Multi-Dimensional Problem Solving

It is never just one problem. You have to view problems with different lenses and at different layers to make sense of things... Layer 1: Customer’s Mental Model / Stated Problem What problem does the customer say they have, in their own words? Layer 2: Ecosystem View (Other Actors’ Perspectives) How do other actors in the customer’s environment interpret or feel the impact of this problem? Layer 3: Human Factors and Behavioral Dynamics What frictions, incentives, norms, habits, or power dynamics are blocking or reinforcing current behaviors? Layer 4: Restated Problem with Status Quo Attempts When we integrate these views and factors, what is the real problem — and why have existing fixes or workarounds failed? Layer 5: Enabling Overlap with Product/Technology How does our product, expertise, or technology directly address these dynamics and create better conditions? Layer 6: Feasible Influence & Needed Capabilities What can we realistically influence today, and what additional capabilities would be needed to expand that influence?

🔧 8D Problem Solving: From Symptoms to Solutions 🚀 In quality and operations, we don’t just fix problems—we solve them for good. That’s why the 8D (Eight Disciplines) Problem Solving Process is a cornerstone of effective root cause analysis. It’s not just a checklist—it’s a mindset of teamwork, rigor, and accountability. Here’s how it works: 🧩 D1 – Form a Team   Bring together cross-functional experts who understand the process and can drive change. 📝 D2 – Describe the Problem   Define the issue clearly using facts, data, and impact—no assumptions. 🛡️ D3 – Implement Interim Containment   Protect the customer and process while the root cause is being investigated. 🔍 D4 – Identify Root Cause   Use tools like 5-Why, Fishbone, and 7M to dig deep and validate the true source. 🛠️ D5 – Define Corrective Actions   Develop targeted solutions that eliminate the root cause—not just the symptoms. ✅ D6 – Implement & Validate   Put the fix in place and confirm it works—through testing, monitoring, and feedback. 🔁 D7 – Prevent Recurrence   Update procedures, training, and systems to ensure the problem doesn’t return. 🎉 D8 – Recognize the Team   Celebrate the people who solved the problem and strengthened the process. 💬 I created the visual below to support team huddles, CAPA reviews, and leadership coaching. Feel free to use it, share it, or ask for a version tailored to your industry. Let’s keep building a culture of ownership, excellence, and continuous improvement—one discipline at a time. #8DProblemSolving #RootCauseAnalysis #QualityLeadership #CAPA #ContinuousImprovement #OperationsExcellence #Manufacturing #MedicalDevices #Teamwork #LeadershipDevelopment #VisualThinking

I'd like to share with you a powerful method that's been instrumental in our journey towards making more nuanced and balanced decisions. The Six Hat Solution, developed by Edward de Bono, is a powerful tool for teams and leaders. It's designed to help people explore different perspectives towards a complex situation or challenge, making our decision-making process more structured and comprehensive. 1. Emotional Viewpoint: Reflecting on our emotions offers initial insights. How does this situation make us feel? Personally, the prospect of our upcoming project invokes a mix of excitement and apprehension. Acknowledging our feelings can highlight potential concerns or areas of strong motivation. 2. Factual Analysis: Grounding our discussion in facts ensures a solid foundation. What are the undeniable truths of our current situation? With our project, the realities include our deadlines, budget constraints, and the resources at our disposal. These facts help clarify the scope of our challenge. 3. Optimistic Outlook: Focusing on the positives, we identify which aspects are most likely to succeed. In our scenario, the creativity and resilience of our team stand out as invaluable assets. This positivity is crucial for maintaining momentum. 4. Critical Perspective: Conversely, acknowledging what might not work allows us to anticipate and address potential issues. For us, the constraints of time and the untested nature of some technologies are concerns that need strategic planning. 5. Creative Exploration: By thinking creatively, we open the door to innovative solutions. Could adjusting our approach or incorporating new methodologies enhance our outcome? This phase pushes us beyond our initial assumptions. 6. Synthesised Solution: Finally, integrating all perspectives, we determine the most viable path forward. A phased project implementation, leveraging both proven and new technologies in stages, appears to be our best strategy. What complex decisions are you facing that could benefit from this multi-perspective approach? #leadership #mindset #culture #growth #success #problemsolving

I am asked often about HOW I approach solving unstructured problems. I start by sharing this statement: "#Choice is affected by #Context." What do you think? This is the first question I pose to my learners whenever I conduct my workshops on #CriticalThinking for Complex Problem Solving. The intent in so doing is to raise #awareness of our natural tendency to make choices without consciously considering the context. Why the need for "Context"? This is the difference between academic research and real world practice. This is the difference between the ideal conditions of a classroom from the actual physical conditions of the ground. The former deals with paper studies, the latter deals with #dynamic #situationalfactors. Knowing the context of problem solving is critical because context determines the rules or #heuristics to approach problem solving. My very first act is always focused on making sense of my operating context. This means gathering facts, evidence and observations which help me identify the cause-and-effect relationships between situational factors and distil the root cause of the problem (usually unseen) to be solved instead of being confused by the symptoms (usually visible). This is about applying the science of physics to question if the data and observations make sense. And about applying the art to #connectthedots and form a theory of HOW situational factors affect each other. So this is my "secret" when I apply myself in tackling unstructured problem solving. Foremost, discover the context of the situation I confront. I will share over the next several installments for HOW I approach unstructured problem solving via a multidisciplinary and nonlinear process that combines Systems Thinking, Systems Architecting, Systems Engineering, Learning Organisation theory, Design Thinking, Operational Thinking/MilArt, Balanced Scorecard, Resource Management, et al. Please join me as I share my knowledge and experience for building #LargeScaleSystems (LSS) and for #Digitalisation gained from over a quarter century of application, successes, and failures.

Not all real-life decisions are about one dimensional outcomes. When applying for a new job, we might care about the compensation, but also the company's mission or their work-from-home policy. Whenever we care about more than one factor, we enter the space of trade-offs instead of just looking for the optimal value for a single variable. These optimal trade-offs are known as Pareto optima. In this week's issue of Causal Python Weekly Alberto D. Horner discusses a brand new paper by Shriya Bhatija and colleagues that proposes a novel framework for multi-objective optimization with known causal graphs. The proposed approach can be used to find optimal interventions in multi-objective optimization scenarios. This methodology can be incredibly useful in both science and industry, and really everywhere where we care about the cost of interventions under multi-objective regime. As Alberto summarizes: "The algorithm is particularly valuable in contexts where running experiments to find the optimal intervention is costly, making it important to prioritize the most informative interventions. Hence, the approach balances two key factors often encountered in real-world scenarios: the cost of running experiments and the value of learning from new information." I recently think a lot about decisions in multi-dimensional spaces and multi-objective functions. Do you find these kind of ideas valuable in practice? Link to the paper in the comments.

In my career, most business problems I've encountered required optimizing multiple KPIs simultaneously. In Mixed Integer Programming #optimization, there are two popular ways of doing this: 0) combining all KPIs into one weighted objective function and 1) solving for each KPI in a particular order, imposing a close-to-optimality constraint for each solved KPI. Option number 1, known as "hierarchical optimization" or "goal programming," is my and many other practitioners' preferred method. (See a related article by Princeton Consultants in the comment section). The advantages of this method are: 0) Unit independence- No need to convert all KPIs to the same units. Keeping them in the original units simplifies the process while making it more transparent. 1) Hands-off: Unlike option 0, with hierarchical optimization, there is no need to figure the right weights among the KPIs. This process requires experimenting and fine-tuning and is instance-dependent, meaning the logic can be easily broken by the data of a particular instance. This is not necessary for hierarchical optimization. 2) Business Priorities: The order in which these KPIs are solved will more closely follow actual business priorities. It forces the modeler to extract the business priorities from the end user and embed them in the solution process. The good news is that modern MIP solvers already have an API call to easily implement both approaches. The code snippet below shows an implementation of hierarchical optimization in #python for FICO's Xpress Solver. Similar functionalities exist for other commercial solvers like Gurobi Optimization and IBM's CPLEX. In this code, we first minimize the deviation from a target capacity consumption, allowing an optimality gap of up to 5%. We then minimize costs while ensuring that the value of the capacity deviation is not worse than the solution found in the previous solve. Let's be prepared for these multi-objective problems that businesses need to solve and help make #decisionintelligence and #operationsresearch the most adopted #AI in business. What method do you use to solve business problems with multiple objectives?

The 8D method, or Eight Disciplines problem-solving methodology, is a structured approach used to identify, correct, and eliminate recurring problems. It is widely used in manufacturing, engineering, and other industries to improve processes and product quality. The 8D process focuses on the root cause of a problem and implementing long-term solutions. Here's a breakdown of the 8 steps (disciplines): 1. **D1 – Form a Team**: Assemble a cross-functional team of people with the knowledge, skills, and authority to solve the problem and implement corrective actions. 2. **D2 – Define the Problem**: Clearly describe the problem in quantifiable terms. Identify what is wrong and how it affects processes or products. 3. **D3 – Implement Interim Containment Actions (ICA)**: Implement temporary solutions to isolate the problem and prevent it from affecting customers or downstream processes. 4. **D4 – Root Cause Analysis**: Investigate the root cause of the problem. Use techniques such as the 5 Whys or Fishbone Diagram to dig deeper into the issue. 5. **D5 – Develop Permanent Corrective Actions (PCA)**: Based on the root cause analysis, propose and plan permanent solutions to fix the issue and prevent recurrence. 6. **D6 – Implement and Validate Corrective Actions**: Implement the proposed corrective actions and verify that they effectively solve the problem. 7. **D7 – Prevent Recurrence**: Modify processes, procedures, or systems to prevent the problem from happening again in the future. Update relevant documentation and train employees if necessary. 8. **D8 – Recognize the Team**: Acknowledge the efforts of the team members and celebrate the success of the problem resolution process. This method is particularly useful for resolving complex problems that involve multiple factors and require a thorough investigation and sustainable solutions.

In AI, particularly in optimization, it’s not just about finding solutions, it’s about finding optimal solutions in a multi-objective world. Optimization in real-world applications isn’t as straightforward as minimizing one objective. You’re often dealing with multiple, conflicting goals, and that’s where the complexity kicks in. ⚖️ Multi-objective optimization: How do you balance between minimizing cost and maximizing performance? Techniques like Pareto efficiency and evolutionary algorithms help find the sweet spot. 📉 When dealing with high-dimensional data, have you considered dimensionality reduction methods like PCA or t-SNE to improve the tractability of your optimization problem? 🤯 For complex constraints, Lagrangian relaxation and dual decomposition allow us to break problems into solvable sub-problems without sacrificing solution quality. I reiterate, optimization is not just about solving: it’s about trade-offs, decomposition, and balancing competing objectives. What’s your approach to tackling multi-objective optimization problems? Let’s dive into the theory and methods in the comments👇 #Optimization #MultiObjectiveOptimization #ParetoFront #DimensionalityReduction #AI #OperationsResearch #AdvancedAlgorithms

Problem Solving: The Art of Navigating Complexity in the AI Era I've learned that in enterprise settings, problems rarely come with neat definitions or clear boundaries. They're messy, interconnected, and often evolving as we work on them, and solutions dont appear magically; you have to work on them from multiple perspectives. While AI excels at solving well-defined problems, the uniquely human skill lies in unpacking complexity by breaking down ambiguous challenges into workable components. This means becoming comfortable with uncertainty, asking better questions, and resisting the urge to jump to solutions. It's like compound interest for problem-solving; the more you invest in understanding the problem space, the greater your returns in solution effectiveness. The most effective problem solvers I work with have mastered four capabilities:   1. Deconstructing multi-layered problems into manageable pieces   2. Studying the problem from different perspectives.   3. Iterating rapidly between hypothesis and testing, and   4. Synthesizing insights across domains and stakeholders. However, I've discovered that AI can serve as an exceptional thought partner in this iterative process. When facing complex challenges, I utilize AI to stress-test my hypotheses, explore potential blind spots I might miss, and rapidly prototype various solutions to the problem. It's like having an always-on collaborator, and a whole slew of subject matter experts in different domains who can help you think through multiple scenarios simultaneously. The future belongs to leaders who can dance with ambiguity while maintaining human agency in defining problems and making decisions. With AI as our thought partner, every one of us can now possess superpowers, accessing knowledge in any domain and accelerating thinking cycles that once took weeks and months to complete, now into minutes and hours. Foundry for AI by Rackspace (FAIR™) D Scott Sanders Ben Blanquera #ProblemSolving #AI #Leadership #CriticalThinking #EnterpriseSolutions #FutureOfWork #ComplexSystems