evaluation, helping companies track product and data quality trends and take action to reduce material waste.

Creating a Culture That Prioritizes Data and Experimentation

Innovation and experimentation require cultural shifts. While data provides the fuel for change, it is only possible if manufacturing companies invest the time and effort needed to create sustainable cultural change. This requires three actions from leadership teams:

• Promote cross-functional collaboration
• Encourage experimentation and iterative prototyping
• Empower frontline teams with tools and technologies.

Roadmap for Building a Sustainable Data Strategy

While data sources and operational objectives differ across organizations, five steps are common in strategy building:

1. Assessment: Evaluate your current data collection practices, connectivity, and collaboration, and pinpoint any data silos. This helps identify valuable use cases.
2. Governance setup: Create your data governance framework. Decide where data will be stored, who will have access, which tools you will use, and how data will be tracked.
3. Data integration: With assessment and governance setup complete, begin integrating new approaches with current tools. Expect some pushback from staff and challenges with interoperability. Best bet? Slow and steady—address problems as they arise rather than trying to deploy too quickly.
4. Use case development: Define beneficial use cases, which may include improved inventory management, enhanced supply chain visibility, or proactive maintenance policies.
5. Scale: Complete steps 1–4 for several use cases. Once you are confident that the processes and policies work, start scaling your strategy across other networks and partner environments.

Pro tip: Focus on small wins powered by case-driven rollouts. These wins serve as proof-of-concept for your data strategy and are instrumental in creating frameworks that deliver value at scale.

Delivering Data Strategy: A Competitive Imperative

Data strategy for manufacturers is not just a nice-to-have—it is now a competitive imperative.

The advent of advanced IIoT, AI, and automation tools allows companies to collect, curate, and apply data in real time, empowering forward-thinking firms to discover new ways to improve operations and streamline supply chains.

In a manufacturing market defined by global instability, labor shortages, and rising demand volatility, companies that master data will lead the way in efficiency, sustainability, and innovation. M

About the author:

 

Eric Wrigley is general manager of predictive technologies at Advanced Technology Services.