How can decision trees help you manage system risk and uncertainty?

Many people jump to solutioning instead of understanding what the issue is and what is confining their options or paths forward. Often there are multiple issues that end up getting merged and confusing the team. So first, each issue needs to be broken down and agreed upon by the key team members, with conclusive data demonstrating the problem exists. This means a well written statement that discusses what the "is" and "should be" consists of with enough but not too much technical detail; often including the who, where, and when. Any additional detail is put in a description. Including end users is critical for making sure the solution meets validation objectives. This will vet out risks if quality, time, and/or cost are the key drivers.

In systems engineering, decision tree diagrams are essential for analyzing complex systems by visually representing decision paths and outcomes.But in my experience, their effectiveness is heightened when integrated with tools like an Issue/Fault Log- which clearly defines problems and historical system challenges- and a Risk and Uncertainty Matrix. This synergetic integration eventually creates a continuous improvement loop, where insights from past issues and strategic risk assessments inform and enhance decision trees. The synergy of these tools allows for a proactive and holistic approach to system engineering analysis, contributing to the resilience and adaptability of complex systems throughout their lifecycle.

Using a combination of Preliminary Hazard Analysis and comprehensive Interface Control Documentation allows issues to b clearly identified prior to them occurring. Should issues arise, both tools allow for accurate decision making.

Einstein's famous quote about how to allocate time if you had an hour to solve a problem comes to mind. 55 mins to define the problem, 5 mins do solutions. Spend the time on the problem definition not just so you have a clear idea of the problem to be solved but also to improve the chances that you're solving the right problem.

Many people mixes up the problem by just looking a component where exact cause has happened. Then it would be difficult to identify the cause. In that regards, it would always a good idea to first do fault tree analysis with breakdown of event of occurrence of problem and then try to solve problem or finding a cause what has happened to that component, Why is always an important retrospective question in this regard as well.

While developing the alternatives, we should pay attention to respect the MECE rule. Alternatives should be Mutually Exclusives and Collectively Exhaustives. This is to ensure we are covering all the scope of the problem and not missing or repeating alternatives.

Prioritize not just the costs but the Level of Effort required to correct or mitigate the problem issue. Many engineers seem to always assume the worst case and you know how Assume is spelled! Lol Sometimes the issue is relatively simple and straight forward. It a good idea to look into the LoE and prioritize those tasks as well as hardware components.

Yes identifying all the possible alternatives is important but at the same time all pros and cons or implications of selecting each alternative shall be clearly defined like; complexity, cost, time required for development, reliability, obsolescence, components availability i.e. indigenously or have to be imported, ancillary industry support required, etc.

I would say it is important to hedge or limit the alternatives to realistic solutions in terms of cost and time. Make decision points at relevant times and don’t postpone till it’s too late and the cost is substantial in terms of money or time to complete

Option analysis with pros and cons with the lens of risk tolerance including cost. Some of the decisions are strategically inclined bypassing option analysis.

If something seems too good to be true, it probably is. There is always a chance that our decision leads to undesired results, even when something seems easy. Estimating probabilities and payoffs helps us decide which alternative has the highest chance to produce the desired outcome with an acceptable level of expenses and externalities. However, please make sure the validity and quality of your data when doing the analysis (garbage in, garbage out).

Estimate the total cost of development, including resources, technology, personnel, & time. Consider both direct costs (like salaries & licenses) & indirect costs For each scenario, estimate the likelihood of occurrence. This can be based on industry benchmarks, historical data, or expert judgment. Use probabilistic models or statistical analysis if possible Calculate the break-even point – how much you need to sell to cover your costs Weigh the total expected benefits against the total expected costs. Consider both tangible (financial) & intangible (brand value, strategic positioning) benefits. Estimate the ROI by comparing the expected benefits to the costs. Use this to evaluate whether the project is likely to be financially viable.

In my experience rapid prototyping and experimentation are more pragmatic tools to learn and improve designs. Simulation tools like decision trees are still limited by the model itself and immune to feedback. However, for simple designs, decision trees can guide the thinking process to identify the cost and feasibility of countermeasures.

In the third step, the focus shifts to estimating probabilities and payoffs for each alternative. Probabilities represent the likelihood of various events or states of nature impacting decision outcomes. Payoffs denote the associated benefits or costs tied to each alternative-event pairing. Estimation methods encompass historical data, statistical models, or subjective judgments. To address uncertainty, incorporating ranges, scenarios, or sensitivity analysis enhances the robustness of these estimates. This step ensures a nuanced understanding of potential outcomes, facilitating a more informed decision-making process.

Begin identifying alternatives and estimating probabilities by outlining various alternatives aligned with established objectives. Estimate probabilities using historical data, statistical models, or expert judgments. Simultaneously, assess payoffs associated with each alternative under different scenarios. To address uncertainty, incorporate ranges, scenarios, or sensitivity analysis. This systematic approach ensures a comprehensive evaluation of potential outcomes, facilitating informed and robust decision-making.

Start by clearly defining the primary decision you need to make. This decision should be at the root of your decision tree List alternatives available for your main decision. These form the first branches of the tree For decision nodes, calculate the expected value by multiplying the payoff of each outcome by its probability & summing these values Assess the risk assoc with each path. Use the probability of outcomes & the variability in payoffs For complex trees, consider using software tools. These can handle large trees, complex calculations, & provide visualizations for understanding Use the decision tree to guide your decision-making process. Choose the path with the best expected outcome, considering the payoffs & associated risks

Decision Support Systems (DSS) need to be extensively used in systems engineering management. Of particular interest are uncertainties decision calculations that help to determine the outcome of the analysis.

In the world of system engineering, the decision tree serves as more than a visual aid; it's a strategic guide, aligning technical choices with overarching system objectives. By incorporating probabilities, payoffs, and a nuanced understanding of uncertainties, the decision tree becomes an invaluable tool for system engineers navigating the complexities of development, ensuring that each choice contributes to the holistic success of the system.

The critical intermediate points have to be clear. Where is possible to introduce changes/ modifications, perhaps some alternatives have to be prepared in every critical point.

Les outils de mind mapping sont nombreux et pourtant indispensables Pour moi, le principal intérêt consiste à regrouper des évènements dans des familles qui au départ n'avaient pas de lien entre eux

An example of analyzing decision tree outcomes is to start by visualizing the tree to understand its decision paths and feature splits. Assess the importance of each feature and the purity of nodes to gauge how the tree classifies data. Evaluate performance using metrics like accuracy and precision, and examine the confusion matrix for insights into prediction errors. Additionally, consider pruning effects and validate the model through cross-validation to ensure generalizability and robustness.

Analyse the decision tree in parallel to the risk matrix. This will help to take effective decisions along with risk mitigation.

In the pivotal fifth step, system engineers analyze the decision tree with precision. The expected value method provides a quantitative basis, but it overlooks decision makers' risk attitudes. The expected utility approach introduces nuanced considerations, demanding a deeper understanding of utility functions. System engineers, mindful of these limitations, explore alternative methods like maximin and minimax regret, offering flexibility that aligns with diverse decision-making criteria. As stewards of comprehensive system development, system engineers balance technical intricacies with overarching objectives, ensuring the chosen method resonates with both quantitative analysis and the nuanced landscape of system engineering.

Analyzing a decision tree involves evaluating different alternatives, assessing probabilities, calculating expected values, and making informed decisions based on various methods and criteria. Here are several methods and criteria used to analyze decision trees: 1. Expected Monetary Value (EMV): Calculation: Calculate the Expected Monetary Value for each decision node by multiplying the payoff of each outcome by its probability and summing these values. EMV = Σ (Probability of Outcome * Payoff of Outcome) Use: EMV helps in determining the average or expected financial gain or loss associated with each alternative or decision.

Le choix judicieux des indicateurs est crucial pour la réussite du projet dans le temps On n'y consacre souvent pas suffisamment de temps On comprend facilement qu'il est difficile de faire bien du premier coup Dans ce cas il s'agit d'être réactif à une conjonction d'indicateurs à la baisse pour réorienter la stratégie

These are key steps for analyzing the outcomes of a decision tree in data science and machine learning: - Visualize the decision tree - Evaluate feature importance - Analyze node purity - Use performance metrics like accuracy, precision, recall - Review the confusion matrix - Pruning effects on the tree - Cross-validation for model validation - Sensitivity analysis - Error analysis and decision path tracing - Decision boundary analysis in classification tasks

In the final step, system engineers translate the analyzed decision into action through effective communication, meticulous planning, and continuous monitoring. They engage stakeholders, secure support, and allocate resources with precision. The decision tree remains dynamic, adapting to new information or changes, reflecting the iterative nature of system engineering. System engineers embrace adaptability, ensuring decisions evolve in response to uncertainties, thereby upholding the system's integrity. This streamlined process embodies the systematic and agile approach of system engineers in decision implementation and refinement.

While it’s important to follow structured and analytical approach for decision making, its equally crucial to consider intangibles as well, if any. Plan B should also be kept in place, should things dont align with the preferred option.

For an alternative model and to validate this Decision Tree approach, compare with the military Command process for developing and assessing ‘Courses of Action’ (CoA). I’ll expand on this later.

In my experience, using a “red” team which you present the tree to and receive feedback from is very beneficial. The role of the “red“ team is to try and find the faults or what was done wrong

This article describes a structured approach to get to evidence-supported decisions about the right design choices for a future product. However, as complexity in modern technical (and also socio-technical) systems is exploding, it is time to lift the method to a next level of formalization: #MBSE approaches allow modeling of systems and their behaviors and interaction in a way that can be analyzed and simulated, not just to visualize the results of a decision to human decision makers but also to automatically verify millions of combinations resulting from variation points against requirements and constraints, and to rank them according to KPIs derived from business objectives. The upcoming #SysML2 language provides the concepts to do so.

Don’t underestimate the value of stakeholders and subject matter experts. When statisticians or engineers conduct studies, they may misinterpret information that would be otherwise clear to the right SME. Involve the right people and seek feedback iteratively throughout all steps to reduce bias and ensure buy-in of results/decisions.