Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution

Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution

Alexius A. Emejom (University of the People, USA), Carl Burgess (University of North Texas at Dallas, USA), Donna Pepper (Benedictine University, USA) and Joan Adkins (Colorado Technical University, USA)
DOI: 10.4018/978-1-5225-7865-9.ch001
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The fourth industrial revolution utilizes artificial intelligence by automating large quantities of numbers to increase the chances of project success. The Project Management Institute lists examples of project outcomes, including but not limited to the Pyramids of Giza, the Great Wall of China, the Panama Canal, and the placement of the International Space Station into Earth's orbit. This chapter highlights how the fourth industrial revolution (Industry 4.0) impacted the evolution of agile project management practices. It discusses how these could be applied in conjunction with traditional waterfall project management or as a standalone approach. Topics discussed include a definition and elements of project management, waterfall vs. agile project management, transitioning to agile methods, developments in agile project management, agile practices, and leading agile projects and project managers.
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Definition And Elements Of Project Management

A project is defined as a set of unique temporary interrelated activities that are executed within a fixed time (schedule), meeting a certain cost, and following limitations (scope) to achieve a specific goal (Project Management Institute, 2017b). PM is the application of knowledge, skills, tools, and techniques to project activities that meet the desired project requirements on time, on budget, and within a defined scope (ibid, 2017).

Although used by industries for many years, PM received minimal recognition until the 1950s and 1960s (Loucks, 2008). Over the last 25 years, PM has adapted to changes in society by increasing professionalism in special projects (Thomas & Adams, 2005). Organizations set and achieved goals with PM through an iterative four-step process of plan, do, check, and act (PDCA). These steps fall into the following process groups in PM: (1) initiating; (2) planning; (3) executing, monitoring, and controlling; and (4) closing (Project Management Institute, 2017b). These phases help the project manager understand the project scope, recognize challenges, and resolve issues connected to PM (Melton, 2004). This process has also assisted businesses and industries to recognize (rather than repeat) mistakes (Owen & Burstein, 2005).

The initiating process group is the first phase for PM. During this phase, the project manager communicates with other members of management to establish objectives and determine their project needs (Suttle, n.d.). When the team has decided on whether to accept or reject the project, they may use descriptive analytics, predictive analytics, and prescriptive analytics (Kelly, 2017). Descriptive analytics provide data aggregation and data mining to inform team members on information concerning the past. Predictive analytics use statistical modeling to understand the future. To assist in possible outcomes, project managers may use prescriptive analytics to optimize and simulate algorithms (Kelly, 2017).

Key Terms in this Chapter

Cyber Physical Systems: A system used to manage projects like robotics, cloud computing, and other autonomous frameworks.

Waterfall: First introduced in 1970 by Dr. Winston W. Royce, this software development uses a particular cascading of steps.

Industry 4.0: Referred to as the integration of computers and automation, Industry 4.0 is the meeting of autonomous computer systems.

Agile Methodologies: A framework with four ideologies: (1) response to change; (2) adaptive planning; (3) speedy delivery; and (4) constant improvement. This project management approach is implemented through software development.

Software Development: A set of processes used for testing, designing, conceiving, and fixing frameworks and/or other software applications and components.

Cloud Computing: An Internet service platform (generally, a pay for storage).

Cognitive Computing: A system that analyzes data by looking for and identifying potential conflicts, patterns, solutions, and suggestions.

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