Best Practice for AIMFG Data Management

Source: Freepik

The following report is culled from vendor advisory and presents best practices that can guide manufacturers when implementing AI in their processes and operations. It is framed in the specific challenges faced by manufacturers and offers guidance that smooths adoption and integration of AI models for process optimization.

Because data management is mission-critical when optimizing manufacturing processes with AI, following best practice during adoption and integration extracts more value from AI tech and tools. The complexities involved demand up-front focus and planning in order to surmount challenges of AI implementation for manufacturers.

Effective data management increases modelling accuracy as it eliminates process steps. And with the transparency that breeds confidence in decision-making based on the outcomes of AI models.

That said, focusing more on data and less on models is the fastest route to process optimization with AI. Consolidation of data reduces the barriers, especially for for Gen AI and LLMs, to synthesizing and analyzing data.

Types and Sources of Manufacturing Data

Manufacturing processes generate an array of data from many sources, including data that is synthesized from processes and operations to supplement those datasets. Capture and categorization of those flows is central to effective data management. They include:

Sensor Data: Collected in real time from the sensors that monitor machines and processes, Sensor Data enriches Machine Data and Maintenance Data. Quality Sensor Data delivered in volume enables greater precision in digital twinning of production processes and models.

Machine Data: Results from the measures of the performance of physical assets are Machine Data. From energy consumption to process times, Machine Data insights give manufacturers greater control of production costs.

Maintenance Data: The information recorded in upkeep and repair logs, including the results of testing and annecdotal accounts, are Maintenance Data. Coupled with Machine Data, Maintenance Data give manufacturers more accurate assessments of machinery health.

Supply Chain Data: Information about the logistical flow of materials from point-of-origin to the factory is Supply Chain Data. Using Supply Chain Data to optimize routing cuts costs for transport.

Inventory Data: The measure at stock level of the materials needed for production informs Inventory Data. Using Inventory Data, manufacturers can cut their costs for carry and warehousing.

Process Data: Sourced from manufacturing workflows and procedures, Process Data illustrates procsses and operations. Manufacturers can use Process Data to reveal where automation and elimination will improve efficiency.

Production Data: The quantity and rates of production are Production Data. When measured against Maintenance Data, Production Data guides improvements in productivity.

Quality Data: The results of benchmark testing of production standards and product reliability are known as Quality Data. Manufacturers can use Quality Data to lower defect rates in with real-time checks and improve customer satisfaction.

Customer Data: Drawing on at order records and customer feedback, Customer Data is used to establish patterns in behavior over time. Using Customer Data, manufacturers can enrich Quality Data and increase the accuracy of demand forecasts.

Best Practices For AI Data Management

Successful AI implementation relies on effective data management, from adoption and integration of tools to implementation and automation of the machine-learning models AI uses to predict outcomes in processes and operations. However, it’s a challenge for manufacturers who for proprietary considerations are deprived of access to wider spectra of Process Data with which to train models.

Thus, making AI more intuitive and user-friendly requires manufacturers to wrangle and supplement data, including with synthetic replication of proprietary data, so that AI is better able to detect and remediate variance at source, including in the models it runs. manufacturers that follow best practice in managing synthetic data can limit the hallucinations and data drift that undercut the accuracy of AI predictions and undermine decision making.

Data Collection and Integration: manufacturers should establish robust processes for collecting and integrating data from various sources, including sensors, IIoT devices, production systems, and external databases. This data may be structured (e.g., from sensor readings and production logs) or unstructured (e.g., from maintenance reports and imaging). Automated collection tools and standardized formats can streamline this process.

Data Quality Assurance: With quality essential for accuracy in AI models, implementing validation and cleansing techniques will identify and correct errors, inconsistencies, and missing values. Assessing data quality in collection and analysis will increase accuracy of AI predictions and decision-making.

Data Security and Privacy: Implementing encryption, access controls, and data anonymization techniques safeguards data from unauthorized access and misuse. It also facilitates regulatory compliance.

Model Training and Testing: Using representative and diverse datasets avoids biasing results and improves model generalization. Synthetic data generation techniques can augment limited or biased datasets, enabling more robust model training.

Model Deployment and Monitoring: Deploying AI models in production environments requires careful planning and monitoring to ensure optimal performance and reliability. Continuous monitoring of model outputs and feedback loops can help identify drift, anomalies, or performance degradation over time, enabling timely adjustments and improvements.

Beyond Best Practice

Just as best practices for data collection, integration, quality assurance, security, and model deployment create sources of value for manufacturers leveraging AI to improve process efficiency. Support for AI adoption and integration can unlock its fuller potential. This includes:

Investing in Data Infrastructure: The infrastructure capable of handling large volumes of diverse data types may include cloud-based storage and computing resources, data lakes, and scalable data processing pipelines. Trade-offs between latency and cost hinge on accessibility and actionability of manufacturing data.

Developing Data Governance Policies: Establishing clear policies and procedures ensures compliance with reregulations and standards. This includes defining data ownership and retention, access and control.

Embracing Collaboration: Foster a collaborative culture in which knowledge is shared among data scientists, engineers, and domain experts leverages collective expertise and insights. Cross-functional teams can better identify data-driven opportunities and address operational challenges.

Investing in People: Along with the sector’s unique AI challenges, the industry-wide shortage personnel versed with AI requires manufacturers to develop training programs, certifications, and partnerships with educational institutions for upskilling existing employees. Recruiting from diverse talent pools can bridge the skills gap in data management and AI modeling.

Staying Agile: Dynamic and evolving manufacturing environments demand agility and adaptability in data management practices. Manufacturing should embrace iterative approaches, feedback loops, and continuous improvement in response to changing requirements and emerging opportunities.

Effective data management is crucial for the successful implementation of AI systems in manufacturing optimization.  With careful planning and strategic execution, manufacturers can navigate AI’s complexities and their unique challenges to drive innovation and growth.