What is manufacturing analytics?

Manufacturing analytics refers to the use of operational and event-related data gathered during the manufacturing process to help organizations improve and streamline their manufacturing operations. It draws on production, equipment, quality and sensor data to track performance, find the causes of downtime and defects, and optimize the shop floor.

As manufacturing processes become automated and logistics and inventory control systems move online, manufacturers have access to more data than ever before on which to make decisions. Manufacturing analytics can provide several benefits, helping organizations:

• Reduce cost and boost revenue
• Manage supply chain risks
• Improve quality and accuracy
• Optimize manufacturing line efficiency

How to use manufacturing data to optimize KPIs

Like many organizations, manufacturers rely on key performance indicators (KPIs) to evaluate the effectiveness of their operations. Manufacturers track metrics related to cycle times, production volumes, maintenance costs, plant utilization, production costs, product quality/defect density and more. All of these metrics are potentially complex and interrelated. For example, a metric such as production cost can be impacted by everything from input cost of goods (COGS) to inventory carrying costs to shipping costs to on versus off-peak power costs.

Business analysts and floor managers typically use analytic dashboards to monitor operational performance and track KPIs related to the manufacturing process. Dashboards can display metrics related to overall equipment effectiveness (OEE), warehouse optimization, process or machine downtime and more.

Driven by a technical revolution and the Industrial Internet of Things (IIoT), equipment on automated factory floors is increasingly instrumented, equipped with multiple sensors. Process engineers can monitor manufacturing performance and receive alerts if equipment parameters exceed critical thresholds. Using predictive analytic techniques, organizations can use gathered sensor data to optimize maintenance schedules, maximize equipment longevity and avoid costly process downtime. Insights generated by analyzing data from manufacturing operations can also help organizations reduce error rates, identify defects quickly and optimize their supply chain and logistics.

Key manufacturing metrics

Common shop-floor KPIs include:

• Overall equipment effectiveness (OEE): availability × performance × quality, the standard measure of how productively equipment is used.
• First pass yield (FPY): the share of units produced correctly the first time, without rework or scrap.
• Scrap and rework rate: the share of output that is discarded or must be reprocessed.
• Cycle time: the time required to complete one unit or batch.
• Unplanned downtime: time lost to equipment failures or unexpected stoppages.
• Mean time between failures (MTBF) and mean time to repair (MTTR): indicators of equipment reliability and maintenance responsiveness.
• Capacity utilization: actual output relative to potential output.
• Schedule attainment: the share of planned production completed on schedule.

Types of manufacturing analytics

Like other forms of analytics, manufacturing analytics is commonly grouped into four types:

• Descriptive analytics shows what happened on the line, such as output by shift, OEE by machine or defect counts by product.
• Diagnostic analytics explains why it happened by correlating downtime or defects with specific equipment, materials, operators or process parameters.
• Predictive analytics uses IIoT sensor data and historical patterns to anticipate equipment failures or quality drift before they occur.
• Prescriptive analytics recommends what to do next, such as scheduling maintenance windows, changing process settings or adjusting production schedules.

Manufacturing analytics vs. operational analytics

Operational analytics is the broader practice of analyzing day-to-day business operations across functions such as sales, finance, HR and supply chain. Manufacturing analytics is a specialized form focused on production: equipment, lines, plants and product quality. It relies heavily on data sources unique to the shop floor, such as manufacturing execution systems, process control systems and sensor streams. Movement of materials into and out of the plant is covered by supply chain analytics.

Challenges of manufacturing analytics

• Siloed systems: ERP, manufacturing execution, quality and maintenance systems often store related data separately.
• High-volume, high-velocity data: sensor and machine data arrives continuously and in large volumes.
• Mixed IT and OT environments: combining business data with operational technology data requires reconciling different formats and identifiers.
• Latency: delays between events on the floor and insight on a dashboard limit the ability to react.

Best practices for manufacturing analytics

• Connect production data with ERP data so shop-floor performance can be tied to cost and revenue.
• Standardize KPI definitions such as OEE across plants for consistent comparison.
• Keep detail at the machine, batch and shift level to support root-cause analysis.
• Start with high-impact use cases such as downtime reduction or defect detection, then expand.

Incorta solutions for manufacturing analytics

To be effective, organizations need an end-to-end view of all of their manufacturing-related data. Incorta's Direct Data Platform™ delivers a rich environment for manufacturing analytics, enabling organizations to quickly ingest, enrich and analyze data from multiple sources. These sources include ERP, manufacturing systems, process control systems and even streaming data from robots and IoT sensors. Incorta's Direct Data Mapping® technology avoids the need for dimensional data models and time-consuming ETL processing.

Manufacturing analytics can also be leveraged for preventive maintenance, inventory risk management, fault detection, defect detection and more, all of which contribute to improved efficiency of manufacturing operations, reduction in costs and increases in revenue.

Intuitive, dynamic dashboards enable manufacturers to analyze and act on their business data with unmatched speed, simplicity and accuracy. With access to real-time production data, manufacturers can not only make better, faster decisions, but they can also improve their overall efficiency, quality and competitiveness.