We understand your challenges
Operational systems provide a wealth of data, but most organizations are unable to take full advantage of that wealth. We understand how to unlock that data and use it to improve and optimize your operations.
Some challenges include:
- Data Silos: Operational data is often siloed in operational systems, only transmitted to corporate IT systems at aggregated levels, and only when it is needed in an ERP.
- Data Engineering: Business intelligence and analytics require different data architectures and formats. Even if your organization has prepared data for business intelligence, it may not be ready for analytics.
- Feasibility: The value and feasibility of analytical solutions may be unclear within your organization.
- Embedding ML: Implementing machine learning to provide feedback and insights in a real-time control system is challenging, primarily due to the numerous often overlooked requirements involved.
As a result of these challenges, organizations may be missing demand or supply expectations, running inefficient operations, or failing to foresee predictable failures.
How we can help
Analytics can play a crucial role in helping optimize operations, reduce costs, and improve overall efficiency. Today, an analyst spends an estimated 80% ensuring the data is valid, complete, and accurate and then 20% on actual data analysis. Streamline’s objective is to flip this ratio, enabling analysts to spend 80% of their time on valuable data analysis.
With the power of cloud computing, vast amounts of industrial data can be collected, processed, and analyzed in real-time. This enables the identification of patterns, trends, and anomalies that might not be visible through traditional methods. By using advanced analytics tools, Streamline’s team can generate actionable insights that can help our clients make informed decisions about their operations in a rapidly changing environment.
The benefits of analytics
Analytics and machine learning can help predict when equipment will fail, allowing you to take proactive measures to prevent downtime and avoid costly repairs. This can be done by analyzing data from sensors and other monitoring systems to identify patterns and anomalies that may indicate a problem.
By integrating these predictions into maintenance management systems, maintenance tasks can be completed before the equipment fails.
Predict demand for energy, and align your trading, maintenance, and operational decisions to minimize affected demand, or maximize profits. This can be done using historical demand and supply data, weather patterns, and other factors that may impact demand. Accurate demand forecasts can be a catalyst for asset optimization, supply chain optimization, customer insights, and energy efficiency.
Optimize the performance of your assets, including power plants, pumps, processing facilities, and other infrastructure by analyzing data from sensors and other monitoring systems to identify opportunities to optimize efficiency or output.
Gain insights into your customers, including their usage patterns, preferences, and behaviours by analyzing data from smart meters, customer surveys, billing, and other sources to predict churn, non-payment, opportunities for cross-sell or upsell, bill shock, and improved customer service.
Analytics can help optimize supply chain operations, ensuring that raw materials, finished products, and other goods are delivered on time and in the right quantities with improved efficiency and reduced waste.
Monitor the quality of industrial processes, ensuring that products meet the required specifications to ensure that products are manufactured to the highest standards. Identify and predict factors that cause quality problems so they can be corrected with minimal impact to quality or output.
Identify inefficiencies and energy waste in industrial processes and experience significant cost savings by integrating analytic insights with cloud-based energy management systems.