Automotive manufacturing must stay nimble to satiate the demands of a dynamic market. Operational challenges will continue to abound in a complex, globally competitive environment and factories that support multiple automotive models with customized optional features will require an agile workflow.
This means that production processes will become less linear and more modular. Single purpose fixed machines must now have flexibility of tasks for different batch jobs within the production environment and turn-around times for setup and tear-down will be constrained and rapid. Furthermore, collaborative robots must be intelligent enough to safely interact with humans at various stages of production, and line-down situations, where equipment must be unexpectedly repaired, can negatively leverage production efficiency for an entire factory.
Automotive manufacturers are turning to the Industrial IoT, among other Industry 4.0 solutions, to solve these complex problems.
Deployment of an automotive IoT strategy in pursuit of increased factory automation efficiency should be approached holistically. It should be considered within the context of the existing infrastructure, human resources, quality, process improvements, and operational decision making. A targeted approach can be taken to those areas of manufacturing that require the most efficiency improvements. In order to properly architect an IoT system for automotive manufacturing, we must first start with the end in mind by answering two fundamental questions. First, what problem or response needs to be solved? Second, what predictor(s) do we need in order to solve it? This will drive the design architecture from the top down.
The integration of an IoT system can be introduced in layers as needed for ROI initiatives. However, a complete design at the onset will discover the sensor hardware, software, and analytical models needed to maximize productivity. Dashboard evidence of factory analytics can highlight the gaps between execution and an ideal factory model.
At the machine level, an asset digital twin encompasses detailed engineering data to simulate the function of an equipment asset. From this simulation, analysis can be performed to extract insight into real-world behavior. Its capability can provide performance data across many operating contexts within their own manufacturing environment. One of the best use case examples of an asset digital twin is when it is used to collect reliability data for better understanding of potential failures so that they can be forecasted and managed in a predictable fashion.
Digital replication of an entire automotive factory can identify areas of improvement to show the ideal optimal performance of many complex systems. The entire process can be supported by an enterprise digital twin simulation that can be compared in near real-time to measured results. Not only will the data from this simulation provide insights into logistics efficiencies, but machine optimization through flexible adaptations can be monitored for fine tuning of the operation.
Quality workmanship within the automotive industry is second to none. It cannot be an afterthought, but rather quality must be inherent within the design architecture of automotive production. With an ultra-low defect rate requirement, where 1 ppm can be improved upon, quality across the entire manufacturing process is paramount. This drives not only material input quality, but optimization of machines and processes during assembly. By monitoring machine performance activity within an IoT infrastructure, real-time process improvements can be realized with workflows that provide insights for actionable quality improvements. This, in-turn, will drive higher quality products across the manufacturing platform.
Automotive manufacturers can struggle to find skilled talent to support increasingly complex machinery. Maintenance can no longer be based upon a run-to-break model, but rather continuous optimization. Although maintenance apprenticeship programs are being expanded, networked sensors across machines can predict their own maintenance and offer solutions for operational improvements. Predictive maintenance and forward-looking prescriptive optimization can be targeted with analytical models that compare actual activity to digital simulation. A strategy that is reactive to what happened on the prior day’s shift can no longer be optimal. Proactive decisions from an IoT deployment to improve upon tomorrow’s activity will be driven by an infrastructure that generates insightful information from machines and operational data.
Practical automation within a factory must drive meaningful return on investment that makes business sense for the enterprise. For legacy incumbent equipment, a new IoT implementation may not always be the best course of action in all areas of the business. The IoT goal must be to achieve a better way to work, not merely to deploy new enterprise systems. However, existing automotive equipment does not need to be an encumbrance to deploying a new IoT strategy. Wireless infrastructures for new equipment can now be layered on top of legacy enterprise systems without disrupting robust wired communications systems. Seamless interaction between the old and the new can be achieved with the proper IoT hardware and networking strategy.
An automotive IoT strategy will need a platform that leverages the expertise already within the factory. Employees with hands-on knowledge already know what poor machine performance looks like when they see it. The insights from an IoT solution should expand on this experience for the staff to utilize this expert team to extract the best insights. The MachineMetrics industrial IoT platform integrates analysis tools that transform raw machine sensor data with time series analytical models. This knowledge can be digested to transform these insights into systems that are driven by data, not just by people that have been given first person experiences.
MachineMetrics dashboards are intuitive for drag and drop type placement within the environment. Training makes logical connections from what has already been experienced on the production floor. Alert triggers to management, floor supervisors and factory workers allow decisions across the organizational hierarchy. Data model algorithms are further trained with new inputs to converge on solutions faster in the future. The data models can become the experts of the operation as additional intelligence is gained about real-world behaviors. MachineMetrics integrates a complete industrial IoT platform for machine monitoring, condition monitoring, predictive maintenance and process optimization for actionable insights within automotive manufacturing.
Discover how MachineMetrics helps automotive manufacturers leverage machine data to make better, faster decisions in real time.
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