Benchmark, monitor, and improve manufacturing productivity
Benchmark & Monitor
From visibility across your enterprise to real-time productivity monitoring on the shop floor, establish a benchmark for manufacturing productivity so you can focus on the biggest opportunities for improvement.
Key Performance Indicators
Your process is different, your tags are different, but your KPIs should be consistent. Whether you track Quality, Yield, First Pass Yield, First Time Yield, or any other metrics, Productivity KPIs offer flexibility with completely configurable formulas. For improved network-wide benchmarking and issue identification, the KPI model supports comparisons across assets, process areas, and even facilities. Productivity KPIs are linked to all underlying data sources so you can understand questions like “What product type has the lowest OEE?”
Configurable dashboards put the power of common data models into the hands of your entire organization. Dashboards are self-service so you can create views specific to each facility, process area, or even line. Utilize dashboards to measure productivity and track production on the shop floor or remotely. Configurable dashboards support a number of visualizations for monitoring like event timelines and histograms along with more advanced analysis to detect production issues in real-time like variance analysis and SPC.
Analyze & Improve
Automatically calculate the ideal production settings by continuously analyzing production runs, identifying the settings that lead to the best results. Prescribe recipes to operators dynamically by detecting current conditions and identifying production settings that are out of specification.
Optimal Machine Settings
Dynamic recipes capture the optimal machine settings for every combination of conditions encountered, including the type of raw materials and environmental conditions, as well as for different types of output (e.g., grades of paper). Recipes are highly configurable and allow for outcome mix optimization. For example, dynamic recipes can determine production settings to minimize cost while maximizing quality and throughput. Recipes automatically update with built-in feedback loops. As your process improves, so too do the recipes.
The Operator CoPilot application prescribes Dynamic Recipes to operators in the control room and on the factory floor. The Operator CoPilot automatically detects current conditions (e.g., raw materials, desired output grade, humidity) and provides recommended machine settings to optimize for the desired outcomes. All machine parameters are tracked in real time, and those that are out of recipe specification are surfaced for immediate operator action.
Start improving manufacturing operations immediately with off-the-shelf manufacturing applications for data exploration, correlation analysis, and process variability studies.
Applications support univariate and multivariate analyses of time-series data, which range from describing behavior over time to investigating correlations between parameters. The Analysis tools are designed to identify variables that exhibit behaviors that may be impacting production processes, uncover relationships between variables, and track performance over time.
Analysis tools support click-through analysis, which recommends workflows through the different applications to uncover additional information.
Perform exploratory data analysis on a common data foundation with real-time applications for data exploration. Data Exploration applications include Data Visualization, Descriptive Statistics, Event Timeline Analysis, and Timeline Analysis.
Understand the relationship between process parameters across your manufacturing operation and process inputs with process outcomes. Correlation applications include Correlation Heatmap Analysis, Curve Fit Analysis, Scatterplot Matrix Analysis, and Time-Series Correlation Analysis.
Unstable or Higher Variability
Identify process parameters that are unstable or have higher expected variability, causing production issues like availability and quality loss. Process variability applications include Statistical process Control and Time-Series Correlation.