experiment-driven-development

Experiment-Driven Development In A Nutshell

Business / By / November 1, 2023

Test-Driven Development (TDD) and Behavior-Driven Development (BDD) are popular agile development techniques. However, they don’t measure application usage or provide guidance on gaining feedback from customers. Experiment-Driven Development (EDD) is a scientific, fact-based approach to software development using agile principles.

AspectExplanation
DefinitionExperiment-Driven Development (EDD) is a software development approach that emphasizes using experiments and data-driven insights to inform the development process. It involves iterative development cycles where hypotheses are formulated, experiments are conducted, and the results are used to guide further development decisions. EDD is rooted in the principles of agility and continuous improvement and is commonly associated with lean and agile software development methodologies.
Key Concepts– Hypothesis-Driven Development: EDD is based on the concept of forming hypotheses about user behavior, needs, or system performance and using these hypotheses to drive development decisions. – Data-Backed Decisions: The approach relies on collecting and analyzing data from experiments to make informed decisions about what features or changes to pursue. – Iterative Development: EDD embraces iterative cycles where small, controlled experiments are conducted, and the results are used to adapt and refine the product. – User-Centric: EDD prioritizes understanding user needs and preferences through experimentation to deliver solutions that resonate with users. – Feedback Loop: It establishes a feedback loop that continuously informs development, reducing the risk of building features with uncertain value.
Characteristics– Continuous Experimentation: EDD involves a continuous process of designing, running, and analyzing experiments throughout the development lifecycle. – Empirical Decision-Making: Decisions are grounded in empirical evidence gathered from user feedback and data analysis. – Adaptability: The development process is highly adaptable, allowing teams to pivot quickly based on experiment outcomes. – User-Centered: EDD places a strong focus on aligning development efforts with user needs and expectations. – Rapid Learning: Teams engage in rapid learning by conducting frequent experiments, leading to faster product improvements.
Advantages– User Satisfaction: EDD leads to products that are better aligned with user expectations, resulting in higher user satisfaction. – Reduced Risk: The approach reduces the risk of building features or changes that may not resonate with users or meet business objectives. – Innovation: EDD fosters innovation by encouraging experimentation and exploration of new ideas. – Efficiency: Teams can avoid investing significant resources in features that do not provide the desired outcomes. – Data-Driven Culture: It promotes a data-driven culture within development teams, fostering a deeper understanding of user behavior.
Drawbacks– Resource-Intensive: Implementing EDD can require additional resources for designing, conducting, and analyzing experiments. – Complexity: Managing multiple experiments and data sources can introduce complexity into the development process. – Misinterpretation: Incorrect interpretation of experiment results can lead to misguided development decisions. – Time-Consuming: Conducting experiments and analyzing data can extend development timelines. – Skill Requirements: Teams may need training in data analysis and experiment design.
Applications– Digital Products: EDD is commonly used in the development of digital products, including websites, mobile apps, and software platforms. – E-commerce: E-commerce platforms use EDD to optimize user experiences, product recommendations, and purchase processes. – Online Services: Online services, such as streaming platforms and social networks, employ EDD to enhance user engagement and retention. – Product Features: EDD informs the development of new features or changes to existing features in a wide range of digital products. – Startup Growth: Startups often use EDD to rapidly iterate on their products and identify growth opportunities.
Use Cases– A/B Testing: A popular use case involves conducting A/B tests to compare two or more versions of a feature or webpage to determine which one performs better with users. – Feature Prioritization: EDD helps prioritize features based on their potential impact, allowing teams to focus on high-value changes. – User Onboarding: Experimentation can optimize user onboarding processes to increase user retention and satisfaction. – Pricing Strategy: EDD can inform pricing decisions by testing different pricing models and strategies with users. – Content Personalization: Media and content platforms use experiments to personalize content recommendations and improve user engagement.

Understanding Experiment-Driven Development

While TDD and BDD help developers enhance code quality and ensure that it behaves according to spec, EDD helps identify the features that should be developed. In other words, what will become the spec.

EDD is driven by split A/B testing, where a baseline (control) sample is compared to several single-variable samples to determine which of the two choices improves response rates. 

This form of feedback collection avoids the need to conduct user surveys, which are often time-consuming for both parties and can be prone to bias.

Implementing Experiment-Driven Development

To implement EDD, it is a matter of following these four steps:

Start with a hypothesis

Instead of beginning with a user story, the project team starts by defining a hypothesis related to customers, problems, solutions, value, or growth.

For example, a growth hypothesis may be “A virtual shoe fitting station in every store will increase shoe sales by 30%.” 

Identify the experiment

In the second step, take the highest-priority hypothesis and define the smallest experiment that will prove or disprove it.

The shoe store may decide to install a virtual fitting station in five stores to begin with and measure the impact on sales.

Run the experiment

This may include creating a minimum viable product (MVP) and then measuring progress based on validated learning from the end-user.

minimum-viable-product
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.

Here, many businesses choose to run experiments based on the Build/Measure/Learn (MVPe) loop. 

product-market-fit
Marc Andreessen defined Product/market fit as “being in a good market with a product that can satisfy that market.” According to Andreessen, that is a moment when a product or service has its place in the market, thus enabling traction for the company offering that product or service.

Debrief

For example, what are the observations?

How were the validated learnings used? Would more time spent on planning have helped?

Based on the results, the team may choose to pivot to a new hypothesis.

Alternatively, they may choose to persevere with the current hypothesis or discard it entirely and move to the next one.

Experiment-Driven Development Benefits

When a business incorporates EDD to complement an existing approach such as TDD or BDD, it can realize several benefits.

These include:

Structure

EDD allows project teams to ask and answer questions in a structured, measurable process.

Since ideas are validated by hypotheses, teams also avoid the testing of ideas simply to validate individual egos or hunches. 

Versatility

Although its scientific foundations may suggest otherwise, Experiment-Driven Development can be used across any business in any industry.

It is not specifically designed for use by R&D teams. 

Objectivity and efficiency

All agile methodologies dictate that value to the end-user is the primary goal.

However, the hypothesis-driven approach of EDD forces teams to define value through validated learning and not assumption alone.

Efficiency is also increased by building an MVP instead of focusing on superfluous features that provide little benefit to the end-user.

Case Studies

E-Commerce Platform: Optimizing Product Recommendations

Challenge: An e-commerce platform wants to improve its product recommendation engine to boost sales and enhance user engagement.

Application of EDD:

  • Hypothesis: “Personalized product recommendations based on user browsing history will increase the average order value by 20%.”
  • Identify the Experiment: The platform introduces personalized product recommendations for a subset of users while the rest continue to see the old recommendations. Data on order values is collected for both groups.
  • Run the Experiment: An MVP of the new recommendation system is implemented for the selected users. The system tracks user interactions and purchase behavior, measuring the impact on the average order value.
  • Debrief: After a defined period, the data is analyzed. If the experiment group shows a significant increase in the average order value, the hypothesis is validated, and the new recommendation system is rolled out to all users. If not, the platform may pivot to a different hypothesis, such as refining the recommendation algorithm.

Outcome: EDD helps the e-commerce platform make data-driven decisions about feature development. If the hypothesis is validated, it can lead to increased sales and customer satisfaction.

Mobile App Development: User Onboarding Flow

Challenge: A mobile app developer wants to improve the user onboarding experience to reduce drop-off rates during registration.

Application of EDD:

  • Hypothesis: “Simplifying the user registration process to two steps will reduce the drop-off rate by 30%.”
  • Identify the Experiment: The developer creates an MVP that streamlines the registration process to two steps. A control group experiences the original registration flow, while another group uses the simplified flow. User drop-off data is collected for both groups.
  • Run the Experiment: Users in both groups are tracked during the registration process. The developer monitors how many users complete the registration and how many drop off at each step.
  • Debrief: After the experiment, the developer reviews the data. If the simplified flow shows a 30% or greater reduction in drop-off rates, the hypothesis is validated, and the new onboarding process is implemented. If not, the developer may iterate on the hypothesis or try a different approach.

Outcome: EDD enables the mobile app developer to make informed decisions about user onboarding. If successful, the simplified onboarding flow can lead to increased user retention.

SaaS Platform: Feature Adoption

Challenge: A SaaS platform wants to improve the adoption of a new feature among its existing customers.

Application of EDD:

  • Hypothesis: “Introducing a step-by-step tutorial for the new feature will increase its adoption rate by 25% among existing customers.”
  • Identify the Experiment: The platform introduces an interactive tutorial for the new feature. Half of the existing customers are exposed to the tutorial when they log in, while the other half does not see it. User interaction and feature adoption data are collected.
  • Run the Experiment: Users’ interactions with the tutorial and their subsequent adoption of the feature are tracked. The platform measures how many users from each group actively use the new feature.
  • Debrief: After the experiment, the platform analyzes the data. If the group exposed to the tutorial shows a 25% or higher increase in feature adoption, the hypothesis is validated, and the tutorial is implemented for all existing customers. If not, the platform may refine the tutorial or explore alternative strategies.

Outcome: EDD helps the SaaS platform make evidence-based decisions to drive feature adoption among its customer base.

Key takeaways

  • Experiment-Driven Development is a hypothesis-driven approach to software development that is based on fact.
  • Experiment-Driven Development incorporates A/B testing, where a baseline sample is compared to a single-variable sample to determine which sample delivers a better outcome. This allows the business to formulate, test, and evaluate hypotheses.
  • Experiment-Driven Development complements approaches such as TDD and BDD, but it does not replace them. EDD can be used in any industry or department as an efficient and (most importantly) objective means of agile software development.

Key Highlights

  • Understanding Experiment-Driven Development (EDD): EDD is an agile development approach rooted in scientific methods. While TDD and BDD focus on code quality and behavior, EDD helps identify features by testing hypotheses with A/B split testing.
  • EDD Process in Four Steps:
    1. Hypothesis: Start with a hypothesis related to customers, problems, solutions, value, or growth.
    2. Identify Experiment: Define a small experiment to prove or disprove the hypothesis. For instance, testing a virtual shoe fitting station’s impact on sales.
    3. Run Experiment: Create an MVP, use validated learning from end-users, and apply the Build/Measure/Learn loop.
    4. Debrief: Analyze observations, learnings, and results. Decide to pivot, persevere, or move to a new hypothesis.
  • Benefits of EDD:
    • Structure: EDD provides a structured process for asking and answering questions based on validated hypotheses.
    • Versatility: EDD is adaptable across various industries and departments, not just R&D.
    • Objectivity and Efficiency: EDD ensures value through validated learning, avoids assumptions, and prioritizes efficient MVPs over unnecessary features.
  • Key Takeaways:
    • EDD is a scientific approach to software development.
    • It uses A/B testing for hypothesis validation.
    • EDD complements TDD and BDD, enhancing agility and objectivity.
    • EDD is versatile and applicable to various industries and departments.

What are the steps to implement experiment-driven development?

The steps to implement experiment-driven development are:

What are the benefits of experiment-driven development?

The benefits of experiment-driven development are:

Read Also: Business Models Guide, Sumo Logic Business Model, Snowflake

Innovation, Agile Methodology, Lean Startup, Business Model

Innovation, Agile Methodology, Lean Startup, Business Model Innovation, Project Management.

Connected Agile & Lean Frameworks

AIOps

aiops
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.

AgileSHIFT

AgileSHIFT
AgileSHIFT is a framework that prepares individuals for transformational change by creating a culture of agility.

Agile Methodology

agile-methodology
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

agile-program-management
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

agile-project-management
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

agile-modeling
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

agile-business-analysis
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

agile-leadership
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. 

Andon System

andon-system
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

bimodal-portfolio-management
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 Matrix

business-innovation
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 Model Innovation

business-model-innovation
Business model innovation 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.

Constructive Disruption

constructive-disruption
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.

Continuous Innovation

continuous-innovation
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.

Design Sprint

design-sprint
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.

Design Thinking

design-thinking
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

devops-engineering
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.

Dual Track Agile

dual-track-agile
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

extreme-programming
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

feature-driven-development
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.

Gemba Walk

gemba-walk
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

gist-planning
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.

ICE Scoring

ice-scoring-model
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.

Innovation Funnel

innovation-funnel
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.

Innovation Matrix

types-of-innovation
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).

Innovation Theory

innovation-theory
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.

Lean vs. Agile

lean-methodology-vs-agile
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.

Lean Startup

startup-company
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.

Minimum Viable Product

minimum-viable-product
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.

Leaner MVP

leaner-mvp
A leaner MVP is the evolution of the MPV approach. Where the market risk is validated before anything else

Kanban

kanban
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

jidoka
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.

PDCA Cycle

pdca-cycle
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.

Rational Unified Process

rational-unified-process
Rational unified process (RUP) is an agile software development methodology that breaks the project life cycle down into four distinct phases.

Rapid Application Development

rapid-application-development
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 Analysis

retrospective-analysis
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

scaled-agile-lean-development
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.

SMED

smed
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.

Spotify Model

spotify-model
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.

Test-Driven Development

test-driven-development
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

timeboxing
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

what-is-scrum
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

scrumban
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

scrum-anti-patterns
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-at-scale
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

six-sigma
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

stretch-objectives
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.

Toyota Production System

toyota-production-system
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.

Total Quality Management

total-quality-management
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.

Waterfall

waterfall-model
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. 

Read Also: Continuous Innovation, Agile Methodology, Lean Startup, Business Model Innovation, Project Management.

Read Next: Agile Methodology, Lean Methodology, Agile Project Management, Scrum, Kanban, Six Sigma.

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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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