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This white paper presents a structured comparison of two foundational project management methodologies—Waterfall and Agile. It examines Waterfall’s linear, phase-based approach and contrasts it with the adaptive, iterative nature of Agile, shaped by the principles of the Agile Manifesto. By exploring key frameworks, practical applications, strengths, limitations, and adoption challenges, the paper equips organizations with the insights needed to align their project management strategies with dynamic business environments and evolving market demands.
As organizations seek to accelerate value delivery, improve collaboration, and remain responsive to change, the choice of project management methodology becomes critical. Agile (with Scrum as a popular framework) and Waterfall, represent two contrasting approaches. While Waterfall is sequential and structured, Agile emphasizes iteration, collaboration, and customer responsiveness.
This white paper explores both the methodologies, highlighting key differences, practical implications and real-world applications.
The Waterfall methodology is one of the earliest software development and project management methodologies. Its linear, phase-driven approach makes it ideal for projects with clearly defined goals and stable requirements.

Each stage flows into the next: Initiating, Planning, Executing, Monitoring & Controlling, and Closing. With minimal iteration, Waterfall relies heavily on upfront planning and documentation. The methodology aligns with traditional project management principles and includes a detailed breakdown of 49 processes distributed across five process groups and ten knowledge areas.


Waterfall remains effective for projects where requirements are fixed and outcomes are predictable—such as infrastructure, construction, or compliance-driven environments.

Image source – “The PMP exam” book by Andy Crowe


Agile emerged as a response to the rigidity of the traditional Waterfall which often led to delays, cost overruns, and products that failed to meet user needs.

These values are supported by 12 guiding principles that advocate continuous delivery, technical excellence, simplicity, and team empowerment.
Agile promotes adaptive planning, evolutionary development, early delivery, and continuous improvement. It prioritizes collaboration across cross-functional teams and close stakeholder engagement throughout the lifecycle.

Agile project management has moved beyond its roots in software development to become equally relevant for mechanical and systems engineering projects. Its iterative and adaptive approach allows teams to respond to change during the development lifecycle, delivering higher-quality products that meet customer needs. Agile fosters accountability, innovation, and continuous improvement, while ensuring that flexibility does not come at the cost of control or predictability. In recent years, agile practices, tools and techniques have gained significant momentum, especially in engineering and software projects. While Agile emphasizes iteration and adaptability, systems engineering ensures disciplined development and delivery of capabilities through structured processes. Combining these approaches strengthens program outcomes, embedding agility into technical rigor.
Transitioning to agile, however, can be challenging, for organizations rooted in traditional project management. Adopting Agile often requires process redesign, especially when embracing DevOps models where development and operations teams collaborate closely to deliver and qualify products.

Agile is not a single framework but a philosophy executed through various methods:

Agile methodology-based engineering leverages system and module models todefine, design, analyze, and validate solutions. These models enable virtual prototyping, iterative development, and efficient communication while reducing reliance on traditional document-heavy practices. The diagram below indicates the Agile system lifecycle model.
A project management framework for engineering projects can be established by analyzing and integrating Agile practices with systems engineering processes and their tools and techniques. Model-based Systems Engineering (MBSE) plays a key role by linking requirements, structure, and behavior with broader system concerns such as safety, security, reliability, and performance



Organizations applying Agile in engineering have realized measurable outcomes:

These results demonstrate that Agile’s non-sequential, incremental approach enables portions of a project to be delivered while others are still under development. This reduces the risk of late defect discovery and ensures that flexibility is embedded across lifecycle stages.

Waterfall is best suited for defined, low-uncertainty projects. Agile thrives in dynamic settings requiring frequent course corrections and stakeholder input.

Scrum Agile to develop valued solutions



There has been evidence that an engineering company where Agile methodology implemented along with integrated project management gained direct benefits like:

The advantage of using agile methods in system engineering is that it follows a non-sequential approach during execution and delivers systems deliverables from one phase to another rather than delivering the final product at the end of a project. This means that part of a project can be open for use while others are under contraction ensuring that defects are identified and addressed on time. The proposed system implementation framework indicates that flexibility is the basis of agile development methodology. For an organization to adapt to changes and still have the ability to deliver a successful product, it is important to identify where flexibility lies on the system and account for it in different life cycle stages of the system ensuring that it is built into the agile development process which will improve the organization able to adapt to changes while delivering a successful product.

Applying Agile in engineering projects enhances flexibility, improves decision-making, and ensures customer satisfaction. At its core, engineering value management is about balancing customer needs with the resources used to meet them. Robust systems remain stable under small changes, adaptive systems adjust to maintain stability under stress, and agile systems evolve rapidly and cost-effectively.
Integrating Agile with systems engineering allows organizations to build systems that are not only robust and adaptive, but also capable of responding quickly to changing conditions. Rather than conflicting with traditional systems engineering, Agile complements it, ensuring relevance in today’s environment of rapidly advancing technologies and competitive pressures.

NVS Narayana Murthy Namburi
NVS Narayana Murthy Namburi, is an engineering graduate with over 16 years of experience in the automotive, rail domains, specializes in automation and customization, design optimization and product development. His expertise spans end-to-end design and deliveries. He is certified Project Management Professional (PMP®), Professional Scrum Master1 and SAFe® Scrum Master, Google project management, Data Science, Technology leadership program and many more. Adept at fostering team collaboration, managing stakeholder relationships, and delivering high-quality products on time. With a track record of delivering high-quality outcomes and recognized for technical leadership and has contributed to diverse international projects.
Cyient (Estd: 1991, NSE: CYIENT) delivers intelligent engineering solutions across products, plants, and networks for over 300 global customers, including 30% of the top 100 global innovators. As a company, Cyient is committed to designing a culturally inclusive, socially responsible, and environmentally sustainable tomorrow together with our stakeholders.
For more information, please visit www.cyient.com
The implementation of the EU AI Act follows a phased timeline to ensure stakeholders have sufficient time to adapt and comply. Below is a general overview based on the regulation’s staged rollout:
AI is rapidly transforming the healthcare industry by enhancing diagnostic accuracy, optimizing treatment plans, streamlining workflows, and improving patient outcomes. Here are some of the major use cases of AI in healthcare, categorized by domain:
Integrating AI into medical devices presents complex compliance challenges, particularly due to the dual regulatory landscape governed by the EU AI Act-2024/1689, and the EU MDR-2017/745 or EU IVDR-2017/746. The intersection of AI functionality and medical safety introduces both technical and procedural hurdles.
AIMD classified as High Risk as per EU AI Act shall comply stringent requirements such as human oversight, transparency, robustness and post-market monitoring
When an AI system is integrated into a medical device or constitutes a standalone AI-enabled medical device, the conformity assessment process is not handled separately under the EU AI Act. Instead, it is embedded within the existing regulatory pathway defined by the EU Medical Device Regulation (MDR 2017/745) or In Vitro Diagnostic Regulation (IVDR 2017/746).
The EU AI Act (2024/1689) marks a transformative step in establishing a robust and harmonized robust regulatory framework for artificial intelligence across the European Union. For the healthcare sector particularly medical device manufacturers integrating AI, this regulation introduces not only new compliance obligations but also opportunities to drive innovation within a well-defined legal and ethical structure.
By adopting a risk-based approach, the Act ensures that AI systems, especially those used in critical sectors like healthcare, are subject to appropriate oversight and accountability. It mandates transparent, safe, and human-centric AI while promoting public trust and technological progress.
To meet these evolving expectations, manufacturers, developers, and deployers of AI systems must align their internal processes with both existing medical device regulations (e.g., EU MDR/IVDR) and AI-specific obligations under this new law.
Proactive compliance will involve:
Ultimately, the EU AI Act not only safeguards individuals but also lays the foundation for sustainable and responsible digital health innovation, supporting the ethical use of AI while enabling Europe to lead in the global AI landscape.

Sathish Kumar Thiagarajan is a seasoned Controls & Automation Engineer with over 18 years of global experience in managing large-scale industrial automation projects involving PLCs, SCADA, and Drives. He specializes in optimizing technical workflows, ensuring regulatory compliance, and leading cross-functional teams to deliver seamless IT/OT integration solutions. Known for enhancing operational efficiency and driving cost-effective innovations, his expertise helps shape transformative strategies in industrial automation.

Srinivasu Parupalli is an experienced Systems Engineer with expertise in program management and delivery across multiple domains, including Industry 4.0, Manufacturing, Embedded Systems, IoT, Software Applications Development, and Cloud Integrations. He has extensive experience in end-to-end product development and has been instrumental in building and training teams on emerging technologies such as Ignition, Solumina, Aveva, and SCADA systems for deployment in diverse customer projects. With a strong background in industrial automation, he has worked across various industries, including Manufacturing, Energy, Utilities, Healthcare, and Process Automation, developing MES, SCADA, and HMI solutions integrated with other applications. His expertise lies in customer engagement, requirements analysis, and risk management, ensuring the successful execution of complex automation projects.


Abhishek Kumar
Subject Matter Expert in Medical Device Regulatory and Quality Assurance
Abhishek Kumar is a Subject Matter Expert in Medical Device Regulatory and Quality Assurance with over 14 years of experience. He has led the EU MDR 2017/745 sustenance program, managed multiple global engagements for top medical device companies, and supported the gap assessment, remediation, and submission of 70+ technical documents across EU MDR, ASEAN MDD, NMPA (China), Taiwan, and 10+ 510(k) submissions. He has authored 40+ Clinical Evaluation Reports (CERs) for Class I–III devices in line with MEDDEV 2.7.1 Rev-4 and developed proposals for market access in the U.S., Europe, and APAC (including ASEAN, China, Taiwan, and Japan). He also prepared and implemented regulatory plans for new product development across 90+ countries through feasibility analysis and cross-functional coordination.
Cyient (Estd: 1991, NSE: CYIENT) delivers intelligent engineering solutions across products, plants, and networks for over 300 global customers, including 30% of the top 100 global innovators. As a company, Cyient is committed to designing a culturally inclusive, socially responsible, and environmentally sustainable tomorrow together with our stakeholders.
For more information, please visit www.cyient.com