The Industrial Internet: 6 Ways Manufacturers Can Fuse Big Data, Automation and IoT for Better Operations

Rapid Costing: In many industries, manufacturing functions are considered as internal suppliers to the product management group or the sales team and, therefore, must provide cost estimates during tendering and business development cycles. Tough market dynamics require rapid costing on price indications about a particular piece of equipment, and this quick turnaround can be a decisive factor in whether the enterprise wins or loses major orders. Historical data including hit-rates, customer preferences, footprint requirements, past tendering records, executed projects and product definitions must be combined in an IIoT strategy to inform tendering feedback, reduce lead time and increase quality of tendering.

Non-Conformance Report (NCR) Analytics: Manufacturing organizations usually collect data points regarding non-conforming events that arise on the factory floor. An NCR is issued when a product, process or procedure does not comply with set standards. It can also represent a significant deficiency. An NCR is generally used as a tool to reduce errors as much as possible and keep faulty products and equipment from reaching customers. IIoT technologies can help analyze NCR data, find relationships between NCRs and support the prediction of future non-conformances. 

Plant Load Optimization: Sales and Operations Planning (S&OP) processes are the core of a manufacturing company. They allow management not only to get a handle on the business but also to create a command and control system that integrates strategic business plans and tactical day-to-day operations. S&OP helps guide daily operations and monthly plans toward long-term business goals and aligns manufacturing, suppliers and customers. Depending on the product´s lifecycle, the S&OP process can define the load forecast over time, which helps determine which products an enterprise will manufacture at which plant—and creates the basis for plant loading. This decision has implications on operational and financial performance. Historical load, industrial footprint, executed projects, scope changes and customer behavior are data points that can optimize plant loading. To understand and balance the trade-offs to optimize loading requires an IIoT strategy.

Shop Floor Operational Improvements: Manufacturers are increasingly interested in the use of low-cost sensors attached to machines for preventive maintenance and condition-based monitoring. Some are finding wireless connectivity and big data processing tools can make it cheaper and easier to collect actual performance data and monitor equipment health. For example, critical machine tools are designed to operate within certain temperature and vibration ranges. Sensors that can actively monitor and send an alert when the tool deviates from these prescribed parameters can aid in preventing malfunctions. When critical equipment fails, operations can quickly fall behind and miss on-time delivery, leading to delayed projects and cost overruns. Big data in an IIoT solution can help improve overall equipment effectiveness (OEE), minimize equipment failure and enable proactive maintenance to reduce or eliminate downtime.

Suppliers and Supply Chain: Access to real-time supply chain information helps identify issues before they happen, reduces inventory and potentially reduces capital requirements. The IIoT can help manufacturers gain a better understanding of this information. By connecting plants to suppliers, all parties involved in the supply chain can trace interdependencies, material flow and manufacturing cycle times. IIoT-enabled systems can be configured for location tracking, remote monitoring of inventory and reporting of parts and products as they move through the supply chain. They can also collect and feed delivery information into ERP, PLM and other systems.

Health, Safety and Environment: Key Performance Indicators (KPIs) for health, safety and environment (HSE) include data for injury and illness rates, short- and long-term absences, near-misses, vehicle incidents and property damage or loss during daily operations. These measurements are typically stored in myriad systems, spreadsheets and emails and are reported sporadically during management reviews or audits. Lagging indicators do not have any relational value and companies rarely perform thorough root cause analyses. A well-defined Industrial Internet and analytics strategy will help isolate and address HSE issues.