Ready to Transition?

Veterans, are you ready to transition? That one single question strikes fear in the heart of more veterans than any other. Once the decision to transition from military service into the civilian work force has been made, there is a relief. The veterans now are able to switch focus and are generally excited about new opportunities. Then, one day, they get a numb feeling in the pit of their stomachs. The questions start flowing int heir mind about a thousand miles an hour! What am I going to do for a living? How am I going to compete? Do my skills translate into the civilian work force easily? This is about the time things start getting real for a veteran in transition. You’re cutting the cords from the military service and embarking on a new environment that is very much alien in every way. At this point, the first thing a veteran needs to do is take a deep breath and relax. Everything is going to be just fine, but you need to understand, you are now in  control of your future. It is time to do what you do best … take charge and be prepared!

Veterans Taking Charge

Now, we’ll say this, it isn’t going to be an easy process. Don’t fool yourself. But, you need to clearly understand that you have faced bigger and more significant challenges and have come out extremely successful. Veterans bring to the table so many qualities that employers are looking for, you have got half the battle won! As a service member, you understand the concepts of being on time, dressing appropriately, paying attention to detail and being committed to a goal. What you have developed in the military, new job seekers in the civilian sector are just developing and learning. You are well ahead of the pack. The next challenge you will face is translating your military service and skills into their civilian equivalent. During your transition, there are resources available to you to do just that. You will be able to easily translate the elements of your skills and training into meaningful civilian equivalent. Don’t disregard any training or experiences you have. Everything counts!

Take Advantage of Your Skills

One of the best situations right now for veterans is Lean Six Sigma. The practice of Lean Six Sigma in the Department of Defense is at the forefront. If you have the opportunity to be trained and work in Lean Six Sigma in the military, take advantage of the opportunity! The practice of Lean Six Sigma is based on excellence and continuous quality improvement. For a veteran, you practice those qualities on a daily basis. In the civilian workforce, Lean Six Sigma professionals are in ever increasing demand. If you are not already trained prior to your transition, enroll in a qualified and quality training program through a reputable provider to attain the necessary skills. When you couple your valuable military experience and personal discipline with the Lean Six Sigma, you will find yourself in a valuable position for an exciting civilian career. Veterans are the perfect candidates for the Lean Six Sigma practice, and transitioning veterans should seriously consider the opportunity for a career.

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Most companies talk of “metrics”, often citing the term as an holy grail – a carrot or a stick: a stick for those who aren’t meeting the metric, and a carrot to those who aspire to meet the metric. In all of this, the term is still nebulous; almost mystical. Okay, not quite, but it’s still, for the most part, vague and undefined in most people’s minds, in my experience.

We know from a previous post that almost everything is a process. A process involves an input, some work, then an output. A process can be found in any business — both virtual and physical businesses. A nice model to keep in mind is the SIPOC model. SIPOC stands for Supplier, Input, Process, Output, and Customer.

Out of this model, we can then begin to see where we might be able to obtain data that will eventually constitue something we can call a “Metric.” Consider the SIPOC Metrics below:

• Top-Level Indicators: This is most likely what senior management — Directors, VP’s, and C-level folks would be most interested in. Data at this level provide overall business health; it’s a high-level perspective for all stakeholders including customers, shareholders, and employees.
• Outcome Indicators: Data at this level is an appropriate measure used to determine the quality of product or service provided to the customer (internally and externally). These data are often “trailing” or “lagging” indicators, often obtained after the service or product has been provided.
• Input or Process Indicators: These are the upstream measures, taken at critical points in the process, for the assessment of the quality of the input to a process and can serve as early warning signs that something will soon be wrong in the Outcome of the process. The term often used to describe these data are “leading indicators” or “predictive indicators.”
• Critical to Quality (CTQ): CTQ metrics are measures of the customer’s expectations of the product or service — that is, what is the customer’s experience with our service or product in both quantitative and qualitative terms.

You can modify the SIPOC model to fit your particular situation. In some situations, there might not be a supplier; in others, the Customer is involved at the beginning and also is the recipient of the output — involved at the end. You decide how this model fits your situation.

Remember, that “metric” is a word derived from the word “measure”. Whatever metric you decide to use will depend on the process and whether data can be obtained at steps within that process — data that is meaningful and representative enough to judge work, effort, quality, timeliness, and satisfaction.

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Does Lean Six Sigma Have a Place in Sports?

Lean methodologies, and Six Sigma in particular, have gained a lot of mainstream attention over the last few years, and people are now exploring more and more creative ways to apply them to various problems around us. It’s clear that the applications of Six Sigma extend beyond areas like manufacturing and production, but just how deeply can we integrate them into our society?

There are some specific areas that are investigating the potential benefits of applying Lean and Six Sigma, specifically the sports industry. Considering that Lean and Six Sigma is all about defining a process and improving it in a systematic way, why should its potential benefits be ignored?

Seeing data that makes sense

One of the most commonly mentioned benefits of applying lean methodologies to any process is the fact that certain patterns start to arise which were not obvious before. This can be particularly useful in sports, as the process of improving one’s skills in any sport mostly comes down to a realistic overview of the athlete’s past records and results, and correct identification of areas where they can use some improvement.

This approach is also beneficial in cases where it’s clear that the athlete has room for improvement in their technique, but they’re unable to identify the exact root cause of the problem. Looking at raw data without any connection between the points usually gets you nowhere, but when you are able to systematically break down your play into its fundamental pieces, the problematic aspects eventually start to surface.

From single players to large teams

A great aspect of Six Sigma is its scalability, and the ability to adapt the core philosophy to a project of any scale. This applies in the context of sports as well – you can use it to improve the game of a single player in a sport like tennis, as well as a whole team in a game like football. The only constraint is how well you’re able to define the important data points for the performance of the player(s), and the rest comes down to proper analysis of that data.

Of course, you wouldn’t be using the exact same analytical techniques in those two cases, but the basic concept stays the same, and Six Sigma can prove very useful when it comes to driving a sports participant forward in their performance.

The most famous example of statistical thinking applied to a sports team is the Oakland A’s major league baseball team, made famous by the book and movie title “Moneyball.” Using data instead of intuition, the team was able to better evaluate talent based on non-traditional measures of success (like on-base percentage, instead of RBIs or batting average), and thus compete with other teams that had much higher salaries.

Continuous improvement matters a lot

Sports is one of the areas where the concept of continuous improvement can really shine, as it’s truly important to always strive for your best here. One of the most common mistakes made by professional players is to become complacent with their current skills, not realizing when everyone manages to sweep past them because they have not been striving for improvement.

This can never happen if the philosophy of Six Sigma is applied to the training process, as one of the core concepts of is to always seek opportunities for improvement, and to realize them as often as possible. A sports player following these concepts will therefore always be able to identify when their game can be improved in a certain area, and they’re going to follow up on those improvements. Tracking and monitoring the correct data will provide the feedback to each athlete that their performance is trending in the wrong direction, or the practice they are putting in is not having the desired impact.

Let’s look at an American football team as an example, to see where process improvement could be applied to these potential metrics

Player talent evaluation:

• Tracking player’s 40-yard dash time over the season to better detect unstated injuries
• QB accuracy at hitting a target, tracked over season
• QB release time from start of throwing motion to release
• QB correct decision % (how often did they select the correct option)
• RB route decision % (how often did they select the correct route based on blocking)
• Placekicker accuracy by hash mark and distance
• WR catch % (how often did they catch it when it was within range)
• Snapper accuracy rating (how close to a target)
• Lineman blocking times (see image below)

In the image above, you can see that tracking the time until their opponent reaches the quarterback (QB) can show graphically how well they perform. Lineman #72 is very consistent (even though not the fastest or slowest), and #77 is the best, as they can hold out the longest and maintains at least 3 seconds for the QB. On the other side, #75 is very inconsistent because of the wide variation, and times near zero.

Some of these techniques are already being applied to player evaluations during high school. For example, National Camp Series KIX rating system evaluates high school placekickers, punters and snappers using Six Sigma concepts to provide objective ratings of players, so college coaches have higher success during recruiting.

Analysis can be performed to confirm the allegations of racial differences in program retention using Six Sigma methods, as explained in this article about the 2020 University of Iowa football program.

Let’s look at other ways that data can be used to make improvements for this football team, outside of the player performance:

Office Area

• Using data to compare employee performance for ticket sales, marketing, customer responsiveness
• Valid experiments and split-testing to compare marketing techniques (responses to Ad Concept #1 vs #2, Web vs. TV vs. Radio advertising, Player A vs Player B on marketing materials, Logo A vs. Logo B)
• Address customer (internal and external) complaints correctly to improve process
• Reduce errors and defects:
  • Tickets delivered late, wrong name, charged wrong amount, improve timeliness of responses, video breakdown delays, rehab process errors, legal issues
  • Identify and prevent “potential” or high-risk issues (reduce failure modes in the process)
  • Litigation, unsafe conditions, customer dissatisfaction, player injuries and illnesses, negative press

Finances

• Advertising sales outstanding, overdue bills to suppliers, late billing, wrong billing, inconsistent quotes, late quotes, missed leads/sales, understanding reasons for low ticket sales, excess inventory for training room/equipment (use kanban instead)

Conclusion

It’s clear that there are some strong, real benefits for many sports players and teams in the adoption of lean thinking and Six Sigma methodologies. However, the results will likely vary a lot from one sport to the next, and it’s important to approach the problem with a critical, yet open mind. Lean methodologies are still seeing active developments as well, and they’re changing constantly, and it’s hard to say what we’ll discover tomorrow that can work even better for sports.

If you like the concept of improving sports teams, check out a great website Lean Blitz by Chad Walters >>>

The post Does Lean Six Sigma Have a Place in Sports? appeared first on 6sigma.

Another thing. And this is super important to understand. Change is NOT an intellectual task. Change, at bottom, is emotional. Influence hearts first, then change their heads and their thinking.

With that said, I’ve also learned that money speaks. To that end, let’s now discuss what is commonly known as the Cost of Poor Quality.

Cost of Poor Quality

Managing the quality function of a company has this much in common with every other business function: it must show a return on the investment the company has made in quality efforts. One of the ways this can be done is by tracking and managing the Cost of Poor Quality (COPQ). Traditionally, the cost of poor quality has been divided into three basic categories.

• Prevention Costs: these are costs that are incurred for activities designed to prevent poor quality in products and services. When companies first begin to track the cost of poor quality, they are usually surprised to find that prevention costs are a small percentage of the total
• Appraisal Costs: these are the costs that we incur by testing, measuring, and auditing products and services. Appraisal costs are basically the cost of any activity required to assess whether or not the product or service meets the requirements
• Failure Costs: these are the costs we incur because the product or service fails to meet the needs of the customer. Failure costs are usually divided into internal failures and external failures
  • Internal failure costs are the failures costs that occur prior to the delivery of the product or service to the customer
  • External failure costs are the failure costs that occur after the delivery of the product or service to the customer

The total cost of poor quality is the sum of all these costs. Note that the total cost of quality should be of great interest to managers, since it represents the difference between the actual cost of products and services and the improved cost that would result from eliminating failure costs.

Using the Cost of Poor Quality above, let us now categorize these items.

The total annual cost of poor quality is almost $2.6 million. Looking at the costs by category, we have the following.

Clearly, failure costs dominate the cost of poor quality for this company and, so, there are lots of opportunities to drive quality improvement by addressing these failure costs.

Now, my guess is that presenting a case like this to your boss will help a little. But, like I said, start with the heart first, then demonstrate logic to influence the mind. But start with heart.

The post How Do I Sell Lean to My Boss and Organization? appeared first on 6sigma.

What is Heijunka?

The Toyota House, or the TPS House, is a great metaphor for the Toyota Production System. The TPS House is based on the idea that “A House Divided Cannot Stand”, Citing the great Abraham Lincoln, who is quoting from the Bible. This means that every part of the house has a role and has a specific purpose.

The foundation of the house is critical. A block in that foundation is Heijunka.

Heijunka is a Japanese term to describe “production leveling”. The distinction between “leveling demand” and “production leveling” is important because we cannot control demand. What we can control is the rate of workload – information, material, raw good, finished goods in fulfillment, or actual production – enters the operation.

Here’s an example:

Not Heijunka

Suppose you run an operation where you make small widgets (11 A), medium widgets (9 B), and large widgets (7 C). You follow a production schedule that looks like this:

Notice that the forecast requirements are met with 11 A widgets, 9 B Widgets, and 7 C widgets.

This is classic batch production. In this example, the company forecasts that their orders will mostly be A, then B, and then C will probably have the least number of orders, which is why there are much fewer C production hours.

The problem with this approach is the following:

• Suppose there’s a big spike in C widgets on Tuesday. This means the customer has to wait until Friday for the order to be fulfilled.
• Suppose the firm decides that the customer shouldn’t have to wait, then the production schedule is changed and an expedited order is created. This creates an overburden on the employee, overtime pay, and instability in the system.
• Suppose the expected demand for C falls, then we end up with more C widgets than the customer needed – overproduction.
• Suppose we find a defect in production for A widgets on hour 5. This means we’ve produced 4 hours of defective products.

Yes, Heijunka

In this example, the forecast requirements are again satisfied.

Notice how the production schedule of A, B, C is dispersed throughout the week. This approach creates a stable and predictable production schedule, less burden on the employee, fewer instances of overproduction, and the ability to fulfill demand during times of uncertain customer demand.

Why Heijunka is a Foundational Block in Lean

We’ve seen from the non-heijunka example above that there are several wastes that come from a non-level production environment. If what I say is true, then much of continuous improvement will be limited if there is no level production. In fact, in that environment most of the mental and physical energy is trying to figure out what is going on. Heijunka is a critical foundation of any application of Lean.

The Challenge of Heijunka

One challenge of Heijunka is in its application. Depending on the industry and business you are in, the application will generally need to adjust. But the principle remains the same – to level production, create stability and predictability.

But to implement Heijunka, we need to first learn a little bit about the Pacemaker Process.

Don’t Forget the Pacemaker

To implement Heijunka, it’s important that you identify the Pacemaker process. Pacemaker is a misnomer in many ways because it doesn’t quite work like the Pacemaker you might find to help someone’s heart beat. The better metaphor for the Pacemaker Process might be that of a conductor of an orchestra.

So, the Conductor of the Orchestra Process is a better metaphor because:

1. The conductor dictates which instruments get played and when
2. The conductor dictates how loud the instruments need to be – when to get louder and when to get softer
3. The conductor manages the coordination between the instruments
4. The conductor sets the pace of the overall song

This metaphor works because that’s exactly what we want to the aspire to and identifying the Pacemaker Process is critical to the success of any Heijunka implementation.

Why?

Because Heijunka should be strategically placed at the Pacemaker process. Below is a 31 minute video that further explain Heijunka and shows how to implement it.

Introduction to Heijunka

Here’s the introduction to Heijunka and How to apply Heijunka in your operation.

This is an example of a Heijunka Board or a Heijunka Box, as it is sometimes called.

What you see are the days of the production week against the models that need to be built for that week. The green cards represent the number of units that have to be build of that model for that day. It works like a Kanban:

1. On the day, pull a green card of the model.
2. When green cards are empty for that day and model, you’re done.

Next is a Heijunka Board for Software Development, sometimes called Kanban Software Development.

In software development, using the Heijunka Board described below is gaining much popularity. This board is used in conjunction with Agile Practices and is used to regulate the rate of feature requests as well as their priority.

[contentblock id=18]

Transcript

Hey guys and welcome to the heijunka section of the stability series.

Now in this section we’ll be covering leveling, a basic heijunka calculation, as well as the basics of single-minute exchange of dies. But before we get started, I want to give you a little illustration about heijunka.

Now, mura is the Japanese term for unevenness. Now, if you go to a grocery store, you’ll notice that certain lines have more people in line than others. That is a form of mura. You’ll also notice that depending upon the time of day lines will be longer and shorter. Again, that’s a form of unevenness.

So in a recent trip to Walmart, I noticed how they were attacking mura. They have a digital board that tells the customer which line to go to. So once the cashier clears the customer, a button is pushed and then the digital board lights up with that particular cash register’s number. The next customer proceeds to that cash register. This is a way of evening lines and fighting mura.

So while I was at Walmart I was leafing through a Cosmo magazine and I came across the mura diet. Conventional knowledge tells us that we should have five to seven servings of breads and grains, four servings of vegetables, two to three servings of meats, and eat fats and oils sparingly.

The mura diet follows the same principle but over erratic periods and quantities. So whereas the food pyramid recommends these foods daily, the mura diet recommends that I eat all meats for two weeks, then switch to grains and breads for eight weeks, and then to vegetables for one month, and then I can reward myself by eating pure lard for three weeks.

I decided to give it a shot and after a solid four months of the mura diet, I began noticing my body changing. It seems no matter how active I stayed, the pounds just kept piling on. This is me just six months after the video footage you just saw at Walmart.

How about a more leveled approach? Perhaps the old food pyramid made sense after all. A heijunka or leveled approach with balanced meals, meals with all food groups represented, eating frequently throughout the day and in small predictable quantities would help me once again look like a Greek god.

Now, this illustration about the mura diet may seem silly, but this is often how we treat our external customers by batching their orders. Now, this is how we can use leveling to help our internal workforce.

Remember the paper airplane exercise? If you’ll recall, worker four had way too much to do with four folds while worker one had only one fold. The resulting system produced 57 pieces of WIP [SP]. We can redistribute work and level-load the line to look like this. This is another way we can use heijunka to fight mura in our internal processes.

If you’ll recall from our waste series, mura is the waste of unevenness. Now, we use heijunka to combat mura. Now, what do I mean by “unevenness”? We know that customers both external and internal can be erratic at times.

So we try to create an environment with even level pull so the customer can pull what’s needed in a calm, even manner. We try to pace the timing in which we replace those items that have just been pulled, and then we try to sequence the items in which we’re replacing in a calm, even manner.

So this is what I mean by level pull. If you recall from our inventory section, the customer is given the option of taking any color plane from finished goods. We create a pull friendly environment by giving the customer the option to pull what is needed so he doesn’t feel compelled to hoard. Whatever is taken, the system reacts in a calm, level manner to replace those goods that were pulled.

Now, this is what I mean by level pacing. This system could be very volatile if we replaced the pace and production to react to short-term changes. Level pacing teaches us that short-term changes in pace at which the customers pull are generally noise. And a sustained calm and level pace producing the takt time is what is actually needed for long-term stability.

Finally, level sequence means we attempt to balance the sequence in which we replenish what has to be produced. So obviously customers don’t consume all of one product, green squares for example, then all red triangles.

So why do we produce this way? Using the level sequence, we mix the order in which we produce to align better with how the customers consume products.

Now, traditionally, efficiency was measured in high machine utilization. Now, when set-up times were 24 hours or more, it just made financial sense to spread the cost of that set-up across a large batch of items.

Now, set-up times have since dropped, but people still keep that mentality. So they use machine utilization and they try to hide set-up across a large batch of items.

So you can see here, the bar represents the total material, labor, and set-up costs for producing a single item in a batch. Obviously, the set-up cost is very high and it would be wasteful to produce only one unit with such high set-up costs. So this is what our cost would look like if we produced five units after setting up. Notice the per unit cost has gone down. Now imagine if we produced 25 units. You could see how one could easily fall into the trap of maximizing batches to hide set-up costs.

Now, you can see the relationship between set-up and batch size. Now, what some people try to do to minimize their set-up is to maximize their batch size. That’s one approach. But the alternative is to minimize the set-up time and then minimize the batch size to correspond to that smaller set-up time.

So here’s our original diagram again. Now, if we apply set-up reduction you can see the impact it has to the total cost model per unit. Once we reach this state, we’re free to produce as many or as few units as we wish because it costs just about the same to produce one unit, five units, or 25 units. At this state, producing in smaller lots is to our advantage. This is what I mean by scaling your batch size to your set-up time.

One of the biggest benefits to heijunka is the reduction in lead time. So this is what the lineup would look like without heijunka, and this is what it looks like with heijunka. So if I was waiting to receive a blue unit, I would have to wait until the end of the production run. See how long this takes? Now, if the lineup was level, this is how long I would have to wait for a blue unit, only a fraction of the time.

Now, another benefit is the reduction in liability when producing a defect. In my traditional model, let’s assume I got a blue unit and I found out it was defective. I ‘d call the factory and they’d most likely find that all the blue units in that production run were defective. Now, they need to produce an entire lot of blue units to replace these defects. Now, in the heijunka model the same blue unit could be defective, but only one was produced in the production run so it costs the company a lot less to replace it.

A benefit related to defect liability is flexibility. In our original scenario, if one defective blue unit revealed that the entire batch of blue units was defective, then the producer has very limited flexibility to rework those defects. Long production runs of other color units would force the producer to either break into the middle of another unit’s production run, or double the batch of the blue units on the next run. Either way, this drives up cost or waiting time for me as the customer. Now, in the heijunka model, the company has a lot more flexibility in inserting a single blue unit into the production run. Placing a single blue unit into the production run, or running a batch of two blue units, is far less disruptive in this scenario than it would have been in the first.

We know that inventory ties up cash and too much can cripple a company. In our original model this could be the case. Because, at any given moment, the production sequence could shift and we could find that the entire batch is defective. We need to keep at least an entire batch’s worth of raw material on hand.

Now, the same assumptions hold true for the heijunka model. But because the batch sizes are so small, one piece in this case, we can hold far less on inventory on hand than if we didn’t have the level production run.

This same scenario applies to finished goods inventory. The customer in our original state has learned that he needs to hoard product when it is available because lead times are so long. This means, as a producer, we need to hold a lot of finished goods because we have inadvertently trained our customers to behave this way.

Now, if we demonstrate to our customers over time that we can produce what is needed, when it is needed, quickly and in small batches, the customer will learn that hoarding is unnecessary. This allows us to decrease finished goods inventory and free up cash.

Companies that successfully implement lean have a good grasp on static and dynamic scheduling. A dynamic model is purely reactive. In our factory we’re very confident that customers order five of each color unit during any given month. We could, in theory, run five of each color in a row every month and this would satisfy customer demand.

But instead, our dynamic scheduling model functions like a giant black box. Orders go in and the black box tells us what to run next. As a result, there is little predictability as to how long it will take to begin production on these green units. All we can tell you is that, on average, it will take 15 days before the first green unit will begin in production.

Now, this is average. Our system is dynamic, so you can influence it by getting a regional sales manager involved, or by befriending the production manager. Then you’re lead time is one day. If the production manager decides the high-five you gave him during the last golf outing wasn’t convincing enough, then you’ll wait 60 days or more. This is why dynamic
scheduling is so dangerous.

Now, notice the static loop we have on the bottom. Having the discipline to establish a static loop is what opens the gate for heijunka. As a quick note, when I say static, I don’t mean you won’t have the leeway to make adjustments. You’ll need to adjust slightly over time to produce the changes in customer demand.

First, five units of yellow, then five units of blue, then five units of red, then five units of green on a set, static cycle. This is set and no amount of chumming up or threats will change this. Now if someone asks when they’ll see another green unit run, you can tell them in exactly 16 days a green unit will pop off the end of the line. Again, locking down and having
the foresight to create a static schedule is the first step to making heijunka work.

Now let’s take it to the next level. We apply set up reduction so you could reduce batch sizes to lots of one piece. Now, in this static model, you can see if someone just consumed their last green unit, they’ll need to wait exactly four days before another green unit will pop off the end of the line.

Notice we’re running in four day static loops in this scenario as opposed to twenty day static loops in the original static loop without heijunka. Either case is much more advantageous to the dynamic black box you saw previously. Finally, notice that 20 days if the absolute max lead time for any batch of any color in this static loop. In the dynamic model there is theoretically no max lead time. This means your order may never make it through the production system.

The final and most important advantage to leveling is shortening order-to-cash. In our original model, we have to wait until the end of the entire production run of blue units to get paid for them all at once. But in the second scenario, we can get paid faster and in smaller increments. Now, this may not seem like a big deal, but imagine if your company decided to start paying your salary once a year. Would you be fine with that?

Obviously, we’ve grown accustomed to getting paid weekly or bi-weekly in small increments of our annual salary. Using that same logic, we need to find creative ways to get our company paid faster and in smaller increments. So again, traditionally, we like to run large batches. We like low variation. Again, people like villainize Ford by saying, “You can have any color, as long as it’s black.” Then we want to minimize the number of set-ups in a traditional run, and we always want to run the largest batches first in a mixed-model system.

To batch would be something like this. You visit a doctor and he tells you that you need your tonsils out. The doctor tells you setting up the operating room is very expensive, so you need to come back in the third week of next month because that’s when he does all of his tonsil cases at once.

Minimizing total set-ups would be something like this. The same scenario with the doctor but it’s expensive to set-up so he tells you to come back when you need your appendix taken out, you have a broken arm, and he gives you a good look and says, “You might want to think about that liposuction too.” This way I can take care of everything at once, and set up the operating room once. Is this how you want to be treated as a patient?

Do you realize this is often how we treat clients when we push orders around and batch them in the name of efficiency? So this is the traditional approach to running production. Notice we’re running four types of product: blue, yellow, red, and green. And we’re producing all four types over four weeks. Traditionally, managers thought that running the largest orders first was more efficient. So the blue units are run first in this model.

Now, take a look at the little truck at the bottom of the board. This truck takes one type of each unit to the client. Notice that the truck has to wait until the end of the fourth week before it can complete an order and take it to the customer. So to compute our heijunka ratio, we take the four types, divide it by four weeks, and this gives us a ratio of 1.0. So heijunka, or leveling, is measured in the form of E.P.E.X., which stands for “every part every ‘x'”. So you have every part every month, every part every three weeks, every part every week. It’s best to run every part as frequently as possible. So in your mixed-model system, obviously running it every week is better than running it every month. Who knows? Maybe you can break this down and run every part every day. The more you can reduce set-up time and reduce batches; this number actually gets smaller, which improves your E.P.E.X. number.

Now, in a lean environment, we like to run in small batches. Lean environments also thrive under high product variation. We try to maximize the number of set-ups, not minimize. And we like to level mixed products. Smaller batches actually run first, rather than last. Remember, running the smallest batches first gets us paid faster. So we’re still running the same four types of units, but we’re running the green units first because there are only seven in total to produce. Now, take a look at the truck at the bottom of the board. It has all four types loaded by the third week of production. So our heijunka ration is four divided by three, or 1.33. This is a 33% gain over the original run, just by sequencing production from smallest to largest.

So now let’s get into a real heijunka calculation. We know the monthly demand for each product. Each month the customer wants 20 blues, 9 yellows, 8 reds, and 7 greens. I divide these by four to get a weekly demand. Obviously, I can’t produce in fractions of units, so I round to the nearest whole number. Then I multiply by four again to get a rounded month of what I need to produce.

Now I have to check my math to make sure I’m not producing too much or too little of one product. I check my rounded month versus my original monthly demand. I see that blue and red work out perfectly, but I’m producing one too few yellow, and one too many green, if I went with the rounded month. So I finally make this adjustment during the last week of production. So during the last week of the month, I’ll make one more yellow and one less green than I would in a normal week. The math rarely works out perfectly, so keep in mind that you have to have an adjustment period just like this.

So let’s apply our math to the production sequence. We’re still running the smallest batch size first. We chose to run red before green because the difference between the two is negligible. Notice the truck fills up with all four types of units within one week. We divide four types by one week and get a heijunka ratio of 4.0. That’s a 200% gain from the second run and a 400% gain from the original.

So this is a very basic heijunka board. First thing you probably notice is there are four different colors: red, green, yellow, and blue. These represent the four different color points we built during the simulation. You probably also noticed there are four different columns: week one, week two, week three, and week four of production. So this board right now represents a full month’s worth of production.

Now, it works really simple. What I do is pull the withdrawal kanban, and send it into the system. This triggers a red plane to be built. Now, the next thing that we built, again, is another red plane. Send it into this system, it would come out. Followed by a green plane, and so on, and so forth, for week one. So you can see that all these are pretty evenly sequenced right now so we don’t have too much of one being produced at once. In theory, I could sequence this all over so all the blues are built at once, all the yellows, then all the greens, then all the reds.

But obviously, we want to balance and level-load what’s being produced every week. So this is why they call this the “central nervous system” of our lean environment, because this literally controls the pulling, the pacing, and the sequencing for the entire shop floor. Next I want to relate value and batch. Now, this is kind of tough for people to swallow and people get upset at this. But, I contend, that only the first piece in any batch is value-add. Now, if you don’t believe me, let’s take for a moment if you consider the entire batch value-add. We would make a bigger batch, and that’s actually opposite of what we’re trying to do. We’re actually trying to minimize batch sizes.

So if you believe that you realize there is waste in every batch. The customer only needs one right now, but they’re buying in the size of a batch because that’s the size in which you produce it. Again, I know that’s a tough pill to swallow. Even though you’re changing the form, fit, or function – which is by definition value – only the first piece in your batch is actually value-add. The rest of the batch is just along for the ride. Keep this in mind, especially when you’re value stream mapping. The time associated with producing the entire batch should not be taken as

value-added time. Yes, this means only a fraction of a second may be value-add for a lead time that takes months. So here are the major steps. First we need to understand demand. And don’t just skim over this. Often times, if you study the data that the customer is actually giving you, you can find that they’re a lot more predictable than you think they are. Then we need to reduce set-up time using SMED.

Reduce batch size. Now remember, these two always go coupled. Don’t just do set-up reduction. You have to reduce batch size to correspond to set-up reduction, or else it’s pointless. Reduce inventory down the line, and upstream. And then shorten the pay cycle. Remember, that’s what this all boils down to. Is owner’s diagram of order-to-cash, we have to compress that. Finally, rinse and repeat. Do this over and over again.

So, Shigeo Shingo is the pioneer behind single-minute exchange of dies, or SMED. Now, I love SMED because once you combine SMED with batch size reduction, that’s when lean thinking really starts to take shape.

He started out in 1950 in a Mazda plant, and they were producing three-wheeled vehicles on three very large presses. He approached the plant manager and asked if he could work on set-up reduction. This is where he developed the concepts of internal and external work. The plant manager wasn’t thrilled about this, but he went ahead and let it go. And seven years later, in 1957, he continued this work at Mitsubishi Heavy Industries. And then, in 1969, he took it over to Toyota, where we see it today.

Single-minute exchange of dies refers to the ability to change over a machine that is running good product, to running good product of a different type, in nine minutes or less. Single-minute refers to nine minutes being a single digit of time. You’re probably already familiar with the pit stop example. During a race, a car stops at a pit stop for refueling and a change of tires. The crew works to minimize this time. But how about an example that’s more time-critical? The U.S. military knows that seconds can be the difference between life and death on the battlefield. According to tactical.com, 50-70% of all combat injuries are extremity wounds. 60% of preventable combat deaths are from extremity bleeding. Now, tourniquets have been used on the battlefield for centuries to minimize the bleeding by constricting the area that has been injured. The issue is, during high-stress situations – such as combat – finding a tourniquet often takes more time than is available. To mitigate this, the military now has built-in tourniquets in critical areas on the uniform. So there’s no need to search for a tourniquet. A soldier can now immediately help an injured comrade. Obviously, we’re not dealing with a life-and-death situation in a factory environment. But, in order to reach a single-minute exchange of die level, you and your team have to come up with innovative ways to save precious seconds, just like these built-in tourniquets do.

So this is generally how set-up breaks down. You generally spend 5% of your time removing the old tooling, 15% of your time installing the new tooling, 30% of time preparing the new material and jigs, and 50% of your time trialing and processing. Obviously, trialing and processing is the largest time bucket. So here’s a quick demo on how to reduce a large time bucket. So this etch-a-sketch is a simple machine that I use to introduce the concept of trialing and processing. Oftentimes, operators rely on their senses to adjust machinery. Some operators are excellent at doing this and repeatedly dial-in a machine to exact specs with little to no problems. Other operators struggle and this causes wasted time, wasted material, and frustration.

So with this etch-a-sketch, I demonstrate that if you understand the knobs and how they affect the machine’s output – the screen – you can replicate any drawing with far reduced trialing and processing time. I’ve taken the liberty of marking each knob with a red line. This serves as a reference point for me. I then calibrate the left knob. It turns out that one complete turn clockwise makes a line traveling right that is 3.4 centimeters long. I turn the right knob 360 degrees, and this makes a line that travels up. This line also measure 3.4 centimeters long.

I then reset the machine and turned the left knob 180 degrees clockwise. This is to measure linearity. I expect the outcome to be a line traveling right that is 1.7 centimeters long. I measure and this is correct. I do the same for the right knob and get the expected result. So I reset the machine and do the same thing for both knobs, this time going counter-clockwise. I get all the expected mirror-image results. Now that I know how the machine behaves, and the knobs have been quantified, I can replicate any image with a reduced time in trialing and processing.

So this image presented here is complex. Now, I could take the traditional approach and mimic what I see using trial and error. But instead, I take detailed measurements and write down my action plan. Here is my action plan, complete with sequence, magnitude, and direction I have to move each knob to replicate this drawing exactly. At this point, I could literally replicate this drawing with my eyes closed. As you can see, the resulting image I made is identical to the one I was presented with. Quantifying and labeling knobs creates a science out of an

art that was known as “trialing and processing”. It also decreases the training time needed on any machine. So this is an illustration of the lean continuum. Now, it’s been my experience that all companies start out doing the obvious things, like 5S, and T.P.M., and value stream mapping. They also all do SMED. Now, some companies notice that with the 5S, and the T.P.M., and the SMED that they’ve done, they’ve had limited financial gain. I consider SMED to be the critical fork in the road that separates the men from the boys in the world of lean.

Companies that see SMED as a final destination and never couple it with batch size reduction and heijunka, never grow up. They inevitably veer off the lean path and wonder why it never worked for them. SMED is nothing more than a methodology that enables you to reduce batch sizes and balance your production load. Again, if you have no intention of coupling SMED with batch size reduction, you should seriously consider your lean journey.

I mentioned that some consider SMED as a methodology to reduce lead time. This is absolutely false. The black bars represent set-up time between batches. Let’s assume the lead time is 20 days and each black bar represents 60 minutes of set-up. Let’s say work like crazy on SMED and you reduce your set-up times from 60 minutes down to 6 minutes. The result is, you only reduce your lead time of 20 days by 3 hours. Now do you see why SMED alone has almost no impact on lead time?

Now imagine you reduce your batch size and level-load your production with your new six minute set-ups. You reduce your 20 day lead time by 16 days. Your new lead time is now only four days. So back to Shingo’s definition of internal external work. Internal work is work that has to be done while the machine is off. External work are actions you can take while the machine is still running.

So in washing dishes, an example of internal work is loading and unloading dishes. You can’t do this while the machine is running. But, while the machine is running, you can pre-soak the next set of dishes to be washed. This is an example of external work. Here are the major steps to SMED as defined by Shingo. First of all, all of your tasks for both internal and external will be mixed throughout your productions procedure. You should then clearly separate those that are internal and external tasks. Then convert as much internal work into external work, then reduce all remaining activities. You finally want to standardize.

When evaluating a set-up, I always watch the person and take detailed notes. Those tasks that are obviously not adding value with respect to set-up, I segregate and eliminate immediately. This way, I start with a clean state when using Shingo’s five steps.

So again, the first step is to recognize that internal and external activities are mixed. Don’t just skim over this step, because it is important for your operators to realize that there is a lot of opportunity, even if this is your second or third wave of SMED. Classroom exercises, examples from other companies, and free, high-quality SMED videos found online help in getting the ball rolling.

Next is to clearly separate those items that are internal and external. It’s important to challenge every single step. As a facilitator, you need to ask, “Does the machine really need to be off for this step?” You may be surprised as to how many steps can actually go into the external bucket of activities.

You then want to convert internal work into external work. You can often purchase cheap alternatives to allow you to perform work externally. For example, this die-cast company. Prior to SMED, they had to wait two hours for the dies to heat up when performing a set-up. Then an operator suggested that the dies be heated externally.

This raised some eyebrows, but a quick trip to Sears and few hundred dollars later, the dies were being heated externally in this cheap, home oven. And the company was indeed saving two hours per set-up on this multi-million dollar machine. So there’s still opportunity to make improvements. I’m big on quantifying knobs on your machines. Operators often have a great feel for what knobs do, but they rarely truly know how the machine behaves. That’s why it’s important to study your machine and understand what the knobs do, just like this etch-a-sketch.

Also, you encourage your operators to make adjustments while the machine is off. Needlessly running while making adjustments is like letting the water run while you brush your teeth. Although the primary metric is time savings, material savings is also very helpful. This is perhaps the hardest part of any lean effort. Everybody always wants to revert to their old way. Make sure you carefully capture the standard sequence for set-up and monitor operators as they work through the new routine.

Growing up in Florida, I loved being on the water, and fishing is one of my favorite pastimes. Believe it or not, fishing is a lot more science than art. On one fishing trip, we were catching far more tarpon than any other boat out there. One boat later approached us and asked how we were so quickly able to zero-in on the depth, tackle, and the bait to use. I showed them my standardized sheets that allowed us to reduce the time of trial and error to allow us to pinpoint fish in less time. I explained that starting out with a large standard array of options, then eliminating those choices that are not working in a standard manner, helps us catch fish faster. He was not impressed.

So in this demonstration, we’re reducing the set-up time of the gas pump. When performing SMED, I always have a copy of the tasks that are being performed. I record the actions. I have a spaghetti and a meatball chart, and of course, a timer. I also use a pedometer to capture the number of steps I take. This gives me two solid metrics to improve. You can pick up a cheap pedometer for about $10.

Now, walking may seem trivial to you, but I’ve measured operators literally walking two miles during a set-up. Remember, set-up time is the elapsed time between good part to good part. In the example you see here, it’s the time that elapses between the last green square and the first red triangle. In our example, it’s the time that elapsed between the last drop of gas that goes into the car in front of me, and the first drop of gas that goes into my car. So the clock starts the moment he stops pumping gas. As a quick side note, 30 seconds elapse before he pulls away and my car is in position. I start by paying for my gas. We have a lot of love bugs in Florida, so I always start by washing my windshields, both front and back. Now, I personally always start by washing my windshields because I find that I always forget when I pump gas first, and I don’t feel like getting back out of the car to do this. You can see that the sponge doesn’t hold much water, so I have to keep walking back and forth to the bucket. I come back and open my gas cap and begin filling my tank. Now, by definition, the moment I start pumping gas, the set-up time is over.

But let’s continue with this exercise to see the total elapsed time. This takes a minute or so, but after I’m done, I replace the pump and close the gas cap on my car. A total of five minutes and thirty-six seconds elapses from the moment the previous guy stops pumping gas, to the second that I’m ready to pull away. My pedometer shows me that I took a total of 88 steps. So let’s follow Shingo’s five steps to reducing set-up time.

So this is step one. Obviously, internal and external tasks are mixed. I recognize this and I’m ready to improve. So, step two. Can I pay for gas while the pump is running? Sure I can. While the guy in front of me is pumping gas, I can go pay in advance, so this is an external task. We have to go through a number of steps to wash the windshield.

Again, does the gas pump have to be off in order for me to wash the windshield? Yes, because I can’t hold the gas handle and wash the windows at the same time. So all the tasks associated with washing the windshields have to happen while the gas pump is off. So by definition, these are internal tasks.

So how about opening the gas cap? I can do this while the previous guy is pumping gas when I go to pay for my fuel. So this is task is also externalized.

Next, pumping gas is by definition an internal task because the machine is running. Now, how about closing the cap when I’m done? Well, I can’t do that while the previous guy is pumping gas, nor can I do it while I’m pumping gas.

But, I can pull away and do it while the guy after me is pumping gas. So I can externalize this task and do it after I free up the gas pump. I definitely don’t want to get back in the car while the gas is pumping, so this remains an internal task.

The next step is for me to convert as much internal work to external work as possible. So I notice there is a little tab on the handle of my gas pump. This frees me up to wash the windshields while I’m pumping gas.

Remember from our history lesson that the Toyota family created a loom that would automatically stop when a thread broke. This freed up workers to perform other tasks while the machine was running.

This handle works the same way. It stops once it detects that the gas tank is full. So I can safely externalize all the tasks associated with washing my windshields.

Step four is to minimize the internal and external tasks. Now, I don’t see many internal tasks I can minimize, but there are some opportunities in the external tasks. Particularly around washing the windshields. I take a look at my spaghetti and meatball diagram and notice that the majority of the numbers are tied to me walking back and forth to the bucket to wet the
sponge on my wiper.

What if I invested $2 and bought a spray bottle, and filled it up with soapy water? I wouldn’t have to walk back and forth to the bucket anymore, saving me many steps. It would also get my windshields cleaner, because I wouldn’t be dipping the sponge back into that filthy water. So armed with my new sequence, I give my new, improved state a go.

Having a set, standard sequence is the last step. I carefully study my sequence and begin. First, I go pay and open my gas cap while the previous guy is pumping gas. Like in the original scenario, 30 seconds elapses before I pull in and I’m ready to go.

Because my gas is paid for and the gas cap is open, I can begin fueling right away. I use the tab on the handle. Now I can go wash the windshields while the machine is running. I already have my spray bottle out, and I walk over to the wash bucket to get the wiper. I wash the windows with the exact same level of care as I did in the first scenario. Eventually, the gas handle clicks, indicating that refueling is complete. I return the wiper to the wash bucket and I put the gas pump away. I refrain from closing the gas cap until I pull away because this is an external task. I pull away, and then close the gas cap. We completed all of our tasks in a minute and fifty-nine seconds in this run. This is a 64% reduction from the original five minutes and thirty-six seconds. My pedometer says I took 22 steps this time compared to 88 steps originally. This is a 75% reduction in steps. I think the $2 spent on a spray bottle will pay for itself in no time.

Now, this little exercise doesn’t do a full SMED-kaizen event justice, but hopefully it opens your eyes to opportunity that you may not have seen before. So here’s a quick review of what we’ve learned. We reviewed mura and we learned that heijunka can be used to combat mura. We learned about production leveling and product leveling. We learned about single minute exchange of dies and how this links to batch size reduction and leveling.

The post Why Heijunka is a Block in the Foundation of the Toyota House appeared first on 6sigma.

The Paper Airplane Game

The paper airplane game is a simple exercise that illustrates in a visible and experiential way the difference between pull systems and push systems. In the videos below, they start with a craft assembly simulation for Round 1, where all 6 workers are used, and one person is the timer. This is an optional part of the simulation. A simpler version is to use two rounds: Mass production (Round 2) and Cellular Assembly (Round 3).

The game goes like this (starting with Round 2):

Participants

• 4 Workers
• 1 Manager
• 1 Material Handler
• 1 Timer

Data Collection

Collect data according to the table below:

Layout

The layout of the assembly line is as follows:

We will use the layout above for both Round 2 and Round 3, but Round 3 will have a slight difference in between workstations.

Paper Airplane Assembly

Planes are to be assembled according to the instructions below:

Worker 1:

Worker 2:

Worker 3:

Worker 4:

Round 2

In Round 2, we will illustrate a push system. Watch the 18 minute video example below to see how to facilitate the Paper Airplane Game and help your audience see the difference between Push Production and Pull Production. Reminder that the first part of the video (Round 1) is an optional simulation around craft production.

• Instruct workers to work at a comfortable pace, but there is a bonus for producing finished planes faster rather than slower.
• The quality will be monitored by the QA associate and only he/she can specify quality rejects.
• Goal: complete 20 airplanes at the Finished Goods Station.

Round 3

In Round 3, we will illustrate a pull system. You no longer need the manager and material handler, only the timer.

• In between the work stations, there is a staging area for Work-in-Process (WIP) – a plane that’s partly finished.
• The staging area between the workstations is the outbox from the previous worker and the inbox for the proceeding worker. In other words,
  • Worker 1 and Worker 2: Outbox for 1 and Inbox for 2
  • Worker 2 and Worker 3: Outbox for 2 and Inbox for 3
  • Worker 3 and Worker 4: Outbox for 3 and Inbox for 4
• A worker cannot put unless the staging area is empty.

The Layout for Round 3 looks like the following:

• The Facilitator places 1 unit of WIP in the staging areas between workers. In other words, place 1 airplane halfway folded between Station 1 and Station 2. Do the similar step for the other staging areas.

Discussion Topics

• Where was the bottleneck? How do you know?
• Was there any finger pointing or blaming or cynicism? Why?
• Why was the cycle time of Round 3 “Pull System” Run lower than Round 2 “Push System” Run?
• Inventory – why was there lower inventory build-up in the Pull System than the Push System?
• Is lower WIP necessarily better than higher WIP? Why or why not?
• For Station 1 and Station 2, what was the difference in Idle Time between both Round 2 and Round 3? Is Idle Time necessarily a bad thing we should avoid or eliminate? Why?
• If you managed an assembly line, which method would you choose? Push System or a Pull System? Why?
• Assuming you’ve introduced the concept of Kanban, ask the participants what represented the concept of Kanban in the airplane game.

I’ve found that the discussion is very lively because the participants have experienced and have visually seen the difference between a push versus a pull system. And, when asked which system is better, it is important that the participants learn to defend their position.

In other words, instead of simply accepting “pull is always better than push” – ask them why and ask them to be very specific.

Remember that as you facilitate this exercise, meet your students wherever they are in their lean journey. But most of all, challenge them to think and challenge them to defend their assertions or claims. Doing so eliminates any unproductive discussions of push versus pull and crushes any dogmatic positions of “pull is better than push”, etc.

Paper Airplane Videos

Below are Part 1 and Part 2 showing the Paper Airplane Game Simulation. Part 1 covers Round 1 and Round 2. Part 2 covers Round 3.

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Video Transcript

Welcome to the simulation portion of our Lean basics training. I was taught this paper airplane exercise when I was first learning Lean and I still use it today. I think it’s a great tool. First of all, it’s cheap because all it costs is a couple sheets of paper and maybe some post-it notes. I’ve seen some pretty complex tools out there, some expensive simulation tools and consulting [inaudible 00:00:25] out there. Some of them are excellent, but they cost a fortune.

The second reason why I like this simulation is everything you need to run it is basically in your office already. You need office paper. You need some post-it notes and some stop watches. You get six small chairs together and you can learn basically a lot of the concepts that these more complex simulations have to offer.

The following point I want to make about this simulation is it serves as a bridge for the rest of the simplex improvement lessons. Pay attention and if you have problems following along, go ahead and download the instructions off the site. As I mentioned, the paper airplane simulation is really easy, but let me explain how it runs.

First of all, you need six total volunteers. Four will serve as workers. One will be a manager and one will be a material handler. Each run lasts for five minutes and there’s something called an X plane. That will show us how long it takes to build a plane from start to finish using different scenarios. While we’re running this simulation, we’ll be measuring a lot of things.

First, we’ll be measuring space. You’ll likely be running this in an office environment with tables, so space will be measured in number of tables used. The second thing is work in process. So, how many planes are partially built at the end of five minutes? The next is good parts. How many of those that are fully complete do I, as a customer, consider done correctly?

Next, we have percentage of good. So, those number of done correctly divided by the total number of planes that they considered finished. Next, we have the lead time. That is the X plane that I mentioned earlier. How long did it take to build a plane from start to finish once I inserted the X plane into the system?

Then, we have number of people. As I mentioned earlier, we start off with six, but as the simulation goes on, they learn to decrease that number. Next, there’s time. All these simulations are going to run for five minutes. Last, we have productivity, which is a measure of good parts divided by people divided by time. For the start of this simulation, let’s follow historical flow. If you

recall, everything before Eli Whitney, everything was an art rather than a science, so everything was craft-based. There were no standards, so if you told someone to build you a rifle, they would you a unique rifle. For the start of this simulation, we will tell them to build this plane. In fact, we’ll give them the model. Will they follow the standard? It’s up to them. They usually do a pretty good job mimicking what this is, sort of like craftsmen do, but the standards really aren’t there. In this portion of the exercise, we’re going to use all six workers to be craftsmen for us. Let’s see how this turns out.

Before we get started with the simulation, I’m going to demonstrate how to build this paper airplane. Really simple. The first thing you do is make a fold down the middle. Then, you fold the nosepiece. Then, you fold the wings. Then, you fold the tips, just two folds on each side. That’s it. So, this is the first run of the simulation. I was pretty fortunate to be able to find a group of undergrad students that were interested enough in Lean to volunteer for this exercise. I gave them the same lesson in history you previously saw and let them know that all six of them will be building planes from start to finish, like craftsmen. It still surprises me today to find out that companies out there pride themselves on using the centuries-old way of thinking. They literally have an individual or small team build a large, complex item, an automobile for  example, from start to finish. Imagine how much training is needed for these craftsmen. If one of these amazing craftsmen were to win the lottery or retire, replacing them would take years.

Notice how much frustration is involved with learning to build even a simple paper airplane from start to finish and notice how slowly they’re being built. Companies  using craft-based manufacturing go as far as to pride themselves on taking months or years to deliver a product that is quote “tailor-suited to you”. For some reason, this niche market they serve seems to not only accept but expect this as a necessary cost of high quality. The reality is craft-based products are generally of lower quality than those that are Lean-produced, or even mass-produced because there are no standards to which they are built. In this simulation and the two to come, I write an X on a blank sheet of paper and start a separate timer to see how long it takes to produce my special order. I allow them to continue to build for five minutes before I stop this portion of the simulation.

So, let’s review the metrics on the first run. We used four tables. There were five in WIP. They only produced four good planes and there were only 15% that were good. The lead time for the X plane was one minute. There were six people producing. The time was five minutes for the total run. Their productivity, which is good parts divided by people divided by time, was 0.133. So, not necessarily a bad start considering craft mentality, but productivity was horrendous. Let’s continue our historical flow and our simulations from mass-production. Then after our history lesson, I mentioned that Henry Ford was the father of mass-production, in that people like to villainize him based off of this. Mass-production is an excellent system and it is a huge step forward from craft-production. One of the downfalls of mass-production is the piece-part metrics. Workers are incentivized to produce a lot. In fact, they drive down the cost of production by producing a lot. So, if two items cost $100 to produce, it cost $50 per item to produce. If that same production cost of $100 were spread over 100 units, then each of those items only cost $1 a piece. Piece-part metrics actually incentivizes workers to produce more than needed, which is over-production, which is a form of waste you’ll learn later.

As a final step in the simulation, we’re going to organize into an assembly line so you’ll see the huge productivity gains we’ll get just from moving from a craft-mentality basis to mass-production and an assembly line. Adam Smith discovered over two centuries ago, there are definite productivity gains to be made by dividing up tasks between workers. In this run, you can see that each worker only had a small component of the total plane to build. There are a couple reasons why this is better than having each worker build a complete plane from start to finish. The first is it speeds up the learning curve. It’ll take a while for me to learn to build this plane from start to finish, but if I only have to learn a fold or two, I can pick it up quickly. But, if the work is passed along an assembly line and each worker is limited to a small set of tasks, like repeatedly tightening a few sets of bolts, then you can train this pretty quickly.

Secondly, there’s that magical productivity gain that Adam Smith discovered from division of labor. When you divide up work, it’s easier for each worker to get faster and faster at his given set of tasks. When you set up these micro-improvements, you’re building the product a lot faster than you were before. I refer to this as magic because the sum of the parts is actually less than the original total amount of time it took to build the plane.

One downside to using mass-production is a high level of stress. The manager continues to push for higher volumes because this drives down the per-unit cost. Each station is working independently and sending work to the next station even though they’re not ready for it. This is called push-production and we’ll talk more about it later on this site.

Also, the material handler adds no value to the process. In a mass-production environment, work in process builds up quickly because there is an incentive to produce more. Work in process in large amounts does nothing but tie up cash and cripples a company. It also makes the material handler appear to be busy when, in reality, his job function as well as that of the manager, add no value to the final product. Because there is so much work in process, my X plane is caught behind a log jam. This is
another downfall of mass-production. It is not designed to quickly respond to rapidly changing customer demand.

So, let’s review our metrics for the second run. In this case, we only used three tables. Work in process, there were 57 units. They produced seven good planes. Seventy percent were good. The lead time for the X plane was three minutes. There were six total people in the system. Again, we ran for five minutes. Productivity, in this case, good parts divided by people divided by time, was 0.233.

Again, the benefits of mass-production over craft-production, they nearly doubled their good planes produced. The work in process was through the roof, 57 compared to 5. You can also see that they had a huge productivity gain from using mass-production. You can see a near 75% productivity gain from the first to the second run.

For our last run of the simulation, we’re going to take Ford’s mass-production system and morph it into Lean-production. We’re going to focus on quality at the source, level-loading, pulling, and pacing. This is what I mean by quality at the source. You can’t inspect quality. It has to be built into the process.

So, this little jig here, make sure that every first fold is correct because we noticed that the majority of the defects were happening early in the process. With these three nails, if you lay the piece of paper on here and you know every single time, you’re getting a perfect fold. This is what I mean by level-loading. You can tell from the second run, that this production line was not very well level-loaded. Worker one only had to do one fold, whereas, worker four had to do four folds. Workers two and three
only had to do two folds a piece.

As a result of the imbalance, you can see that worker one had the least to do. Worker four had the most to do, with four folds. Worker one could out-pace everyone and the majority of the WIP was caused by worker one. As a customer, I need planes every 24 seconds and the way this system is set up right now, they can’t make it because worker four takes about 32 seconds to complete his portion. This is where level-loading comes in. You can see that we divided all the folds pretty evenly across all the operators. You can also tell that they’ll easily be able to meet the customer demand of making a plane every 24 seconds.

For run three, the team decided not to use a material handler and a manager because they were adding no value to the final product. The production line was reconfigured so every was sitting very close to one another. This eliminated the need for the material handler. There were also post-it notes placed between each work station. This was to prevent over-production.

Remember during the last run when there was a staggering 57 pieces of work in process? These post-it notes prevent that. The simple production rule that the team agreed to was to only produce when the post-it note to their immediate right was not covered by a plane that was work in process. Only when the post-it note was exposed by the next operator taking that piece of work in process could the previous worker begin working. This is called pull-production. This is in contrast to push-production you saw during the last run.

This simple control eliminates the need for a manager, as the pace and the amount of production is totally dictated by how often the customer pulls a plane. Notice that with this control in place, the worst case scenario can be three pieces of work in process at any given time. These concepts that Smith, Ford, and Toyota developed don’t have to limited to assembly. If you’re like most companies or hospitals, paperwork is another factor that no one pays attention to. It takes a long time for people working in this area to learn how to process orders or invoices.

Why not set these individuals up in a cell similar to this one? Each worker would process just a component of the order or invoice and pass it along. I’ve personally created numerous cells in office environments for order processing where people didn’t believe a work-cell concept applied. The results were huge gains in productivity and far fewer defects. Notice the slower and more controlled pace the operators are working in. They agreed not to push defects onto the next work station and to stop and fix defects as they find them. Luckily, the jig we built into station one has eliminated the majority of the defects. Even though they are not being managed and working at a slower pace, I noticed planes being completed every 24 seconds. Notice the previous two runs, there was no way to tell how often planes would exit the system. The X plane actually made it through the system in just under one minute.

Let’s review our metrics for the third run. You can see as far as space goes, they only used two tables because they were much more compact. Work in process was only three and that was controlled by the Kanban in between each station. They actually produced 16 good planes, which is nearly double what they produced by using mass-production. One hundred percent of the planes were good because of the quality at the source. Lead time was one minute. In predicatively, one minute, the X plane flowed through.

Number of people in the system was four. They ran again for five minutes. Their productivity, which is good parts divided by people divided by time is .8. You can see that even though they were working at a slower pace, by pulling and pacing, they were actually able to more than triple their productivity. They also were able to more than double the amount of good planes that they built and their quality was impeccable; 100%. This last run is optional. If you have some colored paper and some colored post-it notes, you can run this portion of the simulation. This portion of the simulation is just to show you that it can run with multiple types rather than just one single log. For this final run, we set up colored post-it notes that correspond to each of the four different plane models we’re making; red, yellow, green, and blue. An exposed red post-it lets the operator know that the final customer along the line pulled a red plane. Only the pulled color is replaced.

I also wanted to show you a U-shaped cell. This is traditionally how cells are set up. We’ll develop this further in the cell series, but workers are generally kept on the inside of cells to minimize walking between stations and to lend a helping hand when needed. Also notice that the cell can either expand or shrink depending on customer demand. If demand goes down for colored planes, this cell can run just as well with two operators as it would with four, just at a slower pace. Here’s a summary of what we’ve learned. We learned that Lean has a very long history. I started the lesson at Adam Smith but some say it even pre-dates him. The reason I started with Adam Smith and went forward is because I want to show that Lean has a long history. It’s not just a flavor of the month or something that showed up in the last five years. Next we learned that Lean was developed through a [inaudible 00:14:42] learning process throughout history. We started with Adam Smith, we had Frederick Taylor contributing, Henry Ford, Kiichiro Toyota. Each man taking the previous contributors work and developing it a little more into what we know as Lean manufacturing today. We finally learned that about 60 years ago, Toyota began to formalize all this learning and put it into a comprehensive system that we know as Lean today. Finally I want to mention that Lean has many components. It is one business philosophy. Now this is broken down into easy to understand steps on this website, but it functions as [inaudible 00:15:15]  comprehensive philosophy.

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During the Measure phase, the focus shifts from “do we agree that X is a problem” to “what’s the phenomena that is happening such that it is a problem?” For example, if a machine is failing, it would be good to know how often, when it’s failing, and other items related to the defect. All of these questions in the Measure Phase is answered with Data. So, it’s no surprise then that in Measure we learn how to apply data analysis in a practical way that helps us to narrow down the problem.

In other words, collect data.

But, it’s not just data. It’s gut or hunch also.

Here’s what I mean: often times, we begin with “hunch” or “gut” and that gives us a good place to start of where to collect data. Ideally, we want to marry our gut hunch with data.

The Objectives of the Measure Phase

Here’s the Measure Phase Storyboard that sums up the following objective:

Through more detailed analysis, select a problem and problem space that will have the biggest impact on the organization and set an improvement target.

Measure Phase Roadmap

In the Shmula.com Curriculum, we’ll cover the following topics in this section on the Measure Phase. Each topic will contain a video as well as supporting material such as templates and downloads:

• Role of Data
• Distributions
• 7 Quality Tools
  • Check Sheets
  • Pareto Chart
  • Histogram
  • Scatter Plot
  • Cause and Effect Diagram
  • Control Chart
  • Run Chart
• Process Cycle Efficiency
• FMEA
• Basic Statistics
  • Using Z Values
  • Sample Size Calculations
• Introduction to Variation
  • Measurement System Analysis
  • Gauge R&R

Critical Checkpoints in the Measure Phase

• Plan for and document the results of data collection.
• Establish improvement targets.
• Stratify situation to a component level specific enough to analyze.
• Frame a problem statement clearly and simply, using data.
• Present data using appropriate descriptive charts and graphs.

Measure Tollgate Checklist

By the end of the Measure Phase, you should be able to answer the following:

• How and where did you collect the data?
• Are you using attribute or continuous data and why?
• What does the distribution of your data look like?
• How repeatable and reproducible is your data?
• What is your sampling plan?
• How did you ensure randomness in your data collection?
• What is the current performance of the process?
• How did you establish your improvement target?

This constitutes a basic template for the Measure Phase in Six Sigma and also represents our roadmap ahead in the Shmula.com DMAIC Curriculum.
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