Business Problem Solving Case: Ge Bets On The Internet Of Th

Business Problem Solving Case Ge Bets On The Internet Of Things And Bi

General Electric (GE) is transitioning from traditional manufacturing to a technology-centric business strategy that emphasizes the Internet of Things (IoT) and big data analytics. GE aims to establish itself as the world’s leading Digital Industrial Company by utilizing sensor-generated data from industrial machines—such as gas turbines, jet engines, and locomotives—to monitor performance, prevent failures, and optimize maintenance. The company has allocated $1 billion for installing sensors, connecting devices to the cloud, and analyzing data to enhance productivity and reliability, with the overarching goal of becoming a major software enterprise (Gillin, 2017; Winig, 2016).

At the core of this strategy is GE’s Predix platform, launched in 2015, designed to collect, analyze, and visualize data from industrial sensors in real time. Predix’s open standards allow for flexible deployment on any cloud infrastructure, accommodating large-scale industrial data and enabling the development of custom applications by GE and third-party developers (Babcock, 2015). The platform supports diverse applications, including healthcare, energy, and manufacturing, demonstrating its versatility beyond industrial use cases (Saran, 2015).

GE’s shift to digital solutions transforms its business functions by focusing on predictive maintenance, operational efficiency, and customer service enhancement. For example, GE’s deployment of Predix on jet engines has enabled proactive maintenance scheduling, reducing unplanned downtime and saving costs—an airline that benefits from increased engine lifetime and fuel efficiency exemplifies this (Leonard & Clough, 2016). Similarly, renewable energy provider Invenergy employs GE’s Asset Performance Management, built on Predix, to predict turbine failures and optimize asset management, preventing costly outages (Lugassy, 2016).

Furthermore, GE’s approach supports strategic decision-making at operational and executive levels. Tools developed on Predix facilitate day-to-day maintenance decisions and long-term planning, such as evaluating risk in pipeline management or scenario modeling for plant operations (Saran, 2015). GE’s digital initiatives also extend to analyzing data from non-GE assets, integrating diverse equipment across entire plants to deliver holistic insights and improve efficiency—highlighting a move toward outcome-based pricing and comprehensive asset management (Gillin, 2017).

The value of Predix-supported decisions is significant: predictive maintenance reduces operational costs and minimizes downtime; fleet analytics enhance equipment lifetime and operational efficiency; and risk assessments prevent environmental hazards and economic losses. These capabilities exemplify how data-driven insights directly contribute to increased revenue, reduced costs, and competitive advantage for GE and its customers (Brynjolfsson, Geva, & Reichman, 2016).

GE is progressively becoming a software company by developing and selling analytics applications, establishing a developer community, and offering cloud-based platforms accessible to clients. Jeffrey Immelt’s vision to rank among the top 10 software firms by 2020 underscores this transformation, despite competition from Amazon, Google, IBM, and Microsoft in the IoT platform space (Agrawal, Gans, & Goldfarb, 2017). Whether GE will sustain its position depends on its ability to capture and retain data ownership, differentiate its analytics offerings, and expand beyond its existing hardware legacy (Lacity & Willcocks, 2016).

Regarding location analytics, businesses utilize geospatial data, sensor-based environmental data, and customer location information. For example, geospatial data can optimize delivery routes, sensor data can assess environmental risks affecting pipeline integrity, and customer location data can enhance targeted marketing campaigns. Each type of data informs strategic decisions, operational improvements, and risk mitigation efforts in diverse industries (Davenport, 2014).

The different systems that acquire and represent knowledge include expert systems, neural networks, and genetic algorithms. Expert systems encode human expertise into rule-based formats to support decision-making; neural networks simulate brain-like processing for pattern recognition; and genetic algorithms employ evolutionary principles to optimize complex problems (Alavi & Leidner, 2001; Burtka, 1993). These technologies enable organizations to automate and improve decision processes across different contexts, from diagnostics to scheduling and design optimization (Ask, Facemire, & Hogan, 2016).

Paper For Above instruction

GE’s strategic transformation into a digital industrial powerhouse reflects a profound shift in how manufacturing, maintenance, and operational decisions are made through advanced data analytics and IoT. This evolution is rooted in leveraging sensor data, cloud computing, and analytics platforms like Predix to create better service, optimize assets, and develop new revenue streams. The company’s embracing of software’s potential signifies a long-term vision of integrating industrial operations with digital intelligence, making predictive analytics indispensable for industrial growth (Gillin, 2017).

The core of GE’s digital strategy revolves around the Predix platform, which epitomizes the convergence of hardware and software. By collecting billions of sensor data points from turbines, engines, and pipelines, Predix enables real-time monitoring and proactive maintenance, Drastically reducing costly unplanned outages and extending equipment lifespan (Leonard & Clough, 2016). For instance, GE’s Fleet Analytics, based on Predix, can identify operational inefficiencies across its entire fleet and suggest maintenance actions, resulting in substantial cost savings and efficiency gains (Lugassy, 2016). Such predictive capabilities exemplify the immense value of deploying IoT and big data analytics to support decision-making.

This strategic shift impacts numerous business functions. Maintenance becomes predictive rather than reactive, thereby reducing downtime and operational costs. Asset management is now driven by data-powered insights, allowing companies to anticipate failures before they occur. Customer relationships are also enhanced through outcome-based service contracts, where GE guarantees performance levels and offers tailored solutions based on data analytics (Brynjolfsson, Geva, & Reichman, 2016). In pipeline management, GE’s risk assessment tools allow operators to visualize vulnerabilities caused by aging infrastructure or adverse weather conditions, facilitating better resource allocation and risk mitigation (Saran, 2015).

Decision support facilitated by Predix spans operational and strategic levels. Operational decisions, such as scheduling maintenance or adjusting plant parameters, are optimized based on real-time sensor data. Strategic decisions involve evaluating risks or scenario planning, such as assessing the impact of environmental conditions or operational variables on pipeline safety or plant efficiency (Agrawal, Gans, & Goldfarb, 2017). Each decision supported by predictive analytics adds value by reducing costs, enhancing safety, and improving overall productivity.

GE’s evolution into a software company is evident through its development of Predix and related applications, ongoing creation of a developer ecosystem, and delivery of cloud-based analytics solutions. Despite formidable competition from technology giants and startups, GE’s robust industrial focus and long-term asset management contracts position it advantageously in the digital transformation of industry (Lacity & Willcocks, 2016). Nonetheless, data ownership and security remain strategic challenges, as clients may prefer to manage their own data or choose alternative platforms.

Location analytics provides vital data insights across industries. Geospatial data aids logistics optimization and environmental risk assessments; sensor data from environmental monitoring helps detect potential pipeline failures or environmental hazards; customer location data supports targeted marketing and service delivery. These applications exemplify how different types of location-related data influence strategic, operational, and tactical decisions (Davenport, 2014).

The types of systems that acquire and encode knowledge serve distinct purposes. Expert systems efficiently capture human expertise and rule-based decision logic. Neural networks excel in recognizing patterns and making predictions from complex, unstructured data. Genetic algorithms are optimization tools that simulate natural selection to solve complex problems involving multiple constraints (Alavi & Leidner, 2001; Burtka, 1993). These technologies collectively enable organizations to automate complex decision processes, improve accuracy, and innovate solutions across industries (Ask, Facemire, & Hogan, 2016).

References

• Agrawal, Ajay, Joshua S. Gans, and Avi Goldfarb. (2017). What to Expect from Artificial Intelligence. MIT Sloan Management Review.
• Alavi, Maryam, and Dorothy Leidner. (2001). Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly.
• Babcock, Charles. (2015). GE Predix Cloud: Industrial Support for Machine Data. Information Week.
• Brynjolfsson, Erik, Tomer Geva, and Shachar Reichman. (2016). Crowd-Squared: Amplifying the Predictive Power of Search Trend Data. MIS Quarterly.
• Gillin, Paul. (2017). Industrial IoT Revs Up at Big Renewable Energy Provider. Silicon Angle.
• Lacity, Mary C., and Leslie P. Willcocks. (2016). A New Approach to Automating Services. MIT Sloan Management Review.
• Leonard, Devin, and Rick Clough. (2016). How GE Exorcised the Ghost of Jack Welch to Become a 124-Year-Old Startup. Bloomberg Businessweek.
• Lugassy, Holly. (2016). GE Leverages Pivotal Cloud Foundry to Build Predix, First Cloud for Industry. CloudFoundry.org.
• Saran, Cliff. (2015). GE Predictive Analytics Optimises Irish Power Electricity Production. Computer Weekly.
• Winig, Laura. (2016). GE’s Big Bet on Data and Analytics. MIT Sloan Management Review.