A Deployment Pipeline is a key component of modern software development, enabling teams to automate and streamline the process of building, testing, and deploying software changes. It ensures that code changes progress through various stages, including building, testing, staging, and deployment, with automated testing and validation at each step. This approach enhances software quality, accelerates delivery, and minimizes the risk of issues reaching production. Popular tools like Jenkins and Travis CI facilitate the implementation of deployment pipelines, supporting Continuous Integration and Continuous Delivery (CI/CD) practices.
Element
Description
Implications
Examples
Applications
Deployment Pipeline
An automated sequence of steps used to build, test, and deploy software changes.
Consistency, efficiency, and reliability.
Jenkins, Travis CI, CircleCI.
Continuous integration and delivery of software.
Build
Compilation and assembly of source code into executable artifacts.
Code quality, compatibility, version control.
Compiling source code into binaries.
Creating deployable software packages.
Testing
A battery of automated tests to ensure software quality, including unit, integration, and acceptance tests.
Quality assurance, bug detection, stability.
JUnit, Selenium, Cucumber.
Identifying and fixing defects early in the cycle.
Staging
A pre-production environment where changes are validated before deployment to the production environment.
User acceptance, load testing, performance.
Pre-production servers, environments.
Ensuring readiness for production deployment.
Deployment
The process of releasing new code or changes into a live production environment.
Minimizing downtime, user impact, rollback.
Blue-green deployments, canary releases.
Delivering new features and updates to users.
Monitoring
Continuous monitoring of deployed software to detect issues, collect performance data, and ensure availability.
Real-time visibility, issue resolution.
Prometheus, New Relic, ELK Stack.
Proactively managing and maintaining systems.
Automation
Reducing manual interventions through scripting and automation tools to accelerate the deployment process.
The deployment pipeline is a concept and practice within DevOps that involves the automated execution of various stages or steps, from code commit to production deployment, to ensure the rapid, reliable, and repeatable delivery of software changes. It serves as a conduit for continuously integrating, testing, and deploying code changes across multiple environments in a controlled and efficient manner.
Key Components
Version Control: The foundation of the deployment pipeline, where developers commit code changes using version control systems such as Git or SVN.
Continuous Integration (CI): The process of automatically building and testing code changes whenever they are committed to the version control repository.
Automated Testing: The execution of automated tests, including unit tests, integration tests, and acceptance tests, to validate the functionality and quality of the software.
Artifact Repository: The storage of built artifacts, such as compiled binaries or Docker images, for subsequent deployment stages.
Deployment Automation: The automated deployment of tested and validated code changes to various environments, including development, testing, staging, and production.
Benefits of Deployment Pipelines
Accelerated Delivery
By automating and streamlining the software delivery process, deployment pipelines enable organizations to deliver software changes rapidly and frequently, reducing time-to-market and enhancing agility in response to customer feedback and market demands.
Consistent Quality
Deployment pipelines enforce consistent quality standards by automating testing and validation processes, thereby reducing the risk of defects and ensuring that only thoroughly tested and verified code changes are deployed to production environments.
Enhanced Collaboration
Deployment pipelines promote collaboration and transparency among development, operations, and quality assurance teams by providing visibility into the status of code changes and facilitating cross-functional communication and feedback.
Challenges in Implementing Deployment Pipelines
Complexity Management
Managing the complexity of deployment pipelines, especially in large-scale or distributed systems, can be challenging, requiring careful design, configuration, and maintenance to ensure scalability, reliability, and performance.
Pipeline Orchestration
Coordinating the execution of multiple stages and tasks within a deployment pipeline, including parallel and sequential steps, can be complex, requiring robust orchestration tools and frameworks to manage dependencies and ensure proper sequencing.
Cultural Resistance
Overcoming cultural resistance to automation and DevOps practices, including concerns about job security, change management, and organizational inertia, is essential for successful adoption and implementation of deployment pipelines.
Real-World Examples of Deployment Pipelines
Continuous Delivery at Amazon
Amazon employs a sophisticated deployment pipeline known as the Continuous Delivery Pipeline (CDP) to automate and streamline the delivery of software changes across its vast ecosystem of services and applications. The CDP encompasses various stages, including code commit, automated testing, artifact creation, deployment orchestration, and production rollout, enabling Amazon to deliver software changes rapidly, reliably, and at scale.
Netflix’s Spinnaker Platform
Netflix leverages the Spinnaker platform, an open-source continuous delivery tool, to manage its deployment pipelines and orchestrate the delivery of microservices and applications across multiple cloud environments. Spinnaker provides a flexible and extensible framework for defining and executing complex deployment workflows, enabling Netflix to achieve rapid, safe, and automated software delivery across its global infrastructure.
Best Practices for Deployment Pipelines
Infrastructure as Code (IaC)
Adopting infrastructure as code (IaC) practices enables organizations to define and provision infrastructure resources, including servers, networks, and environments, programmatically and automatically, thereby enhancing the consistency, reliability, and scalability of deployment pipelines.
Canary Deployments
Implementing canary deployments, where new code changes are gradually rolled out to a subset of users or servers before being fully deployed, allows organizations to validate changes in production environments safely, mitigate risks, and gather feedback before wider rollout.
Monitoring and Observability
Integrating comprehensive monitoring and observability tools into deployment pipelines enables organizations to monitor the health, performance, and reliability of applications and infrastructure in real-time, facilitating proactive detection and remediation of issues.
Conclusion
The deployment pipeline serves as a cornerstone of modern software delivery practices, enabling organizations to automate and streamline the process of delivering software changes from development to production environments rapidly, reliably, and efficiently. By embracing deployment pipelines, organizations can accelerate delivery, maintain consistent quality, enhance collaboration, and respond to customer feedback and market demands with agility and confidence, thereby driving innovation and competitive advantage in today’s fast-paced digital landscape.
Related Frameworks, Models, or Concepts
Description
When to Apply
Deployment Pipeline
– A Deployment Pipeline is an automated workflow that enables the continuous delivery of software updates from development to production environments in a repeatable and reliable manner. Deployment pipelines consist of a series of stages or gates, such as build, test, and deploy, through which code changes progress as they undergo automated testing, validation, and approval. By establishing deployment pipelines, teams can automate the build, integration, and deployment processes, detect defects early, and ensure that changes are deployed safely and consistently across environments.
– When adopting continuous integration and delivery practices, or when striving to accelerate software delivery, improve deployment reliability, and reduce manual intervention in the release process. – Applicable in industries such as software development, IT operations, and cloud computing to streamline the software delivery lifecycle and enable rapid, reliable deployments using deployment pipeline workflows and automation tools.
Continuous Integration (CI)
– Continuous Integration (CI) is a software development practice where code changes are automatically integrated into a shared repository and tested frequently, typically multiple times a day. CI aims to improve collaboration among developers, detect integration errors early, and ensure that code changes do not break the build. By automating the build and test process, CI helps teams deliver software more quickly, reliably, and with higher quality.
– When developing software applications using Agile methodologies, or when multiple developers are working on the same codebase concurrently. – Applicable in industries such as software development, IT operations, and web development to streamline the integration and testing process and accelerate software delivery using CI practices.
Continuous Delivery (CD)
– Continuous Delivery (CD) is an extension of Continuous Integration (CI) where code changes that pass automated tests are automatically deployed to production environments. CD aims to minimize manual intervention in the deployment process, reduce lead times, and enable teams to release software updates to customers quickly, safely, and frequently. By automating the deployment pipeline, CD helps teams deliver value to users continuously and respond rapidly to changing market demands.
– When implementing Agile and DevOps principles, or when striving to achieve shorter release cycles and faster time to market for software products and digital services. – Applicable in industries such as e-commerce, fintech, and SaaS to establish a culture of continuous delivery and enable teams to deliver value to customers continuously using CD practices and tooling solutions.
Infrastructure as Code (IaC)
– Infrastructure as Code (IaC) is a DevOps practice where infrastructure is defined and managed using code and version-controlled repositories. IaC enables teams to automate the provisioning, configuration, and management of infrastructure resources such as servers, networks, and storage using declarative or imperative code. By treating infrastructure as code, teams can achieve consistency, repeatability, and scalability in their infrastructure deployments, reduce manual errors, and improve overall operational efficiency.
– When deploying and managing infrastructure in cloud environments such as AWS, Azure, or Google Cloud Platform, or when adopting DevOps practices to automate infrastructure provisioning and configuration. – Applicable in industries such as cloud computing, DevOps engineering, and IT operations to streamline infrastructure management and enable agile, scalable deployments using IaC techniques and tooling solutions.
Microservices Architecture
– Microservices Architecture is an architectural style where software applications are composed of small, independently deployable services that are organized around business capabilities and communicate via lightweight APIs. Microservices promote modularity, flexibility, and scalability by decoupling services and allowing them to be developed, deployed, and scaled independently. By breaking down monolithic applications into smaller, more manageable services, teams can improve agility, facilitate continuous delivery, and enable faster innovation and experimentation.
– When designing and developing modern, cloud-native applications or when migrating existing monolithic applications to a microservices architecture to achieve greater agility and scalability. – Applicable in industries such as e-commerce, social media, and financial services to enable rapid development and deployment of scalable, resilient software solutions using microservices architecture principles and patterns.
Containerization
– Containerization is a lightweight virtualization technology where applications and their dependencies are packaged together in a standardized format called containers. Containers provide a consistent runtime environment that is isolated from the underlying infrastructure, enabling applications to run reliably across different environments. Containerization platforms such as Docker and Kubernetes automate the deployment, scaling, and management of containerized applications, allowing teams to deliver software quickly and consistently across diverse environments.
– When developing, deploying, and managing cloud-native applications or when building scalable, portable software solutions using containerization technologies such as Docker and Kubernetes. – Applicable in industries such as cloud computing, software development, and DevOps engineering to streamline application deployment and improve infrastructure utilization using containerization platforms and orchestration tools.
Monitoring and Observability
– Monitoring and Observability are practices that involve collecting, analyzing, and visualizing data about the behavior and performance of software applications and infrastructure in real-time. Monitoring focuses on tracking metrics, logs, and events to detect and diagnose issues proactively, while observability emphasizes understanding the internal state and interactions of systems through instrumentation and telemetry data. By monitoring and observing applications and infrastructure, teams can identify trends, detect anomalies, and troubleshoot issues more effectively, ensuring the reliability and performance of their systems.
– When operating and maintaining software applications in production environments or when implementing DevOps practices to improve system reliability and performance. – Applicable in industries such as IT operations, site reliability engineering, and cloud services to monitor and optimize the performance of applications and infrastructure using monitoring and observability tools and techniques.
Automated Testing
– Automated Testing is a DevOps practice where software tests are executed automatically using test automation frameworks and tools. Automated testing helps teams validate software functionality, performance, and security quickly and efficiently, enabling them to detect defects early and deliver high-quality software with confidence. By automating repetitive and time-consuming testing tasks, teams can accelerate release cycles, reduce manual errors, and improve overall test coverage and reliability.
– When developing software applications using Agile methodologies or when implementing continuous integration and delivery pipelines to automate the testing process. – Applicable in industries such as software quality assurance, DevOps engineering, and cybersecurity to ensure the reliability and security of software products using automated testing practices and tooling solutions.
Configuration Management
– Configuration Management is a DevOps practice where infrastructure configurations and application settings are managed and maintained systematically to ensure consistency, reliability, and compliance across environments. Configuration management tools such as Ansible, Puppet, and Chef automate the provisioning, configuration, and deployment of infrastructure resources and software components, allowing teams to enforce desired state configurations and manage change effectively. By standardizing and automating configuration management processes, teams can reduce manual errors, minimize configuration drift, and improve infrastructure agility and stability.
– When managing and scaling infrastructure resources in dynamic, cloud-based environments or when deploying and maintaining complex software systems with multiple dependencies. – Applicable in industries such as IT operations, cloud computing, and software development to standardize, automate, and control configurations using configuration management tools and best practices.
Version Control
– Version Control is a software development practice where changes to code and other artifacts are tracked, managed, and coordinated using version control systems such as Git, Subversion, and Mercurial. Version control enables developers to collaborate effectively, track changes over time, and revert to previous states if necessary, ensuring the integrity and traceability of software assets. By adopting version control, teams can streamline code management, facilitate code reviews, and enable parallel development, leading to improved code quality and productivity.
– When developing software applications collaboratively with multiple contributors or when managing code repositories for versioning, branching, and merging. – Applicable in industries such as software engineering, web development, and open-source projects to manage code changes and track revisions using version control systems and workflows.
Infrastructure Monitoring
– Infrastructure Monitoring is the practice of collecting and analyzing data about the health, performance, and availability of IT infrastructure components such as servers, networks, and databases. Infrastructure monitoring tools provide visibility into key metrics, alerts, and dashboards that help teams detect and respond to issues proactively, optimize resource utilization, and ensure the reliability and performance of critical systems. By monitoring infrastructure in real-time, teams can identify bottlenecks, troubleshoot problems, and make data-driven decisions to improve operational efficiency and user experience.
– When managing and maintaining on-premises or cloud-based infrastructure resources or when operating mission-critical systems and applications that require continuous monitoring and performance optimization. – Applicable in industries such as IT operations, network management, and cloud services to monitor and manage infrastructure health and performance using infrastructure monitoring tools and platforms.
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
Agile started as a lightweight development method compared to heavyweight software development, which is the core paradigm of the previous decades of software development. By 2001 the Manifesto for Agile Software Development was born as a set of principles that defined the new paradigm for software development as a continuous iteration. This would also influence the way of doing business.
Agile Program Management is a means of managing, planning, and coordinating interrelated work in such a way that value delivery is emphasized for all key stakeholders. Agile Program Management (AgilePgM) is a disciplined yet flexible agile approach to managing transformational change within an organization.
Agile project management (APM) is a strategy that breaks large projects into smaller, more manageable tasks. In the APM methodology, each project is completed in small sections – often referred to as iterations. Each iteration is completed according to its project life cycle, beginning with the initial design and progressing to testing and then quality assurance.
Agile Modeling (AM) is a methodology for modeling and documenting software-based systems. Agile Modeling is critical to the rapid and continuous delivery of software. It is a collection of values, principles, and practices that guide effective, lightweight software modeling.
Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.
Agile leadership is the embodiment of agile manifesto principles by a manager or management team. Agile leadership impacts two important levels of a business. The structural level defines the roles, responsibilities, and key performance indicators. The behavioral level describes the actions leaders exhibit to others based on agile principles.
The andon system alerts managerial, maintenance, or other staff of a production process problem. The alert itself can be activated manually with a button or pull cord, but it can also be activated automatically by production equipment. Most Andon boards utilize three colored lights similar to a traffic signal: green (no errors), yellow or amber (problem identified, or quality check needed), and red (production stopped due to unidentified issue).
Bimodal Portfolio Management (BimodalPfM) helps an organization manage both agile and traditional portfolios concurrently. Bimodal Portfolio Management – sometimes referred to as bimodal development – was coined by research and advisory company Gartner. The firm argued that many agile organizations still needed to run some aspects of their operations using traditional delivery models.
Business innovation is about creating new opportunities for an organization to reinvent its core offerings, revenue streams, and enhance the value proposition for existing or new customers, thus renewing its whole business model. Business innovation springs by understanding the structure of the market, thus adapting or anticipating those changes.
Business modelinnovation is about increasing the success of an organization with existing products and technologies by crafting a compelling value proposition able to propel a new business model to scale up customers and create a lasting competitive advantage. And it all starts by mastering the key customers.
A consumer brand company like Procter & Gamble (P&G) defines “Constructive Disruption” as: a willingness to change, adapt, and create new trends and technologies that will shape our industry for the future. According to P&G, it moves around four pillars: lean innovation, brand building, supply chain, and digitalization & data analytics.
That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problem and not the technical solution of its founders.
A design sprint is a proven five-day process where critical business questions are answered through speedy design and prototyping, focusing on the end-user. A design sprint starts with a weekly challenge that should finish with a prototype, test at the end, and therefore a lesson learned to be iterated.
Tim Brown, Executive Chair of IDEO, defined design thinking as “a human-centered approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.” Therefore, desirability, feasibility, and viability are balanced to solve critical problems.
DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.
Product discovery is a critical part of agile methodologies, as its aim is to ensure that products customers love are built. Product discovery involves learning through a raft of methods, including design thinking, lean start-up, and A/B testing to name a few. Dual Track Agile is an agile methodology containing two separate tracks: the “discovery” track and the “delivery” track.
eXtreme Programming was developed in the late 1990s by Ken Beck, Ron Jeffries, and Ward Cunningham. During this time, the trio was working on the Chrysler Comprehensive Compensation System (C3) to help manage the company payroll system. eXtreme Programming (XP) is a software development methodology. It is designed to improve software quality and the ability of software to adapt to changing customer needs.
Feature-Driven Development is a pragmatic software process that is client and architecture-centric. Feature-Driven Development (FDD) is an agile software development model that organizes workflow according to which features need to be developed next.
A Gemba Walk is a fundamental component of lean management. It describes the personal observation of work to learn more about it. Gemba is a Japanese word that loosely translates as “the real place”, or in business, “the place where value is created”. The Gemba Walk as a concept was created by Taiichi Ohno, the father of the Toyota Production System of lean manufacturing. Ohno wanted to encourage management executives to leave their offices and see where the real work happened. This, he hoped, would build relationships between employees with vastly different skillsets and build trust.
GIST Planning is a relatively easy and lightweight agile approach to product planning that favors autonomous working. GIST Planning is a lean and agile methodology that was created by former Google product manager Itamar Gilad. GIST Planning seeks to address this situation by creating lightweight plans that are responsive and adaptable to change. GIST Planning also improves team velocity, autonomy, and alignment by reducing the pervasive influence of management. It consists of four blocks: goals, ideas, step-projects, and tasks.
The ICE Scoring Model is an agile methodology that prioritizes features using data according to three components: impact, confidence, and ease of implementation. The ICE Scoring Model was initially created by author and growth expert Sean Ellis to help companies expand. Today, the model is broadly used to prioritize projects, features, initiatives, and rollouts. It is ideally suited for early-stage product development where there is a continuous flow of ideas and momentum must be maintained.
An innovation funnel is a tool or process ensuring only the best ideas are executed. In a metaphorical sense, the funnel screens innovative ideas for viability so that only the best products, processes, or business models are launched to the market. An innovation funnel provides a framework for the screening and testing of innovative ideas for viability.
According to how well defined is the problem and how well defined the domain, we have four main types of innovations: basic research (problem and domain or not well defined); breakthrough innovation (domain is not well defined, the problem is well defined); sustaining innovation (both problem and domain are well defined); and disruptive innovation (domain is well defined, the problem is not well defined).
The innovation loop is a methodology/framework derived from the Bell Labs, which produced innovation at scale throughout the 20th century. They learned how to leverage a hybrid innovation management model based on science, invention, engineering, and manufacturing at scale. By leveraging individual genius, creativity, and small/large groups.
The Agile methodology has been primarily thought of for software development (and other business disciplines have also adopted it). Lean thinking is a process improvement technique where teams prioritize the value streams to improve it continuously. Both methodologies look at the customer as the key driver to improvement and waste reduction. Both methodologies look at improvement as something continuous.
A startup company is a high-tech business that tries to build a scalable business model in tech-driven industries. A startup company usually follows a lean methodology, where continuous innovation, driven by built-in viral loops is the rule. Thus, driving growth and building network effects as a consequence of this strategy.
As pointed out by Eric Ries, a minimum viable product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort through a cycle of build, measure, learn; that is the foundation of the lean startup methodology.
Kanban is a lean manufacturing framework first developed by Toyota in the late 1940s. The Kanban framework is a means of visualizing work as it moves through identifying potential bottlenecks. It does that through a process called just-in-time (JIT) manufacturing to optimize engineering processes, speed up manufacturing products, and improve the go-to-market strategy.
Jidoka was first used in 1896 by Sakichi Toyoda, who invented a textile loom that would stop automatically when it encountered a defective thread. Jidoka is a Japanese term used in lean manufacturing. The term describes a scenario where machines cease operating without human intervention when a problem or defect is discovered.
The PDCA (Plan-Do-Check-Act) cycle was first proposed by American physicist and engineer Walter A. Shewhart in the 1920s. The PDCA cycle is a continuous process and product improvement method and an essential component of the lean manufacturing philosophy.
RAD was first introduced by author and consultant James Martin in 1991. Martin recognized and then took advantage of the endless malleability of software in designing development models. Rapid Application Development (RAD) is a methodology focusing on delivering rapidly through continuous feedback and frequent iterations.
Retrospective analyses are held after a project to determine what worked well and what did not. They are also conducted at the end of an iteration in Agile project management. Agile practitioners call these meetings retrospectives or retros. They are an effective way to check the pulse of a project team, reflect on the work performed to date, and reach a consensus on how to tackle the next sprint cycle. These are the five stages of a retrospective analysis for effective Agile project management: set the stage, gather the data, generate insights, decide on the next steps, and close the retrospective.
Scaled Agile Lean Development (ScALeD) helps businesses discover a balanced approach to agile transition and scaling questions. The ScALed approach helps businesses successfully respond to change. Inspired by a combination of lean and agile values, ScALed is practitioner-based and can be completed through various agile frameworks and practices.
The SMED (single minute exchange of die) method is a lean production framework to reduce waste and increase production efficiency. The SMED method is a framework for reducing the time associated with completing an equipment changeover.
The Spotify Model is an autonomous approach to scaling agile, focusing on culture communication, accountability, and quality. The Spotify model was first recognized in 2012 after Henrik Kniberg, and Anders Ivarsson released a white paper detailing how streaming company Spotify approached agility. Therefore, the Spotify model represents an evolution of agile.
As the name suggests, TDD is a test-driven technique for delivering high-quality software rapidly and sustainably. It is an iterative approach based on the idea that a failing test should be written before any code for a feature or function is written. Test-Driven Development (TDD) is an approach to software development that relies on very short development cycles.
Timeboxing is a simple yet powerful time-management technique for improving productivity. Timeboxing describes the process of proactively scheduling a block of time to spend on a task in the future. It was first described by author James Martin in a book about agile software development.
Scrum is a methodology co-created by Ken Schwaber and Jeff Sutherland for effective team collaboration on complex products. Scrum was primarily thought for software development projects to deliver new software capability every 2-4 weeks. It is a sub-group of agile also used in project management to improve startups’ productivity.
Scrumban is a project management framework that is a hybrid of two popular agile methodologies: Scrum and Kanban. Scrumban is a popular approach to helping businesses focus on the right strategic tasks while simultaneously strengthening their processes.
Scrum anti-patterns describe any attractive, easy-to-implement solution that ultimately makes a problem worse. Therefore, these are the practice not to follow to prevent issues from emerging. Some classic examples of scrum anti-patterns comprise absent product owners, pre-assigned tickets (making individuals work in isolation), and discounting retrospectives (where review meetings are not useful to really make improvements).
Scrum at Scale (Scrum@Scale) is a framework that Scrum teams use to address complex problems and deliver high-value products. Scrum at Scale was created through a joint venture between the Scrum Alliance and Scrum Inc. The joint venture was overseen by Jeff Sutherland, a co-creator of Scrum and one of the principal authors of the Agile Manifesto.
Six Sigma is a data-driven approach and methodology for eliminating errors or defects in a product, service, or process. Six Sigma was developed by Motorola as a management approach based on quality fundamentals in the early 1980s. A decade later, it was popularized by General Electric who estimated that the methodology saved them $12 billion in the first five years of operation.
Stretch objectives describe any task an agile team plans to complete without expressly committing to do so. Teams incorporate stretch objectives during a Sprint or Program Increment (PI) as part of Scaled Agile. They are used when the agile team is unsure of its capacity to attain an objective. Therefore, stretch objectives are instead outcomes that, while extremely desirable, are not the difference between the success or failure of each sprint.
The Toyota Production System (TPS) is an early form of lean manufacturing created by auto-manufacturer Toyota. Created by the Toyota Motor Corporation in the 1940s and 50s, the Toyota Production System seeks to manufacture vehicles ordered by customers most quickly and efficiently possible.
The Total Quality Management (TQM) framework is a technique based on the premise that employees continuously work on their ability to provide value to customers. Importantly, the word “total” means that all employees are involved in the process – regardless of whether they work in development, production, or fulfillment.
The waterfall model was first described by Herbert D. Benington in 1956 during a presentation about the software used in radar imaging during the Cold War. Since there were no knowledge-based, creative software development strategies at the time, the waterfall method became standard practice. The waterfall model is a linear and sequential project management framework.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
