How can you design a system for optimal performance in a dynamic environment?

In my experience, defining Big Business Y's, such as system output you like to optimize…and linking these Big Business Y’s to metrics that are affected externally or internally within our processes that we can measure, is essential. If we take such approach, then impact of any change whether coming from internal or external can be easily tracked and we can understand their impact on our core metrics and KPI’s by our businesses. Establishing a virtual model that links inputs, the levers you can change, to outputs, Big Y's you like to optimize or control, and investigating in technology and resources to mimic the rules of our external world would give us a chance to control and optimize our business or system outputs.

One thing I've found useful is identifying "improvement over prior art" approaches at the beginning -- and early feasibility analysis to identify weaknesses. The more effort invested in any effort, the harder it is to "kill" -- so product design (systems are products) should test assumptions rigorously. Experience has shown huge wasted resources when solution feasibility isn't challenged at inception.

One of most important concept of system is the environment. Environment shold be a part of system design due to the principle of completeness. System, in other words, is a subsystem of a system of system. This approach can produce good requirements, models and performance and risk indicators to be evaluated by the managers to commit a product to be developed. It is more than an environment static or dynamica analysis (parametric).

Develop a CONOPS: Understand system needs, dynamic life cycle, system life cycle, temporal considerations. Provide for effective requirements: Derived requirements Requirements testing Requirements development Requirements compliance Requirements revision Requirements traceability Requirement documentation Requirement language Non Redundant requirements Inductive and deductive requirements Associated standards and codes...

You notice the AI missed the second premise of this article series, the "dynamic environment" part. So it sent me for a loop. This is a clean sheet rote answer. It doesn't work. No business, outside of the first year of a startup, is clean sheet. Existing businesses have gobs of constraints. The second problem is the fake goal of "optimality" What is the real measure of success? Zero defects, more sales, lower costs. The first and third are hyperbolic at best, and the second, according to the CFO, is boundless. This paragraph, is where most problems arise. Tools are irrelevant without 1)clear, shared knowledge of constraints, how to get them changed,2)where you are in comparison to some lofty goal that someone is calling "optimal".

Modelling is a science and an art. Before start to model we need to establish ‘what’ and ‘why’ of the model. What to model and why? This will help us to reduce model complexity otherwise model will not be fit for purpose. Once model is ready, it needs to be validated against data collected from the actual system. A validated model will help to find the weakest link in the design.

One thing that I have found helpful is to add a visual map to showcase the inputs, outputs, feedback , noises, etc. Once that map exists, it would directly lead to understanding the DOEs which are needed to optimize the performance

Yes this will led to open discussion and looking for solutions. The goal is to incorporate team building with feedback and collaboration from the team

Models are useful tools, but it is notoriously difficult to characterize every system component. One "black box" component within the system can nullify overall performance measures. Characterizing the "black box" elements in the system early should be a priority. Divide and conquer, but conquer the unknowns first.

As a systems engineer, designing a system model for optimal performance in a dynamic environment requires a comprehensive understanding of the system’s behavior and its interactions with the environment. A system model should be designed to capture the system’s dynamics and the feedback loops that drive its behavior. The model should be tested and validated using real-world data to ensure that it accurately represents the system’s behavior. Additionally, the model should be designed to be flexible and adaptable to changing conditions in the environment. This can be achieved by incorporating feedback mechanisms that allow the system to adjust its behavior in response to changes in the environment.

The tools and the processes to measure the performances shall be defined during the conception. Not once the product or system is integrated or finalized.

Once the expectations are reached and defined start measuring the progress and development effectiveness. By focusing on what is working and building on the progress the system performance is improving

Yes. Evaluate the performance for every appreciable change in dynamic environment, observe changing direction and iterate design for optimised performance, implement and repeate.

Always remember that performance and costs are tradeoffs. Design for normal usage, expect higher usage and determine what the difference means in terms of cost. Ensure you are not building in capability just for the sake of having increased performance when the cost is unsustainable.

Using lessons learned and reviewing the results will help to achieve the final outcome. Review and validation of the objectives will optimize the performance

Weighting cost functions to achieve constrained optimization is inherently subjective, so solutions are not unique. Intuition is a key asset -- and common sense.

Nuevamente, la participacion del cliente / usuario final es fundamental para validar que la solución cumple con las expectativas y objetivos buscados.

Functional tests sometimes require production data to ensure proper integration, but systems are often tested in a test environment with limited external interfaces. And if production data is sensitive, then representative data sets will need to be scrubbed or obfuscated — this can sometimes take up valuable time and resources. This can be mitigated through data tagging and data management.

Validation needs to be architected at different levels, including (but not limited to) design, component, system, and customer usage scenarios, with clear understanding that one cannot replace another. Constraints and assumptions made in early architecture and design phases must be carefully examined and validated, in addition to the function and performance expectations. A robust mindset for validation should encourage breaking the system in the validation phase rather than leaving the exposure to customers.

System specifications, derived from marketing and functional specifications, are prerequisite for system validation. Generally they start at stage 1 and are tweaked through the modeling stage -- then validated and revised through iteration stages.

Check out John Boyd’s OODA loop. It fully describes the process. The faster you can iterate across the process the faster you can converge on an optimal design.

Iterate the design for optimised performance for every appreciable change in dynamic environment, implement and review. If found optimised solution for a specific dynamic environment then store n implement everytime you encounter similar dynamic environment. Repeat, reiterate, review and implement....

Again, at every step evaluate performance vs cost. Sometimes it is worth taking a performance hit at peak operation times to save cost. Design for flexibility to allow increases in performance later. A good example is in cloud based systems where additional resources are brought online when needed or that can be added later if usage increases.

If you are designing a socio-technical system, especially when you want to create platform interfaces to enable and empower the value interactions that you want to mobilize in the arena of interest you want to specialize in, you got to learn to do a deep listening to sense and see the potentials, what is wanting to be born in the ecosystem as a platform designer while working very closely with the platform shaper team.

The principles of Systems Engineering and their implementations are the key to cost-efficient & high-quality product development. Hence, having a team of skilled Systems Engineers & Architects as part of the Systems Engineering Organisation is a must. When I say skilled, I do not mean the ones with tens of years of industry experience but the ones with an appetite to implement Systems Engineering principles in a way that suits the overall organisation the best. Handle things by book but at the same time know what to do when things go sideways. Unfortunately, the lack of this makes the organisations, established or young ones, bleed so much of money thanks to the chaos created by so-called "experienced or industry experts".

Expose your solution to colleagues in engineering design reviews, especially those with different perspectives/talents. It helps if you have "shot down" their ideas in past reviews -- they will vet your solution more vigorously.

Remember that the optimal performance of a system in a dynamic environment is an ongoing process. Regularly review and update the system architecture and practices to adapt to evolving requirements and technology advancements. Continuous improvement and a proactive approach to system optimization are key to maintaining optimal performance over time.

A dynamic environment by definition is one which changes. Therefore the system must be designed for a range of environmental conditions. This may be accomplished by including features such as redundancy, flexibility, and modularity and require a tradeoff on an optimization for any specific set of environmental conditions.