The virtuous circle: Implementing continuous improvement in a toy factory with TOC
By: Didier Varlot
Tags: Theory Of Constraints, Continuous Improvement, Manufacturing, Production, Improvement

At night, Alexandru calls me and describes what he has seen during his inspection.

I answer: “This station is your constraint in this line. As every line is separated, you may have a different constraint in each line, but probably they will look the same. Now that you have identified the constraint, you need to determine how you will exploit it.”

I keep asking him some questions to guide him to find and express what ideas could be applied to manage the constraint.

Finally, he comes up with some ideas: “As making always the same movement is tedious, people switch between stations every hour. Shouldn’t it be better to change them only every two or three hours to let them the time to reach a better efficiency in the station before to change them?”

“Yes, this is a good beginning, two hours between changes would allow losing less time for changes during the shift and let people perform in their station for a longer time.”

Then I add: “Are you sure that everyone is performing equal?”

“Of course, not. Some are better than others in some task.”

“How could you make use of this information?”

“We could make sure that the best people only switch between the most demanding stations. In this way, those difficult stations are always staffed with the most efficient people.”

“You told me during our first call that you are often switching the toys made by a line. How does this impact the production?”

“We have first to empty the line completely, then we bring the parts of the new toy. This is to avoid mixing the parts and making mistakes. The process takes between twenty and thirty minutes to complete.”

“This is a very long time lost for the constraint. Is there a way to avoid losing this time of production of the constraint?”

“We could limit switching toys at the end of the shift or even better at the end of the second shift as we are already emptying the lines. It would avoid any loss of time of production.”

And so, he does. The next day, he limits the switch between stations to every two or three hours and takes care that every person is assigned to stations where they can perform well. He also limits the switch of toys of a line to the end of the second shift as much as possible.

The result is immediate. Within one week, the quantity of toys ready to deliver increases, and he sees much more consistency in the production. One side effect is that expediting is becoming barely necessary. It becomes much easier to schedule the production.

On the next call, he tells me how satisfied he is.

But I have to temper his enthusiasm: “Don’t stop there. Nothing is finished. Don’t let inertia become your constraint. Now you need to subordinate your process to the constraint. You need to limit the work in progress to prevent discovering quality issues too lately. You need to make sure that the stations with more capacity than the constraint doesn’t produce more than what the constraint can process.

Another advantage is that some resources will partially become available. These resources can be used to reinforce the capacity of the constraints. In this way, you will also elevate your constraint.”

Alexandru takes less than one week to implement the measures we discussed, and he is feeling an immediate improvement in the delivery of toys.

A few weeks ago, he was constantly falling short of his target by as much as 20% (800,000 toys instead of 1,000,000), but after only a few weeks, he is close to his target (nearly 950,000 toys per week).

April 2020

In the next call, Alexandru wants to analyze another production line.

He asks: “Now that the semi-automatic lines are performing much better, how do I determine the constraint on automated lines. These lines have automated machines at the beginning and the end with exactly the same capacity while the six middle steps are manual.”

I explain: “Well, the statistical variations may give you the impression that the bottleneck is switching randomly between the two automated machines. In that case I would not worry too much and choose which one is the easiest to manage as the constraint. I would choose the first one. You have the great advantage that your resources are flexible and you can choose which is your constraint. The worst case would be to have one middle operation as the constraint, as it would be much more complicated to manage. What are the measures you propose to make sure that the first automated step is the constraint and how do you intend to manage it?”

“I must make sure that all intermediary steps are filled with skilled people that will be able to sustain the takt time imposed by the first automated machine. This is no big issue, I have enough trained people for that.”

“Isn’t there anything else you should be sure about?”

After thinking a moment, Alexandru tells: “well, the automated lines stop from time to time for maintenance. I should be able to schedule this maintenance during the night shift not to interrupt the work during the day shift.”

“This is a good idea. What else shall you make sure of making profit out of every minute of the machine you choose as your constraint?”

“It also happens from time to time that the machine stops because of a lack of parts. The feeder operator has to feed the machine every 10 minutes. But when he goes to the warehouse to bring more parts, it may remain more than 10 minutes away from the machine. The machine stops because of a lack of parts for some minutes, only the time for the feeder to come back. I can ensure that whenever he is away from the line, someone else will take temporally his place.”

“This seems good, now let’s implement this and see the result.”

The next day, Alexandru implements the additional measures at the first automated line. The following day, he implements them at the other lines and only after two weeks, he already sees an improvement of the production. He reaches for the first time this year his target (1,028,500 toys per week). This improvement was achieved with only a part of the workers at work, as some of them are kept at home because of the pandemic.

Alexandru is feeling so confident that he decides spontaneously to impose a new target at 1,500,000 toys per week. At the end of the month, he reaches 1,232,500 toys per week, his best result of the year.

May 2020

This month doesn’t start as well as Alexandru expects.

The production struggles to maintain the rate that was already achieved in the previous month. The perturbations seem random and machines are stopped from time to time for defaults in parts.

Alexandru calls me and describes the new situation.

I ask him: “What are the symptoms you see that could cause these perturbations? Could you identify what has changed?”

Alexandru takes some hours to discuss with my team, and uncover issues. Then, he calls me back.

“We have new toys in production. The flyers that are put in the envelopes are different from the previous one. Some of them are badly cut, so the machine block when such flyer is introduced.

Furthermore, the toys are new and more difficult to install in the envelope, so the workers take more time to assemble these toys.

Some workers that were previously at home because of the pandemic are back at work and are not used to our new way of working.”

“You should not waste a minute of your constraint. You must make sure that it only processes good parts. Processing defect parts is wasting the time of the constraint. The time of your constraint is the most valuable asset you have. You must take all the necessary measures to avoid processing bad parts. What do you intend to do for that?”

“I can put someone to verify the flyers before introducing them into the machine and taking out the ones that could jam the feeder and block the machine.

For the people who come back to work now, I had detailed instructions per station prepared for the new toys, and they are being trained by more experienced people.”

Alexandru goes on implementing these measures, and in a few weeks he is reaching his target of 1,500,000 toys per week.

June 2020

During the first call in June 2020, we comment the curve that Alexandru sent me. It is spectacular how much has been made within less than 10 weeks. Alexandru is very proud of what he and his team achieved in the factory.