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Tag: Methodologies

PMTimes_Aug21_2024

Six Sigma in Project Management

Have you ever wondered how some companies consistently deliver top-quality products and services while others struggle to meet basic expectations? What sets the best apart is often not just their technology or talent but the methodologies they use to manage and improve their processes. One such powerful methodology is Six Sigma.

Developed by engineers at Motorola in the 1980s, Six Sigma is a data-driven approach focused on process improvement and quality management. The name “Six Sigma” itself refers to a statistical term that measures how far a process deviates from perfection. In Six Sigma, the goal is to limit defects to 3.4 per million opportunities. In simpler terms, it’s about getting things right 99.9997% of the time!

In this article, we’ll explore how Six Sigma integrates with project management, the methodologies it uses, and the tools and techniques that can make your projects more successful.

 

Key Principles of Six Sigma

At its core, Six Sigma revolves around a few key principles:

  1. Customer Focus: Everything starts with the customer. Six Sigma emphasizes understanding customer needs and expectations and then tailoring processes to meet or exceed those expectations.
  2. Data-Driven Decisions: Six Sigma relies on data and statistical analysis to identify problems and measure progress, ensuring that decisions are based on facts.
  3. Process Improvement: Six Sigma encourages teams to constantly look for ways to refine processes, eliminate waste, and increase efficiency.
  4. Employee Involvement: Six Sigma requires active participation from all levels of the organization. Everyone is encouraged to contribute ideas and take ownership of process improvements.
  5. Proactive Management: Instead of reacting to problems after they occur, Six Sigma promotes a proactive approach. It helps teams identify potential issues before they become major roadblocks.

As for project management, using Six Sigma in this field helps foster a culture of continuous improvement, where every team member is empowered to look for ways to enhance the project’s outcomes. This focus on quality and efficiency not only helps in achieving the project’s objectives but also builds a foundation for long-term success.

 

Six Sigma Methodology

Six Sigma offers two primary methodologies – DMAIC and DMADV – each serving different purposes but both aimed at improving processes and ensuring quality.

 

DMAIC Process

The DMAIC process is the most widely used methodology in Six Sigma, especially in project management. It stands for Define, Measure, Analyze, Improve, and Control. Here’s a quick breakdown of each phase:

  1. Define: Identify the project goals and customer deliverables. In this phase, project managers define the scope and objectives, ensuring everyone is aligned on what needs to be accomplished.
  2. Measure: Gather data to understand the current performance level. This involves identifying the key metrics and collecting relevant data to establish a baseline for improvement.
  3. Analyze: Dig into the data to uncover the root causes of defects or inefficiencies. This phase is about understanding why the process isn’t meeting the desired standards.
  4. Improve: Develop and implement solutions to address the root causes identified in the analysis phase. Here, teams brainstorm and test various strategies to enhance the process.
  5. Control: Monitor the improved process to ensure the changes are effective and sustainable. This step includes establishing control plans and continuously tracking performance.

 

DMADV Process

On the other hand, the DMADV process, also known as Design for Six Sigma (DFSS), is used when a new process or product is being designed from scratch. It stands for Define, Measure, Analyze, Design, and Verify:

  1. Define: Similar to DMAIC, this phase involves defining the project goals and customer requirements.
  2. Measure: Collect data on critical factors that could impact the quality of the new process or product.
  3. Analyze: Evaluate the data to develop design alternatives. This phase focuses on ensuring that the new design will meet customer needs and business objectives.
  4. Design: Develop detailed designs for the new process or product. This involves creating prototypes and conducting simulations to test the design’s effectiveness.
  5. Verify: Test and validate the final design to ensure it meets the necessary standards and performs as expected in real-world conditions.

Choosing between DMAIC and DMADV depends on the nature of the project. If you’re improving an existing process, DMAIC is your go-to approach. If you’re creating something new, DMADV is the way to go.

 

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Six Sigma Tools and Techniques in Project Management

Six Sigma is a toolkit filled with a variety of tools and techniques that help project managers and teams identify problems, analyze data, and implement solutions.

Here’s a quick look at some of the most popular tools used in Six Sigma projects:

1. Pareto Chart

The Pareto Chart is based on the Pareto Principle – also known as the 80/20 rule – which states that 80% of problems are often due to 20% of causes. This chart helps project managers visually prioritize issues by showing the frequency of defects or issues, making it easier to focus on the most critical areas first.

2. Cause-and-Effect Diagram (Fishbone Diagram)

Also known as the Ishikawa Diagram, the Cause-and-Effect Diagram helps teams brainstorm potential causes of a problem and organize them into categories. This visual tool is particularly useful for identifying the root causes of defects or inefficiencies in a process.

3. Control Charts

Control charts are used to monitor the performance of a process over time. By plotting data points, project managers can quickly see whether a process is stable and within predefined limits. This tool is crucial for maintaining control over a process and ensuring consistent quality.

4. Process Mapping

Process Mapping, also known as Flowcharting, involves creating a visual representation of the steps in a process. This tool helps teams understand how a process works, identify bottlenecks, and find opportunities for improvement.

5. Root Cause Analysis (RCA)

Root Cause Analysis is a technique used to identify the underlying reasons for a problem or defect. By asking “why” multiple times, project managers can drill down to the fundamental cause of an issue and develop effective solutions.

6. Statistical Analysis

Six Sigma heavily relies on statistical analysis to make data-driven decisions. Techniques such as Hypothesis Testing, Regression Analysis, and ANOVA (Analysis of Variance) help project teams analyze data, test assumptions, and validate their findings.

 

Six Sigma Certification Levels and Their Role in Project Management

Six Sigma is also a structured training and certification system that equips individuals with the knowledge and skills to lead and implement Six Sigma projects effectively. In project management, these Six Sigma certifications serve as a roadmap for career development.

Let’s take a look at the different Six Sigma certification levels and how they align with various project management roles:

1. White Belt

White Belts have a basic understanding of Six Sigma concepts and terminology. They assist with change management and participate in local problem-solving teams that support larger projects, making them valuable team members on any project.

White Belts often serve as team members who support data collection and process improvement activities. They are usually new to Six Sigma and are eager to learn from more experienced colleagues.

2. Yellow Belt

Yellow Belts have a deeper understanding of Six Sigma principles and can assist Green and Black Belts in data collection and analysis. They play a crucial role in identifying processes that require improvement. They also assist project leaders in implementing process changes and can lead smaller projects or initiatives under the guidance of higher-level Belts.

3. Green Belt

Green Belts have a solid understanding of Six Sigma methodologies and tools and can lead smaller projects or serve as team members on larger projects led by Black Belts. Green Belts are responsible for analyzing and solving quality problems and are actively involved in process improvement.

Green Belts often manage individual projects within their areas of expertise. They lead teams, apply Six Sigma tools and techniques, and are responsible for delivering project results.

4. Black Belt

Black Belts are experts in Six Sigma methodologies and are capable of leading complex projects. They mentor Green and Yellow Belts and are responsible for managing multiple projects across different departments.

Black Belts are focused on strategic process improvements and are key to driving significant changes within an organization.

5. Master Black Belt

Master Black Belts are the highest level of Six Sigma certification. They have extensive experience in Six Sigma and are responsible for training and mentoring Black Belts and Green Belts.

Master Black Belts play a strategic role in the company. They oversee the implementation of Six Sigma methodologies across the enterprise, provide expert guidance on complex projects, and ensure that Six Sigma initiatives align with the company’s overall strategy and objectives.

 

Conclusion

Six Sigma has proven itself as a powerful and versatile tool in the world of project management. By embracing its methodologies, tools, and certification programs, organizations can significantly enhance their process efficiency, reduce errors, and consistently meet customer expectations.

As project managers, integrating Six Sigma principles into your practices can transform projects and the way your team approaches problem-solving and quality management. So, are you ready to take your project management to the next level with Six Sigma?

PMTimes_Aug07_2024

Owning the Rules of Project Management

Project management (PM) developed over time and will continue to evolve as innovative technologies and practices are embraced. Rules of PM are sometimes brandished around as if they are things that never waiver, things that are a must-have. These include scoping the project, creating the project charter, asking for stakeholder input, managing budgets and timelines—and the list goes on. But rules need to be malleable; they need to adapt to the project.

 

First Things First

No doubt, it’s hard to let go of things we learn, things that courses, seminars, webinars, and experience teach us. Things our gut says to pay attention to. There are times, though, when those things fade into the background like a sunset dissolving into the western horizon. Everything about project management should unfold according to the project’s needs and not based on rules defined by instructors and books. We need to be accommodating.

After earning my project management certification (whew!), and after a few hours of in-house PM training geared to enlighten various management and professional teams about the value of structured project management—and company-designed forms to use—my ethos was one of a rule enforcer (kind of an inherent trait of mine anyway): This is how it’s done. This is how to ensure the project will be successful!

I can honestly say that this rigid mindset did not get far. I am not going to say that I threw a hissy fit when someone refused to follow a certain “rule”, but I voiced my concern. Someone in management challenged me with the question: Why is it so important that things be done this way?

My response was: Because this is proper project management. This is what I learned from PMI (the Project Management Institute), and this is what we learned in-house.

Sidebar: The people involved in this matter were in the same in-house sessions as I was.

I knew what I was doing, right?

Wrong … sort of.

I am a detail-oriented person, and I believe in structure and rules. Those traits can be too stringent and can get in the way of managing a project from the stakeholders’ perspectives. I needed to unlearn—well, maybe adapt—my inherent beliefs if I wanted to survive as a good project manager. I needed to satisfy the stakeholders needs, and not my own.

At the end of the day, the “rule” was not going to be followed for this project (and mostly all projects since then).

Did I feel defeated at first?

Yes.

Did I get over it?

Yes.

Every company will have its own unique way of managing projects. Each project will demand its PM to lead it in a way that suits the scope, goals, stakeholders, timeline, budget, and, more importantly, the company’s culture and style. Your corporate culture is not something you can be taught in a PM course. You must know it and make it part of your PM skills.

 

Communication

You need to know your stakeholders and what they need from you. Communication needs are not one-size-fits-all. Emails, status reports, and meetings need to be tailored to your audience. As examples:

  • The sponsor wants a weekly high-level status report.
  • The technical team lead requires a thirty-minute face-to-face meeting every two weeks.
  • The business manager only cares about monthly budget and timeline updates.
  • The functional subject matter experts team doing the project work needs weekly meetings.

Managing a project involves stakeholder registers and communication plans which ensure everyone is informed when and how they want to be.

It is important to note that communication with third parties is crucial. Vendors and suppliers, at least in my experience, are not psychics. They must be listed on your stakeholder register and assigned the same level of value as those in your company. No secrets! If there is a change in the timeline or resources, it is beneficial for them to know so they can adjust accordingly.

 

Objectives, Scope and Deliverables

We need to keep perspective when it comes to project scope and objectives. Complex projects may require occasional check-ins with team members and sponsors when new learnings trigger a flurry of “what if” questions. There almost always are unknowns, things we cannot predict, that could change some facet of the project. Nothing is set in stone, and things about a project can be adjusted if necessary.

Always keep risk management in the forefront when it looks like an aspect of the project needs adjusting. Assess the impact of the change and make sure that all requests are feasible.

The Requirements Traceability Matrix

When I first learned about the Requirements Traceability Matrix, I at once felt a bond that almost matched my love for Excel (I cannot envision a world where I could live without Excel, at least not in a business setting). I created a version of an RTM I found online that I liked, and then I adapted it to my needs. It really helped get me through a large, multi-year project. No one else referred to it. Everyone thought it was overkill.

A rule of thumb – use what works for you but expect that it may not work for anyone else on your project team. And that’s okay. If it keeps you focused on the tasks in the pipeline, the successes, and the near misses, that is what is matters.

 

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The Plan

Let’s not forget about the project plan. Regardless of the size of the project, you need a plan. What are the tasks? When do they need to be complete? What is contingent upon something else? What is the status of each task? Have communications gone out as planned?

Plan the work, work the plan—that was something my instructor at UMUC (now UMGC, University of Maryland Global Campus) told us one day during class. Those words still stick with me today, even when it’s related to personal projects and tasks. For me, this is a golden rule. No caveats.

 

Prioritization

Prioritizing your work is tantamount to success. And that means assigning priority to non-project work, too! I am not a full-time project manager. Along with my projects, I run supply chain models, pull data for various analyses and GHG reports, and take care of monthly reports and transportation management system support.

When you feel like you are overwhelmed (and it will happen), it’s good to step back and assess your priorities. Usually, that is done throughout the day as emails arrive in your inbox, impromptu meetings pop-up on your calendar, the phone rings, and a myriad of other things vie for your attention. STAY CALM and think rationally. Things will get done! Make sure to communicate if anything needs to move to the backseat instead of being in the driver’s seat.

The bottom line is that it is okay to make the rules up as you go along. Each project’s requirements will be different—sometimes only slightly while other times a major overhaul is needed. Be adaptable and responsive to the static and changing needs of your stakeholders and the project in general. Enjoy the plethora of challenges presented by project management.

PMTimes_July30_2024

87% of Project Managers Report an Increase In The Use of EQ Over The Past 2 Years

Capterra’s latest research study investigates the use of emotional intelligence (EQ) within project management and its impact on overcoming project challenges.

EQ is the ability to use, understand, and manage one’s own and others’ feelings. EQ-based techniques, such as listening actively or being open-minded, allow project managers to better engage with stakeholders and enhance their decision-making skills.

Almost half (47%) of the U.K. project managers we surveyed say they always use EQ-based techniques when enacting their project management responsibilities, while 51% say they do so often. Additionally, 87% of surveyed project managers report a significant or moderate increase in EQ usage over the past two years.

 

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The vast majority (95%) state that their company’s leadership understands the importance of EQ in project management. Moreover, 97% believe that a high EQ level can significantly or moderately impact a project team’s ability to achieve goals.

Project management responsibilities most positively impacted by EQ, according to respondents, include:

–          Decision-making (60%)

–          Problem-solving (58%)

–          Team management (49%)

–          Risk management (32%)

–          Time management (27%)

While project managers speak highly of EQ’s positive impact on teams, the story is different when it comes to its influence on senior stakeholders—only 17% of managers believe EQ positively impacts senior-level stakeholder management.

Conflict resolution is an aspect of EQ that most project managers struggle with, as 45% grapple with handling conflicts. Moreover, 36% of project managers have trouble communicating needs or expectations, 30% find it hard to identify emotions and 29% have difficulties managing relationships.

Eduardo Garcia, content analyst at Capterra U.K., comments:

While emotional intelligence is innate, there are aspects that can be further developed by project managers. Therefore, businesses should educate project managers on the effective use of EQ. Understanding and managing emotions not only enhances decision-making and problem-solving but also improves team dynamics and project outcomes.

To harness EQ effectively, businesses should prioritize training in conflict resolution, communication and relationship management to ensure projects and teams are managed efficiently.


Methodology:

Capterra’s 2024 Impactful Project Management Tools Survey was conducted in May 2024 among 2,500 respondents in the U.S. (300), U.K. (200), Canada (200), Brazil (200), Mexico (200), France (200), Italy (200), Germany (200), Spain (200), Australia (200), India (200) and Japan (200). Respondents were screened to be project management professionals at organizations of all sizes. Their organization must currently use project management software. For this study, we analyzed data from a sample of 200 U.K. respondents.

PMTimes_July24_2024

The Role of Artificial Intelligence in Project Management

1. Introduction to Artificial Intelligence

In the field of project management, AI can provide three significant benefits.

 

The first is improving project planning. Deep learning can be used to predict the tasks and resources needed, and intelligent virtual assistants can find optimal and efficient project plan scheduling under resource constraints.

The second benefit is in helping avoid risks and complications by identifying patterns that predict potential problems. The third is through enhancing decision-making regarding further resource management, strategic responses to change, and resolving potential resource conflicts.

The third and final benefit is helping project management evolve in the direction of adaptive projects. Natural language processing algorithms can analyze and distill project documents into accurate structural data used by adaptive project management, as can machine learning and data visualization employing AI.

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal.

The term “artificial intelligence” is often used to describe machines or computers that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving.” As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of “artificial intelligence,”  having become a routine technology. Capabilities currently classified as AI as of 2020 include successfully understanding human speech, competing at the highest level in strategic game systems, such as chess and Go, autonomous cars, intelligent routing in content delivery networks, and military simulations.

 

2. Applications of AI in Project Management

To sum up, the principle behind machine learning is above all to detect an algorithm based on a set of training inputs and outputs, and once these are entered, the computer understands how to reproduce the outputs of those inputs. One of the applications of artificial intelligence in project management, as we have already mentioned, is to be able to estimate costs and completion dates. However, this is a very narrow use of everything AI can offer. There are many applications of artificial intelligence in project management, but here we are going to show you the most important ones.

One of the terms closest to artificial intelligence is the concept of machine learning. The concept that closely fits artificial intelligence when it comes to project management is the idea of machine learning. When we refer to the concept of machine learning, we are talking about computerized systems that have learned to perform a given task. The principle is similar to how humans learn. In other words, it consists of training computers so that they are able to learn from the data we have prepared them. It is a question of learning to do things and not simply of giving precise orders to a computer.

 

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3. Challenges and Limitations of AI in Project Management

Currently, project management software products do not offer AI features. The incorporation of AI features can make intelligent project management tools more attractive, leading to a competitive advantage. However, there are also limitations of AI in project management. Many popular AI techniques, such as neural networks, are “black box” models, i.e., they are difficult to interpret. This makes it hard to identify the key inputs and processes and also judge their quality. It is therefore challenging to verify the correctness of the results and the decisions made by these models. Consequently, it is not practical to solely rely on AI for the control of project management problems.

AI assistance to support human decision-making is likely to be the most practical application of AI to project management. Despite major progress, current AI capabilities give relatively poor results when the demands for actions are more complex, and project management activities involve complex human interplay or cognitive decision-making requiring business intuition and strong business communication skills. The more cognitive AI can do to improve its abilities to make complex decisions, navigate uncertainty, and efficiently communicate project management activities, the more frequently it will be asked to confront problems that are outside of its current expertise.

It is difficult for AI systems to detect when unintended consequences occur and be covered effectively by different forms of liability standards and frameworks. There is a recognized need to regulate AI to ensure that the technology is used ethically and does not bring unintended harm. Despite the significant potential benefits of applying AI and ML to project management, the risks associated with their use must be addressed. Legislation and industrial standards need to be developed to mitigate these risks accurately while furthering innovation.

Currently, many challenges require the attention of researchers to make AI techniques more competitive in their applications to project management. Machine learning techniques are sensitive to the quality and size of data. Insufficient and low-quality data can lead to overfitting problems. Project data availability is often an issue. Data sets tend to be quite small in the project management domain and require advanced methods to deal with such high-dimensional and small-size problems.

AI techniques involving NLG and image processing are complex to design and manage. It remains an NP-hard problem to search for the best sub-network. In these settings, different specific indexes will have a combined negative effect on the heuristic search complexity and the performance of the translations. There are generally limited best algorithms for the training of high models and yet to identify effective features. Moreover, problems related to biased training data, privacy, and interpretability are becoming increasingly critical. The development of domain-independent AI techniques is important to explore the potential power of AI in complex project management tasks.

 

4. Future Trends and Implications

In particular, AI’s applications have the potential to revolutionize the interactions and dynamics within the project environment. AI has the capacity to transcend some specifically human deficiencies as it can perform some tasks more quickly and accurately, around the clock. Other important characteristics of AI include its ability to decrease costs, provide alternatives that can take into account diverse sources of data and information, and help reduce misunderstandings due to communications across different parties throughout the project lifecycle.

AI brings new instruments that can support this complex chain across the asset lifecycle, such as project management that ranges from planning, monitoring, quality control, risk probability assessment, resource allocation, closure, and feedback. Tools range from the use of virtual reality for providing training of operators well in advance of assets in operation, building up the cognitive learning curve, and obtaining optimal and sustainable production. However, successful implementation of AI does raise a set of technical and ethical issues that need to be addressed, such as the real-world impact of AI, privacy, and security concerns.

Disruptive technologies have a great opportunity to revolutionize project management practices. In this context, this chapter reviewed recent research efforts in two general categories about artificial intelligence (AI) strategies: redefining project management through AI and AI for project management.

It aimed to present the potential of AI applications in making projects more efficient and contributing to maximizing asset performance throughout the whole asset lifecycle. A closer collaboration is also needed between the project management and the AI research fields to create a better understanding of project management requirements with respect to AI.

The exploration of the possible impacts of future trends in the implementation of AI is a great gap to be filled in the coming years. The technology, the organization, the project structure, and the environment are also core determinants in the success of AI incorporation in the industry.

PMTimes_Jun25_2024

Agile – Autonomy & Self-Organization

In one of our coaching workshops, we were discussing what makes an Agile coach successful. We had a good discussion on this topic, and we came up with two concepts: autonomy and self-organization, which seem slightly similar, but they are mostly used interchangeably.

So first, let’s see what these terms talk about.

[Note: These are very vast topics for discussion; we cannot conclude this in a few lines of writing; I have only highlighted them briefly here in this article.]

 

Autonomy: The cultural steps toward empowerment

Agile development relies more on people, their mindset, and their culture than on processes.

Leadership,

Collaboration,

Informal communication,

Flexible and participative,

Encouraging, cooperative

Are other characteristics of agile software development.

 

Many organizations are embracing agile ways of working in an attempt to build faster, more customer-focused organizations. They are redesigning themselves to create a culture where decision-making is transitioned away from middle management towards those working with customers on the front lines, i.e., teams.

Ultimately, they seek engagement in order to create a culture where the team is more empowered to truly delight customers. Autonomy is the critical ingredient for this change.

Autonomy is always implemented through leaders. Leaders should have thought that employees should get well engaged with the organization, and if Leaders really want a high standard of engagement, they have to look for self-direction, empowerment, and a and a little bit of control from employees over what they have to do—their task, over when they have to do—their time, over who they have to do with—their team, and over how they have to do their technique.

If organizations and leaders think about these aspects, then employees will surely do things better.

 

Autonomy is not where leaders or bosses tell employees exactly what to do and precisely how to do it; Leaders take away all employee choices of any kind and largely control what they should do; and Employees are compliant with leaders and follow their instructions without digging up their own thoughts and experiences. This is very bad, controlling, and hijacking the working relationship between employees and their leaders.

However, autonomy is often misunderstood as power.

Autonomy should not be confused with the need for power, which is entirely a different matter and one that some employees will avoid at all costs. The difference between power and autonomy can be summed up as follows: Power is the desire to control not just one’s own actions but the actions of others, while autonomy is concerned with the ability to operate independently.

(more control and less autonomy)

 

Self-Organization: The Desire to be self-managed and self-driven

At its simplest level, a self-organizing team is one that does not depend on or wait for a manager to assign work. Instead, these teams find their own work and manage the associated responsibilities and timelines; they do require a mentor who can help grow their skills.

 

Defining self-organizing teams

A group of motivated individuals who work together toward a goal have the ability and authority to take decisions and readily adapt to changing demands. Let’s look at some important ingredients for a self-organizing team:

  • They pull work for themselves and don’t wait for their managers to assign work. This ensures a greater sense of ownership and commitment.
  • They manage their work (allocation, reallocation, estimation, delivery, and rework) as a group.
  • They still require mentoring and coaching, but they don’t require “command and control.”
  • They communicate more with each other, and their commitments are more often to project teams than to the Scrum Master.
  • They continuously enhance their own skills and recommend innovative ideas and improvements.

 

Five essentials of self-organizing teams

  • Competency: Individuals need to be competent for the job at hand. This will result in confidence in their work and eliminate the need for direction from above.
  • Collaboration: They should work as a team rather than as a group of individuals. Teamwork is encouraged.
  • Motivation: Team motivation is the key to success. Team members should be focused and interested in their work.
  • Trust and respect: Team members trust and respect each other. They believe in collective code ownership and are ready to go the extra mile to help each other resolve issues.
  • Continuity: The team should be together for a reasonable duration; changing its composition every now and then doesn’t help. Continuity is essential for the team.

 

Creating a self-organizing team

A common criticism of self-organizing teams is, “We cannot just put eight random individuals together, tell them to self-organize, and expect anything good to result.”

Creating a self-organizing team can be considered a three-step process.

Training: Proper classroom training can help satisfy many of the principles that self-organizing teams require. Specifically, hard skills training is needed to make each employee competent in a particular domain or technology. Soft skills training is also helpful.

Coaching: Once the team starts working together, adopt a coaching style to see if the members are facing any difficulties. They may require more support and guidance at the beginning. Some indicators of a self-organizing team are: scrum ceremonies, team enjoyment of the work, and teams pulling tasks for themselves.

By the end of this phase, you will know the team is self-organizing. However, keep your eyes open to observe the team’s behavior and provide need-based coaching.

 

Mentoring: Once a team starts self-organizing, the journey has only just begun. Team members still require mentoring to grow their skills and maintain the balance of the team. This mentoring should also help with continuity by ensuring everyone grows together and remains motivated. As mentioned earlier, a self-organizing team doesn’t need “command and control,” but it does need coaching and mentoring.

Teams are not always static; they change over time, but the frequency matters. Building a self-organizing team is an on-going process. Whenever a team’s composition changes, we need to repeat the whole team-building lifecycle (forming, storming, norming, and performing).

 

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How can we relate these two concepts?

Autonomy and self-management are two different concepts, but they are starting to be used interchangeably, as I said before.

Okay, to set up the stage, let’s see from 30,000 feet and consider, for now, these terms as

Autonomy is at the top management level, whereas self-organization is at the team level.

 

In self-organized teams,

  • There are no managers. Everything is self-directed and self-driven.
  • There is no one to set goals; teams decide their own learning path.
  • In self-organizing, a team sets their destination, sets accountability for the tasks, and decides how to reach the destination.
  • How many tasks, how often they have to do them, how many hours they have to do—it’s totally up to the team.

Autonomy, on the other hand, is different.

  • Autonomy means that there is someone who sets strategic direction and the goal for the employee [let’s call that someone a leader or top management], but the employee has the freedom to decide how to achieve the given goal.
  • Managers are there to guide, provide feedback, and advise employees, but they will not watch over the employee’s shoulder every step of the way.
  • There is always someone to review you, give you feedback, and promote you.
  • In autonomy, the team themselves decides and is accountable for how to reach the destination. But the destination is not set by the team; it is set by someone.

 

 

 

1A: Micromanagement Culture: no high-level purpose; just shut up and follow orders. The team is also not mature enough, and the manager takes almost all the decisions. Teams are management-compliant. Management always says we are here to decide what is good for you; you just follow what we are saying. This is leading to poor performance.

1B: In a large organization, autonomy is a tricky balancing act. For example, suppose you have hired a junior developer. First, they will need training, direction, and coaching. Then, over time, they will become more skilled and experienced. And then they will understand the company’s business model. As this happens, you can trust them with larger pieces of work and with less supervision.

Teams are similar in this quadrant. They aren’t all ready for autonomy right away, where team maturity is low, which again leads to poor performance.

2A: This quadrant exactly talks oppositely to 1B. So leaders are good at communicating what problems need to be solved, but they are also good at telling teams how to solve them. However, teams are well mature and self-organized; they know how to approach the given goal; this level of autonomy leads to dissatisfaction and a loss of motivation in teams.

 

2B: High autonomy with higher team maturity means leaders focus on what problems to solve and let the teams figure out how to solve them. This culture always brings continuous improvement and a healthy working environment.

Autonomy is the biggest factor when people decide to leave their current place of employment. Often, employees will stay in a position even if the salary is low, so long as they maintain some level of control over how they perform their work. Autonomy provides employees with a sense of collective ownership; they have organizational citizenship and, thus, a sense of belonging.

Yes, autonomy plays a critical role in reshaping our workplaces, but don’t forget to balance autonomy with self-organization for better results.

Even if at the organization level, leaders promote autonomy culture, it does not mean at the team level we achieved self-organization immediately. There are certain stages (mentioned above) that lead to self-organizing and performing teams for better results.

 

Studies found that in many organizations, there is a lack of system for team support, and reduced external autonomy is an important barrier to introducing self-organizing teams. These findings have implications for software development managers and practitioners.

Still, the process of designing, supporting, and coaching agile teams is not adequately addressed and understood in the context of software development organizations.

Further, there is a need for new knowledge on how companies should organize for the right level of autonomy and utilize self-organized agile teams to attain better performance, productivity, innovation, and value creation, and thus increase competitiveness.

 

 

Follows:

-Jacob Morgan

-Daniel Pink

https://www.planview.com/resources/articles/what-is-self-organizing-team/