Revealing the Truth: How Accurate is Your ERP Data on Machine Effectiveness?
In pursuing industrial excellence, understanding the true performance of your machines is crucial. Yet, many companies rely heavily on Enterprise Resource Planning (ERP) systems for performance data, which might not always reflect the real scenario (e.g., data are collected manually and not continuously updated). This reliance can lead to significant discrepancies between real and measured Overall Equipment Effectiveness (OEE).
Case in Point: Incorrect Manual Data Entry
In an example from the automotive industry [1], manual data entries were found to be error-prone due to a combination of human error and misinterpretation of machine status. Workers sometimes recorded downtime inaccurately, either because they misunderstood the nature of the stoppage or because the interface was not user-friendly. This led to overestimating machine availability in the ERP system, skewing performance metrics and operational decisions [2].
Misalignment with Actual Machine Conditions
Another common issue occurs in the food and beverage sector, where ERP systems recorded production quantities based on standard cycle times. However, slight variations in raw material quality and environmental conditions frequently adjusted actual cycle times, resulting in the ERP system reporting higher or even lower efficiency than was indeed achieved.
The Reliability of Direct Machine Data
Direct data collection from machines through sensors and integrated systems provides a more accurate and timely representation of machine performance. For instance, real-time data collected directly from equipment in the semiconductor industry allowed for immediate adjustments in production processes, significantly reducing waste and increasing yield. This direct data approach minimizes human error and provides a more truthful picture of machine effectiveness.
Conclusion
Relying solely on ERP data might give you a distorted view of your equipment's effectiveness. By incorporating direct data collection methods, such as IoT sensors and automated systems, you can obtain a more accurate and reliable measure of your machine's true performance. This shift not only enhances operational efficiency but also supports more strategic decision-making based on precise data.
Our Solution
Snaption is at the forefront of transforming how companies measure and understand the effectiveness of their machines. By integrating measurements directly from machines and analytics, Snaption provides a robust solution that bypasses common pitfalls associated with ERP-based data reporting. Snaption’s DigiFactor directly interfaces with machines to capture real-time and continuous operational data, reducing the risk of human error and providing a minute-by-minute account of machine performance. At Snaption, we recognise the value of ERP information and propose integrating real machine data with ERP insights. With this information and leveraging workers’ expertise, companies can achieve the highest levels of accuracy in measuring OEE. This enhances decision-making, which in turn supports the optimisation of operations and guides investment strategies.
[1] Breiter, Stephan, Jonas Gottwald, and Julia C. Arlinghaus. "Manual Collection of Data on Disruptions: Determinants to Increase the Intention to Use." IFAC-PapersOnLine 55.10 (2022): 952-957.
[2] Hedman, Richard, and Peter Almström. "A state of the art system for managing time data in manual assembly." International Journal of Computer Integrated Manufacturing 30.10 (2017): 1060-1071.
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