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Complementary to agile methods, design thinking is a creative design approach based on brainstorming and collaborative creation.

What is design thinking?

Design thinking is a collective innovation approach. At the crossroads of analytical and intuitive thinking, it recommends involving future users in a collaborative creativity logic. The idea is to get them to capitalize on collective intelligence to come up with innovative product or service prototypes.

If design thinking has come back into fashion in recent years, it is a method that originated in the 1950s with the invention of brainstorming by the American advertising executive Alex Osborn, a concept that made the business world aware of creative thinking. The concept of design thinking was more widely evoked in the book “Visual Thinking” by Robert H. McKim, published in 1973. It was then developed in detail in the 1980s by Rolf Faste at Stanford University.

What is the purpose of design thinking?

The aim of design thinking is to stimulate innovation in an organization, whatever its size, by organizing collective creation sessions. The objective is to come up with a concept or even a prototype of an innovative product or service.

Design thinking is based on four main principles:

1. Identify the problem to be solved,
2. Take into account the users’ needs,
3. Fostering collective and multidisciplinary intelligence,
4. Test several solutions, do not be afraid of failure to progress.
   

What are the three basic criteria of design thinking?

Design thinking takes shape in a process that was defined by the American designer Rolf Faste. It is based on three major complementary evaluation criteria:

1. Desirability which refers to the question of whether users really want the proposed solution, and ultimately whether they will adopt it,
2. Feasibility which implies having the necessary resources to develop this solution (budget, technologies, skills…).
3. Sustainability which echoes the interest of deploying such a solution, and more precisely its profitability, i.e. its potential return on investment.

What are the 5 phases of design thinking?

According to the Stanford University d.school, design thinking can be reduced to 5 stages which are:

1. Empathy With customers/users to get to know them,
2. The definition of the problem to identify their expectations,
3. Idea generation to find an answer to these expectations,
4. Prototyping to shape the solution,
5. The test phase to test the final product.

What are the main design thinking tools?

The design thinking method relies on many tools, such as the customer journey map which traces all the interactions of a consumer with a product, to create a user journey. It is also possible to use affinity diagrams, moodboards, personas or mind maps. Then come the tools specific to design:

• Brainstorming,
• Co-creation meetings,
• Idea menus,
• The positioning matrix.

Some design thinking use cases (examples)

Combining the analytical thinking of engineers or marketers and the intuitive thinking of creatives, design thinking focuses on the user experience (UX). Going through three key stages (inspiration, imagination and implementation), design thinking can be applied to all areas involving interaction between the user and the product or service. From there, design thinking can be found in many fields: fashion, software man-machine interfaces, car interiors and dashboards, interior design, smart cities, connected objects…

Several famous brands have used design thinking to find innovative solutions for their customers. For example, Lapeyre called on students from d.school Paris. Their objective was to rethink the bathroom to adapt it to seniors. After more than a year of discussions with senior citizens, caregivers and carers, they identified the needs of the elderly: to be able to sit down, to protect their belongings from water, to be able to see better up close, to be able to move around easily, to have good lighting, etc. By analyzing these needs and implementing the 5 steps of design thinking, they were able to create the “Concept” Care modular furniture.

With the design thinking method, personas are used in the definition phase. They allow to make visible and concrete the users to better understand their expectations. Personas allow marketing teams to remember who their target is when developing a product or service.

How do design thinking and agility complement each other?

Agile methods and design thinking are complementary, not to say intimately linked. In the agile project management cycle, design thinking is positioned in the solution research phase. The various agile methods then cover the way in which this solution is developed by implementing regular feedback between the team of developers and the end users. Their objective: to create and deploy a product through successive iterations, constantly validated by the business client, both upstream and downstream, in order to avoid any drift.

Design thinking training

Taking up all these theoretical writings, design thinking training teaches this entrepreneurial approach which is now most often summarized in three key steps: identify a problem and understand its environment, find the idea that will solve this problem, and transform the idea into a concrete project.

The teaching of design thinking insists on the fact that this entrepreneurial method requires a logic of co-creation, intellectual gymnastics between analysis and intuition, but also a field study. If design thinking can be learned through workshops or Mooc. IBM makes its internal design thinking guide available to everyone online.

Alexa, Google Home and Siri voice assistants are all based on automatic language processing technologies. Objective: to have the ability to understand, process and generate voice messages.

What is natural language processing (NLP)?

Natural language processing (NLP) is a branch of artificial intelligence that focuses on giving machines the ability to understand, generate or translate human language as it is written and/or spoken. Chatbots are among the most popular NLP software. Other famous NLP applications are the voice assistants Alexa, Google Home or Siri.

Why NLP?

Natural language processing aims at providing software with automatic processing of vocal or textual language. From there, it covers many cases of use more or less elaborated:

• Text classification,
• Text recognition,
• Automatic summarization,
• The automatic translation,
• Chatbot, voicebot or callbot,
• Intelligent assistants.

NLP combines artificial intelligence and language processing. The latest generation of NLP technologies is based on artificial neural networks or simple statistical machine learning models. Learning models will have been trained on large volumes of text.

The objective can target several types of automatic processing: speech-to-text and text-to-speech, named entity recognition (names of people, places…), sentiment analysis (positive, negative, neutral), text summarization, aspect extraction (targeting the intent of the text) or topic modeling.

What are the two types of machine learning models in NLP?

Overall, natural language processing falls into two broad categories of machine learning models:

• The machine learning models oriented NLU (natural language understanding), which are concerned with grasping the meaning of a language and a discourse in its context,
• The machine learning models oriented NLG (natural language generation) which aim to generate a text in the manner of a human.

Some examples of NLP oriented deep learning models

Although natural language processing has been around for a long time, recent progress in this field has been considerable. The number of NLP projects oriented towards artificial intelligence has multiplied among the digital giants. Among the most advanced models, we can mention:

• Google AI’s BERT and ALBERT models,
• Models derived from this first family such as RoBERTa (Facebook), StructBERT (Alibaba), DeBERTa (Microsoft), DistilBERT (Hugging Face),
• Alternative models such as GPT-2 and GPT-3 (OpenAI), XLNet (Carnegie Mellon University), UniLM (Microsoft), and Reformer (Google).

With the development of artificial intelligence, robotics is booming. Automata are now joined by humanoid robots or autonomous computer programs: bot, chatbot…

What is robotics?

Robotics is a field of activity covering the study, design and manufacture of robots or automated machines. It involves skills in computer science, electronics and mechanics.

What is a robot ?

To be considered as a robot, a machine must be equipped with sensors, which analyze the environment, but also with a logic processing system and actuators.

From the information obtained in its environment, the latest generation robot can act autonomously and intelligently based on embedded learning models or machine learning. It thus enters the AI family.

What is the origin of the word robot?

The term robot was conceptualized in 1920 by the Czech writer Karel Çapek. He used this term in one of his plays (Rossum’s Universal Robots) to designate an android invented by a scientist to perform tasks normally done by humans.

Robots types:

Robotics encompasses a very broad and diverse field of activity. Here are some examples and fields of application:

• Industry: assembly line management robot…
• Army: drone, spy robot, mule robot…
• Security: video surveillance…
• Health: ultrasound, assisted surgery…
• Aerospace: NASA explorer robot…
• Transportation: autonomous car…
• Domestic use: vacuum cleaner robot, lawnmower robot…
• Accompaniment: automated toy, humanoid robot…
• Computer science: chatbot, voice assistant…

Robots revolution ?

1. The first robots were nothing more than mechanical automatons. In the 16th century, Leonardo da Vinci is said to have created the first android automaton that could coordinate the movements of its limbs and jaws.
2. Cybernetic robots emerged in the early 20th century. They are robots programmed to perform specific tasks. The most advanced are the first rovers sent to other planets.
3. Humanoid robots have emerged from the 2010s. These are robots that take on a human appearance and are designed to interact with humans. This generation integrates the latest developments in AI, especially in deep learning.

What are the three laws of robotics?

In order to limit the behaviors or harmful effects that could be caused by robots with autonomy, three laws of robotics have been enacted. These laws were enacted by the science-fiction writer Isaac Asimov and the editor John Campbell. These three laws appeared in the short story “Runaround” published in 1942:

• A robot may not harm a human being or, by remaining passive, allow a human being to be exposed to danger.
• A robot shall obey orders given to it by a human being, except where such orders conflict with the first law.
• A robot must protect its existence so long as such protection does not conflict with the first or second law.

What are the related jobs in robotics?

Among the jobs in robotics are robot designer, maintenance technician, mechatronics technician (a profile combining electronic and computer skills) or digital control operator.

When did robotics first appear?

The term robotics, derived from the word robot, was first used by the Russian writer Isaac Asimov. It is found in the science fiction story “Liar!”, published in 1941.

Robots, inspired by the first automatons, appeared at the beginning of the 20th century. They are equipped with sensors allowing them to collect information on their environment, which then influence their activity. The development of robotics is strongly linked to the industrial field, which has developed since the middle of the 20th century. The latest intelligent robots created are autonomous objects, voice assistants, humanoid robots…

Speech synthesis allows the machine to translate the voice into text. With artificial intelligence, its operation becomes optimal. Many free speech-to-text tools exist.

What is speech synthesis?

Speech synthesis is a computer technique that consists in generating an artificial voice. It relies on linguistic processing techniques to convert the text produced by the machine into a phonetic version, and then on signal processing techniques to transform the latter into a digital sound rendering that can be broadcast through a loudspeaker.

In contrast to speech-to-text (automatic speech recognition), text-to-speech refers to the transformation of computer text data into artificial voice.

What is a synthesized voice?

A synthesized voice results from the conversion of a text into a sequence of phonemes aiming to be as close as possible to a human voice.

What is the best text-to-speech?

In 2020, the Mozilla Foundation published a study in collaboration with Carnegie Mellon and Northwestern Universities to estimate the quality of text-to-speech applications. Google’s wavenet text-to-speech model came out on top of this benchmark, ahead of Windows and Amazon Poly.

Free text-to-speech tool

There are many free text-to-speech tools available online. They are designed to translate text into voice on the fly. Some of these voice generators include:

Text-to-speech in Word is offered in Office 2019, Office 2021 and Microsoft 365. To activate it, you need to follow these steps: from Azure Text to speech for the first

1. Go to the Review menu,
2. Click on “Read Aloud”,
3. From the command menu, select “Read” to have Word read the text aloud.

Realistic speech synthesis

Amazon, Google and Microsoft each offer text-to-speech cloud services based on giant artificial intelligence engines. The goal: to get the most realistic voice possible. The first one is Amazon Polly, the second one is Google Cloud Text-to-Speech and the third one is Azure Text-to-Speech.

To create a synthesized voice, it is necessary to use a text-to-speech (TTS) engine. It allows to shape autonomously an artificial voice from a text produced by the machine, thanks to the use of AI and deep learning.

It is important to differentiate between intelligent TTS software and simple automated voice response software, based on pre-recorded words in a database. There are many websites offering a free text-to-speech service (see list above).

What is a text-to-speech engine?

A text-to-speech engine consists of a front-end and a back-end. The front-end is dedicated to splitting the text into words by associating each one with its phonetic transcription. This phonetic analysis step by fragmentation of the text occurs first.

Then, the voice synthesis engine uses its back-end system (synthesizer) to convert the linguistic and phonetic strings thus cut into sound. The synthetic voice is created by this last process.

What is the contribution of deep learning in speech synthesis?

Deep learning, through the use of deep artificial neural networks, allows to optimize speech synthesis in order to bring the sound rendering closer to the human voice. It reproduces voice inflections, intonations, tone variations, even accents…

Deep learning also introduces changes in rhythm and pronunciation. These elements will contribute to a better understanding by the target audience, but also more flexibility in language programming.

On Android, Google offers a text-to-speech application. Available on Google Play, it allows to use an Android smartphone to control applications by voice. In concrete terms, it translates a vocal request into a written request that can be understood by the software. It also allows to transcribe a text that has been recorded vocally.

Within its cloud offer, Google proposes a speech-to-text API for developers. It allows them to integrate text-to-speech features into their applications on a pay-per-use basis.

Examples of speech-to-text applications

Speech synthesis concerns different fields of application, such as:

• Audio books,
• Audio working documents (example of ReadSpeaker software that also underlines the written text),
• Techniques for reading without looking at a screen (visually impaired),
• Intelligent automated telephone services,
• GPS,
• Bank machines with integrated voice,
• Voicebots,
• Intelligent voice assistants (Alexa, Google Home…)…

Reinforcement learning reproduces the “natural” mechanism of knowledge acquisition. Robot, chatbot, autonomous car, its applications are multiple in artificial intelligence.

Reinforcement learning implements learning algorithms that learn from repeated experiences by trial and error. It thus reproduces the “natural” mechanism of knowledge acquisition.

To guide learning in the desired direction, reinforcement learning algorithms validate the decisions made by the machine via a reward or penalty mechanism. One could compare the process to training.

What are reinforcement learning algorithms?

The two most famous reinforcement learning algorithms are TD learning (for temporal difference learning) and Q-learning. These learning models are inspired by the human (and animal) process of acquiring knowledge through trial and error.

What is the advantage of reinforcement learning?

The main advantage of reinforcement learning is that to program a robot, for example, there is no longer any need for long and tedious development work. The computer will learn to operate, to react to this or that event or request by itself.

Whether the robot is physical or virtual, the learning phase will be carried out in the form of a digital simulation. This operating mode optimizes the learning time.

What is deep reinforcement learning?

Classified as automatic (or unsupervised) machine learning, reinforcement learning generally relies on neural networks in order to efficiently estimate the validity of a “complex” strategy, with a large number of choice criteria to be taken into account. This is called deep reinforcement learning (DRL). The main challenge is to achieve a system that encourages the desired behaviors, without undesirable side effects.

Often presented as the ultimate AI, DRL allows the creation of software capable of reaching or even surpassing human intelligence in several domains. The most famous system taking advantage of the method is none other than DeepMind, Google’s AI platform (resulting from the 2014 acquisition of the British company of the same name). It is on it that the American giant based itself to develop AlphaGo, the supercomputer known for having defeated in 2017 the world champion of go, the Chinese Ke Jie.

Examples of reinforcement learning

Deep reinforcement learning is used in many areas:

• Robotics in factories and warehouses to allow automata to learn by themselves how to lay down a new part model without prior programming.
• Calibration and quality control of industrial systems, whether they are focused on manufacturing, supply chain or energy production,
• Finance to optimize automated trading or market risk management,
• Text summarization to estimate the overall quality of a summary by extracting from a word by word logic,
• Game and recommendation engines for developing strategies in uncertain environments,
• The autonomous car to improve the vehicle’s ability to react to a given traffic event,
• …

The strong artificial intelligence would be in the image of the man with a superior cognitive intelligence. But is strong artificial intelligence possible? Definition and examples.

What is strong artificial intelligence?

According to the theory of strong artificial intelligence, AI could develop an autonomous consciousness, sensitivity and will, on the model of human beings.

The goal of strong AI is to create a complex intelligence designed to address the full range of problems that humans are capable of grasping, as the human brain can naturally do.

Is strong artificial intelligence possible?

Several aspects of intelligence belong to the field of strong intelligence:

• cognitive intelligence (or cognitive computing),
• emotional intelligence,
• social intelligence,
• psychomotor intelligence.

Today, the concept of strong AI is theoretical rather than practical. Most of the programs based on artificial intelligence appeal mainly to cognitive and cognitivist abilities:

• rationality,
• organization,
• planning,
• learning,
• autonomy,
• problem solving.

Examples of strong artificial intelligence are still in the realm of science fiction. There are machines integrating AI, with great performances, in particular cognitive ones, but these machines are not provided with any autonomous consciousness.

For some, the exceptional performance of deep learning, based on artificial neural networks with a complex architecture, augurs well for strong AI. A concrete example of strong artificial intelligence, which is still highly perfectible, is the autonomous car. In its most accomplished version, it can make a decision (brake, stop, turn) and avoid some incidents, but not all.

Today, the autonomous car can assist the human being, and even, for some models, ensure 98% of the driving load. But the other 2% must still be managed by human intervention. The autonomous car has neither consciousness nor sensitivity and, faced with particularly complex situations, is not able to react.

What is the greatest artificial intelligence?

The largest machine learning models are the large language models (LLMs). One of the most famous of them is GPT-3 (for Generative Pre-trained Transformer 3). Unveiled by the company OpenAI in 2020, it is a giant neural network for automatic language processing with no less than 175 billion parameters. It is based on a Transformer architecture.

What is the difference between strong and narrow artificial intelligence?

Weak artificial intelligence uses AI for specific tasks. It can perform calculations, process large amounts of data, solve specific problems and learn in an automated way. But it has no consciousness of its own and acts according to the way it has been programmed by humans.

Strong AI is the AI vision of tomorrow, it aims to create autonomous machines with consciousness. Today, the AI integrated into applications and machines belongs to the weak AI. Strong AI, on the other hand, is the challenge and certainly the most ambitious project in AI.

An integrated web-based customer relationship management solution, HubSpot covers sales, marketing, customer service and e-commerce management applications. Its entry-level service is free.

What is HubSpot?

HubSpot is a CRM platform. With more than 150,000 customers, it targets primarily mid-sized companies. Offering a free entry-level edition, it aims to meet the main needs related to customer relationship management via several applications:

• Marketing Hub is oriented towards the management of inbound marketing campaigns. It covers e-mailing, chat, form management, marketing automation, intelligent content management, A/B testing…
• Sales Hub manages sales by integrating the follow-up of e-mails, notifications, calls, appointments, but also the automation of tasks, the management of pipelines…
• CMS Hub is a web content management system including a no-code web editor, an online store, a chatbot, as well as webperf, SEO tools…
• Service Hub orchestrates customer service and support actions across all channels. The application is built around a portal based on a knowledge base.
• Operations Hub is designed to drive and align relationship management teams.
• A centralized customer database The suite offers more than 5000 integrations, bi-directional data synchronization…

What is the purpose of HubSpot?

HubSpot is an all-in-one web-based customer relationship management solution. Unlike other CRM solutions, such as Salesforce, HubSpot was developed as an integrated offering from the start. It brings together content, messaging, automation, data and reporting. For executives, it provides global visibility into the performance of their teams, with standardized dashboards for the entire company. This integration also streamlines CRM processes around a single platform and a single customer and prospect database.

To complete the picture, HubSpot offers a marketplace of over 5,000 integrations. Among the most popular are Gmail, Google Calendar, WordPress, Outlook, Zapier, Facebook Ads, Google Ads, Zoom and Slack. In parallel, HubSpot offers a hub of nearly 6,500 service providers who can help customers implement its technology.

In the logic of code-free development, the platform can be enriched with new data categories (persona). This functionality allows, for example, to customize the criteria of customer or prospect profiles. In the background, this process contributes to structuring HubSpot’s database to better segment it.

Is HubSpot free?

HubSpot offers a free version of its platform to start using its sales management, customer service, and CMS features. Only the marketing management features are not free. Their price depends on the number of contacts used.

How much does HubSpot cost?

The HubSpot platform has an entry price of 41 dollars per month with a limit of 1,000 marketing contacts and two collaborators for sales and customer service management tools respectively. This is the HubSpot Starter version.

HubSpot Professional (from 1 474 dollars) and HubSpot Enterprise (from 4 593 dollars) unlock these limitations. The first allows 2,000 marketing contacts, and the second 10,000. Both allow 5 users for both the sales management tool and the customer service management module.

Does HubSpot have a signature generator?

HubSpot does offer a signature generator to create an email signature with ease.

All you have to do is go to the platform’s website www.hubspot.com, select the product that suits your needs and create a free account. Hubspot then guides users through the various steps involved.

Quite similar to the famous Trello software, Microsoft Planner is an ideal tool for managing tasks and teamwork. Available for Office 365 Enterprise subscribers, it facilitates collaborative work.

What is a planner?

Planner is a task manager included in the Microsoft 365 productivity suite (formerly Office 365). It offers a simpler and more modern solution than Microsoft Project, the historical project management tool from Microsoft.

This new generation solution is based on a task management with the possibility to control the evolution of projects through kanban boards.

Is Planner free ?

Microsoft Planner is included in the subscriptions of the Microsoft 365 Enterprise suite, whose entry price is 35.40 dollars per user and per month with an annual commitment (Microsoft 365 E3 plan).

Why use Planner ?

For Microsoft 365 users, Planner is a solid and efficient solution for task management. Oriented towards teamwork, it allows you to assign tasks, to share them with your collaborators by integrating any associated content, to follow the progress of each one, to visualize the due dates…

Planner is accessible online through tasks.office.com, but also as apps for iOS and Android. It also integrates with Microsoft Teams unified messaging through the Tasks app. A collaborative brick that is also designed to include actions managed via Microsoft To Do, Microsoft’s other task management tool. At the same time, Planner connects the tasks to be performed to the specification documents stored in SharePoint. Finally, it can use Power Automate to develop specific project management processes in no-code mode.

Planner allows you to create plans. That is to say, projects broken down into tasks assigned to project team members. To create a plan, the following procedure must be followed:

1. Click on “New plan” on the Planner homepage,
2. Enter the name of the plan,
3. Assign this plan to an existing Microsoft 365 group or create a new group,
4. Select + to create a task,
5. Select its due date,
6. Choose “Add a task”.

Planner allows you to export a task calendar once created in Outlook, so you can receive a reminder when a task is due or the deadline has passed. The task calendar can also be shared on Teams.

Planner can be integrated with Teams, to notify each user when a task is assigned, removed or completed. To do this, simply go to the team channel in charge of the associated project, add a tab, and select the Planner application. After programming the task scheduler, it is accessible to all members of the dedicated Teams channel.

Is it possible to manage a Gantt chart in Planner?

Planner does not allow the creation of a Gantt chart. For this, it is necessary to use Microsoft Project or Excel, also offered in the Office 365 suite.

To get Planner on Windows, simply go to your Microsoft 365 account, select the application launcher and choose Planner. On mobile, the Planner app can be downloaded from the Google Play for Android, and the App Store for iOS. Once the download has started, you only need to provide access to the Microsoft 365 account in order to use the tool.

Microsoft Planner vs Trello

Quite similar, both tools are widely used in companies to optimize teamwork. While Trello works with tables, lists and cards, Planner has plans, tasks and compartments. Despite the different vocabulary, the way it works remains the same. The big advantage of Planner, however, is its integration with Microsoft 365 tools such as ToDo and Teams.

Developed by a French start-up, Bubble Plan is an online planning tool. It allows, among other things, to manage project portfolios by creating Gantt charts on the fly.

What is Bubble Plan?

Through an ergonomic and secure online environment, Bubble Plan offers a planning-oriented application designed for project portfolio management. The tool is based on a task management system with various visual management features, from a Kanban board to a Gantt chart, including individual or collective calendar views.

What are the features of Bubble Plan?

In order to supervise a portfolio of projects, Bubble Plan includes the following tools:

• A planning tool which allows you to drag and drop the tasks to be carried out on a timeline by distinguishing the phases (scoping, pilot, development, deployment), sprints, stages, meeting dates… All identified by a color code,
• A Gantt chart which manages the constraints and dependencies that may exist between tasks or between different projects,
• A reporting tool to monitor the progress of projects with warning indicators in case of delay,
• A Kanban view to follow in detail the progress of each project,
• A schedule of the participants to manage and track task allocation, workloads, anticipate new projects and potential areas of tension,
• A task management tool designed to manage workloads by team,
• A schedule dedicated to each speaker,
• A personal dashboard to allow everyone to manage their priority actions, their meeting schedule, and their deadlines,
• A cat integrated into each project schedule to facilitate collaboration and teamwork,
• A comment thread manages the discussions around the projects as well as the moderation and validation processes.

In addition, Bubble Plan offers a data hosting service, with security features.

To make a Gantt plan in Bubble Plan, you need to navigate to the milestones and bubbles menu, then select the “Links” option. This feature allows you to manage the main action, as well as past and future subtasks. This includes the possibility to add “cause/effect” type constraints. From this point of view, the activation and connection by keyword of the parameterized actions are also possible.

Is Bubble Plan free?

Including the main features of the application, Bubble Plan offers a free entry-level service that limits the number of users to two. So this edition is limited to the discovery of the offer.

What is the price of Bubble Plan?

In its paid version, Bubble Plan has an entry price of 12 dollars per month for 10 users. This is the Bubble Plan Team offer. For 22 dollars per month, Bubble Plan Business allows 20 users and extends to the management of custom views.

Finally, Bubble Plan Enterprise offers a decreasing price according to the number of users. This edition includes a dedicated architecture and a separate database.

Editors feedback on Bubble Plan?

Bubble Plan stands out first of all by its clear interface and an immediate handling thanks to the “drag and drop” principle. The functionalities remain easy to access and are exhaustive to manage a portfolio of projects and their dependencies. This is one of the main strengths of this application.

Collaborative tools allow to perfect the offer, and to guarantee productive exchanges. Bubble Plan relies on a scalable system with regular updates.

Open source project management application, Redmine has gained in popularity until it competes with proprietary and paid applications. Presentation, tutorials, prices… Overview.

Redmine is an app belonging to the category Project management software. Open source, Redmine is free. Under the GNU GPL license it can be called free software. This license allows to use it for one’s own purposes, but also to redistribute it as long as one redistributes modified versions.

Written in the Ruby language, Redmine is based on the Ruby on Rails framework. It is available for Linux, Mac, Unix and Windows. On the database side, Redmine can use MySQL, PostgreSQL or SQLite.

Redmine is available for download from the open source project of the same name. The application is available for Windows or MacOS.

Why Redmind ?

Redmine covers all the key features of a project management application worthy of the name. Among the main ones are:

• Multi-project management,
• Management of user groups,
• The multi-project wiki,
• Customization of request fields,
• Management of roadmaps,
• A discussion forum by project,
• Gantt charts to visualize the progress of a project,
• Support and ticketing management,
• Etc.

Like most of its competitors, Redmine has tutorials to learn how to use its open source project management environment. Available online, you just have to open an account on the application’s website to start using it. Note that the tool is mainly used with the Scrum agile method.

On the site of the open source project, a user guide for Redmine is available in French. It covers the installation and migration phases, administration with different dimensions (project, user, collaboration group…) and advanced configuration (version management, notifications, authentication…).

After downloading Redmine from the project website or as a Docker image, you first need to install Bundler to manage the Ruby gems dependencies. Then comes the deployment and database setup phase, and the configuration of the environment as such (see the tutorial in French on the open source project website).

To make the process easier, you can turn to Bitnami Redmine Stack. It is an all-in-one installer. It helps you to install Redmine on your computer very easily and for free, regardless of the operating system you use (Windows, Linux, Mac).

As mentioned above, Redmine is an open source solution, usable “freely”. Nevertheless, it benefits from a commercial distribution, for a fee, which has been able to impose itself on the market. Called Easy Redmine, it extends the functionalities of the environment to facilitate the management of complex projects. Marketed in cloud mode by the London-based editor Easy Software, it has an entry price of 58 dollars per month for 25 users.

Easy Software’s offering includes a series of complementary extensions. They cover resource management, agile management and DevOps, financial management, electronic document management (EDM), BtoB CRM and help desk.

Redmine has a rather rich and eclectic repertoire of graphic themes developed by the ecosystem and the community around the open source project.

Among the more recent graphical templates developed for Redmine, we can mention Boostmine, Zenmine, Abacus Office, Gitmike or SASS. Some of them are commercial themes, for a fee, others are open source.

Redmine being an open source project management software, it regularly benefits from plugins (like those proposed by Easy Software) that allow to improve its features. Many developers work hard to make Redmine as efficient as possible. They allow you to choose an ergonomics and to benefit from a declination adapted to smartphones.

Called Theme changer, a plugin is designed to manage the selection and installation of graphic themes once Redmine is installed. On its website, the open source project details step by step how to develop a custom theme.

Redmine exposes some of its data through a REST API. It supports XML and JSon formats with resources such as issues, projects, users, time entries, wiki pages, and many more. Basic operations (create, update, delete) can be applied to these resources.

Redmine offers both project and ticket management. Many consider it a free ticketing tool that is ideal for managing multi-channel tickets in IT support.

Jira is probably the best known of Redmine’s competitors. Although it has superior features with no less than 3,000 integrated third-party applications, its price and its heaviness are two elements that counterbalance its strengths against Redmine.

GitLab is a software building platform based on Git. Developed by GitLab Inc, it covers bug tracking, continuous integration and continuous delivery (CI/CD). Areas where Redmine is not present. Just to manage access to source code repositories, Redmine has to rely on third party tools.

On the other hand, Redmine allows to create, customize and orchestrate allocation and event management processes (bugs, defects…) via a console called Trackers. GitLab does not yet offer the same degree of customization via its issue management system.