Posts Tagged pull system

Musings About FIFO Lane Sizing “Math”

First in, first out (FIFO) lanes are the core of sequential pull. When properly sized, constructed and managed they ensure process and conveyance sequence, provide a buffer to facilitate flow during upstream changeovers, chronic failures, etc., and guard against overproduction. FIFO lanes, among other things, must reflect a maximum level of inventory – number of parts or pieces or total work content (minutes, hours, or days). Without enforced maximum levels the upstream process may produce more or faster than the downstream process can routinely consume.

So, how do you size your FIFO lane? There’s different levels of math that can be thrown at it. Often folks apply some pretty rudimentary thinking, especially initially if they’re in the midst of value stream analysis. Generically, the equation is:

FIFO lane max = desired lead time/takt time (TT)

Of course, then you have to get into the definition of desired lead time. In a perfect world it would be zero, but very few value streams are perfect. In fact the reason we typically use a FIFO lane is that we cannot connect the upstream and downstream process via continuous flow (or supermarket pull, for that matter). So, there obviously are barriers to continuous flow (and pull) – like those pesky changeovers, cycle time mismatches between upstream and downstream, process instability, shelf-life considerations, cure times, shared processes, etc. We must always try to eliminate the barriers, but in the meantime, we often need to live with sequential pull.

…Anyway, back to desired lead time. Below are a handful of possible equations that can be applied. Admittedly, they are not failsafe, but they do prompt some necessary thinking. Like kanban sizing math (often much more complicated), these are principle-based and should be tested out and adjusted as necessary first through table-top simulations and again after real-life piloting and forever, really. You can definitely get carried away calculating factors of safety, applying standard deviation driven coefficients to address variation and the like. I’ll leave that for another time. For now, here are a handful of equations that may be helpful.

If we’re talking cure time, for example:

  • FIFO lane max = (cure time/TT) X factor of safety (i.e., to address cure time variation and/or upstream stability issues)

If the issue is shelf life, it can be:

  • FIFO lane max = (shelf life/TT) – factor of safety (it makes sense to have margin here)

If the upstream operation has significant set-up time and thus there is a risk that it may “starve” the downstream, then the calculation may look something like:

  • FIFO lane max = (Upstream internal set-up time/TT) X factor of safety

The same type of thinking can be applied if the upstream process is shared (i.e., supplying other value streams). Here we may need insight into the “every part every interval” and translate it into an every line every interval (ELEI…just made that one up) thing. The equation may then be:

  • FIFO lane max = (ELEI/downstream TT) X factor of safety

If the upstream operation has substantial and chronic failures (i.e., unplanned downtime), and frankly this issue is probably implicit within most factors of safety referenced above, then you may want to consider something like:

  • FIFO lane max = (average upstream unplanned downtime event/TT) X factor of safety (to address unplanned downtime duration variation and/or time between unplanned downtime events)

Within a mixed model value stream, sometimes the cycle time (CT) of the downstream process is greater than the upstream CT for some models. (Of course, the average weighted CT of the downstream process is less than or equal to the average weighted CT of the upstream process.) In that situation, the math may look something like:

  • FIFO lane max = ((longest downstream CT – TT) X batch volume for longest CT item)/TT

I am sure there is other (and better) math out there. Please share your expertise here!

Of course, lean practitioners aren’t only concerned about the maximum levels. When we exceed maximum levels, we definitely have an abnormal condition that requires real time response. But what about when the FIFO lane has dwindled, when do we signal an abnormal condition? Obviously, when the FIFO lane is empty; but that’s a bit late. This is where we can, for example, use the factor of safety (divided by TT) to help calculate the “red zone.” And there are other conventions that can be used. For another time…

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Ready! Fire! Aim!…Maybe, We Should Have REALLY Simulated First!?

ready fire aim picOne of kaizen‘s unofficial taglines is, “Just do it.” And it makes sense. We try to spin the PDCA wheel as fast and as frequently as possible in order to experiment and quickly learn and make adjustments. But, sometimes we should just do it AFTER careful and extensive simulation. It seems wimpy, but it’s about managing risk. Lean leaders should care about that.

So, when does it make sense to simulate an improvement? We actually do it all the time when we trystorm. Trystorming is a melding of brainstorming and simulation. It can be really simple stuff or it can be much more involved. People tend to be fairly OK with the simple stuff, but start getting weak in the knees when meaty simulation is required. They don’t want to take too much time simulating. It can be slow and tedious.

Simple simulation. People can tolerate simple simulation like pantomiming the new standard work sequence with a draft standard work sheet and standard work combination sheet in hand before they try it out for the first time. Then they can make adjustments on the way. Hey, who wouldn’t be OK with that level of effort and spontaneity?

More extensive. The more extensive simulations take time and require a certain rigor. Why do we need to endure this pain? Because the implementation of improved or brand new systems can cause big problems if we don’t iron out some of the more substantial flaws. Often we don’t know what we don’t know. Here are two types of extensive simulations.

  • Many people apply 3P (production preparation process) when developing substantially new or improved processes  and/or products.  As we all know, locking in a poorly designed product or process is a recipe for long-term pain and suffering. In brief, 3P is a team-based methodology in which the members down-select from multiple alternatives to seven different ways for a new improved process (or product), simulate the new process with crude, inexpensive, and quickly applied materials (PVC, cardboard, wood, duct tape, etc.), then whittle down the options to three best process designs (as measured against predetermined selection criteria), followed by more trystorming and then ultimate selection.
  • Supermarket pull is a wonderful thing when properly applied, but you’ve got to get it right in order to ensure that the downstream customers are not starved and that there is no excess inventory. Pull system or kanban system simulations are extremely valuable. Using production kanban as an example, after taking a first cut at demand analysis, percent load analysis, determining what the kanban strategy will be (i.e., in process, batch – pattern, batch board, triangle), sizing the kanban, formulating the draft standard work (how/who/when regarding kanban posts, emergency kanbans, scheduling protocol, etc.), etc., we need to simulate the system using real historical demand data and some invented surprises.  The simulation requires cards for all of the inventory, mock kanban posts, “scheduling,” capacity analysis…the whole nine yards! It is critical to find out when and where the system breaks in a big way and then figure out what needs to be adjusted…before it goes live.

So, what are your experiences with either high intensity simulations or implementations where it would have been a good idea to simulate (or simulate better)?

Related posts: Kaizen Principle: Be like MacGyver, use creativity before capital!, Check Please! Without it, PDCA and SDCA do NOT work.

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