How do we determine what’s important? When we drive our vehicles we pay attention to lights, lines, and signs. When we hear the beeping of a truck backing up, we stop, pause and make sure we’re not in the way. These daily beeps, lights, and signs help us cut through the noise of our environment and determine what’s important to pay attention to and what’s not. These principles are important to understand, because they can be applied with great effect to our own productivity and work.

Problem solvers often prioritize the usability and performance of a product or interface, focusing on intuitive layouts and achieving specific user goals like maximizing clicks. However, an equally crucial consideration for designers is the potential for their designs to fail under non-optimal conditions, a factor often overlooked in discussions of design and user experience.

Typically, design efforts concentrate on optimizing user experience within what is known as the Optimal Design Domain (ODD) — the normal circumstances of use. Yet, evaluating design excellence also involves examining how well a product or system performs when faced with failures and unexpected conditions that lie outside the ODD. This broader perspective helps in creating designs that are more reliable and resilient.

We see signs everywhere. From road signs which indicate how fast to drive, when stop, and where to go, to warning labels on household products, and everything in between. In business we also use signs everywhere. These signs tell us how to operate equipment, where to store materials and maybe even what do to in case of an emergency. These signs are incredibly effective at communicating information and supporting a safe work environment.

Systems are a set of interconnected things. People, technology, molecules, and cells. They behave in a way that produces their own signature pattern of behaviors and responses to forces in the world. Our lives are a milieu of interconnections between machines, people, and the environment. This is why understanding and improving systems is the one of the most impactful and effective way to increase efficiency and productivity. Whether it's in industries like manufacturing or healthcare, in our personal, daily lives, a well-functioning system can make all the difference in achieving our goals and realizing our dreams. Most of the systems we encounter are complex and even the simplest systems can benefit from improvement, particularly if it consistently underperforms to its expectations. In this article, we'll explore five effective strategies to enhance any system, ensuring better outcomes with greater ease.

One of the toughest aspects of problem solving is the implementation of solutions. If you have an analytical mind, you usually have no problem with the early stages of the problem solving process. Defining the problem, chartering the project, selecting the key KPIs, even analyzing potential root causes happen quickly and seamlessly. Those with a creative and collaborative mind, also thrive when it comes to generating new ideas, engaging stakeholders and partnering with them or trying out some low resolution prototyping.

Who is at fault when things go wrong? Is an individual always the root cause or are there instances when things are beyond the control of individuals and their performance is determined by other factors? These questions have been at the heart of business, management, and organizational psychology since the early 20th century. The answers to these questions have far reaching consequences for how organizations are designed, how they manage performance, and how they grow and improve.

Problem solvers are constantly faced with decisions about technology. Questions about technology are not simply about which technologies to use, but also how to manage the technology once it’s integrated into the business or system. Today there is much talk about Artificial Intelligence (AI), but the questions surrounding AI is focused mainly on what it can do, which applications it is right for, and the impact that might have on jobs. This is a logical starting point, but there’s much more that ought to be considered.

At it’s core, AI like many other modern technologies (Robot Process Automation, Driverless Vehicles, Factory Robots, etc.) are tools of automation. They speed up a process or system, they take the human person out of the picture, and they are supposed to perform with greater speed, quality, safety, and make better decisions than humans would. In short, the value offered by these modern, revolutionary technologies is the same value that the steam engine offered in the 18th century. Yes the technologies today are more sophisticated. They make better decisions, or have greater applications, but the difference is in degree, not in kind. We gain value through their ability to automate.

For us, this is a very good thing. For several generations we’ve been working with automation and there are many people who have done a good job of explaining the best ways to use automation to achieve business objectives while also decreasing risks like safety and quality. The luminary electrical engineer, and human factors pioneer Jens Rasmussen had one of the most insightful ways to think the potentials of automation as well as the risks. He called this the “Automation Conundrum.”

Standards are some of the most impactful and cost effective solutions. They’re cheap. They’re quick to implement. They don’t require lots of iteration. They create stability and structure that helps elevate the company to even higher heights. There are many other benefits as well, from knowledge sharing and ease of training to improvements in quality and reductions in errors. But in all of these discussions, sometimes we fail to do a good job of answering the question, “What exactly is a standard?”

First, to give a definition, a standard is a known procedure, product or service level agreement that signifies or attains an accepted level of quality. But beyond this we need to disambiguate a little further. There are actually two different categories we could be referring to. Here we describe what these categories are, as well as their differences.

If you’re in the workforce you’re almost certainly working with technology. Whether that’s a work computer, specialized software and applications, or large physical equipment, the use and interaction between us and machines is unavoidable. For a few select occupations their interactions with technology go beyond mere use. Software programmers develop new applications and novel ways of doing things. Data Analysts determine which methods will be used for data collection, cleaning, and analysis. User Experience Designers design the human machine interactions. Engineers and project managers evaluate, select and integrate technologies into organizations with an expectation of making changes for the better.

Problem solving is not just a discipline built on processes, techniques, and tools. Soft skills are just as important as the more rigid elements in problem solving. In fact, it is often the softer, more interpersonal skills that truly set apart effective problem solvers. In this post, we will delve into the five most important soft skills for problem solving: resilience in the face of failure, comfort in ambiguity, teamwork and communication, storytelling, and creativity.

One of the largest challenges that faces problem solvers is the temptation to solve a problem without seeing and observing the problem firsthand. The way this temptation materializes can take many forms. Data Analysts scour over heaps of data, looking for trends and curiosities. Seasoned managers rely on past experiences and assume things about the problem that are possibly incorrect and unconfirmed. Subject Matter Experts like engineers and maintenance professionals may attempt solutions to problems based on their intuition and deep expertise.

In each of these cases, the approaches leave much to be desired and many questions unanswered. Problem solving is a boots-on-the-ground activity, and without observing the issues first-hand you face an uphill battle to develop an effective, sustainable, and impactful solution. Here, we’ll look at some of the key benefits to making that extra effort to go and see the problem firsthand.

We live in a paradoxical world. The world is a dangerous place. At the root of most of these dangers is human error and yet we don’t trust machines to take on some of our most critical decisions. Nuclear power plant operators, air traffic controllers, pilots, and surgeons are all critical pieces of our economy and will never be replaced by machines, yet, all of them can make mistakes at any moment. The impact of human error is pervasive. Quality issues on a production line, medical mistakes, plane crashes, and even nuclear meltdowns all hinge on humans and whether or not they will make correct judgements at the correct time. The stakes are well-known but what's often overlooked is the root cause of many of these issues: why are we so fallible? Why do humans make mistakes? In this article, we'll delve into the world of human error, exploring its predictability, typical forms, and how the Skills, Rules, Knowledge (SRK) framework can help us better understand and mitigate it.

One of the most important problem solving skills is communication. To be an effective problem solvers, it’s not enough to manage projects and create solutions, you must also communicate what you’ve done to a broader audience. Presenting a project is not merely about showcasing data and solutions; it's about engaging your audience in a compelling narrative. You must get them to care about what you care about. To accomplish this, you must engage your audience’s minds but also their hearts, getting them to feel the pain of the problem, the confusion of the analysis, the hope and opportunity of the solution to the problem. If you care about demonstrating your value as a problem solver, then storytelling is not just a tool but a necessity.

“An ounce of prevention is worth a pound of cure.”

Ben Franklin asserts a time-honored proverb that many can relate to. It seems like to prevent an issue from occurring is always better than responding to the issue. But is he right? Are there ever any circumstances where we might prefer a response to a problem, rather than its prevention?

As Ben Franklin’s quote makes clear, there are two distinct ways to solve problems. Preventative and responsive.

The ability to adapt and innovate is paramount for survival and success. Problem-solving has a natural tendency to create innovations, largely because of the need to generate new ideas to solve current problems. However, people often misunderstand some of the best practices for generating innovative ideas and solutions. They incorrectly assume that the approaches center around extensive planning, prediction, forecasting, large investments, and high risks. In reality however Problem-Solving best practices reveal a more agile and efficient way to tackle challenges, foster innovation, and generate creative solutions. Enter the concept of "Little Bets."

Most problem solving experts know that the first step to solving a problem is to clearly state what the problem is. The Six Sigma methodology has greatly developed this idea into one of the most effective tools in the six sigma catalog - the problem statement. Essentially, The problem statement is a formula to guide problem solvers into creating a clear definition of what the problem is, how it will be measured and what kind of performance improvement the project is expecting to attain.

Becoming an effective and successful leader and facilitator of problem-solving initiatives is a crucial step in the realm of leadership. To be an effective problem-solving leader, you not only need to understand fundamental skills of problem-solving like root cause analysis, brainstorming techniques, and implementation strategies, you also need to be an effective communicator, an organized manager, and a charismatic personality that people gravitate towards.

Problem-solving is the process of identifying, analyzing, and resolving difficulties or obstacles to achieve desired goals or outcomes. It involves systematically assessing the situation, exploring possible solutions, evaluating their effectiveness, and implementing the most appropriate course of action.

Root Cause Analysis (RCA) is a problem-solving strategy that seeks to determine the root cause of a particular problem. This strategy is highly effective because it helps problem solvers resolve the problem at the lowest level, making for efficient and effective solutions that do more than simply put a band-aid on problem’s symptoms.

Root Cause Analysis is also very popular because of the numerous benefits that result from the strategy’s successful implementation. One of these benefits is just how intuitively we naturally gravitate to root cause analysis when dealing with problems in our day to day lives.

Take for instance treating a sick child. Perhaps the child has a fever. You intuitively know, as does every pediatrician, that in order to make the fever go away, you don’t treat the fever but the underlying illness. This usually occurs through the efforts of the body’s immune system, but other interventions like anti-biotics are widely used and incredibly effective for the very reason that they treat the illness at the source, the root cause, and not the symptoms.

The Kano Model, developed by Noriaki Kano in 1984, provides a way of understanding and visualizing how unique product attributes satisfy customer wants, needs and desires differently. In particular, the model establishes and categorizes the relationships between product performance and customer satisfaction.