Manufacturers want to create a more digital and streamlined future to cater to the growing and diverse needs of their customers and end-users. Data-driven manufacturing is the next wave that will drive efficient, intelligent, and responsive production systems. But in order to get beyond this hype, manufacturing leaders need to understand some of its underlying challenges and associated paradigm shifts.
Data management. Photo via Shutterstock
Photo: Shutterstock
With the amount of information already being generated, collected, and stored within their organizations, you would expect industrial businesses to be well versed in unlocking and leveraging the value contained within their data.
However, that has not been the case yet – at least for large swathes of the manufacturing landscape. According to Mckinsey, the global management consulting company, manufacturers have not yet made good use of this mountain of potential intelligence; one that can bring about up to 50% reduction in equipment downtime and up to 10% improvement in company gross earnings.
One of the largest challenges of digital transformation is in the area of data management. In this article, we outline a few key aspects of overcoming data management issues in manufacturing.
Integration
Part of the integration challenge comes from the diverse nature of plant data, where variations in formats, granularity, and time periods are inevitable. Inside a plant, you'll find multiple levels of systems. For example, at the equipment level, there are PLCs, SCADAs, control systems, and data loggers; then at the enterprise level, there are the downstream decision-making and productivity tools like Computerized Maintenance Management Systems (CMMS), Business Intelligence (BI), and Enterprise Resource Planning (ERP) systems.
Most of these systems have traditionally lived on an “island”. They do one thing well, but they do so in isolation. The result is substantial complexity in connecting these various streams of data. Building bridges between them improves their performance, but is expensive, complex, and time-consuming.
Yet, it is essential for the success of modern manufacturers. Getting data to flow seamlessly across the plant and integrate it with upstream and downstream systems will create significant opportunities for actionable insights into processes and analyzing overall operational effectiveness.
Modern data management systems and tools are designed to facilitate raw data transformation in a way that is compatible with upstream and downstream systems. They come with in-built integration toolkits for most major systems using application program interfaces (APIs) created specifically for the everyday user.
Another trend is the migration of data to the “edge”, especially in the areas of IIoT. Similar to the lean technique of storing tooling at the point of use, data computation is done at the “edge,” meaning it is processed at the machine where it is generated. Insights can therefore be pushed directly to equipment operators and maintenance technicians. As data continues to proliferate, edge computing reduces the overall burden on a computer network by distributing some of the processing work to a network’s outer equipment nodes to alleviate core network traffic and improve application performance by keeping it more lean.
Bryan Christensen, CEO, Limble
The author, Bryan Christiansen is the founder and CEO of Limble CMMS.
Infrastructure
The promise of achieving significant, measurable business value from data-driven maintenance can be realized only if organizations establish a data management infrastructure that supports the rapidly growing volume, velocity, and variety of data. This includes initiatives to address data accessibility, security, and scalability without greatly impacting plant operations.
Modern technologies such as gateways for IoT devices enable remote access without compromising security. Additionally, most modern manufacturers have dedicated cybersecurity protocols and teams that vet new installation and software before onboarding and installation to ensure due diligence.
Furthermore, an increasing number of CMM systems are now cloud storage enabled. Storing data in physical servers on-site can increase redundancy risk as well as significantly drive up real estate costs. Cloud-based solutions offer an easy way out of that problem by leveraging vendor’s economies of scale while ensuring easy accessibility for an increasingly distributed workforce.
Centralization
Most legacy systems in manufacturing were designed independently as monolithic applications and weren’t designed originally to talk to each other.
Modern maintenance management technologies have introduced the flexibility to integrate multi-model data and connect them together. Not only does this allow for complex queries, but manufacturers can also trace event lineage and track data provenance to ensure quality. This enables data tracing throughout its lifecycle–its origins, the path it takes, and the transformations it undergoes. This provides visibility and allows data to be trusted as a single source of truth.
Instead of “silo”-ed data that requires manual consolidation or tracking spreadsheets data can be queried in one centralized repository.
Conclusion
Whether you call it Industry 4.0, Industrial Internet of Things (IIoT), smart manufacturing, digital transformation, or something else, the manufacturing industry is getting more data-heavy. A centralized, well-integrated, accessible, and reliable plant production database is the foundation on which connectivity, automation, and analytics can thrive.
- The author, Bryan Christiansen is the founder and CEO of Limble CMMS.
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