AIMFG Insights Compatibility Assessments Guide

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Compatibility is a key consideration for manufacturers as they go about evaluating AI systems, products and platforms. Before integrating AI with MES and ERP systems, it is useful to conduct compatibility assessments of the tools and tech available to augment production lines with existing systems and processes. A rule of thumb: The more robust the examination, the greater the chances of project success.

Compatibility assessments of manufacturing execution systems (MES), automation technologies, robotics, and human-centered processes all are the necessary first-steps when considering AI. Because they reveal aspects of organizational readiness, applying AI in areas best suited for seamless integration can lay the ground for extending AI across the breadth of processes and operations.

Working on their own, manufacturers can inventory assets and map processes, identifying sources of the data needed to drive AI applications, their formats, warehousing and access, its quality and gaps that exist in dataflows. The results will inform decisions about vendor products, about supplementing AI models with synthetic data, and about the centralization that lowers latency, providing foundations for AI best-practice.

Optimal Areas for AI

Production Processes:

AI can optimize production scheduling and resource allocation, using Data Analytics on Process Data for workflow management that increases OEE (Overall Equipment Efficiency). AI insights culled from Machine Data help MES use less energy and generate less waste on a safer shop floor.

Quality Assurance:

AI systems can detect defects, anomalies, and deviations from production benchmarks, making for consistency of product quality and reducing rework. From such diverse sources as calibrators, cameras and customer feedback, structured and unstructured data run through ML machine learning) models improves the accuracy of AI monitoring.

Supply Chain Management

With Data Analytics,  AI can optimize inventory levels of materials and packaging, forecast demand based on customer histories, supplier pricing and economic conditions, and optimize logistics for faster transport at lower cost. Compatibility allows manufacturers to expand sources of Supply Chain Data, including from suppliers and service-providers, with deeper ERP (enterprise resource planning) integration enabling moves along the value chains of customers and suppliers.

Assessing AI Compatibility

After identifying processes and operations where AI tools and tech can add value, manufacturers then can determine systems and organizational readiness for AI integration in those areas. This review should inventory existing MES, automation technologies, physical assets like robots and tools, and human-centered processes with a view to knowing whether and how they can be augmented with AI.

Because Data is central to the accuracy of AI algorithms, assessing the availability, accessibility, and quality of data required by ML models and ensuring compatibility with existing data sources and formats. Data silos may necessitate consolidation, either on-premises or in the cloud. Meanwhile, insufficient data to ensure the accuracy of the ML models that underpin AI decision-making may require supplementing with synthetic data created from proprietary flows.

Compatibility assessments should aim to identify challenges posed by data silos, systems compatibility, and interoperability constraints that can slow AI integration projects before they arise. Consultants from ERP systems providers can help with integrating AIMFG insights deeper into the business.

Evaluating the readiness of the organization in terms of skills, resources, and cultural alignment is one more measure of compatibility for  AI tools and tech. Because employee buy-in is a lynchpin of successful AI integration, the capacity for workforce education and training can dictate the size and speed of those projects.

Steps to Compatibility

Consult Internal Stakeholders

Gather IT, operations, and engineering teams to provide insights and perspectives on potential compatibility issues and integration challenges in manufacturing processes. Involve Finance in inventory and supply chain assessments, and Human Resources for upskilling.

Invest in Skills Development

Provide training and upskilling opportunities for employees to deepen their understanding of AI’s effects on processes. Alongside continuous improvement of ML models, innovation that occurs among workers and technicians dealing directly with the technology is a source of business value.

Pilot Sizing

Small-scale pilots for real-world application allow for early identification of overlooked challenges to AI integration. Manufacturers processes and environments that run on closed data loops are ideal sites for these projects, with easier mitigation instilling organizational confidence in AI.

Leveraging AI Consultants and Vendors

Engaging with AI consultants helps to identify opportunities and develop strategies that hinge on compatibility with legacy MES and ERP.  Platform operators and vendors can provide or supplement IT teams making AI interoperable with the existing production infrastructure.

By systematically evaluating existing systems, processes, data, and organizational readiness, manufacturers can identify potential challenges and mitigate risks associated with AI integration. Through collaboration with internal stakeholders and leveraging expertise from third-party vendors, manufacturers can streamline the compatibility assessment process and pave the way for effective deployment of AI.