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Predictive analytics is a new frontier for business intelligence, offering manufacturers the opportunity to forecast future outcomes and plan for them before they happen.

This is accomplished through tools such as Microsoft Azure, which allows manufacturers to make more accurate predictions. The data generated from this type of analysis can be used for forecasting demand, improving product quality, reducing time-to-market, and increasing profits.

The Microsoft Azure ecosystem offers several of the most powerful tools manufacturers can use to leverage the power of predictive analytics.

Azure Machine Learning Studio for manufacturing predictive analysis

Manufacturing companies are always looking for ways to increase their operational efficiency and quality. Predictive analytics can improve your decision-making process at every level of your company, all while reducing human error. Additionally, it can help optimize plant floor operations and other aspects of a business by applying statistical algorithms that determine which variables impact productivity and how these variables interact with one another. Having access to insights into possible future scenarios enables you to better prepare for what lies ahead, such as demand spikes or changes in input costs, such as fluctuations in energy prices.

This type of cutting-edge analysis enhances performance by identifying relationships between different key metrics early on. It can also help companies assess the current market environment against historical conditions, spotting notable events that could affect their future performance. The possibilities are vast, but for algorithms to be effective, they need to be tested properly beforehand.

Microsoft Azure has built an end-to-end, cloud-powered solution for manufacturing predictive analysis. With Azure Machine Learning Studio, customers can easily combine data from across their operational systems and third-party services using a guided interface that enables them to create, test, and deploy predictive AI solutions.

Azure Machine Learning also offers a wide range of visual tools for companies to create predictive models. These models can be integrated with applications such as Azure SQL Database and Dynamics 365, thereby extending the core capabilities of these products.

Mapping processes: getting the most out of your Azure predictive analytics solution

By using machine learning algorithms that are based on modern statistical techniques, it’s possible to identify patterns in historical data sets containing millions of variables and predict how these patterns will interact with one another in the future. This mapping process is particularly useful for companies operating across multiple locations or divisions because they have access to huge amounts of raw data which can be difficult for them to manage without proper tools.

The mapping process consists of 4 main steps, data gathering, data pre-processing, algorithm implementation, and result interpretation. As various variables are manipulated, statistical tests implement standardised mathematical techniques to highlight potential value-adding associations between different datasets.

Once these connections have been identified, they can then be ranked according to their relevance or weight. Once the most relevant correlations have been highlighted by the system, manufacturers will be able to adjust their strategies accordingly and make more informed decisions.

Azure Stream Analytics enables up-to-the-minute decision-making capabilities

Azure Stream Analytics is a fully managed cloud service that enables real-time analytics on streaming data. It takes advantage of the elasticity, scalability, and intelligence of Azure to provide users with fast insights from multiple data sources. Stream Analytics helps businesses unlock insights from their streaming IoT data enabling them to react faster and improve responsiveness of their operations through:

  • Real-time processing and analytics of millions of events per second: Big Data doesn’t have to mean big delays, the ability to capture and process streaming data from IoT sensors can happen in real-time.
  • Predictive analytics with Azure Machine Learning: Data scientists, business analysts, and developers can use the same tools, including R and Python integration, to build powerful predictive models that help predict equipment failure or maintenance requirements. Operationalize those models using a simple drag-and drop experience for deploying them as cloud services.
  • REST API allowing fine grained control over processing logic: Customers want flexibility when it comes to access their stream analytics jobs through APIs or directly through portal dashboards. Stream Analytics provides an open REST interface for customers who prefer accessing their jobs programmatically.
  • Stream Analytics scaling options: A key benefit of Azure Stream Analytics is the ability to scale from 10s, up to 100s GB data into processing topology. This allows organizations to process big data sets with similar ease as small datasets.

Getting a competitive edge

Predictive analysis in a manufacturing setting can be summarised as the examination of trends from existing data to predict future business outcomes. These may be in the form of performance or quality data, but the key objective is to find a link between different datasets and extract meaningful information through statistical analysis – with a view to creating a competitive advantage.

To get started with Azure and predictive analytics, contact the experts at Hanu today for your free consultation.