OLSOM | WHITEPAPER
data exchange with other systems in real time. Through no-code and low-code data engineering features, AGW breaks down silos and enables the cross-system data consolidation essential for sophisticated analysis.
To address master data accuracy, AGW incorporates anomaly detection capabilities that alert users to potential data quality issues before they compromise decisions. This proactive approach ensures that insights driving operational improvements rest on solid foundations.
Ultimately, the vast amount of data AGW collects( sub-second, bi-directional data exchange that cloud-only architectures cannot accomplish), combined with proper contextualisation between disparate data sources, serves as a differentiator in the market. OLSOM customers are therefore able to unlock unprecedented and hidden analytics that can be used to maximise optimisation.
“ We managed to reduce downtime by 30 % and no longer have supply or assembly issues”
OLSOM Customer
Measurable impact The value of this approach becomes clear in real-world applications.
A project OLSOM delivered for a major manufacturer in Tangier Automotive City in Morocco demonstrates the tangible benefits of data-driven optimisation.
Prior to AGW implementation, the facility’ s entire pre-production area – comprising approximately 40 machines including injection molding machines( IMMs) and decoration machines( metallisation, hard-coating, base-coating) operating around the clock – suffered from unstable performance, limited visibility and frequent unplanned downtime that directly impacted the assembly line. The production team lacked the insights needed to identify problems early or address root causes effectively.
By connecting machines from four different brands through AGW, the manufacturer enabled continuous collection of more than 50 parameters per machine, including pressure, temperature, cycle time, clamping force and injection speed. Real-time dashboards, downtime analysis and historical reporting provided complete visibility into overall equipment effectiveness( OEE), quality trends and scrap rates.
The results were substantial: a 30 % reduction in downtime based on production team data, significant improvements in machine availability and process stability, enhanced communication between
56 March 2026