The meaning of the word “robust” can shift depending on the context. It can be used to describe an organization that’s grown to a significant size, a person with a lot of natural stamina or the hearty flavor of a gourmet soup. However, in the context of process management, robustness describes the ability of a process to handle unexpected or sub-standard input without compromising profitability or product quality.
Overview: What is robustness?
Even though the term is nebulous in general, the robustness of a process is usually quantifiable through analysis of operational performance and output cost or quality. A robust process is one that can handle variations in different types of input successfully. What counts as success is ultimately up to the process designers and operators.
3 benefits of robustness
Robustness is a purely positive characteristic, so it’s one that companies should maximize whenever possible.
1. More room to improve
2. Reduce operational uncertainty
3. Better overall performance
Ultimately, a robust process will simply outperform one that isn’t, especially when circumstances aren’t ideal. It’s up to the process designers to balance the investment cost of robustness versus the potential value addition.
Why is robustness important to understand?
Robustness is a fundamental characteristic of a good process and one that will be successful in a lean manufacturing or operational environment.
Defining quality attributes
Before worrying about robustness, companies need to know the critical quality attributes (CQAs) of each product or service they deliver to customers. These attributes are the ones that are most essential to the value of the solution to the final recipient.
Relating to process parameters
Process designers also need to identify critical process parameters (CPPs) for each key process based on the critical quality attributes. Processes that directly or greatly impact key attributes are the ones that need to be robust.
Achieving the right balance
Every process in a successful Six Sigma company has some robust elements, but robustness is not the only element of a strong process. Achieving the right balance requires leaders to keep the full scope of their operations in view when investing in certain processes or prioritizing changes.
An industry example of robustness
The operator of a small food truck serves items like hotdogs, hamburgers and side items to customers at a popular location. Due to high demand at peak hours and small cooking surface, the operator cooks burgers at a high temperature for minimal time. Unfortunately, this increases the risk of overcooking or under-cooking the meat.
After assessing the situation, the operator decides to improve the robustness of the process by investing in a larger cooking surface. This allows him to cook burgers at a lower temperature since he can do more simultaneously. This reduces the risk of burning or under-cooking the food if his attention is on customer service or another food item.
4 best practices when thinking about robustness
Robustness isn’t a complicated subject and it’s one that often comes naturally from following basic best practices in research, development and implementation.
1. Design for robustness
The best place to incorporate robustness is during the initial research and development phase. That’s why it’s important for businesses to understand their critical parameters and attributes as quickly as possible.
2. Adopt cyclical improvement
Robustness is also something that’s built over the course of trial and experience. Cyclical improvement creates a framework and environment that allows for continuous growth and greater efficiency over time.
3. Focus on value
It’s easy to focus too much on the process and not on the result. You need to make sure you stay in touch with the things that your customers are really concerned about with your products or services.
4. Quantify when possible
Not all characteristics of a process are quantifiable, but the impact on final deliverables can always be measured. It’s always a good idea to look for opportunities to leverage data tools and metrics.
Frequently Asked Questions (FAQ) about robustness
How do you measure robustness?
Outside of complex statistical analysis, robustness is measured by its results. Incrementally increasing variability of each type of input to gauge impact on output is the simplest way to gauge its overall tolerance to change.
What’s the difference between robustness and reliability?
Robustness describes the characteristics of a process, while reliability describes the process itself. A robust process is more likely to be reliable than one that isn’t.
How do you make a process more robust?
This depends entirely on the type of process as well as the critical parameters and quality attributes. Typically, processes are made more robust by investing in different equipment, changing technique or shifting priorities.
Start thinking robust
Robust doesn’t always mean big, but it always helps keep your company growing in that direction. The recent pandemic was only one of many examples of the type of crisis or shift that can demand robustness across entire industries. Whether you are reducing variability on a micro or macro scale, it’s always better to think robust.