The buzz around Artificial Intelligence (AI) in manufacturing is everywhere – and for a good reason. AI is a powerful tool when deployed to the shop floor, from taking quality control to a whole new level to predicting equipment failures, optimizing maintenance schedules, and interfacing with robots, the potential for AI to transform factory operations is immense. Manufacturers are increasingly looking to AI to lower cost, enhance efficiency, productivity, and innovation – especially as they navigate global supply chain disruptions and labor shortages.
However, the path to successful AI implementation isn’t always straight and narrow. Many manufacturers are discovering that while AI models offer incredible capabilities, they are fundamentally reliant on one crucial element: data. There’s a well-known principle in computing, often summarized as “Garbage In, Garbage Out” (GIGO). When it comes to manufacturing AI, this principle isn’t just a technical caveat; it’s a critical business reality.
However, the data challenge can be overcome and the sooner manufacturers start, the better. In this blog we’ll explore why a robust data strategy and diligent data cleansing are non-negotiable foundations for achieving real value from your manufacturing AI investments, especially for predictive maintenance applications.
The Data Deluge on the Factory Floor
Manufacturing is an industry drowning in data. With the proliferation of sensors, connected devices, and digital systems on the factory floor, manufacturers generate terabytes of data daily from production equipment, supply chain systems, quality control processes, and customer interactions. This data is the necessary fuel that AI models need to learn, identify patterns, and make predictions or decisions.
Yet, simply having a lot of data isn’t enough. The manufacturing data landscape is often complex and messy: data resides in disconnected silos across disparate systems, legacy equipment, and even manual spreadsheets. This data can be in many different formats, making it challenging to unify and utilize effectively. Manufacturers are adding more sensors and edge devices, further increasing the complexity of this data landscape. Despite collecting all this information, many manufacturers struggle to make sense of what the data is saying and how to use it to improve their systems.
“Garbage In, Garbage Out” in Action
The “Garbage In, Garbage Out” principle means that if the data used to train or operate an AI model is of poor quality – meaning it’s inaccurate, incomplete, inconsistent, mislabeled, or lacks necessary context – the AI’s outputs will also be poor.
The consequences of poorly trained models on the factory floor can be devastating:
- Quality Control: Training an AI model for visual inspection requires large datasets of images or videos, accurately labeled with types of defects or non-defects. If the images have inconsistent lighting, poor resolution, or incorrect labels, the AI model will learn incorrectly, leading to high rates of false positives or false negatives. In our own work we have seen how bad images – in our case a good percentage of extremely over- and underexposed images – prevent the algorithm from properly learning to detect defects.
- Predictive Maintenance: AI models for predictive maintenance analyze sensor data from machines to predict potential failures. If the sensor data is incomplete, contains erroneous readings, or isn’t properly timestamped or linked to specific equipment, the model’s predictions about when a machine is likely to fail will be unreliable, potentially leading to unexpected downtime. We found data quality (as well as availability) the main hurdle hampering the implementation of predictive maintenance solutions. Even if data is available, it is often siloed and in a format that cannot be readily ingested by AI.
- Process Optimization: Optimizing manufacturing processes using AI often involves analyzing historical production data. If this data is inconsistent in how process parameters or yield are recorded, or if data from different production shifts or lines isn’t standardized, even the most powerful AI model won’t be able to identify accurate patterns or make valid recommendations for improvement.
Data availability and quality issues are frequently cited as primary roadblocks that cause delays in AI project implementations. Many manufacturing organizations currently struggle with data integrity.
Building the Essential Data Foundation for Manufacturing AI
For manufacturing AI to truly be a game changer, a strong data foundation is essential. Digitalization is fundamentally transforming companies into data-driven enterprises where data quality is paramount. Addressing data integrity is often the critical first task.
Data must be accurate, complete – this is where data cleansing becomes job one. Data cleansing involves profiling your data sources to understand their state, cleaning inaccurate or missing entries, and standardizing formats and definitions across different systems.
Beyond just cleansing, a mature data strategy leveraging accurate data is a necessary first major step for digitalization and AI. This includes:
- Defining a clear technical blueprint that outlines the required data architectures and data requirements for your AI initiatives.
- Establishing robust data acquisition processes.
- Putting in place protocols for data security, privacy, and compliance, especially crucial with sensitive operational data generated on the factory floor.
- Planning for data growth and designing a scalable infrastructure that can handle increasing volumes and velocity of data.
- Ensuring clear ownership and responsibility for data quality across the organization.
Furthermore, AI requires connectivity and integration. Systems need to be able to communicate and share data in real-time to provide the transparency needed for effective AI deployment.
The Payoff: Realizing the Potential of Manufacturing AI
Investing the time and resources in building a robust data pipeline and focusing on data quality pays significant dividends. Relevant, clean data is essential for AI deployments that deliver tangible business value, e.g. enabling reliable predictive maintenance models that optimize schedules and reduce unplanned downtime and associated costs.
Conversely, neglecting data readiness carries significant risks, including falling behind competitors, increasing employee stress due to inefficient processes, and missing out on new market opportunities. A lack of clear ROI from AI projects is also a barrier often linked to implementation challenges, which robust data can help overcome.
Getting Started with Your Data Strategy
So, where do you begin this intimidating process of getting your data AI-ready? On a high-level, these are the steps:
- Start with Strategy: Don’t jump straight to technology. Begin by examining your business needs and assessing your current data state. Understand what data you have, where it lives, and its current quality.
- Prioritize Scalability: While you might start with a specific use case (and we also recommend that you do that), design your data collection and infrastructure with scalability in mind to support future AI initiatives.
- Begin with a Pilot: A proof-of-concept study (POC) is an excellent way to establish the value of AI. The process of implementing a POC will quickly highlight the state of your underlying data and where cleansing and governance efforts are most needed. Starting small allows you to tackle data issues in a more manageable scope while demonstrating value and building momentum.
Recognize that tackling data problems, including dealing with legacy systems and data silos, is a typical and necessary step in the AI journey for most manufacturing organizations today.
Conclusion: Data Is the Engine
AI holds incredible promise for manufacturing, but its transformative power is inextricably linked to the quality and accessibility of your data. Prioritizing a robust data strategy – focusing on cleansing, governance, integration, and establishing clear ownership – isn’t just a technical task; it’s a fundamental business imperative for successful digital transformation. By investing in data readiness today, you can build the essential infrastructure that will fuel the intelligent, efficient, and competitive factory of tomorrow.
Check in with us to discuss how we can help you overcome data challenges and successfully implement AI on the shop floor.
Other blogs that might interest you:
Seeing Is Believing: How AI is Changing Visual Inspection for Good
Challenges of AI in Manufacturing – the Perspective of an Implementation Professional