A service mesh is a dedicated infrastructure layer that facilitates communication, observability, and security among microservices in a distributed application. It abstracts networking complexity, provides traffic management capabilities, and enhances visibility and control over service-to-service communication.
Service meshes typically utilize sidecar proxies, such as Envoy or Linkerd, to intercept and manage traffic between services, enabling features like load balancing, service discovery, circuit breaking, and encryption. By abstracting networking complexity and providing centralized control and visibility, service meshes empower organizations to build and operate resilient, scalable, and secure microservices architectures.
Service meshes employ sidecar proxies, such as Envoy or Linkerd, deployed alongside each microservice instance to intercept and manage inbound and outbound traffic. Sidecar proxies handle tasks like load balancing, service discovery, circuit breaking, and encryption, offloading networking concerns from the application code.
Control Plane
Service meshes feature a centralized control plane responsible for configuration management, policy enforcement, and telemetry collection. The control plane provides a unified interface for defining and enforcing traffic routing rules, security policies, and observability settings across the service mesh.
Service Discovery
Service meshes offer built-in service discovery mechanisms to dynamically locate and connect to available microservice instances. Service discovery enables automatic registration and discovery of services, ensuring that traffic is routed to healthy instances and promoting fault tolerance and high availability.
Traffic Management
Service meshes enable advanced traffic management capabilities, including load balancing, traffic splitting, and circuit breaking, to optimize performance, reliability, and resilience. Traffic management features empower developers to control how traffic is routed and distributed across microservice instances based on predefined rules and policies.
Strategies for Implementing Service Mesh
Gradual Adoption
Implementing a service mesh involves gradually introducing the mesh infrastructure into the existing microservices architecture. Organizations can start by deploying sidecar proxies alongside select microservices and gradually expand coverage to additional services over time, minimizing disruption and risk.
Configuration as Code
Implementing a service mesh requires defining configuration settings, routing rules, and security policies using declarative configuration files or infrastructure-as-code (IaC) tools. Adopting configuration-as-code practices enables organizations to automate provisioning, deployment, and management of the service mesh infrastructure.
Telemetry and Observability
Implementing a service mesh includes integrating telemetry and observability tools to monitor and analyze service-to-service communication and behavior. Telemetry data, including metrics, logs, and traces, provides insights into service performance, latency, error rates, and dependencies, enabling proactive troubleshooting and optimization.
Security and Compliance
Implementing a service mesh involves implementing security measures, such as mutual TLS (mTLS) encryption, authentication, and authorization, to protect communication between microservices. Service meshes enforce security policies and compliance requirements, ensuring that only authorized services can communicate and that sensitive data is encrypted in transit.
Benefits of Service Mesh
Network Abstraction
Service meshes abstract networking complexity and provide a consistent communication layer across microservices, regardless of underlying infrastructure or deployment environments. They shield developers from low-level networking concerns and enable seamless communication between services, simplifying application development and deployment.
Traffic Management and Resilience
Service meshes offer advanced traffic management features, such as load balancing, traffic splitting, and circuit breaking, to optimize performance, reliability, and resilience. They enable organizations to implement traffic control policies, perform A/B testing, and gracefully handle failures, ensuring optimal user experience and application uptime.
Observability and Troubleshooting
Service meshes enhance observability and troubleshooting capabilities by providing real-time insights into service-to-service communication and behavior. Telemetry data, including metrics, logs, and traces, enables organizations to monitor service health, diagnose performance issues, and identify bottlenecks quickly, facilitating rapid incident response and resolution.
Security and Compliance
Service meshes strengthen security and compliance by enforcing encryption, authentication, and authorization policies for service-to-service communication. They provide a centralized control plane for defining and enforcing security policies, ensuring that communication is secure, auditable, and compliant with regulatory requirements.
Challenges of Service Mesh
Complexity and Overhead
Service meshes introduce additional complexity and overhead to the microservices architecture, requiring careful planning, configuration, and management. Organizations must invest in training, tooling, and automation to effectively deploy and operate service mesh infrastructure at scale.
Performance Impact
Service meshes may introduce performance overhead due to the added latency and processing incurred by sidecar proxies intercepting and managing traffic. Organizations must carefully evaluate and optimize service mesh configurations to minimize performance impact and ensure optimal application performance.
Operational Complexity
Service meshes increase operational complexity by adding another layer of infrastructure and configuration management to the microservices environment. Organizations must develop and maintain expertise in service mesh technologies, implement best practices for configuration management, and establish monitoring and alerting mechanisms to detect and respond to issues promptly.
Compatibility and Interoperability
Service meshes may face challenges related to compatibility and interoperability with existing infrastructure, platforms, and tooling. Organizations must ensure that service mesh solutions integrate seamlessly with existing systems, support interoperability standards, and align with architectural principles and best practices.
Implications of Service Mesh
Developer Productivity
Service meshes enhance developer productivity by abstracting networking concerns and providing a unified communication layer across microservices. They enable developers to focus on application logic and business requirements without having to worry about low-level networking tasks, accelerating development cycles and time-to-market.
Operational Efficiency
Service meshes improve operational efficiency by automating traffic management, security enforcement, and observability tasks across microservices. They enable organizations to standardize configuration settings, enforce policies consistently, and troubleshoot issues proactively, reducing manual effort and operational overhead.
Scalability and Resilience
Service meshes enhance scalability and resilience by providing advanced traffic management and fault tolerance mechanisms. They enable organizations to scale microservices independently, handle traffic spikes gracefully, and recover from failures quickly, ensuring high availability and performance under varying load conditions.
Security and Compliance
Service meshes strengthen security and compliance by enforcing encryption, authentication, and authorization policies for service-to-service communication. They provide centralized control and visibility over security measures, enabling organizations to mitigate risks, protect sensitive data, and ensure compliance with regulatory requirements.
Conclusion
A service mesh is a dedicated infrastructure layer designed to facilitate communication, observability, and security among microservices in a distributed application.
Key components of a service mesh include sidecar proxies, control plane, service discovery, and traffic management capabilities.
Strategies for implementing a service mesh include gradual adoption, configuration as code, telemetry and observability, and security and compliance measures.
Service meshes offer benefits such as network abstraction, traffic management and resilience, observability and troubleshooting, and security and compliance.
However, they also face challenges such as complexity and overhead, performance impact, operational complexity, and compatibility and interoperability concerns.
Implementing a service mesh has implications for developer productivity, operational efficiency, scalability and resilience, and security and compliance, shaping efforts to build and operate resilient, scalable, and secure microservices architectures.
Related Frameworks, Models, or Concepts
Description
When to Apply
Docker
– Docker is a popular containerization platform that allows developers to package applications and their dependencies into lightweight, portable containers. Docker containers provide a consistent runtime environment that is isolated from the underlying infrastructure, enabling applications to run reliably across different environments. Docker simplifies the process of building, distributing, and deploying containerized applications, making it easier for teams to adopt containerization and microservices architectures.
– When developing, packaging, and deploying applications in containerized environments, or when seeking to improve application portability, scalability, and efficiency using containerization technologies such as Docker. – Applicable in industries such as cloud computing, DevOps engineering, and software development to streamline application deployment and infrastructure management using Docker containers and container orchestration tools.
Kubernetes
– Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Kubernetes provides features such as automatic scaling, self-healing, and service discovery, allowing teams to deploy and manage containerized workloads at scale with ease. Kubernetes abstracts away the underlying infrastructure and provides a declarative API for defining and managing application resources, making it a powerful tool for building and operating cloud-native applications.
– When deploying and managing containerized applications in production environments or when building scalable, resilient software solutions using microservices architectures and Kubernetes orchestration. – Applicable in industries such as e-commerce, fintech, and SaaS to enable rapid, automated deployment and scaling of containerized workloads using Kubernetes clusters and infrastructure as code (IaC) practices.
Container Orchestration
– Container Orchestration is the process of automating the deployment, scaling, and management of containerized applications using container orchestration platforms such as Kubernetes, Docker Swarm, and Apache Mesos. Container orchestration platforms abstract away the complexity of managing containerized workloads and provide features such as load balancing, auto-scaling, and service discovery, allowing teams to operate containerized environments efficiently and reliably. Container orchestration simplifies tasks such as deployment rollouts, resource optimization, and application lifecycle management, enabling teams to focus on building and delivering value to users.
– When deploying and managing containerized applications at scale or when building microservices architectures that require automation and orchestration of containerized workloads. – Applicable in industries such as cloud computing, DevOps engineering, and digital transformation initiatives to streamline application deployment and infrastructure management using container orchestration platforms and best practices.
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.
Infrastructure as Code (IaC)
– Infrastructure as Code (IaC) is a DevOps practice where infrastructure configurations and provisioning are managed programmatically using code and version-controlled repositories. IaC enables teams to automate the deployment, configuration, and lifecycle 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 provisioning and managing infrastructure resources in dynamic, cloud-based environments or when deploying and maintaining complex software systems with multiple dependencies. – Applicable in industries such as cloud computing, DevOps engineering, and IT operations to standardize, automate, and control infrastructure deployments using infrastructure as code principles and tooling solutions.
Service Mesh
– Service Mesh is a dedicated infrastructure layer that provides a network of interconnected services with features such as service discovery, load balancing, and encryption. Service meshes such as Istio and Linkerd are designed to handle complex communication patterns between microservices in distributed architectures, providing visibility, control, and resilience to service-to-service communication. By offloading networking concerns from application code to the service mesh, teams can simplify microservices development, improve security, and enhance observability and reliability in their deployments.
– When building and deploying microservices architectures that require advanced networking capabilities, traffic management, and security features or when seeking to improve visibility, control, and reliability in service-to-service communication. – Applicable in industries such as cloud-native development, containerization, and DevOps engineering to enhance microservices deployments using service mesh technologies and best practices.
Cloud-Native Computing
– Cloud-Native Computing is an approach to building and running applications that leverage cloud-native technologies, practices, and architectures to deliver value to users more quickly and efficiently. Cloud-native applications are designed to be scalable, resilient, and portable across different cloud environments, using containerization, microservices, and DevOps practices such as continuous integration and delivery (CI/CD). By embracing cloud-native principles, organizations can accelerate innovation, improve agility, and reduce time to market for their software products and services.
– When developing and deploying applications in cloud environments such as AWS, Azure, or Google Cloud Platform or when adopting modern software development practices and architectures to achieve greater agility and scalability. – Applicable in industries such as SaaS, e-commerce, and digital media to enable rapid development and deployment of cloud-native applications using cloud-native computing principles and technologies.
Immutable Infrastructure
– Immutable Infrastructure is an architectural pattern where infrastructure components such as servers and containers are treated as immutable artifacts that are replaced rather than modified in place. Immutable infrastructure deployments involve creating new instances of infrastructure components with each change, rather than making in-place updates, which helps eliminate configuration drift, reduce security vulnerabilities, and improve reliability and reproducibility. By embracing immutable infrastructure, teams can ensure consistency, predictability, and scalability in their deployments, enabling them to recover quickly from failures and maintain desired system states effectively.
– When deploying and managing infrastructure resources in cloud environments or when seeking to improve reliability, security, and scalability through immutable infrastructure practices. – Applicable in industries such as software development, IT operations, and cloud computing to standardize, automate, and secure infrastructure deployments using immutable infrastructure principles and techniques.
Hybrid Cloud
– Hybrid Cloud is a cloud computing environment that combines on-premises infrastructure with public and private cloud services to support varying workload requirements and business needs. Hybrid cloud architectures allow organizations to leverage the scalability and flexibility of public clouds for certain workloads while maintaining control, compliance, and data sovereignty on-premises. By adopting hybrid cloud strategies, organizations can optimize costs, improve agility, and mitigate risks associated with data residency, regulatory compliance, and latency-sensitive applications.
– When deploying and managing workloads across multiple cloud environments or when integrating on-premises infrastructure with public cloud services to achieve flexibility, scalability, and resilience in hybrid cloud deployments. – Applicable in industries such as healthcare, finance, and government to balance the benefits of public cloud scalability with the control and security of on-premises infrastructure using hybrid cloud architectures and solutions.
Multi-Cloud Strategy
– Multi-Cloud Strategy is an approach to cloud computing where organizations use multiple cloud providers to host their applications and workloads. Multi-cloud architectures enable organizations to avoid vendor lock-in, mitigate risks associated with cloud outages or service disruptions, and optimize costs by leveraging the strengths of different cloud providers for specific use cases. By adopting a multi-cloud strategy, organizations can achieve greater flexibility, resilience, and agility in their cloud deployments, allowing them to innovate and adapt to changing business requirements effectively.
– When selecting cloud providers and planning cloud migrations or when seeking to diversify risk, optimize costs, and improve resilience by distributing workloads across multiple cloud environments. – Applicable in industries such as finance, e-commerce, and enterprise IT to reduce dependency on single cloud providers and maximize flexibility and control using multi-cloud strategies and architectures.
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.
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