How to Solve Any Problem with the Eight Disciplines (8D)

Who is this article for?

• Manufacturing, engineering, and technology companies
• Operations, quality assurance, and product development departments
• Managers, engineers, analysts, and team leaders involved in problem solving

These roles need effective problem solving skills to identify and resolve issues that impact product quality, process efficiency, and customer satisfaction. A structured methodology can help align teams and drive continuous improvement.

A Structured Approach to Problem Solving

Problem solving is the process of identifying problems, determining root causes, prioritizing potential solutions, and implementing corrective actions. While the specific steps may vary, most problem solving methodologies aim to enable faster issue resolution and prevent problem recurrence.
For example, the 8 Disciplines (8D) approach, originally developed by Ford, outlines a series of steps:

1. D0: Prepare and establish a team
2. D1: Use a team approach
3. D2: Describe the problem
4. D3: Develop interim containment actions
5. D4: Determine and verify root causes
6. D5: Verify permanent corrections
7. D6: Implement and validate corrective actions
8. D7: Take preventive measures
9. D8: Congratulate the team

Similarly, methodologies like Six Sigma and Lean focus on structured problem solving to eliminate defects and streamline processes. Hippel (1994) notes that the locus of problem solving tends to shift to where needed information resides, as “sticky” information that is costly to transfer often dictates where problems get solved.

Key Elements of Effective Problem Solving

Assemble a Cross-Functional Team

Complex problems often span multiple departments and disciplines. Assembling a diverse, cross-functional team ensures you incorporate different perspectives and expertise into the problem solving process.
Aarikka-Stenroos and Jaakkola (2012) highlight the importance of supplier-customer collaboration in knowledge-intensive problem solving. Suppliers and customers jointly diagnose needs, design and produce solutions, and manage conflicts through an interactive co-creation process.

Clearly Define the Problem

Investing time upfront to thoroughly define the problem pays dividends later in the problem solving process. Use frameworks like the 5 Whys and Ishikawa diagrams to dig deeper and uncover the true nature of the problem you’re trying to solve.
Be specific in your problem statement. Quantify the issue in terms of defect rates, costs, lost productivity, or other relevant metrics. Clarify the desired target state you want to achieve.
Schrader et al. (1993) argue that problem solvers choose the levels of uncertainty and ambiguity to operate under, rather than treating them as exogenous factors. Framing the problem therefore shapes the trajectory of the problem solving process.

Take Interim Containment Actions

Once you’ve defined the problem, take immediate steps to limit further damage. Containment actions like halting production, blocking shipments, or rolling back changes prevent the problem from growing while you investigate root causes and develop permanent solutions.
Balance the need for swift action with the potential risks and costs of containment. Consider factors like customer impact, compliance requirements, and resource constraints. Communicate plans with all affected stakeholders.

Identify and Verify Root Causes

With containment in place, it’s time to dig into root causes. Resist the urge to jump to conclusions. Gather data, analyze processes, and test hypotheses to identify the underlying factors contributing to the problem.
Techniques like the 5 Whys, fishbone diagrams, and statistical analysis help structure your investigation. Look for patterns and trends that point to systemic issues. Validate root causes by seeing if you can turn the problem on and off by removing and reintroducing the suspected cause.
MacDuffie (1997) emphasizes the importance of organizational factors in successful root cause analysis. Effective problem solving relies on rich data from multiple perspectives, fluid problem categories, common language, framing problems as learning opportunities, and seeing standardization as a foundation for further improvement.

Implement and Verify Corrective Actions

Armed with validated root causes, generate a range of potential corrective actions. Evaluate options based on criteria like effectiveness, feasibility, cost, and time to implement. Select and execute the solution that best addresses the root causes.
Develop a detailed implementation plan with milestones and responsibilities. Provide any necessary training and resources to support the rollout. Verify the effectiveness of corrective actions by measuring results against baseline data and targets.
Thomke (2000) advocates “front-loading” the problem solving process by shifting more problem identification and experimentation upstream. Techniques like rapid prototyping, computer simulation, and digital design allow for faster iterations and reduce the cost of downstream changes.

Implement Preventive Measures

Corrective actions fix the immediate issue, but preventive measures stop problems from recurring. Based on learnings from the problem solving process, update standards, policies, and procedures to eliminate the root causes.
Consider what similar issues could arise in the future. Proactively address potential failure points through process improvements, training, and early detection mechanisms. Document and share knowledge gained to drive organizational learning.
Postrel (2002) notes that the degree of knowledge specialization versus mutual understanding shapes problem solving effectiveness. Developing “islands of shared knowledge” in key areas like product development, while allowing specialization elsewhere, optimizes overall performance.

Common Problem Solving Pitfalls to Avoid

• Jumping to conclusions without validating assumptions
• Implementing solutions that don’t address true root causes
• Failing to contain problems, allowing them to grow unchecked
• Not considering the systemic impact of changes
• Declaring victory too soon without verifying long-term results

Hsieh et al. (2007) argue that problem complexity shapes the optimal approach to problem solving. Simpler problems benefit from experiential, trial-and-error learning, while complex problems require more theoretical analysis and knowledge integration. Failing to match your problem solving approach to the problem at hand leads to delays and suboptimal solutions.
Liao (2002) cautions against “knowledge inertia” that stems from over-reliance on existing knowledge, routine procedures, and past experiences. While established expertise provides a valuable starting point, it can also inhibit the search for novel solutions. Effective problem solvers balance leveraging current knowledge with exploring new possibilities.

How Tallyfy Enables Effective Problem Solving

Tallyfy is a workflow management platform that digitizes and streamlines business processes, including problem solving. With Tallyfy, you can:

Structure intake

Go from standalone forms to trackable workflows. Structure the intake process to capture all necessary problem information upfront.

Set conditional rules

Use simple if-this-then-that logic to route problems to the right people and automatically set task assignments and deadlines based on problem criteria.

Track real-time status

Monitor problem solving progress in real-time with automatic notifications and audit trails. Quickly identify and address bottlenecks.

Collaborate with customers

Provide secure external access for customers to submit issues, provide input, and track case status without needing a separate login.

Standardize problem solving

Build problem solving templates that codify best practices like 8D. Improve consistency while allowing flexibility to handle case-specific details.
By digitizing problem solving workflows, Tallyfy helps you resolve issues faster, increase customer satisfaction, and facilitate continuous improvement. Tallyfy provides the structure and visibility needed to drive effective problem solving across your organization.

In summary, effective problem solving requires a structured approach, cross-functional collaboration, and a commitment to addressing root causes. By following proven methodologies and avoiding common pitfalls, you can turn problems into opportunities for improvement. Tallyfy provides the digital foundation to optimize your problem solving processes and drive better business outcomes.

How Is AI Changing the Way We Solve Problems?

Artificial intelligence and related technologies like machine learning are fundamentally transforming how we approach problem solving. Rather than relying solely on human cognition and reasoning abilities, AI allows us to leverage the power of computing to tackle problems in radically new ways.

One of the key shifts is that AI enables a more data-driven approach to problem solving. With machine learning, algorithms can be trained on massive datasets to identify patterns, correlations and insights that would be difficult or impossible for humans to discern. This allows problems to be dissected and understood in much more granular detail.

For example, a study by Brusoni (2005) looked at the impact of modular design and specialization on problem solving in engineering projects. The research found that there are cognitive limits to the division of labor in problem solving. Coordinating increasingly specialized knowledge requires firms with broad in-house capabilities that can integrate knowledge from different domains. AI could potentially expand these limits by enhancing an organization’s ability to manage complexity.

AI is also changing problem solving by enabling much more complex problems to be tackled in the first place. Many real-world issues involve huge numbers of variables interacting in nonlinear ways. These kinds of systems are difficult for humans to model mentally. But with AI and massive computing power, we can build sophisticated models and simulations of complex systems to understand their dynamics. Agent-based models and reinforcement learning are some of the AI tools enabling this.

Another impact of AI is in enabling more automated and autonomous problem solving. Rather than just being a tool to augment human intelligence, advanced AI systems can be set up to explore problem spaces and search for solutions on their own, with minimal human intervention. Evolutionary algorithms and generative design powered by AI are examples of this.

A study by Hsieh, Nickerson and Zenger (2007) presents a theory of the entrepreneurial firm based on opportunity discovery via problem solving. They argue that as problems become more complex, experiential “trial-and-error” search becomes less effective compared to cognitive search guided by theories and heuristics. AI could support this kind of cognitive search at scale.

What Are the Potential Future Impacts of AI on Problem Solving?

As AI continues to advance, it is likely to have even more transformative impacts on problem solving in the future:

• AI could enable more proactive and predictive problem solving, identifying and resolving issues before they manifest
• Collaborative human-AI problem solving could become the norm, combining the strengths of human and machine intelligence
• AI may allow us to solve problems that are currently considered intractable, such as curing diseases or reversing climate change
• Automated AI problem solving could handle an increasing share of routine issues, freeing up humans for higher level creative work

Liao (2002) discusses the issue of “knowledge inertia” that can arise from routine problem solving procedures and relying on past experience. AI could potentially help organizations overcome this inertia by dynamically generating novel solutions and approaches.

At the same time, increased reliance on AI in problem solving raises important questions and concerns. There are risks of over-automation leading to brittleness and failures when facing “edge cases”. Potential biases in training data and algorithms could lead to discriminatory or unethical outcomes. And there are challenging questions around transparency and accountability when AI is involved in high-stakes decisions.

Despite these challenges, the overall potential of AI to enhance human problem solving is immense. By expanding our cognitive limits and enabling more complex, data-driven and proactive approaches, AI promises to help us tackle some of the most vexing issues facing organizations and society. Continuing to develop effective human-AI collaboration and interaction will be key to realizing this potential in a beneficial way.

Tallyfy Tango – A cheerful and alternative take

Two coworkers, Zoe and Max, are taking a coffee break in the office kitchen. Zoe looks frustrated as she stirs her latte.

Max: Hey Zoe, why the long face? Did the copier jam on you again?

Zoe: No, it’s this project I’m working on. I’ve hit a wall and can’t seem to find a solution. I feel like I’ve tried everything!

Max: Ah, the dreaded problem solving roadblock. We’ve all been there. Have you tried looking at it from a completely different angle?

Zoe: What do you mean? Like standing on my head while brainstorming ideas?

Max: No, no, nothing that drastic! I mean, try to approach the problem in a way you haven’t considered before. What if you pretended you were explaining the issue to a 5-year-old? Or imagined you were an alien from another planet trying to tackle it?

Zoe: Hmm, I never thought about it like that. Maybe if I break it down into really simple terms, I can see it from a fresh perspective.

Max: Exactly! And don’t forget the power of collaboration. Sometimes just talking it through with someone else can spark new ideas and solutions.

Zoe: You’re right. I’ve been so focused on solving it myself, I forgot I have a whole team of brilliant minds to tap into. Thanks Max, I feel better already!

Max: Anytime! Now let’s put our heads together and show this problem who’s boss. With a little creative problem solving, there’s no challenge we can’t overcome!

Related Questions

What are the 7 steps to problem-solving?

While there are many approaches to problem-solving, a simple 7-step process can often help you work through the issues and find solutions:

1. Identify the problem clearly
2. Understand everyone’s interests
3. List the possible solutions
4. Evaluate the options
5. Select an option or options
6. Document the agreement(s)
7. Agree on contingencies, monitoring, and evaluation

How to solve the problem?

Solving problems effectively requires a systematic approach:

• First, analyze the problem to get clear on what exactly the issue is.
• Next, generate potential solutions – get creative and come up with options.
• Then, evaluate the pros and cons of each solution.
• Finally, implement the solution you choose and monitor the results to see if it’s working.

Problem-solving is a process – remain open to trying different approaches until you find one that works.

What are the 4 strategies of problem-solving?

Four key problem-solving strategies are:

1. Brainstorming: Generating a list of potential solutions freely without judgment
2. Research: Gathering more information to better understand the problem and potential solutions
3. Breaking it Down: Separating a large, complex problem into smaller, more manageable sub-problems to solve individually
4. Trial and Error: Testing out different solutions to see what works, learning and improving with each attempt

What are the 4 processes of problem-solving?

The problem-solving process can be broken down into four key steps:

1. Define the problem – Analyze the situation to identify the specific issue that needs to be resolved
2. Generate alternatives – Use strategies like brainstorming to come up with potential solutions
3. Evaluate and select – Assess the pros and cons of each option and choose the best solution to implement
4. Implement – Put the chosen solution into action and monitor the results to ensure it is effective

Following a step-by-step problem-solving process can help you approach issues in a structured way and increase your chances of finding an optimal solution.

References and Editorial Perspectives

Aarikka‐Stenroos, L., & Jaakkola, E. (2012). Value Co-Creation in Knowledge Intensive Business Services: A Dyadic Perspective on the Joint Problem Solving Process. Industrial marketing management, 41, 15 – 26. https://doi.org/10.1016/j.indmarman.2011.11.008

Summary of this study

This study examines the collaborative process of value co-creation between suppliers and buyers of knowledge intensive business services. It provides a framework depicting the joint problem solving process, including activities like diagnosing needs, designing solutions, managing resources and value conflicts, and implementing the solution. The insights are relevant for understanding how workflow software can facilitate problem-solving between different parties.

Editor perspectives

At Tallyfy, we find this study fascinating because it highlights how a structured joint problem-solving process is key to delivering value in knowledge-based services. A workflow platform like ours is the perfect tool to enable this type of collaborative problem-solving framework between service providers and clients.

Brusoni, S. (2005). The Limits to Specialization: Problem Solving and Coordination in ‘Modular Networks’. Organization studies, 26, 1885 – 1907. https://doi.org/10.1177/0170840605059161

Summary of this study

This paper argues there are cognitive limits to the division of labor and specialization of knowledge, even with modular products. It explains why firms with broad in-house capabilities are needed to coordinate specialized suppliers and integrate distributed knowledge to solve complex problems. This highlights the importance of knowledge integration in problem-solving.

Editor perspectives

We believe the insights from this research are very relevant as more companies rely on specialized external partners. While workflow tools are great for coordinating modular tasks, this study shows you still need strong internal expertise to solve overarching problems that span different knowledge domains. It’s given us food for thought on how Tallyfy can better support this type of knowledge integration.

Gray, P. (2001). A Problem-Solving Perspective on Knowledge Management Practices. Decision support systems, 31, 87 – 102. https://doi.org/10.1016/s0167-9236(00)00121-4

Summary of this study

This study develops a framework for categorizing knowledge management practices based on their role in problem-solving and the type of problem addressed. It finds that practices can be grouped into two higher-order factors corresponding to exploration and exploitation. The research emphasizes the importance of focusing knowledge management on enabling problem-solving to drive business value.

Editor perspectives

At Tallyfy, we’re always looking for ways to make knowledge more actionable for solving real business problems. This problem-solving lens for knowledge management practices really resonates with us. It’s a good reminder that documenting and sharing knowledge is not enough – we need to bake it into the workflows where problems actually get solved to have an impact. This framework has inspired us to think more about how our platform can directly enable problem-solving activities.

Hippel, E., v. (1994). “Sticky Information” and the Locus of Problem Solving: Implications for Innovation. Management science, 40, 429 – 439. https://doi.org/10.1287/mnsc.40.4.429

Summary of this study

This influential paper introduces the concept of “sticky information” that is costly to acquire, transfer and use in a new location. It explores how the stickiness of information determines the locus of problem-solving and innovation. Key insights are that problem-solving occurs where sticky information resides, may iterate between multiple sites of sticky information, and can be partitioned or unstuck through investments. This has important implications for understanding patterns of problem-solving.

Editor perspectives

The concept of information stickiness is so relevant for how we think about workflow design at Tallyfy. Often the reason workflows are inefficient is that the information needed to complete them is stuck in certain people’s heads, legacy systems, or siloed departments. If we can use our platform to make information more fluid and less sticky, we can dramatically streamline and accelerate problem-solving for our users. This research is a great conceptual foundation for that.

Hsieh, C., Nickerson, J., A., & Zenger, T. (2007). Opportunity Discovery, Problem Solving and a Theory of the Entrepreneurial Firm. Journal of management studies, 44, 1255 – 1277. https://doi.org/10.1111/j.1467-6486.2007.00725.x

Summary of this study

This paper presents a theory of the entrepreneurial firm grounded in opportunity discovery via problem-solving. It argues that opportunities equate to problem-solution pairings, and that discovery occurs through either trial-and-error experiential search or cognitive search based on theorizing. Cognitive search is more useful for complex problems but requires knowledge sharing, which markets, authority-based hierarchy, and consensus-based hierarchy each facilitate to different degrees. Entrepreneurs with strong opportunity recognition abilities can efficiently adopt authority-based structures for a wider range of complexity.

Editor perspectives

We love how this research connects organizational structure to problem-solving efficiency – it’s a link we think about a lot at Tallyfy. The distinction between experiential and cognitive search also maps well to different use cases we see for workflow software, from standardizing best practices to facilitating collaboration on novel problems. It would be really interesting to explore how our platform could more directly support these different problem-solving modes for entrepreneurs and innovators.

Liao, S. (2002). Problem Solving and Knowledge Inertia. Expert systems with applications, 22, 21 – 31. https://doi.org/10.1016/s0957-4174(01)00046-x

Summary of this study

This study examines how knowledge inertia, the tendency to rely on past routines and knowledge, can both enable and inhibit problem-solving for individuals and organizations. It proposes a knowledge-based architecture incorporating case-based, heuristic, and rule-based knowledge to manage inertia. A case study of a military training institute overcoming inertia to implement training innovations is presented.

Editor perspectives

At Tallyfy we’ve definitely seen how past processes can become ingrained and hard to change, even when better ways of doing things are available – the “this is how we’ve always done it” mentality. But this research helps explain why that knowledge inertia exists and how it can be overcome. I think workflow software has a big role to play in codifying institutional knowledge while also making processes more visible and agile so they can evolve as needed. The knowledge architecture proposed here gives us a nice blueprint to work from.

MacDuffie, J., P. (1997). The Road to “Root Cause”: Shop-Floor Problem-Solving at Three Auto Assembly Plants. Management science, 43, 479 – 502. https://doi.org/10.1287/mnsc.43.4.479

Summary of this study

This paper uses case studies of problem-solving for quality issues at three auto plants to examine organizational factors behind process improvement. Focusing on early problem-solving stages, it finds that improvement is influenced by how the organization shapes members’ cognitive processes. Beneficial factors include: capturing diverse problem perspectives, using “fuzzy” problem categories, having common language, framing problems as learning opportunities, and treating standardization as a starting point for further improvement.

Editor perspectives

These insights about organizational influences on frontline problem-solving are so relevant for any operational excellence initiative. At Tallyfy we’re always thinking about how to shape users’ interactions with workflows to drive the right problem-solving behaviors. Things like using flexible templates, supporting cross-functional collaboration, and making processes living documents that are easy to optimize are all ways we try to enable the positive dynamics described in this research.

Postrel, S. (2002). Islands of Shared Knowledge: Specialization and Mutual Understanding in Problem-Solving Teams. Organization science, 13, 303 – 320. https://doi.org/10.1287/orsc.13.3.303.2773

Summary of this study

This paper examines when it makes sense for specialists to invest in mutual understanding versus remain in mutual ignorance. Using a model of product design, it shows that specialist knowledge and trans-specialist understanding are substitutes, implying that learning across specialties is only sometimes efficient. Situations where cross-specialty learning is valuable are rare in the economy overall but common where key managerial activities occur, making “islands of shared knowledge” important.

Editor perspectives

This is such an interesting economic perspective on a challenge we see companies grappling with all the time – how to balance specialization and knowledge sharing across functions. The concept of “islands of shared knowledge” is a useful way to prioritize where to focus cross-training and collaboration efforts. As a workflow platform, Tallyfy is well-positioned to facilitate these islands by making it easy to create shared process understanding in pockets where it matters most, like new product development, while letting specialization reign in other areas.

Schrader, S., Riggs, W., M., & Smith, R. (1993). Choice Over Uncertainty and Ambiguity in Technical Problem Solving. Journal of engineering and technology management, 10, 73 – 99. https://doi.org/10.1016/0923-4748(93)90059-r

Summary of this study

This paper argues that problem-solving involves choices about how much uncertainty and ambiguity to engage with, which are determined by the problem framing process rather than being entirely external. Uncertainty and ambiguity require different approaches and resources to manage. Problem-solvers select the levels of each to contend with based on factors like past experiences, organizational context, and available resources. The fit between the levels engaged and the supporting context shapes problem-solving efficiency and outcomes.

Editor perspectives

We spend a lot of time thinking about how to help people make good choices when designing their workflows, so the idea that problem-solvers have some agency in how much uncertainty and ambiguity they take on is really intriguing. It suggests that the way you structure a workflow can actually shape the types of problems you surface and have to solve downstream. There’s an opportunity for Tallyfy to provide more explicit support for navigating ambiguity and uncertainty in workflows, perhaps with branching logic, parallel paths, or low-code flexibility.

Thomke, S. (2000). The Effect of “Front-Loading” Problem-Solving on Product Development Performance. The Journal of product innovation management, 17, 128 – 142. https://doi.org/10.1016/s0737-6782(99)00031-4

Summary of this study

This article examines how “front-loading” problem-solving, or shifting more problem identification and solving to earlier stages of product development, can improve development efficiency. Methods for front-loading include transferring knowledge from past projects, using technologies that enable faster iterations, and combining new and traditional technologies optimally. A case study of Toyota demonstrates systematic front-loading in practice.

Editor perspectives

Front-loading is such a powerful concept – it’s essentially about shifting the curve of when you incur the costs of solving problems to get the benefits sooner. At Tallyfy we’re huge believers in putting in the work upfront to design robust processes that prevent issues downstream. A lot of the techniques described here, like using templates and post-mortems to enable knowledge transfer between projects, are exactly the types of things our platform is great for. We’d love to see more workflow owners adopt this front-loading mindset, and we’re thinking about how to build even more front-loading best practices into the Tallyfy product itself.

Glossary of terms

Ambiguity

Ambiguity in problem-solving refers to a lack of clarity about the problem definition, interpretation, or appropriate solution method. It arises from missing, complex, or conflicting information. Ambiguous problems often require more judgment and exploration to solve compared to problems with clear uncertainty.

Front-loading

Front-loading is the strategy of investing more effort in the early stages of a problem-solving or development process to prevent problems and maximize efficiency downstream. Methods can include transferring knowledge from past projects, using technologies to enable faster iterations, and systematically identifying issues as soon as possible.

Knowledge inertia

Knowledge inertia refers to the tendency for individuals and organizations to rely on existing knowledge, routines, and problem-solving approaches even when better alternatives are available. Some degree of inertia is necessary for efficiency, but too much can inhibit innovation and adaptation. Overcoming inertia often requires deliberate unlearning.

Problem framing

Problem framing is the process by which a problem-solver defines a problem and its boundaries. Framing involves determining the scope, variables, constraints, and solution space to consider. The choice of frame influences the level of uncertainty and ambiguity engaged with and the ultimate efficiency and effectiveness of the problem-solving process.

Sticky information

Sticky information refers to knowledge that is difficult and costly to transfer or apply in new contexts. Information can be sticky due to attributes like tacitness, complexity, and context-dependence. The location of sticky information often determines where problem-solving occurs, as it is more efficient to bring the problem to the knowledge than vice versa.