Determine The Root Cause: 5 Whys

Asking “Why?” may be a favorite technique of your 3-year-old child in driving you crazy, but it could teach you a valuable Six Sigma quality lesson. The 5 Whys is a technique used in the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology. It is a great Six Sigma tool that does not involve data segmentation, hypothesis testing, regression or other advanced statistical tools, and in many cases can be completed without a data collection plan.

By repeatedly asking the question “Why” (five is a good rule of thumb), you can peel away the layers of symptoms which can lead to the root cause of a problem. Very often the ostensible reason for a problem will lead you to another question. Although this technique is called “5 Whys,” you may find that you will need to ask the question fewer or more times than five before you find the issue related to a problem.

Benefits of the 5 Whys

◈ Help identify the root cause of a problem.
◈ Determine the relationship between different root causes of a problem.
◈ One of the simplest tools; easy to complete without statistical analysis.

When Is 5 Whys Most Useful?

◈ When problems involve human factors or interactions.
◈ In day-to-day business life; can be used within or without a Six Sigma project.

How to Complete the 5 Whys

1. Write down the specific problem. Writing the issue helps you formalize the problem and describe it completely. It also helps a team focus on the same problem.

2. Ask Why the problem happens and write the answer down below the problem.

3. If the answer you just provided doesn’t identify the root cause of the problem that you wrote down in Step 1, ask Why again and write that answer down.

4. Loop back to step 3 until the team is in agreement that the problem’s root cause is identified. Again, this may take fewer or more times than five Whys.

5 Whys Examples

Problem Statement: Customers are unhappy because they are being shipped products that don’t meet their specifications.

1. Why are customers being shipped bad products?

– Because manufacturing built the products to a specification that is different from what the customer and the sales person agreed to.

2. Why did manufacturing build the products to a different specification than that of sales?

– Because the sales person expedites work on the shop floor by calling the head of manufacturing directly to begin work. An error happened when the specifications were being communicated or written down.

3. Why does the sales person call the head of manufacturing directly to start work instead of following the procedure established in the company?

– Because the “start work” form requires the sales director’s approval before work can begin and slows the manufacturing process (or stops it when the director is out of the office).

4. Why does the form contain an approval for the sales director?
– Because the sales director needs to be continually updated on sales for discussions with the CEO.

In this case only four Whys were required to find out that a non-value added signature authority is helping to cause a process breakdown.

Let’s take a look at a slightly more humorous example modified from Marc R.’s posting of 5 Whys in the iSixSigma Dictionary.

Problem Statement: You are on your way home from work and your car stops in the middle of the road.

1. Why did your car stop?
– Because it ran out of gas.

2. Why did it run out of gas?
– Because I didn’t buy any gas on my way to work.

3. Why didn’t you buy any gas this morning?
– Because I didn’t have any money.

4. Why didn’t you have any money?
– Because I lost it all last night in a poker game.

5. Why did you lose your money in last night’s poker game?
– Because I’m not very good at “bluffing” when I don’t have a good hand.

As you can see, in both examples the final Why leads the team to a statement (root cause) that the team can take action upon. It is much quicker to come up with a system that keeps the sales director updated on recent sales or teach a person to “bluff” a hand than it is to try to directly solve the stated problems above without further investigation.

5 Whys and the Fishbone Diagram

The 5 Whys can be used individually or as a part of the fishbone (also known as the cause and effect or Ishikawa) diagram. The fishbone diagram helps you explore all potential or real causes that result in a single defect or failure. Once all inputs are established on the fishbone, you can use the 5 Whys technique to drill down to the root causes.

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A Solution Template to Help in Hypothesis Testing

One of the most difficult topics for those learning how to use statistics is hypothesis testing. Solving a number of examples will help convince potential and new Six Sigma practitioners of the importance of the concepts behind this tool. However, the necessary steps and their formulation take some additional effort. An appropriately designed solution template for this purpose can ease the difficulties of the learning process.

Suppose that we want to decide whether the mean (m) of the population under consideration exceeds, does not exceed or differs from a given value (m0). To make that decision, we take a random sample, compute the mean (), and then apply a statistical inference technique called hypothesis testing. First, we describe the hypothesis tests for one-population mean. The results from this exercise will translate to other hypothesis-test analogies of the one-sample z-interval and one-sample t-interval confidence-interval procedures, respectively. The third is a nonparametric method called Wilcoxon signed-rank test, which applies when the variable under consideration has a symmetric distribution. For other hypothesis tests beyond the tests for one-population mean, the formulas for test statistics (Z) will be used in Step 3 instead of test statistics:

Hypothesis Tests for One-population Mean

We will solve the following hypothesis tests for a one-population problem using the template to be designed. The solution text will appear as underlined or as a choice to be selected or deleted, appropriately.

Example: The Food and Nutrition Board of the National Academy of Sciences states that the recommended daily allowance (RDA) of iron for adult females under the age of 51 is 18 milligrams (mg). A sample of iron intake in was obtained during a 24-hour period from 45 randomly selected adult females under the age of 51. It revealed that the sample mean () was 14.68 mg. At the 1 percent significance level, does the data suggest that adult females under the age of 51 are, on average, getting less than the RDA of 18 mg of iron? Assume that the population standard deviation is 4.2 mg.

P-Value Approach to Hypothesis Testing

This can also be modified to examine a second approach to hypothesis testing, the p-value approach with a minor modification. The p-value (also known as the observed significance level or the probability value) indicates how likely or unlikely observation of the valueobtained for the test statistics wouldbe if the null hypothesis (H0) is true. In particular, a small p-value (close to 0) indicates that observation of the value obtained for the test statistics would be unlikely if the null hypothesis (H0) is true. Accordingly, Steps 4 and 5 will be modified as follows for the p-value approach. For a two-tailed test, as required, the amount a of will be halved in the alterative Steps 4 and 5.

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Using Control Charts or Pre-control Charts

Every process falls into one of four states:

1. Ideal: produces 100 percent conformance and is predictable
2. Threshold: predictable but produces the occasional defect
3. Brink of chaos: not predictable and does not produce defects
4. Chaos: not predictable and produces defects at an unacceptable rate

Processes tend to migrate toward chaos if not effectively managed.

Pre-control Charts

There are two basic philosophical differences between those who support control charts (or Shewhart charts, named for their developer, statistician Walter A. Shewhart) and those who support pre-control charts. The pre-control folks tend to view any product within specification as being of equal good. All outcomes are considered to be “good” or “bad” and the dividing line is a sharp cliff. A part that barely meets specification is as good as a part that is perfectly centered on the target (T) value. Producing product tighter than the specification limits is viewed as an unnecessary expense.

Figure 1: “Good” Within Product Specifications

Rath & Strong consultants, including statistician Frank Satterthwaite, developed pre-control charts in the 1950s. This technique focuses on the voice of the customer in that the pre-control limits are based on upper and lower specification limits (USLs and LSLs). These limits are chosen such that the hard stop limit to pre-control charts are at the customer specification and cautionary limits are at ±50 percent of the specification (see Figure 2).

Figure 2: Example of a Pre-control Chart

To establish process capability, five consecutive units must fall between the pre-control limits in the green region. After this condition is met, two successive units are periodically sampled. If the two units fall in the green zone, continue production. If one unit falls in the green zone and the other falls in the yellow, continue production. If both units fall in the yellow zone, stop and adjust the process. If one unit falls in the red zone, stop and adjust the process. To resume normal production five units in a row must be within the green zone. The sample frequency is determined by dividing the interval between stoppages by six.

Control Charts

Control chart philosophy more closely follows the Taguchi Loss Function even though control charts were developed in the 1920s and the Taguchi Loss Function was not introduced until the 1960s. The Taguchi Loss Function states that as the parameter (x) varies about the target (T) there will be a loss [L(x)] to society. Thus, a part produced at the target is more valuable than a part produced at the specification limits. This is because throughout the value stream accommodations have to be made to be tolerant to that variation from the target value. That adds cost to subsequent steps in the value stream. (See Figure 3.)

Figure 3: Taguchi Loss Function

Shewhart chart control limits are chosen so that time is not wasted looking for unnecessary trouble. The practical goal is to take action only when necessary. Control limits are calculated by estimating the standard deviation of the sample data adjusted for sample size and multiplying that number by three. That number is then added to the average for the upper control limit and subtracted from the average for the lower control limit. Shewhart gave us constants to use that ease these calculations. The control chart tests are design to flag points that are not behaving “normally” (i.e., exhibiting special cause variation).

The Shewhart chart focuses on the variation that is due to the process itself. Control limits are developed from the process data and not tied to the specification limits. This is commonly referred to as voice of the process (VOP) as the process is providing information about itself.

Figure 4: Example of a Control Chart

Shewhart charts determine what kind of variation the process is exhibiting. Common cause variation is systemic, chronic variation that is produced by any process. It is often thought of as “random” variation and is produced by the process itself. It can be large or small. Special cause variation is caused by a unique disturbance. It is unpredictable and can be large or small. The cause may be known or unknown and is not always bad.

What is the concern in identifying our observed variation as common cause or special cause? Treating common cause variation increases variation as illustrated by Dr. W. Edwards Deming’s funnel experiment described in Out of Crisis. The experiment shows that treating common cause as special cause degrades process performance. Dr. Deming called this tampering.

Figure 5 displays results from a simulation to illustrate the effect of tampering. It shows that treating common cause variation as special cause variation greatly increases variation from the target value; by treating common cause like special cause, the problem worsens. If special cause variation is treated like common cause variation, the root of the problem is not found. Additional variation and cost to the process are likely to be introduced.

Control Chart Test for Special Cause Variation

There are eight control chart tests that can be done to reveal special cause variation. (Refer to Figure 4 for Zone references.)

1. One point beyond Zone A detects a shift in the mean, an increase in the standard deviation or a single aberration in the process.

2. Out-patient workload

3. Nine points in a row in a single (upper or lower) side of Zone C or beyond detects a shift in the process mean.

4. Six points in a row steadily increasing or decreasing detects a trend or drift in the process mean. Small trends will be signaled by this test before Test 1.

5. Fourteen points in a row alternating up and down detects systematic effects such as two alternately-used machines, vendors or operators.

6. Two out of three points in a row in Zone A or beyond detects a shift in the process average or increase in the standard deviation. Any two out of three points provide a positive test.

7. Four out of five points in Zone B or beyond detects a shift in the process mean. Any four out of five points provide a positive test.

8. Fifteen points in a row in Zone C, above and below the center line detects stratification of subgroups when the observations in a single subgroup come from various sources with different means.

9. Eight points in a row on both sides of the center line with none in Zones C detects stratification of subgroups when the observations in one subgroup come from a single source, but subgroups come from different sources with different means.

Figure 5: Effects of Tampering

Control Charts or Pre-control Charts: An Example

In much of the literature that supports the use of pre-control control, claims are made that control charts are a waste of time and are too cumbersome to use. Often those who hold to control charts claim that pre-control charts will cause users to tamper with their process and actually increase variation. Which group is correct? Consider the following example.

A set of 500 normally distributed data points with a mean of 100 and a standard deviation of 5 was created. Setting specification limits at 100 ±15 results in a Cpk of 1, which is optimum in pre-control terms. The data being normally distributed and centered on the target value is a fair condition for traditional control charts.

Figure 6: Probability Plot

The individuals chart (Figure 7), which is the closest Shewhart chart to the pre-control chart, flags the points as greater than three standard deviations from the process mean. This is expected as the process is centered on the specification mean for this example; 1 in 370 points are expected to fall beyond three standard deviations in a normal distribution. The individuals chart is also the most sensitive of the Shewhart charts but should always be used in conjunction with the moving range chart.

Short term variation is not investigated in an individuals chart. That is the job of the moving range chart (Figure 8). The moving range chart indicates that seven moving range points seem to be behaving abnormally and should be investigated.

The pre-control chart (Figure 9) flags eight additional adjacent pairs as falling two standard deviations away from the specification mean and, thus, require process adjustment. Following the pre-control rules would lead to tampering. A total of 59 points require additional evaluation beyond the Shewhart method in this example.

Figure 7: Individuals Chart

Figure 8: Moving Range Chart

Figure 9: Pre-control Chart

It appears that the pre-control chart would have a higher false positive and encourage tampering. Pre-control measures compliance with customer specification, the voice of the customer. Control charts are measuring process variation or VOP. Control charts offer power in analysis of a process especially when using rational subgrouping. Rational subgrouping also reduces the potential of false positives; it is not possible with pre-control charts.

Pre-control charts have limited use as an improvement tool. Pre-control does not detect shifts, drifts and trends with statistical certainty as control charts or run charts do. See the table below for a side-by-side comparison of the two tools.

Comparison of Control and Pre-control Charts
	Control Charts	Pre-control Charts
Protects the Customer	In conjunction with process capability	The goal of pre-control charts
Useful in Process Improvement	Highly useful	Minimally useful
Variation Inflation Risk	Minimal	Likely
Ease of Use	1. Readily available software 2. Chart-based	1. Must develop manually or write custom software 2. Charting not required
Broadly Accepted	Yes	No
Conducive to Rational Subgrouping	Yes	No
Statistically Valid	Yes	Questioned

Many quality professionals have declared that pre-control charts have gone the way of the Dodo bird. They are, however, a helpful tool to use after changeovers. Pre-control charts can help to roughly center the process until there are enough points to calculate control limits and reestablish capability – but only if the rules are slightly modified. “If…, stop and adjust the process” should be changed to “If …., stop and investigate the process.” In the event of a pre-control chart trigger, problem-solving analysis tools should be employed rather than blindly adjusting the process.

By using this slightly modified pre-control charting as part of a changeover procedure the customer can be protected until stability, control and capability can be established. There is a great deal of variation as to the number of points required to calculate control limits, from as low as 14 to as high as 100; 30 is the most common. If an institution uses a higher number of points, there might be a place for pre-control charts in its changeover practices.

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From Quality Control to Quality Improvement

Everybody is familiar with control charts for quality control. An example of a control chart is shown below. In the example a packaging company who made blisters for the pharmaceutical industry found the process average for a critical characteristic was out of control. There were some ideas about possible causes but, as in most other companies, they were lacking the knowledge and resources to perform experimental design to find the causes of variation. They decided is to use their statistical process control (SPC) program to make the step from quality control to quality improvement.

Figure 1: Control Chart Example

The first step the company took is to add extra information columns to their data. These columns can be used for tracking and tracing information like operator, lot number, batch number, etc. Other columns could be sources of variation like process parameters, machine batch numbers, temperature, humidity, etc. The company extracted this data from PLCs (programmable logic controllers) and from the company’s ERP (enterprise resource planning) system.

Figure 2: Control Chart Data

The control chart shows us when the process is out of control, but we can also use the same measurements to analyze if there are differences in the process between different settings or different material batch numbers.

The second step is to analyze the control chart and indicate where process changes are made. This can be done using vertical bars, but the charts are even more meaningful if we show the variation with each process change in zones. An example of the chart with specific zones for each material batch number is shown in the picture below.

Figure 3: Control Chart Zones

Each zone in the graph above indicates a material batch change. The graph clearly shows that there are differences between batches and it seems that the process within a zone is statistically in control.

Another way to present the same data is the multi-vari chart (below is an example of a multi-vari chart).

Figure 4: Multi-vari Chart

We can see for example that the average of 6724 is higher than the average for 9988359. The obvious question is whether this is a significant difference or just random variation.

To compare the averages of two data sets we need to perform a T-test, but first you need to check if the two data sets are coming from a normal distribution and if the variability of the two datasets are more or less the same (F test).

In the example the F- and T-tests are calculated for the two selected datasets marked with a circle. For each point the actual data can be shown in the form of a small histogram standard with a normal curve superimposed on top of it. This allows us to check the data for normality (see screen shot below).

Figure 5: Multi-Vari Chart

From this analysis the company found out the material batch number was the main cause of variation in their process.

The method above shows a very quick and effective way to make the step from quality control to quality improvement, and it helps closing the gap between using control charts and applying experimental design. The benefits of the approach described above are:

1. Analysis can be performed by many more people than the specialists trained in experimental design. The techniques described above are much easier and faster to learn than experimental design.

2. By adding extra columns with parameter information to the control charts and making operators responsible for recording data, you can perform many more experiments than by applying experimental design alone.

3. The mindset of people using control charts will shift from process control to analysis for improvement.

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The 5S of Communication

Lean 5S (sort, simplify, shine, standardize, sustain) are about organizing work space so we can be more efficient, effective and productive. All Lean concepts are about how work gets done. There are many benefits Lean 5S can provide including improving safety, decreasing down time, raising employee morale, identifying problems more quickly and establishing convenient work practices. They are also used to strengthen employees’ pride in their work, to empower employees to sustain an organized work area and even to promote stronger communication among staff.

How effective is your communication? How Lean is your communication?

Communication is a two-way dance and involves an offer that is accepted by the audience. It is as much about how content is delivered as the content itself. The most effective communication happens when content is delivered in a process that can be “heard” by the audience. This involves attention to not only words, but also to tones, gestures, postures and facial expressions.

Effective communication has many strong connections to Lean principles and concepts, including 5S. When communication is done right, it helps employees and leaders work together in a safe and open environment. It increases the quality of personal and professional lives. Everyone feels respected and appreciated for their unique gifts. Down time is decreased as positive conflicts are handled without drama, which means that effective problem solving is expedited. Because employees feel safe and connected, they feel pride in working in an organization where their gifts are valued; therefore, they continue to contribute to improving work processes.

Like Lean, effective communication is about the how; it is about how communication takes place, as well as what is said. With that in mind, here are the suggested 5S of communication: size up, seek, simulate, stabilize and sustain.

Size Up

Be open, trustworthy and transparent. Be open to understanding different personality types and individual needs; be able to assess employees’ preferred communication styles and respond accordingly. Sizing up is about being self-aware of your personal communication needs, strengths and blind spots, and being able to shift communication styles to cater to others’ needs.

Seek

Seeking is about aligning the way you communicate to cater to individual preferences. It is about being resourceful, willing to discover ways to effectively connect with others, encouraging creativity, innovation and problem solving while appreciating and leveraging personality differences.

Simulate

Polish your communication and compassion skills so you know how to motivate employees based on their different needs. Simulating is about being inquisitive and curious about what makes people diverse and leveraging that diversity.

Stabilize

To stabilize means to consistently apply effective communication by connecting with, and motivating employees, based on their needs to resolve conflict and eliminate drama in the workplace. This requires a leader who is persistent, willing to stick with employees who are different, willing to help without judgement, who can listen and empower.

Sustain

Be proficient in communication practices so you can motivate employees based on their needs and connect at their level, consistently and effectively. It is about accepting people the way they are – listening and holding themselves and others accountable. It is about recognizing your own propensity to create or participate in drama, and having the compassionate skills needed to lead a healthy organization.

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Building a Project Meeting Structure That Works

As a project leader, you’re required to chair meetings before, during and after any major project. To ensure everyone is on the same page and working in the right direction, you need to plan these meetings effectively. But how do you plan and execute a project meeting structure that works?

1. Set your objectives

If you’ve ever been to a meeting that seemed to last hours but had no clear conclusions, chances are the meeting had no clear goals set beforehand. Positive meetings have a clear set of identified goals. Make sure these are outlined at the start of the meeting and assess your progress before the meeting ends.

2. Agenda

Don’t let the meeting drift. Nobody wants to be in a meeting any longer than they have to. Make sure the agenda is circulated before the meeting and give clear timings for each matter on the list.

3. Materials

Make sure you have any required materials in place and organised before you start. No one wants to sit there as you sift through reams of documents to locate the right information. Keep any information short and to the point.

4. Attendees

Make sure only the relevant team members are in attendance. Being asked to attend a meeting that doesn’t concern you can be frustrating, as you may know. As a general rule, the fewer people in attendance the better. If decisions need to be made, make sure all the people needed to make them are in the room.

5. Environment

Too hot, too cold, wrong venue? These environmental factors can scupper a positive meeting. Make sure everyone is comfortable, and sort out drinks or other requirements before you start so there are no interruptions.

6. Stick to the plan

Other matters come up in meetings that are not on the agenda but it might be better to save these for another time. Try to make sure you start and finish the meeting on time. If it’s clear that the meeting will go way past the planned finish time, then it might be worth scheduling another.

There are many types of project management meetings, but the above principles always remain the same. Effective meetings are well planned and well executed. If you are unsure about planning a meeting, then utilise tools such as a project meeting agenda sample or project board agenda template. This will help you to plan and direct your meeting and help you achieve your prioritised goals.

As you become more experienced chairing meetings, you develop an understanding of how they work. But when you start out, it’s all about the details. Making sure you are prepared and that the meeting has been planned correctly are key to success.

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The Qualities of a Good Project Manager

Understandably, we believe that being certified in project management is a sure-fire way to boost your career chances, knowledge of best practice and overall skillset. However, without the right personal skills, a qualification can only take you so far. We’ve pulled together 4 essential attributes you can use to take your PM skills from good to great.

1. Organisation

People tend to believe that you’re either naturally organised or you’re not, and that for those who have the gift, staying organised is a walk in the park. Neither of these are true. You can teach yourself a number of organisational tactics that suit your personal working style, but they require hard work, consistency and perseverance.

Disorganised project managers either struggle or fail. The best project managers know how to tailor helpful organisational tools, such as Gantt charts, to their own company’s practices, and apply a range of techniques to organise both themselves and others. Set yourself reminders, create to-do lists and be sure to update both regularly; organisation takes time.

2. Communication

Project Management could be defined by this word alone. The project manager will liaise between their team, stakeholders, project board, suppliers and more, so will have to relay information accurately and succinctly. Being able to communicate in a number of voices will help. That includes adopting a more formal tone for stakeholders than you’d use with your team.

Another important, but often overlooked, aspect of effective communication is to know when not to give someone information. This obviously means keeping sensitive information to yourself, but also means not copying everyone from the CEO to the janitor into your email chains. Consider whether the information is actually need-to-know for your target audience, and tailor it appropriately.

3. Logic

Logic can cover a number of bases. One project management practice where logic is essential is Risk Management. This requires you to use linear thinking and historical information to draw possible conclusions. Logic will also help with both organisation and communication, as previously discussed.

Logic also takes time. As with Risk Management, your past experiences will inform how you apply logic to future projects. Take time to properly assess how your team’s actions and processes will affect the final outcome, and if anything doesn’t make sense, trust your gut and take a critical look at processes and solutions.

4. Empathy

Empathy is one of the most important soft skills a project manager needs for effective leadership. It’s in your interests as a PM to have your team feel as though they can trust you, both with issues and with ideas. Being honest, open and understanding can set an exceptional project manager apart from an average one.

As with any of the skills listed in this article, empathy isn’t an innate, unteachable talent. You can cultivate empathy like any other ability: with practice. Make conscious decisions to listen to your team more carefully, and pay attention to how your actions and their circumstances may affect their mood and performance. Remember, being empathetic doesn’t mean being a push-over – be sure to make your own expectations and boundaries clear too.

Never underestimate the impact your soft skills can have on your project management prowess. Taking time to develop these more intangible skills can give you and your team’s work a serious boost, and combining it with a tangible methodology or framework will set you firmly apart from other PMs.

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How to Manage Multiple Projects

When things get busy in the office, it can be a real challenge figuring out how to manage multiple projects. There just doesn’t seem to be enough hours in the day to get it all done.

Handling multiple projects can quickly get out of control, and taking your eye off the ball can quickly lead to extra work, mistakes or even failure. So how do you manage several different projects at the same time? And what is the secret to effective multi-tasking?

Get a strategy

There are many strategies for facing the challenges of multiple projects. But the first thing to think about is training. There are project management training courses out there which teach you all the basics of how to run successful projects concurrently. PRINCE2 courses teach the strategy behind effective management and help you fully integrate this approach into everything you do at work.

In essence, there are several key principals behind effective project management that you need to know. These are:

1. Prioritise

This is fundamental knowledge that is absolutely key to being a successful project manager. If you are not able to identify which areas are the most important and pressing, then you can spend all day wasting time flitting between one area and another, never really achieving or moving anything forward. This is especially important if you are working in a team and can delegate certain tasks. Using your time wisely to do the most important jobs first is always a good idea.

2. Block time

To help you prioritise, you need to be able to block your time to focus on one area without being distracted by the long list of other things you have to do. If you aren’t constantly switching between jobs, you can get into a flow state. That means you can achieve work goals much more rapidly.

3. Review

You need to able to accurately assess how you are getting on with your workload. So take time out to look over what you have done and think about what you need to do next. Set aside a specific time each week, either on Friday afternoon or Monday morning and go over where you are in each project. This will inform where you are overall and help identify areas that need more work.

4. Manage expectations

Sometimes projects run into problems because people are expecting too much too quickly. As the manager, it is your job to make sure all stakeholders have a realistic view of what is involved and how long things take. Of course, your job is to deliver, but it is always better to deliver quality than rushed work. You need to be able to communicate clearly and effectively if you need more time or assistance to bring projects in to brief. As the manager, this is a key part of what you do.

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