Microsoft SQL Server R Services, a new R-based analytics feature in SQL Server 2016, helps Microsoft IT manage our supply chain with insights that drive more efficient manufacturing operations and better customer service. We’ve compiled a few top scenarios for R Services in supply chain management—including linear programming to predict best outcomes, text analysis of customer feedback, machine learning to create predictive maintenance models, and association rule mining to refine shipping processes.
Wayne Applehans, Senior Product Marketing Manager, talks with Dave Langer, Microsoft IT Principal Program Manager, about how Microsoft IT is improving supply chain management with SQL Server R Services, a new feature in Microsoft SQL Server 2016. Along with predictive analytics and machine learning, R-based analytics—such as linear modeling and clustering—help us discover business insights that increase efficiency in our logistics management, inventory strategy, cost optimization, and demand forecasting.
At Microsoft, we’re redefining how and where we work, and how we stay secure. We manage staggering amounts of data and embrace self-service tools. We’re more mobile and productive than ever. Our global Finance team is at the forefront, driving insights with Power BI analytics, adopting Skype for Business unified communications, using Office 365 Enterprise E5 advanced security and threat protections, and managing data discovery and compliance—for an intelligent, collaborative, and more secure work experience.
At Microsoft, we want to use data to influence every decision. Our IT executives use business intelligence to help inform decision making and effective communication. Executive Insights dashboards—powered by Power BI—intuitively visualize data, facilitate deeper analysis, and help us make more informed decisions. MyAnalytics quickly analyzes—in aggregate—consumption and actions taken on qualified IT executive group email messages. And MyAnalytics data lets them fine-tune their communication strategies.
To avoid costly problems in the supply chain and production line, manufacturers must monitor and optimize their operations. For Microsoft, hardware manufacturing for Microsoft Surface, HoloLens, and Xbox is a multibillion-dollar business. But curating production data took time away from proactive decision-making. Microsoft IT and the Microsoft supply chain used Microsoft Power BI and data analytics to transform operations in one factory, which led to higher productivity and faster anomaly detection.
Meeting company sales targets and accurately forecasting sales revenue are critical to the success of Microsoft and our customers. To help sales teams make informed decisions and accelerate opportunities, Microsoft IT uses predictive analytics models, Azure Machine Learning, and algorithms like latent semantic analysis and regression analysis. These models don’t replace human judgment—they augment it with seller feedback and ongoing model retraining, which result in analytics-based insights.
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