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Archive for category Lean Math

Labor Density – When Dense is Good

Sep 7

Posted by markrhamel in Lean Math | No Comments

Labor density is not a measurement that is thrown around very often, at least explicitly. Conceptually however, it must be resident somewhere in the lean thinker’s headset. Hey, it was important enough for Taiichi Ohno to discuss!

Labor density is a measure of value-add intensity relative to total worker motion. The measurement provides insight into the extent that a worker’s motion transforms the materials or information (or in the instance of health care – helps the health or comfort of the patient) into something that is valued by the customer. Ideally, the labor density should be 100%.

The waste of motion, both physical (searching, twisting, bending, etc.,) and virtual (searching within a database, moving from computer screen to screen), consumes time and resources, but does not add value. While total work content is not necessarily limited to only motion, labor density can help highlight wasted motion whether it is an act of omission (motion that substitutes for real value-added work, like “apparent” work instead of properly securing the required three fasteners) or plain old, waste of motion.

The math:

  • Labor density = work / motion
  • Example:  If value-added work is 32” and total worker motion is 40” per cycle: 80% = 32” / 40”

Admittedly, the labor density measurement is not very sexy at all, but it should challenge us to more rigorously observe, identify and eliminate waste!

Related post: Musings About FIFO Lane Sizing “Math”

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Lean Metric: Waste Elimination Effectiveness

Jul 1

Posted by markrhamel in Lean Math | 8 Comments

It happened about 15 years ago, but I remember it very clearly. My sensei, never one to mince words, shared his thoughts on the performance of the four teams. He grabbed a flipchart and scratched out a formula – one that I now call “waste elimination effectiveness.”

The W.E.E. = identified waste X acknowledged waste X eliminated waste. It’s cumulative, like rolled throughput yield (i.e., 80% X 60% X 65% = 31%). A low % in any of the factors is NOT good, multiple factors, disaster.

Some teams fared a lot better than others in the sensei’s semi-quantitative assessment. I don’t remember the scores. Not really important. What is important are the underlying principles and perspective. Here are some of my humble W.E.E. reflections.

The great Hiroyuki Hirano calls the practice of identifying waste “wastology.” Pretty cool term.  In my estimation, it’s about 85% technical skill and 15% behavioral. In other words, with study, hard work , the right tools/techniques, and a lot of practice, you can learn how to identify waste. In order to drive the W.E.E.’s waste identification number, you also have to apply sufficient rigor and stamina.

Now, you can teach a person to identify waste, but you can’t MAKE them acknowledge it (kind of like that horse and water thing). The willingness to acknowledge waste is primarily behavioral. I put this at a 10% technical and 90% behavioral “skill mix.” A retributive culture and/or a lack of humility will minimize acknowledgment. Of course, lazy folk know that if they don’t acknowledge the waste, then they won’t be obligated to try to eliminate it (“Waste? What waste?”).

…And even if people acknowledge the waste, you can’t MAKE them eliminate it.  Some just don’t have the killer instinct. I see elimination as a 50%/50% split between technical and behavioral. A lack of bias for action or aggressiveness will limit waste elimination. Similarly, from a technical perspective, if the kaizener does not apply adequate countermeasures, and apply them against the real root cause(s), they’re just spinning their wheels.

So, generating a high waste elimination effectiveness level is not easy…but, pretty much anything worth accomplishing isn’t easy.

Related posts: Kaizen Principle: Bias for Action, Time Observations – 10 Common Mistakes, The Truth Will Set You Free!

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Musings About FIFO Lane Sizing “Math”

Jun 7

Posted by markrhamel in Lean Math | 1 Comment

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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