The future of manufacturing rests squarely on the shoulders of Big Data. The lure of low-cost implementation of high-end technologies using the Internet has companies around the globe embracing data-driven manufacturing.
The opportunities this presents for efficient and responsive production are potential game changers, but before your job shop jumps on the Big Data bandwagon it’s important to understand that there are some significant challenges on the way to successful implementation.
The reality is that data-driven manufacturing isn’t going to replace traditional processes in one fell swoop. As the slow but steady change to Internet-connected production takes place across industries, manufacturing plants, and job shops, consideration must be given to:
Shifting from Time-triggered Production to Event-triggered Production
Today’s production systems are planned, with production runs initiated at specific intervals triggered and monitored through ERP and MES systems. The entire process—from pulling materials through delivering products to customers—is governed by time. Data-driven manufacturing, on the other hand, is event-triggered, meaning production doesn’t start until a data signal within the system indicates customer demand (an order is placed). The complicated network of equipment, data collection, and electronic inter-communication can not only slow system response times but also cause system instability that engineers then must routinely deal with—a drain on time and resources.
Making the Transition from Data Exchange to Data Sharing
ERP systems and computer-aided design (CAD) have long benefitted manufacturing, simplifying many procedures and processes, including design finalization and manual export of manufacturing and process data. However, these “transition-oriented” ERP systems were built to be independent and don’t necessarily “talk” to one another. Lacking system integration, taking it up a level to actual Big Data sharing instead of limited data exchange could cause production-derailing miscommunication among design, production, distribution, and selling.
Integrating Legacy Systems
The advanced technologies around data-driven manufacturing are essential, but there can be a disconnect between them and well-established and proven systems. Since job shops often have multiple systems in place, revamping them to accommodate data-driven manufacturing is an expensive undertaking that could also introduce risks to data maintained in legacy systems.
The Internet of Things (IoT) is a central component of data-driven manufacturing, providing the wireless connections among equipment and devices foundational to production. The traditional IT cloud network is well-proven and protected by authentication policies and rules. However, industrial control systems often have unique protocols and data gateways that aren’t yet offered these same protections—putting your data and job shop in jeopardy of being hacked or otherwise compromised.
Overcoming these challenges and enabling “smart” manufacturing is not just a matter of adjusting to new technologies, equipment, and processes; it’s imperative knowledge shared among industries, agencies, manufacturers, and job shops to fill any gaps that may exist between existing methodologies and the Big Data wave.
Participating in online networks of professionals from industry, research, academia, and governmental disciplines can foster open communication and leverage expertise to:
- Define and discuss obstacles that emerging industrial technologies and innovations present
- Develop platforms to model, share, and innovate among those who understand industrial needs and those who can model and solve impediments to meeting those needs
- Be thought leaders in smart manufacturing policymaking that may shape overarching regulations, funding, and resources related to data-driven manufacturing
Big Data is going to play an increasingly big role in your job shop. Contact a Wipfli expert today to discuss how you can prepare for the impact and make a successful transition.