For many manufacturers, performance losses significantly impact total production within a given timeframe. These losses represent the gap between theoretical production capacity and actual output, often caused by factors such as micro stops and speed losses. For example, a typical pharmaceutical manufacturer with a theoretical production capacity of 10 units may produce only 8 units due to such performance losses.
Analyzing performance losses at a granular, line-by-line level is crucial for identifying bottlenecks. However, understanding whether micro stops, speed losses, or both are contributing—and to what extent—can be challenging without actionable data. Effective analysis requires automated reporting systems powered by real-time sensor data. This allows executives to pinpoint lines associated with performance losses and implement targeted improvements.
The new Sensor Deep Dive feature has been designed to address this need, empowering manufacturers to enhance operational efficiency. In this article, we will introduce Sensor Deep Dive and explore its key applications, demonstrating how it can serve as a valuable tool for tackling performance issues and improving overall equipment effectiveness (OEE).
An Introduction to Sensor Deep Dive
Sensor Deep Dive is a powerful component of SCW.AI’s OEE Tracker, designed to provide manufacturers with in-depth insights into production activities. It captures and displays key metrics such as changeovers, unplanned downtime, and runtime, while also tracking production counts during operational phases. By analyzing the distribution of sensor data, including zero counts during production runs, Sensor Deep Dive identifies performance losses such as micro stops and speed losses.
One significant advantage of Sensor Deep Dive is its ability to gather and display automated, real-time data, eliminating the need for manual reporting. It offers granular insights, allowing executives to examine the production speed of each line on an hourly basis for detailed analysis. For a broader perspective, users can switch to daily overviews, helping them identify chronic bottlenecks and recurring inefficiencies.
Like all SCW.AI Digital Factory Solutions, Sensor Deep Dive presents data in a user-friendly format. It uses intuitive color codes in the activity bar—for example, red for unplanned downtime, grey for idle time, pink for changeovers and green for runtime—to ensure that insights are easily understandable at a glance.
A dedicated segment for micro stops is also included, customizable to factory-specific definitions. For instance, if micro stops are defined as interruptions lasting less than five minutes, longer durations will be classified as unplanned downtime and should be deducted from availability losses instead of performance losses. In this case, Sensor Deep Dive visually distinguishes micro stops with green highlights, while longer interruptions are marked in pink, enabling precise analysis and targeted action.
When paired with other features of the OEE Dashboard, Sensor Deep Dive enables executives to gain a comprehensive understanding of loss factors on the shop floor. This holistic view empowers them to take informed, targeted actions to maximize output and profitability.
4 Key Applications of Sensor Deep Dive for Manufacturers
In this section, we explore 4 practical use cases of Sensor Deep Dive to highlight its benefits for line leaders and executives.
1. Identify and Eliminate Micro Stops with Precision
With Sensor Deep Dive, identifying micro stops becomes straightforward. These brief interruptions in production are visualized on the micro stops bar as green highlights, based on factory-defined thresholds. During these periods, sensor data shows zero production, making it easy for line leaders and executives to pinpoint their occurrence.
Identifying micro stops is critical for optimizing shop floor performance, particularly because traditional methods, such as Excel or paper-based tracking, often fail to capture these short events. Moreover, manufacturers frequently underestimate the importance of micro stops due to their fleeting nature.
While a single micro stop may seem insignificant, their cumulative effect can be substantial, and constitute up to two-thirds of total performance losses. Eliminating micro stops can boost throughput by 2% to 15%, depending on the initial OEE level. Beyond productivity, micro stops may also signal quality concerns; for example, imperfect machine operation during these interruptions can lead to rework and negatively affect first-pass yield, driving up costs.
Once lines with frequent micro stops are identified, solutions can be tailored to address root causes. In some cases, simple measures of total productive maintenance such as more frequent machine lubrication, can significantly reduce these interruptions.
Labor activities can also contribute to micro stops. Issues like incorrect line cleaning or improper setups may cause recurring inefficiencies. Using Labor Performance Report, manufacturers can analyze runtime variances to identify patterns linked to specific workers. If consistent issues are observed, targeted training may be necessary. Generative AI tools can assist by creating effective training materials, such as videos or step-by-step guides, to address skill gaps.
In more complex cases, micro stops may stem from systemic issues that require advanced solutions, such as machine learning-driven predictive maintenance. By analyzing a range of manufacturing and maintenance KPIs, predictive maintenance tools can generate proactive schedules to maximize runtime and minimize interruptions per Deloitte.
2. Detect and Address Speed Losses in Real-Time
Another key application of Sensor Deep Dive is detecting and addressing speed losses—situations where the production count per minute falls below the target speed. Speed losses are often accompanied by fluctuating product count data, as machines configured to operate at a constant speed typically exhibit minimal variability in healthy production lines. Ideally, manufacturers should observe a steady, nearly linear production trend with minimal standard deviation (See Image Below).
Similar to micro stops, the causes of speed losses can vary widely. In some cases, they may stem from non-optimal machine configurations, often linked to operator errors during setup or adjustments. Alternatively, speed losses may indicate maintenance needs, such as wear and tear on machinery that prevents it from running at peak efficiency.
3. Enhance Production Quality and Minimize Rework
Sensor Deep Dive serves as a powerful tool for achieving world-class manufacturing standards by enhancing production quality and reducing costs. As previously mentioned, fluctuating product counts per minute and frequent small stops are not only cost concerns but also indicators of an unsteady production line. Such instability suggests the line is struggling to operate as planned, which can lead to increased scrap rates, rework, and compromised overall quality.
Once lines with fluctuations are identified, Digital Factory Platform enables further examination of quality-related inputs using tools like OEE Waterfall Analysis and Station View. These tools provide deeper insights into quality metrics, allowing manufacturers to correlate specific lines with high scrap or rework rates. If a pattern emerges, immediate corrective actions—such as targeted maintenance or operator training—can be implemented to address the root cause and restore optimal performance.
4. Distinguish Availability Losses from Performance Losses
In many factories, particularly those in the early stages of digital factory transformation with no OPC connections, operators are often required to manually input information about whether a line is running, being cleaned, or undergoing maintenance. This manual process can lead to mislabeling availability losses as performance losses, creating inaccuracies in loss data and hindering effective problem-solving.
For example, if a line stops for a duration exceeding the defined threshold but is incorrectly labeled as a run phase in the digital system, it may appear as a micro stop and a performance loss. In reality, this scenario represents unplanned downtime and should be classified as an availability loss. Similarly, if a changeover takes longer than planned, it may delay the start of the run phase, with the resulting lack of production mistakenly attributed to performance losses instead of the true cause: an availability loss (See Image Below).
When loss data is inaccurate, manufacturers may address the wrong bottlenecks, wasting resources and failing to resolve the underlying issues. Sensor Deep Dive eliminates these errors by providing real-time, automated data that accurately reflects the actual phase of production. Moreover, it encourages operators to label activities correctly, as the visual clarity of the tool allows line leaders and executives to instantly identify discrepancies. This improved transparency fosters greater accountability on the shop floor and ensures that manufacturers can focus on the right issues to enhance efficiency and maximize uptime.
Maximize Your Run Time with SCW.AI
SCW.AI’s modular Digital Factory Platform offers a turnkey, end-to-end solution for manufacturers embarking on or advancing their digital transformation journey. Designed to be simple, scalable, and fast, this platform provides a suite of tools to meet a wide range of manufacturing needs. Its modules address critical aspects such as:
- Monitoring: Gain real-time insights into production activities, labors and assets.
- Execution: Streamline operations and ensure seamless production workflows.
- Compliance: Maintain adherence to regulatory and operational standards.
- Planning: Optimize production schedules and resource allocation.
- Intelligence Automation: Leverage AI to drive smarter decision-making.
- Automated Data Collection: Eliminate manual data entry with real-time, sensor-driven updates.
To learn more about how our solutions, including Sensor Deep Dive, can revolutionize your operations, we invite you to get in touch with us.
Experience the transformative potential of SCW.AI’s Digital Factory Platform firsthand—book a demo with us today.
