Published on 12/16/2016 | IoT Index
Industry 4.0 technologies also enable improved operations. In the “plan,” “source,” and “make” stages of the value chain, various physical-to-digital and digital-to-physical connections can transform planning, support, and factory operations.
When planning for production, manufacturers often encounter a host of uncertainties across the manufacturing value chain. IT and OT can support several transformations in this area:
Demand sensing and planning - Collect and analyze data to persistently monitor demand patterns; track movement of goods throughout the supply chain for purposes of demand planning; proactively suggest product replenishment to customers as needed;
Supply planning and supplier transformation - Enable vendors to monitor and own inventory in the OEM supply chain; develop better understanding of supplier capacity levels and lead times; use outside market information to improve pricing decisions;
Outbound network optimization - Track inventory in forward networks; alter routes for distribution vehicles in real time based on unforeseen events; allow customers to track delivery status by exact location.
Demand sensing and planning using IT (for example, sensors, signal aggregation, optimization, and prediction) enable manufacturers to gather data throughout the value chain. Data can be analyzed to uncover patterns, track movements, and, ultimately, understand what customers want, and where—so they can better plan to provide it at the right time and place.
For example, Ridgeline Pipe Manufacturing, a manufacturer of polyvinyl chloride (PVC) pipes, dealt with constantly changing customer demand and short lead times. The company needed to anticipate and plan in the face of uncertain demand, rapidly adjust to unforeseen changes, and reduce production changeover time. Using legacy systems, waste, costs, and inflexibility had risen to unacceptable levels. The company adopted a flexible production platform, in which automated production controllers managed the manufacturing equipment while providing access to information on diagnostics and performance. The system also analyzes production data to offer predictive failure analytics.
Factory: Creating a digital link between OT and IT
Perhaps no other segment better encapsulates the physical-to-digital transformation inherent in Industry 4.0 than the intelligent factory. The industry 4.0-enabled factory utilizes physical-to-digital technologies such as augmented reality, sensors and controls, wearables, and the Internet of Things to track movement and production, monitor quality control, and manage the tooling life cycle, among other capabilities. In this way, Industry 4.0 on the factory floor can enable enhanced capability effectiveness, production asset intelligence, and activity synchronization and flow:
Enhancing labor productivity and effectiveness - Enhance capabilities with regard to fabrication and assembly; labor efficiency tracking; monitoring worker movements and productivity; and real-time safety monitoring of both workers and equipment;
Production asset intelligence - Use proactive sensing and quality control for detecting defects; predictive maintenance of factory machinery; and tooling life cycle management;
Activity synchronization and flow - Use technology for dynamic routing during the production process; virtual build simulation to maximize effectiveness of engineering changes to the production floor; accommodation of varying environmental factors that might impact machines.
Industry 4.0 technologies can enable safer conditions for workers, enhancing labor productivity and effectiveness. Joy Global, a mining equipment manufacturer, added about 7,000 sensors to its remote-controlled extraction device, enabling it to mine in extremely deep mineshafts—areas often dangerous to workers who typically perform the work. Similarly, Boeing uses a positioning system to pinpoint worker location and assess the status of their safety harnesses, improving worker safety.
Beyond labor productivity and safety, IT/ OT can transform product asset intelligence. Harley-Davidson, for example, uses smart systems to detect defects during production processes. A smart system in its York, Pennsylvania, plant monitors equipment performance and initiates action autonomously. Upon detection of measurements beyond acceptable ranges, the machinery is automatically adjusted, preventing malfunctions.
Once a part or product has been developed, manufactured, shipped, and sold, Industry 4.0 technologies can impact support in at least three key ways (table 8).
Learning the causes behind a failure can enable manufacturers to more effectively address the root of the problem, rather than its symptoms. For example, Schneider Electric examined both maintenance and historical data collected over the course of one year for a 110 MW steam model turbine which had required regular, ongoing maintenance for an escalating series of ongoing breakdowns, realizing that technicians had been addressing symptoms rather that root causes for a quality issue. Analysis enabled Schneider to address the root cause—thermal expansion problems—before they led to “symptoms”—bearing vibration—that caused equipment shut- downs. The company estimates that predictive maintenance offers millions of dollars in potential savings along with far fewer days of equipment downtime.
In another predictive analytics example, Caterpillar is partnering with a company named Uptake, analyzing data gathered through telematics devices in its machinery to predict failures and engage in proactive repair. The companies see future opportunities to monetize this capability by offering new data products and services to customers.
When responding to field failures, wearables and augmented reality can allow remotely located technicians to walk users through maintenance procedures. An industrial equipment manufacturer, for example, faced challenges as it expanded its operations to China, including increased operational costs and more frequent downtime of machinery. These issues were, in large part, rooted in a shortage of seasoned talent to train employees within the new manufacturing facilities. The manufacturer piloted a smart-glass, wearable technology so that remote experts could see alongside the equipment operators in the facility, and offer step-by-step instructions and training. These improvements were also accompanied by risk reductions in the overall production process due to better quality management:
Aiding productivity and quality of field repair - Enable remote, “see-what-I-see” field support; leverage digital overlay augmented reality for training; combine digital and mobile technologies for product manuals;
Predicting part, product, or service failure - Use advanced analytics to ensure proper selection of tools for field technicians; use customer data to identify common problems and adapt designs; enact end-user smart training;
Responding in a timely, accurate, and effective manner to that failure—sometimes preemptively - Use data to plan support-network allocation; optimize spare parts inventory mix; 3D-print spare parts and tools.
