Product Development in Complex System Design

History (Waterfall Model)

Before iterative design became an industry standard in product development the waterfall model was largely popular. The waterfall model originated in industries that are highly structured physical environments such as manufacturing and construction where changes to the design after building are incredibly expensive if not physically impossible to do. The waterfall method is made up of a conceptualizing, initializing, analysing, designing, constructing, testing and implanting/producing and maintaining phase[1].

It is important to note each phase in the waterfall model appears only once in this process. After the product is designed and analysed to theoretical satisfaction it is locked in. Any missteps that become evident after construction can, often, not be changed. Developers using this method have no opportunity for recourse. As the time and financial investment increase the ease of design changes diminishes; such is the case early in the waterfall method. It is for this reason that an iterative design model and prototyping are powerful tools, particularly when developing complex systems.

While this is a mechatronic design class what we are tasked to develop is a prototype for a product. Thus its design should have people and the environment it will exist in in mind. It is also for this reason that product development methods utilized in industry are highly relevant to this process.

Iterative design process is made up of 6 steps: ideate, design, construct, test, improve and iterate [2]. The more this process is repeated the higher the quality of the finished product’s design. However, there is point where this process begins to produce diminishing returns for the time and financial investment it requires thus careful consideration needs to be made with every cycle as to what more the team expects to learn by repeating the process. The more trivial the insights become the more likely you are to having reached the point of diminishing returns.


The first step in this process is ideation. This phase has a high impact on final achieved design quality. The goal at this stage is to have a high quantity of design solutions to choose from. All suggestions need to be made without fear of judgement because of the impact that one idea may have on another. This in terms of sparking a second superior idea from another team member as well as the original team member continuing to contribute to the ideation process. Studies like integrated product development implement techniques that encourage developers to think of the problem through different assignments or lenses. The first assignment is to think of the social, economic and technological factors that exist and are related to the product[3]. Under social an example would be the apprehension that some people have towards close human-robot interaction for fear of harm. Under economic an example would be: countries like Dubai invest heavily in structural expansion and favour taller buildings. And under technological one could identify various advancement in robotic technology such as machine learning.

A second lens that the product can be viewed in is in terms of value opportunity attributes. This challenges developers to think of who the key stakeholders or users of this product are and what their desires would be. Stakeholders for a robot could include robot operators, companies that purchase the robot or hire the robot providing service and people that directly engage or exist in close proximity to the robot. Once the most important stakeholders have been identified the group should define and rank the features that these stakeholders would find most important in the product. There is an already established list of attributes that have been identified for product development purposes but the list needs to be made specific to individual use cases through attribute definitions [4].

The third tool in the ideation process is to evaluate opportunity gaps using the insights garnered in the first two steps. At this stage one is looking for areas that are under addressed by existing technology and solutions [5]. This is done because there are often several ways to solve a single identified problem and this helps focus the team on creating the best solution to meet one or two of the most valuable of these under addressed areas. For instance, if the goal is clean windows without human involvement one might literally build a robot that cleans using traditional processes (sponge, cleaning solution, etc) or a programmable system that redirects rain water in such a way that it would clean the window could address the problem too. The latter is of course also a robot. Identifying opportunity gaps helps get the team to a unique solution addressing the most urgent needs in the problem space and allowing for the team to unify their skillsets behind it. Looking at opportunity gaps will ultimately allow for the largest scope of creative solution production with a single problem in mind. At this stage a team should come up with over 100 opportunity gaps for addressing a single problem.

Solution Generation

With all this pre-solution work having been completed the team will have built up a wealth of early knowledge that they can use to generate solution. From this point the ideation process falls into generating ideas. There are various methods that exist for brainstorming as an individual and as a team. In teams processes like round robin– where team members are put on the spot in turn to come up with an idea for a solution—and the 635 method—where 6 people are tasked to come up with and develop 5 ideas through 3 iterations [6].

After the solution generation phase is complete the team needs to apply reduction criteria to narrow their options down to their top solutions. This is often done using a decision matrix that the team creates including consideration like cost, time, availability, etc. The identified criteria can be weighted based on importance to ensure a more reliable outcome. Other options for reduction include a democratic vote and conversation amongst the group. The latter has the advantage of providing further insight into team members’ thinking. This portion of the iterative process can be revisited as needed later on.

After the ideation process is complete it is time to design the solution. Referral to the SETs, VOAs and the chosen POGs as well as the idea generating techniques discussed in the above paragraph is very useful. Trade studies can also provide valuable insight at this stage. It is important to keep the problem as the focus of the process and not the solution. This will help keep the group on track and not accept design ideas that seem brilliant but fail to address the identified requisites of the product. In addition, this is the first stage that will intentionally be repeated numerous times in the iterative process. Between each prototyping stage the group should test their prototype, improve their design and prototype again.


Once the initial design is formulated the team should engage in prototyping. Prototyping is described as coming in 3 form: low fidelity, medium fidelity and high fidelity [7]. The core difference between these is the time and financial input they require as well as the types of insights that can be garnered from them. Early on in the iterative cycle it is imperative to keep financial and time input low. This is because the ability and willingness of the group to change design ideas is inversely proportional to these 2 factors. One should expect that their design is not perfect early on and thus high commitment should be avoided. Low fidelity prototypes should be created first, followed by medium fidelity and lastly high fidelity prototypes. The team should engage in reflection of what lessons or discoveries have been made after each prototype and keep note of these. This helps in thinking of what areas to improve and protects them from recommitting former design mistakes that have been discovered in far earlier iterations.

Low fidelity prototype is a quick and easy translation of your design into a physical artifact. This level of fidelity includes easy to shape materials such as foam core, cardboard and wood. The time commitment is a matter of minutes or hours and low dollar financial investment. In the case of a robot it will likely give you insights into how systems will interact with each other as well as giving the team an idea of movement. If there is a human interaction perspective that needs to be considered, for example in terms of graphic user interfaces, this is a fidelity level that can give high output for low input as people have the ability to quickly bridge the gap between the basic prototype they are presented with to the higher level envisioned. Because it is cheap and easy groups should look to exploring as many of their top ideas as possible utilizing this level of fidelity. On the software control side low fidelity may include process flow or finite state machines written out on paper and mirrored by physically moving or manipulating the prototype.

A mid fidelity prototype is comprised of harder to form materials and, in some cases, materials that are under consideration for the final prototype. There are two versions of prototyping at this level: narrow scope in terms of prototyped capability but great depth in terms of functionality or wide scope in terms of prototyped capability but very shallow functionality. Which type you engage in is dependent on what you hope to learn from your prototype. One can expect a higher financial investment (on the order of tens or hundreds of dollars) and time investment on the order of hours if not days. At this level Rapid prototyping methods such as 3D printing and computer aided design as well as cheap part sourcing are usually utilized. Using CAD you are then able to conduct stress analysis and get insight into the material properties your final product will have. Through 3D printing you are able to create complex shapes (such as racks and pinions) and again see sub system interaction using a more robust material. Semi-final design contenders should be prototyped and iterated a limited number of times on this level. At this point you have begun to make some commitment to a final design but the time and money investment level still leaves room for pivoting as necessary. At this stage functional coding can be incorporated where appropriate to test basic movement control of the robot and identifying limiting mechanical constraints that need to be investigated for the highest level fidelity to work.

When one reaches a high level fidelity level of prototyping the design should be well iterated upon. All discoveries that could have been made without utilizing final materials should have been made. It is at this stage that you are likely to be locked into your design thus you must have done everything possible before hand to ensure this is the best design. High fidelity prototypes happens using materials identified to be used in the final product where possible. If the real material is not accessible for some reason substitutes need to be carefully considered such that they give the closest physical result to the real material. Traditional manufacturing methods (eg: turning, milling, etc) or part sourcing is utilized at this stage. In general, this prototype should happen as close to final product specifications as possible. The software should have the same high quality finish as the physical prototype (ie: programmed to near finished state). This serves as your final proof of concept for your product design. While an iteration can be done for this level of fidelity the team should accept that smaller changes may only be accessible at this point.


In conclusion, while this process was created for industries where usability and design are central to the products made, the iterative design process is becoming a widely accepted industry standard. High fidelity mistakes are expensive. Companies have seen immense value in failing fast and failing cheap thus time and effort should be directed towards the ideation, design and lower fidelity prototyping. Throughout this process it is imperative to fall in love with the problem and not the solution, this mind-set will deliver the best product solution. There is good reason to engage with and practice this method of development in a classroom setting where stakes and consequences are still minimal.


  3. Cagan, Johnathan and Vogel, Craig M.. 10/22/2001. Creating Breakthrough Products. Pearson Education. New Jersey, USA
  4. Cagan, Johnathan and Vogel, Craig M.. 10/22/2001. Creating Breakthrough Products. Pearson Education. New Jersey, USA
  5. Cagan, Johnathan and Vogel, Craig M.. 10/22/2001. Creating Breakthrough Products. Pearson Education. New Jersey, USA
  6. 25 Useful Brainstorming Techniques.
  7. Nielson, Jakob. 03/03/1992. The Engineering Usability Lifecycle. Morgan Kaufman. CA, USA

I would recommend site 6 as a resource because I have found many of the exercises mentioned on this list useful for unlocking more creative thinking. The best solutions are not usually those thought of first or that come easily, they are those that have outcompeted a plethora of other good ideas and won. The deeper that you dive into the ideation or creative process the better the quality of your final idea.