Vous concevez un système complexe. Comment maintenir l’innovation tout en assurant la fiabilité ?

Innovation and reliability are not mutually exclusive. Innovation leads to new ways to solve problems or design systems, but reliability comes during the build process. Iterative testing and refinement will continuously improve the reliability of any system, whether that solution is innovative or not.

Innovation in complex system design hinges on iterative refinement. As an aerospace engineer with a deep dive into data science and IT systems, my approach emphasizes embracing iteration. Starting with innovative prototypes, I leverage my experience in sensor technologies and automation to iterate through testing and feedback cycles. This method not only fosters creativity but also enhances reliability by systematically identifying and rectifying flaws. This iterative process aligns seamlessly with my career goal of transitioning towards bioinformatics, where precision and innovation converge to drive impactful solutions.

Maintaining innovation while ensuring reliability in a complex system requires a balanced approach that integrates creativity with rigorous engineering practices. Here are some key strategies: Clear Requirements and Goals: Define clear and achievable requirements and goals from the outset. This provides a solid foundation that guides innovative efforts while ensuring they are aligned with the system's purpose and user needs. Implement a modular architecture that allows for innovation within specific components or modules without compromising the entire system's reliability. This approach enables independent development, testing, and integration of innovative features.

The passage you've shared emphasizes the importance of iteration in the process of innovation and system design. Let's break down the key points and implications: Starting with a Prototype Purpose: Prototyping serves as a tangible representation of your innovative ideas. Advantages: It allows for practical testing and initial feedback gathering. Action: Begin with the core innovative concepts to see how they function in a real-world scenario. Testing and Gathering Feedback Purpose: To assess how well the prototype works and its reception by users or stakeholders. Advantages: Helps identify flaws, weaknesses, and areas for improvement early on.

the Best options is create the project with prototype, because you can keep the currently design that already have quality, concepts approved and through prototype if something not working, issue that appear during developing, you can change to solve before the final design. the other point high importance, is deploy the system in little system, in complex system, when you deploy in minor system, you can identify more easy issues and fix before finish the design.

I developed my own AI algorithms, based on artificial neural network (ANN) theory, the basis of today's AI's, for curve fitting tasks back in the 1980's. The advantage of ANN's is that they can fit themselves to data like chameleons. Their disadvantages are two. First, they're great at interpolation but fail at extrapolation. The second is that because they train themselves to the data there is no traceability as to how they actually function. They are the ultimate black boxes. For those that use them, the ANN's are doing the innovation and those that programmed them are hoping for good results. One must be really really careful in understanding what AI's can and cannot do. They have shown they can lie just like humans. Bon chance.

An agile methodology is critical to keep going in a positive direction. This keeps the goal of the application in mind. It means the goal can change when it is discovered that it needs to change and there is not a loss of time or manpower beccause it is found early.

Well, this one is easy. The K.I.S.S. (Keep it simple, stupid) principle is monumental here. Until we validate what our consumer wants, there’s no need to build something massive, only to learn they hate it.

Assessment of Impact Purpose: To understand the potential consequences if a failure occurs. Action: Evaluate how each identified risk could impact the system, users, stakeholders, or the organization as a whole. This helps in prioritizing which risks need immediate attention. Proactive Risk Mitigation Purpose: To devise strategies to minimize or eliminate identified risks. Action: Develop mitigation plans or strategies early in the design process to address high-priority risks. This proactive approach ensures that potential issues are managed before they escalate.

Maintaining innovation while ensuring reliability in designing complex systems demands a strategic blend of methodologies, in my recent endeavor with Regulatory Science & Affairs R&D, Risk Characterization is even more significant. Risk analysis is pivotal, identifying potential failure points early to prioritize mitigation strategies effectively. Modular design fosters both innovation and reliability by allowing components to be upgraded independently. Continuous testing and feedback loops ensure ongoing validation and improvement. Knowledge sharing promotes collective innovation while maintaining standardization. These practices collectively safeguard reliability while nurturing innovation across the system's lifecycle.

the Risk Analysis the important is identify possible failure and create one tree failure, each failure, what is the solution for the failure and the way to be avoided, if one failure witch system affected and study the probability of failure occurring. if the failure is isolate or one failure that affect other system.

También es importante evaluar si el costo de la solución excede el impacto del riesgo. Ya que si es así, no vale la pena invertir en la solución y se tenga que tomar otras medidas, como evitarlo o anularlo

Reduce complexity as much as reasonably possible. Focus on robust designs. If your innovation allows to include proven subsystems, don't reinvent wheels. For risk analysis and design review, consider involving external experts to bring other views.

Totally agree that modular design is powerful tool to ballance between innovation and reliability in complex system Innovation by allowing incremental upgrades. Reliability by isolating faults

Approach: Simplifies fault isolation and troubleshooting. Action: When a fault occurs, modular design allows you to localize the issue to a specific module. This makes it easier to diagnose and rectify problems without impacting the overall system’s functionality.

Modularity IS a tool useful in this. Once a design is in a version that is mature enough to be analyzed, the team could be tasked with assessing the highest risk modules / elements using the whatever standard practice the organization employs. Then ask the team not just to improve the risk of those areas, but to re-think the problem such that the ones shown would evaluate out as among the LOWEST one third of the modules in terms of risk. It is that sort of thinking that yields innovation since it requires leaps of thought, not iterative steps. That is often the key to innovation -- requiring leaps. (and by the way, leaps are always required, even on 'normal' days, but we get away with not doing them, so we innovate less than we could)

First build “Lego” pieces. The great thing about them: 1. Allows to iterate quickly 2. Allows to pivot quickly 3. Allows to flex quickly In case you didn’t get the memo: it keeps us quick.

Another advantage to modularity is that you can utilise off the shelf hardware where appropriate, allowing an innovative overall concept/design to take advantage of the reliability and dependability of tested, optimised, cost effective designs. It's not always best to reinvent the wheel!

Modular system you can identify one failure easy and isolate for the other system, complex system with modular system, make the system independent. if you need to modify the system or improvement, the modular system is the best options.

Without a doubt, the modular design will allow the entire system to be managed in a better way, allowing to have equipment by subsystems that will guarantee their availability. This contributes to the reliability of the system, maintaining the other functional parts in case of failures, without needing to affect other areas and thus be able to efficiently address solutions to the problems that arise.

Be clear on your interfaces connecting your modules. Use standards, if possible. Understand the behaviour of your modules. Understand the impact of module failures on your system.

Continuous testing is the key to allow and promote innovation and new approaches. It is a kind of safety net to secure users, customers and developers.

Continuous testing is essential for maintaining reliability throughout the development process. By integrating testing early and often, teams can ensure that software meets quality standards, performs reliably, and meets user expectations. This approach not only reduces the likelihood of defects slipping into production but also fosters a culture of quality and collaboration within development teams.

I agree, the only aspect that I can add is that if there are failures all parameters must be recorded "video, photographs" plus your review and restest strategy must be rock solid.

In most aircraft systems I have operated they take into account failure of the system and associated problems should it be lost and duplicate or triplicate within the design to enable safe completion of the task. Generally in my experience the Kiss (keep it simple stupid) is the way to go although at times may not be possible. Obviously testing the system by causing the failures to see exactly what happens proves the design or not. We did once cut through a power shaft with an oxyacetylene torch to prove a free turbine runaway trip prevention system, luckily it worked.

One great but infrequently - used example of a feedback loop is the "pre - deployment failure analysis". There are other names for it but basically you ask the team to imagine that their design is adopted, was implemented as planned, but failed catastrophically. Even though this failure is hypothetical, of course, their task is to describe, in detail, the one or two most likely causes of the failure. This gives them 'permission' to attack their own design in ways no one else could. The insights they get from doing this could lead to an innovation that could actually ENHANCE, rather than just preserve, reliability.

Integrating feedback loops into the design process is essential for creating successful products and systems. By continuously gathering and integrating feedback from end-users and stakeholders, designers can ensure that their designs are user-centered, effective, and aligned with business goals. This iterative approach not only improves the quality of the final product but also fosters a collaborative and customer-focused design culture within organizations.

Innovation for the sake of innovation is overrated. First focus on getting the core functionality right, and ensuring that it works to a high standard. Any additional discoveries can be developed by actually placing the system in its operating environment. This will yield new insights and can kickstart evolutionary growth into the more complex form which the Agile framework is best suited to.

Try to get real world experience as soon as possible. Collect inputs from critical voices. Include the learnings in your next prototype (see "iterations" above).

In designing complex systems, balancing innovation with reliability is crucial. Embracing iteration, rigorous risk analysis, modular design, continuous testing, feedback loops, and knowledge sharing are integral to this process. Promoting knowledge sharing (electronic notebook) within teams fosters both innovation and reliability by leveraging collective wisdom from past projects and industry developments. This approach not only anticipates potential challenges but also nurtures creative solutions that uphold system integrity. In these situations repository management and version control are crucial to ensuring cybersecurity, but also to enable the use of collaborative workflows such as Agile and Scrum to assign roles and tasks.

In my experience, sharing ideas (technical brainstorming) with worldwide partners ever offers a chance to know and discuss something that was made in a revolutionary way with different clients around the world. Clients have different perspectives on what is expected and is up to you to offer the best solution, ever. Artificial Intelligence, document repository, and official product support also can help to increase the options list.

Perform lessons learned sessions. Present your findings to colleagues who could benefit from your experience. Offer to be (project-)external reviewer for other innovation projects.

Innovation during design should not be inhibited by reliability concerns, especially from the start. The process of design or the Design Cycle as it's popularly known inherently brings in reliability. A more practical approach according to me is to keep doing 'What If Analysis' with every iteration and then normalise the solutions with standards if available or in absence of standards, undertake risk assessment. This is essential as requirements for reliability would restrain innovation.

Designing a complex system implies : innovation, reliability, emergence and invariance to change of scale. Necessary cooperations unveils causal relationships between variables, eliminating reductionism, common sense and vague language. Denotation and referencing are the sticking points in data analysis. Key stages : data research, interviews, modularization, validation of security criteria and control, within a permanent cycle of prototyping and testing. System designing starts from the workshop, not from the design office (Kelly Johnson), in order to stick to physical realities. Retrodictions are always necessary but never sufficient. Security is a the heart of innovation.

Innovation - tied to Creative side Reliability - usually tied to having lots of checks, testing, risk etc. Question here was how to have both of these. The first thing to be considered is to start thinking how without putting a STOP or BAN start thinking on how to reach a solution. Eg: developer having full control of system and executed simple recursive delete command 2 ways to handle 1. BAN/ STOP culture - Restrict the developer access. Or add additional process and make it difficult to access 2. Ideation culture - how fast the entire system can be brought back to original state and how easily can you do it - create backups, or auto restore having the deleted data back. Question is what do you choose?!

This might be a bit counterintuitive, but I would constrain, and pretty tightly, the design team by some key parameter OTHER than reliability and then ask them to be able to defend the approach in light of its reliability. Innovation, I think, is driven by need, seldom by plenty. So, rather than have them 'focus on reliability' or even on 'innovation', I would place a stress on the team that requires innovation. Doing this, I could perhaps see new tradespaces I wouldn't otherwise have been aware of.