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Abstract

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.

Introduction

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.

Waterfall Methodology 

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. 

waterfall-methodology

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-model-agile-model

 

Initiating Process Group

  • Objective: Define the project and obtain authorization to proceed.
  • Software development Phase: This corresponds to the Requirements Gathering and Analysis phase in Waterfall.
  • Key Activities:
    • Develop the Project Charter: to formally authorize the project.
    • Identify stakeholders and their needs.
    • Establish high-level project goals and constraints (time, cost, scope).

Planning Process Group

  • Objective: Develop a detailed project management plan and define the scope, schedule, cost, quality, resources, and risks.
  • Software development Phase: This corresponds to the System Design phase in Waterfall.
  • Key Activities:
    • Develop the Project Management Plan: This is a comprehensive plan that defines how the project will be executed, monitored, and closed.
    • Define detailed project requirements based on the initial analysis.
    • Scope Management: Document detailed requirements and create a Work Breakdown Structure (WBS) to break down the project into manageable components.
    • Schedule Development:strong> Create a detailed project schedule that outlines all tasks, milestones, and dependencies.
    • Cost Management:strong> Develop a detailed cost estimate and budget.
    • Risk Management:strong> Identify potential risks and create a risk management plan.
    • Quality Planning:strong> Define quality metrics and processes to ensure the product meets the required standards.

waterfall-methodology-process
 

Executing Process Group

  • Objective: Coordinate people and resources, manage stakeholder expectations, and execute the project plan.
  • Software development Phase: This corresponds to the Implementation (Coding) phase in Waterfall.
  • Key Activities:
    • Execute the project according to the plan, starting with the development or coding of the system.
    • Team Management: Assign and manage resources to carry out the work as per the project plan.
    • Quality Assurance: Ensure that the work meets the quality standards defined in the planning phase.
    • Communication: Keep stakeholders informed about progress, risks, and issues.
    • Procurement Management: If external resources or services are required, manage procurement processes.

Monitoring and  Controlling Process Group

  • Objective: Track, review, and regulate project performance and make adjustment as necessary.
  • Software development Phase: This corresponds to the Integration and Testing phase in Waterfall.
  • Key Activities:
    • Monitor and Control Project Work: Track project progress against the project management plan and make adjustment as needed.
    • Perform Integrated Change Control: If changes are required, evaluate and approve them through a formal change control process.
    • Scope Control: Ensure that the project stays within the defined scope and prevent scope creep.
    • Schedule Control: Track the project schedule and take corrective actions if there are delays.
    • Cost Control: Monitor the project budget and ensure the project stays within financial constraints.
    • Quality Control: Perform inspections, reviews, and audits to ensure that the deliverables meet quality standards.
    • Risk Monitoring: Continuously assess risks and implement mitigation plans if new risks arise.
    • Testing: Conduct testing to verify that the product meets the defined requirements and functions correctly.

Closing Process Group

  • Objective: Finalize all project activities, complete the project, and close it.
  • Software development Phase: This corresponds to the Deployment and Maintenance phase in Waterfall.
  • Key Activities:
    • Close Project or Phase: Formally close the project or project phase, ensuring that all deliverables have been completed and accepted by the stakeholders.
    • Conduct Lessons Learned: Document what went well and what could be improved for future projects.
    • Finalize Documentation: Ensure all project documentation is complete, including final deliverables, test results, and any required user manuals or documentation.
    • Obtain Formal Acceptance: Ensure that the project deliverables are formally accepted by the client or stakeholders.
    • Release Resources: Release the project team and any other resources that were dedicated to the project.
    • Post-Implementation Review: Conduct a review to assess how well the project met its objectives and what can be improved in future projects.
    • Maintenance: In the Waterfall model, post-deployment support and maintenance are handled as ongoing tasks after project completion, ensuring that any issues are resolved and the system continues to operate effectively.

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

 

waterfall-table

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

Strengths and Limitations of Waterfall

 

strengths-limitation-table

 

strengths-limitations-waterfall

 

The Agile Methodology

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.

 

the-agile-methadology

 

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

 

The 12 Agile Principles are:

  1. Satisfy the customer through early and continuous delivery of valuable software.
  2. Welcome changing requirements, even late in development.
  3. Deliver working software frequently, with a preference for shorter timescales.
  4. Business people and developers must work together daily throughout the project.
  5. Build projects around motivated individuals, giving them the environment and support they need.
  6. Face-to-face conversation is the most efficient and effective method of communication.
  7. Working software is the primary measure of progress.
  8. Agile processes promote sustainable development, maintaining a constant pace indefinitely.
  9. Continuous attention to technical excellence and good design enhances agility.
  10. Simplicity—the art of maximizing the amount of work not done—is essential.
  11. The best architectures, requirements, and designs emerge from self-organizing teams.
  12. Regularly reflect on how to become more effective, then adjust accordingly.

Cyient's Role to Enable Customers Adopt Agile Methodology

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.

 

Phases of Agile Project Management

 

phase-of-agile-project-management

 

Frameworks for Agile Implementation

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

framework-for-agile-implementation

 

Proposed Agile System Lifecycle Model

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

propose-agile-system

 

Phases of the Agile System Lifecycle

agile-system-lifecycle

agile-phases

Organizations applying Agile in engineering have realized measurable outcomes:

agile-measureable-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.

 

Comparison: Agile vs. Waterfall

agile-vs-waterfall

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

waterfall-vs-agile

 

Delivery Method

Scrum Agile to develop valued solutions

Scrum-Agile

agile-workshop

 

Benefits of Agile Adoption

agile-adoption-benefits

 

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

agile-benefits

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.

application-of-agile

 

Conclusion

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.

About the Authors

Narayana-Murthy-Namburi

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.

About Cyient

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

EU AI Act Implementation Timeline

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:

EU AI Act Implementation Timelin

Use of AI in Healthcare

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:

Use of AI in Healthcare

Possible Challenges that Deployers/Manufacturers/Providers of AI for Medical Purpose Might Face

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.

Dual Regulatory Burden
  • Manufacturers must comply with both the EU MDR/IVDR and EU AI Act.
  • Requires harmonization of conformity assessments technical documentation and quality system across two frameworks.
  • Potential for duplicated or conflicting requirements.
High Risk AI classification

AIMD classified as High Risk as per EU AI Act shall comply stringent requirements such as human oversight, transparency, robustness and post-market monitoring

Data Governance & Quality
  • Ensuring training and test data are: Relevant, representative, free of bias, Statistically appropriate, Collected lawfully under GDPR
  • Challenge: Medical AI often uses retrospective, non-standardized, or anonymized datasets with quality or bias issues.
Transparency & Explainability
  • AI outputs must be understandable to the intended user (e.g., physicians).
  • Difficult for complex models like deep learning or black-box AI to meet these criteria.
  • Manufacturers may need to redesign interfaces or limit the use of opaque algorithms.
Human Oversight
  • Manufacturers must ensure that human operators can understand, intervene, or override the AI system.
  • Manufacturer must have explicit design features, documentation, and training materials to enable human oversight.
Robustness, Accuracy & Cybersecurity
  • AI systems must maintain accuracy and performance throughout their lifecycle.
  • Real-world data often differs from training environments, impacting performance.
  • Cybersecurity risks increase with connected AI-enabled medical devices (e.g., remote updates, API access).
Continuous Learning & Software Updates
  • Many AI systems are adaptive or continuously learning, which conflicts with the static certification model under EU MDR and the EU AI Act.
  • Challenge: Validation & monitoring of that changes postdeployment.
Technical Documentation & Traceability
  • Manufacturers must produce detailed documentation about:
  • Model architecture
  • Training/testing data
  • Risk management
  • Logging and traceability mechanisms
  • These are not always readily available for third-party or opensource AI components.
Post-Market Monitoring
  • Requires active surveillance of AI model performance, bias, and safety over time.
  • Need for data pipelines, feedback loops, and incident reporting mechanisms tailored to AI.
Notified Body Expertise Gaps
  • Many Notified Bodies lack AI-specific expertise.
  • Manufacturers may face delays or inconsistencies in conformity assessment procedures.
  • Ongoing need for capacity building among conformity assessment entities.
Ethical and Fundamental Rights Compliance
  • AI must respect human dignity, privacy, non-discrimination, and autonomy (restricted from AI prohibited practices as per EU AI Act). In case of High-Risk AI model/ system it becomes complicated to demonstrate the above features for the AI involved in life-and-death decisions, triage, or behavior prediction.
Cost & Time of Compliance
  • Compliance adds substantial regulatory, engineering, and legal costs.
  • Small or medium manufacturers may find this particularly resource-intensive.

Conformity Assessment Strategy for AI-enabled Medical Device (AIMD)

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).

Conformity Assessment Strategy for AI-enabled

Compliance Checklist for Deployers, Manufacturers, and Providers of General-Purpose, and High-Risk AI systems

Compliance Checklist for Deployers, Manufacturers, and Providers (1)

Conclusion

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:

  • Integrating regulatory requirements early in the design and development phase
  • Investing in technical documentation and risk governance
  • Leveraging recognized standards to establish traceability and conformity
  • Promoting AI literacy and human oversight across all operational levels

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.

About the Authors

Sathish Kumar

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

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.


shutterstock_2486517429

About the Author

Abhishek Kumar-2

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.

About Cyient

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