Case Study 61: Real Bets On The Internet Of Things And Big ✓ Solved

Case Study 61read Ge Bets On The Internet Of Things And Big Data Ana

Read "GE Bets on the Internet of Things and Big Data Analytics." Answer the questions: Identify and write the main issues discussed in the case (who, what, how, where, and when). List all indicators (including stated "problems") that something is not as expected or desired. Briefly analyze the issue with theories found in your textbook or other academic materials. Decide which ideas, models, and theories seem useful. Apply these conceptual tools to the situation.

As new information is revealed, cycle back to sub-steps a and b. Identify the areas that need improvement (use theories from your textbook). Specify and prioritize the criteria used to choose action alternatives. Discover or invent feasible action alternatives. Examine the probable consequences of action alternatives. Select a course of action.

Create a design and implementation plan/schedule. Create a plan for assessing the action to be implemented. Provide a strong conclusion or summary.

Writing Requirements: 3–5 pages in length (excluding cover page, abstract, and reference list). APA format. Use the APA template located in the Student Resource Center to complete the assignment. Use the Case Study Guide as a reference point for writing your case study.

Additionally, for discussions, address the following: What is a spatial join? What primary characteristic distinguishes a spatial join from an attribute join? Minimum 120 words for discussions.

Paper For Above Instructions

The case study titled "GE Bets on the Internet of Things and Big Data Analytics" presents an in-depth analysis of GE's strategic initiatives in leveraging the Internet of Things (IoT) and big data technologies to enhance operational efficiency, drive productivity, and unlock new business opportunities. The critical issues in the case revolve around the company's approach to digital transformation, the challenges posed by data management and integration, and the need to foster an organizational culture that embraces innovation and agility.

Main Issues in the Case Study

The central issues identified include GE's significant investment in digital infrastructure, the transformation of its legacy systems, and the integration of IoT technology across multiple sectors such as aviation, healthcare, and energy. With a focus on data-driven decision-making, GE aims to optimize its assets and services while addressing the increasing competition in the industrial landscape. The question of 'who' pertains to GE's leadership and workforce, indicating a need for upskilling in data analytics and IoT competencies.

Further, the 'what' includes the strategic shift towards becoming a digital industrial company. The 'how' relates to the processes employed to integrate IoT solutions and the challenges in this integration process. 'Where' signifies the operational environments in which these changes take place — notably manufacturing and service delivery. Finally, 'when' encapsulates GE's initiative, which gained momentum in the early 2010s and continues to evolve.

Indicators of Problems

Several indicators suggest that outcomes have not been as expected. These include the slow adoption rate of IoT technologies by traditional sectors, the complexity of data integration from various sources, and the cultural resistance to change within the organization. For instance, the challenge of training personnel to handle and analyze vast amounts of data effectively is a significant barrier. Additionally, the initial high investment costs and the struggle to demonstrate immediate ROI on digital initiatives have also emerged as critical challenges.

Theoretical Analysis

Utilizing theories from organizational change management, such as Kotter’s Eight Steps for Leading Change, can provide insight into GE's approach. For instance, creating a sense of urgency about digital transformation within the workforce is essential to overcoming resistance and fostering engagement. Furthermore, the Technology Acceptance Model (TAM) can be applied to analyze factors that influence employees' acceptance of new technologies, including perceived ease of use and perceived usefulness.

Areas for Improvement

Identifying areas requiring improvement involves assessing both technological and human factors. From a technological perspective, investing in user-friendly interfaces and streamlined data integration tools can facilitate smoother transitions to IoT solutions. On the human side, developing continuous training programs that empower employees to utilize big data effectively can increase productivity and innovation. Theories such as the Diffusion of Innovations can provide frameworks for understanding how new ideas and technologies spread within the organization.

Criteria for Action Alternatives

When selecting action alternatives, it is vital to establish criteria that prioritize the feasibility, cost, scalability, and potential impact of proposed solutions. A systematic approach to evaluate each alternative against these criteria will help GE make informed decisions that align with its strategic objectives and corporate vision for digital transformation.

Feasible Action Alternatives

Feasible action alternatives may include cross-departmental collaborations to enhance data sharing, investing in cloud-based platforms for better data accessibility, and engaging in partnerships with tech firms specializing in IoT solutions. Each alternative should be assessed for its potential to support GE's digital strategy and empower its workforce with data analytics capabilities.

Examination of Probable Consequences

Examining the consequences of these alternatives reveals that enhanced collaboration may lead to improved business outcomes through innovative solutions, while investment in technology could yield long-term cost savings and operational efficiencies. However, there might be resistance from employees adjusting to new workflows and technologies, which must be carefully managed.

Selected Course of Action

The selected course of action involves prioritizing investment in training initiatives, upgrading data management systems, and fostering a culture of innovation. This approach aims to align employees with GE's digital objectives while equipping them with the necessary skills to leverage IoT and big data effectively.

Implementation Plan

The design and implementation plan includes specific milestones over the next three years. Year one focuses on establishing training programs and foundational upgrades to IT infrastructure. Year two will involve piloting IoT solutions in selected departments followed by a company-wide rollout in year three, accompanied by ongoing assessments to evaluate the impact on performance metrics and employee engagement.

Conclusion

In conclusion, GE's commitment to embracing the Internet of Things and big data analytics is crucial for maintaining its competitive edge in today's industrial landscape. By identifying key issues, analyzing them with relevant theories, and developing a strategic action plan, GE can not only address current challenges but also position itself as a leader in digital transformation. A successful implementation of this strategy will require continuous assessment and adaptation to ensure that both technology and personnel are adequately prepared for the future of industry.

References

  • Chui, M., Manyika, J., & Miremadi, A. (2016). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly.
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review.
  • Fitzgerald, M., Kruschwitz, N., Ryan, A., & Dubner, D. (2014). Embracing Digital Technology: A New Strategic Imperative. MIT Sloan Management Review.
  • Porter, M. E., & Heppelmann, J. E. (2014). How Smart, Connected Products Are Transforming Competition. Harvard Business Review.
  • Rogers, E. M. (2010). Diffusion of Innovations (4th ed.). Simon and Schuster.
  • Schmidt, G. (2015). From big data to big knowledge: The role of information quality. Journal of Business Research.
  • Simon, H. A. (1997). Administrative Behavior: A Study of Decision-Making Processes in Administrative Organization. Free Press.
  • Vial, G. (2019). Understanding Digital Transformation: A Review and a Research Agenda. The Journal of Strategic Information Systems.
  • Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.
  • Yoo, Y. (2010). Computing in Everyday Life: A Call for More Innovative Research. ACM SIGMIS Database.

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