Who Took My Money ?

Diner time, my wife tell me about her frustration at work. She had a long meeting with her boss together with others functional managers.

In the meeting, her boss shouted to all the people; “Our sale is going up, but we are making loss!! What happen?”

I reply her; you are not the first one telling me this story. A friend of mine running own plastic injection molding business also tell me the same story once upon a time. It is a common problem facing by many “Chinese man” companies.

One of the root causes of the problem may be related to the “invisible” process hided in the main operation that eat out their earning.

I name the “invisible” process as “Hidden Factory”

End of the day, the more the company produce, higher the loss the company generates.

Attitude Problem

I was in bad mood since early morning. because of some one’s working attitude that contradict with my expectation.

As a project manager, timeline to me is very important. I have to make sure completion of assignment between timeline.

Team member’s attitude most of the time determine the success of a project. In general, the output of a project is the effort of teamwork.

We work in a team, beside delivering quality work on time, we also have to take care the urgency of the team objective.

A team member should have the attitude to complete their assignment as soon as possible. High light to the team when encounter problem.

I’m very scared those team member who like to keep quiet when encounter problem. Always, it was too late when I knew it. It was too late for me to adjust. We end up in fire fighting and finger pointing. The whole team spirit spoiled.

Why must we end up in this kind of situation? If you have problem, tell me early lah. I send out the assignment 2 week the work start, why must wait until today only you tell me you are not free to work, what happen to the resources booked per your request?

I’m considered lucky today because I walk to his office to follow up with him the status, else, I may not know the story till next week.

In fact, I seldom show off my anger like this in my work place. You should know my level of dissatisfaction today.

Gage R & R

I wanted to put up this post long time ago, anyway I didn’t make my wish realize until today because of laziness.

I almost forget this topic since seldom use it, someone show me a GR&R report and ask me to explain, make me busy for the whole afternoon to flip through my training material to refresh my memory.

Thank you Lim PC….

This is how I interpret the GR&R report below.
(Chart interpretation in anti clockwise)

Components of Variation

The sources of variation that are represented in the graph are:

• Total Gage R&R, which is the variation due to the measuring system including multiple operators using the same gage.
  
• Repeatability, which is the variability in measurements obtained when the same part is measured multiple times by the same operator.
• Reproducibility, which is the variability in measurements obtained when different operators measure the same part.
• Part-to-Part, which is the variability in measurements across different parts.

In a good measurement system, the largest variation component is Part-to-Part variation. If large amounts of variation attributed to Gage R&R, corrective action is needed.

R Chart By Operator
The R chart consists of the following:

• The plotted points, which represent the difference between the largest and smallest measurements on each part measured by the operator. Because the points are arranged by operator, you can see how consistent each operator is.
• The green center line represent the grand average for the process.
• The red control limits, which represent the amount of variation expected for the subgroup ranges. These limits are calculated using the variation within subgroups.

If any of the points on the graph go above the upper control limit (UCL), then that operator is having problems consistently measuring parts. The UCL value takes into account the number of measurements by an operator on a part and the variability between parts. If the operators are measuring consistently, then these ranges should be small relative to the data and the points should stay in control.

All of the parts data are “in control” indicating that the three operators are measuring consistently.

X Chart By Operator
The X-bar chart consists of the following:

• The plotted points represent the average measurement on each part measured by each operator.
• The green center line represent the overall average for all part measurements by all operators.
• The red control limits (UCL and LCL), which are based on how much variability there is between parts and the number of measurements in each average.

Because the parts chosen for a Gage R&R study should represent the entire range of possible parts, this graph should ideally show lack-of-control. Lack-of-control exists when many points are above the upper control limit and/or below the lower control limit.

For the parts data, there are many points beyond the control limits. This indicates that the measurement system is adequate.

Operator * Part ID Interaction
The graph shows the average measurements taken by each operator on each part in the study, arranged by part. Each line connects the averages for a single operator.

Ideally,

• the lines will follow the same pattern
• the part averages will vary enough that differences between parts are clear

For the parts data, the lines follow each other fairly well. The operators may have a problem consistently measuring part 10.

Measure By Operator
The graph shows all of the measurements taken in the study, arranged by operator. The measurements are represented by dots; the means by the circle-cross symbol. The black line connects the average measurements for each operator.

Ideally,

• the measurements for each operator will vary an equal amount
• the part averages will vary as little as possible

For the parts data, this seems to be the case. There will always be some variation, but it appears the operators are measuring consistently.

Measure By Part ID
The graph shows all of the measurements taken in the study, arranged by part. The measurements are represented by dots; the means by the circle-cross symbol. The black line connects the average measurements for each part.

Ideally,

• the multiple measurements for each individual part will vary as little as possible (the dots for one part will be close together)
• the averages will vary enough that differences between parts are clear

For the parts data, the measurements for part 10 vary quite a bit. This variation may be due to the system’s (operator and/or gage) inability to consistently measure that part. The averages also vary significantly. This variation in averages should occur because the parts chosen for the study should represent the entire range of possible parts.

Here’s My Data

Here the case. A turning machine is producing high precision metal rod for a printer paper roller component. Customer specification call for rod diameter 10.00 +/- 0.5mm.

Due to many field return and customer complaint, I were asked to investigate and solve the problem. Before I start anything, I did a simple data collection that covers 3 different shifts.

My nightmare started when I forward the data to the related manager. I am busy to answer the question like this;

From the data;

1. Mean = 10.49. Spec call for 10mm, why control at 10.49? Who set up the machine?
2. Yield = 56.67%. Why?
3. Max diameter = 11.57 mm, why operators cannot detect the reject? Which QC buyoff the lot?

Many more unexpected questions that I can’t think off will come to me.

Lets see what other info can be translate from the data.

Step 1: Data summary.

Beside the basic info, graphical presentation also provides important information of the process. From the histogram, I can see that the rod diameter increasing from 10.00mm to max 11.574mm. Most likely, the machine was set to 10.00mm at the beginning of production, but dimension run out of spec after certain cycle.

Step 2: Process stability analysis.

Opp.. what happen? The process looks out of control when machine nearly 16 hours. Why? I remember the line technician told me, machine become unstable when cutting tool hit 50% of the expect life spend. This chart confirms the feedback valid.

What should I do now?

1. I set a special control for this item. Reduce machine tool maintenance cycle time. Machine will automatic stop for cutting tool maintenance when production cycle over 45% of the original expected tool life spend.
2. What’s wrong with the cutting tool material?
3. What’s wrong with the metal rod material?
4. What’s wrong with the coolant?
5. What’s wrong with the program parameter? Feed rate, spindle speed, cutting dept all ok?

It is time for me to clear my doubt mentioned. Long way to go man!

Where Is Your Data

I was tight up with lot of data collection activities recently. Just because of a failure in my project.

When ever problem rise, people start Kan Chiong asking data. They will ask all kind of data from me. I have to collect all the data they want to fulfill their requirement. From the data, they will ask me thousand of funny question. But non of them ever advice me the potential root cause of the failure, or any constructive ideas that can help me solving the problem.

This is the consequent of Five Why analysis. There are managers really make “fool” use of it when come to problem solving.

In general, what they Kan Chiong is not really because of my failure. They ask data to assess the situation. They will keep quiet if data shows less impact to them. Whereas, they will keep pushing me for commitment, the time line I plan to solve the problem and put the data on track. This is common advice from them.

Usually the “advice” from them useless for me to solve the problem, I have to review the data I have again and again, to sort out the information and present to them the potential root cause, I have to propose my contingency plan as well as corrective action plan.

Well… I will write something about my data analysis on my next post.

Can Six Sigma Deployments In Medium Size Company?

It is one of the hot discussion topics among my Six Sigma community. There is no concrete answer actually. The answer could be “Yes” or “No”, depend on how you see it.

My answer to the topic is “Yes”. It is possible to deploy Six Sigma in medium size company.

Before I elaborate my point further, let’s list down some major concern that I used to receive from the business owners when this topic put on the discussion table.

• High initial investment cost for infrastructure set up and training for existing workers

• Additional human resources needed to drive Six Sigma deployment and Six Sigma project.

• Heavily data driven, most medium size companies do not have proper data retention system set up. Application of statistical tool in process control is very limited. Resultant difficulties in Six Sigma project that required huge number of historical data for performance analysis.
  

Well, it is just a concern from the business owner who has limited knowledge in Six Sigma program.

Now I tell you why I vote for “Yes” in this topic;

• Six Sigma is a concept of problem solving with a set of systematic DMAIC problem solving approach.

• There’re many tools developed for problem solving. However, only applicable tools were selected for respective Six Sigma project.

• Historical data in Six Sigma project provide information about previous process performance. Loss of historical data is not a big issue actually. New data could be collect before project start up as a baseline for future performance benchmark.

• Statistical analysis is just a potential problem assessment with data collected. The complexities of the analysis depend on the user’s requirement. It could be very complicated or as simple as ABC. It is up to you to decide!
  

Technically, the concept of Six Sigma is applicable for big and small companies. As long as challenges exist, it can be use.

Actually, the biggest challenges for any program deployment medium size companies are the shortage of human resources. Human resource in these companies usually limited. Workers are expected to spend all their time to perform their functional duties. No time were allocated for them to participate in others small group activities such as QCC or Six Sigma projects.

Because of that, Six Sigma doesn’t works in this type of organization?

I don’t think so, Six Sigma deployment could be cheap and simple. Below simple solution may be you can refer;

• Form a steering committee that consists of General Manager, Black Belt and various department managers.

• Train up all the department managers as Green Belt (at least). Green Belts and Black Belt will work together on the Six Sigma project assign to them.

• Steering committee members should meet up regularly to list down problem they are facing in daily operation as well as opportunities for break through improvement. All challenges then turn into Six Sigma project and drive by the respective departmental managers (Green Belt).
  

What are the benefits?

It is more effect to drive Six Sigma project through respective department managers. This is because they know their department operation well, they have the power to changes any process and they have the resource to work on the improvement activities.

It is their departmental problem; they have to solve it anyway. The managers are responsible for their departmental result. By nature, managers will put in effort to ensure Six Sigma project completion and achieving the target.

Company who apply Six Sigma DMAIC as a standard problem solving approach should have better performance in term of operation efficiency and business profitability.

In general, building up the habit does not require huge investment as what people thought. The most important ingredients to ensure the success of the deployment is the attitude and commitment from the top management.

If they themselves don’t care… who care?

Problem Statement

When talk about the “Problem Statement”, I like to refer to the case study below.

It is a real case that I experience long time ago. Where I having conversation with a engineering manager.

Me :	Hi sir, good morning. I would like to follow up with you the condition of the machine that we hand over to you.
Me :	I learnt from the engineers, they had rectified the “problem” that you high lighted. Machine also buy off by your guys before they hand over to you.
Me :	So far, do you still encounter any problem?
EM :	Oh yes, we still facing a lot of problem.
Me :	Really? Could you tell me in further detail about the problem that not being solve?
EM :	My problem? The machine still cannot run production.
Me :	Is the machine still giving same problem as before? Or there are new problem coming out?
EM :	I don’t know, as feedback from my guys, the machine still giving problem, therefore production cannot start.
EM :	You as a project manager, you should ensure machine is in good condition before being transfer to production. This kind of problematic machine, how am I going to run production?
EM :	I don’t want to talk so much about the problem; you go to the line and investigate yourself!

What’s wrong with him? I spend 15 minute talking to him, and this is the outcome. I feel my time wasted in this kind of conversation.

I really go to the production line to have a better understanding about the “problem” that manager mention to me.

I interview a line technician that I know him well.

Me :	Hi, how the machine? Is everything ok?
LT :	No ler, still having problem. On and off the machine stop, about 4 to 5 time per shift the machine stop.
Me:	Is it same failure mechanism?
LT :	Yes, same defect and same location. Anyway better than before. Before that the machine totally cannot move, now at least production can start with special attention given.
LT :	I notice something. The problem only occurs after cutting tool life exists 100K cycle. The tool life spans only 50% of the estimated life. How ar?
Me:	Since the problem only occur 4 to 5 time per shift, why the machine take so long to complete a lot? The estimated UPH not matched with the recorded output.
LT :	The product looks complicated to them, need special skill to handle. When the machine stops at night shift, none of the production operators nor technician dare to attend the machine. They just leave the machine idle until next morning for your engineer. That’s why it takes long time to complete a lot.

I feel happy to have the conversation. At least, I have some result from the short discussion.

What I m trying to tell from this case is, a good “problem statement” is very important in problem solving. People only can solve your problem if they fully understand your problem. If you yourself unable to describe your problem clearly, you are actually not clear what goes wrong.

A good problem statement should consist of four major components

1. What: What is the failure mode or what is the failure effect
2. Where: At which location the problem was detected
3. How: How serious the effect of the problem (Major, moderate or minor)
4. Measurable failure effect: The effect of the problem should be measurable.

What kind of problem statement the manager describes?

Other than “Problem” and “Production cannot start” nothing else.

What kind of problem statement the technician describes?
Let see…

	Answer
Did he tell me “What” is the problem he is facing?	Yes, same like last time
Did he tell me the “Location” he encounter the problem?	Yes, same like last time
Did he tell me “How” serious the effect of the problem?	Yes, the machine stop 4 to 5 time per shift Problem only occurs when cutting tool life exist 100k cutting cycle. Production folks not dare to touch the machine, lack of experience and training
Did he tell me “Measure” the effect of the problem?	No (Never mind, I will find way to measure)

Good enough, at least I know what the problems are now and I can do something to over come.

Well done the Line Tech

The guy did not go through the training to form a good problem statement, but he can tell the problem precisely compare to the engineering manager. Why?