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Benchmarking: When Measurement Becomes the Crime

“But mom, all the other kids are doing it!”   We’ve all heard, and probably quoted, that line at one time or another.  And just as inevitability, we have all been subjected to one of the two most likely retorts, “I don’t care what other kids are doing, they’re not mine,” or my personal favorite “If all the other kids jumped off a bridge would you?”   To which, in the days when my wit overshadowed my judgment, I usually answered, “well that depends on how high the bridge is, whether there’s water under it, how deep the water is….” but by the time I got that far, if not sooner, I was usually dismissed by a headshaking sigh in the best case, or a sharp, quick slap in the back of the head in the worst case.  Either way it was clear—what other children did never justified duplicating their actions.   In all likelihood it was the weakest argument one could proffer to a supervising adult when seeking permission for some as yet never before allowed behavior or activity.    So given this almost universal youthful experience, why then does the exact opposite logic apply to most applications of benchmarking in very adult arenas of distribution and logistics?

Most of us have had conversations about operational performance benchmarking with our colleagues, or have reviewed benchmarking within our industry with the hope of understanding how our operation performs.  Being confronted with a productivity rate from another operation that is significantly higher than the same functional rate within your operation can be disheartening or motivating, but it should be neither until you know more about the benchmark rate than just the number of cartons picked per hour that it purports to be the ‘industry standard’ or ‘best in class’.    The value of a benchmark productivity rate to the operation evaluating itself is proportional to the similarities between the operation or operations which established the benchmark and the operation benchmarked against it.

There are some very simple questions to pose when evaluating any benchmark, the most important of which is not “How can I duplicate or surpass that performance?” but “Does that operation’s benchmark apply to my operation?”  Answering that question first will avoid wasting time chasing after performance benchmarks that  are completely irrelevant to your operation, and allow for devotion of effort to benchmarks that are bon-a-fide targets for a particular operation and should be pursued to elevate and operation to higher levels of performance.

To be clear, the benchmarks we are talking about here are distribution center facility operating benchmarks.  These are most commonly metrics around productivity, but are also frequently focused on throughput, accuracy, storage density, and even shrink percentages.   The simplest benchmarks to discuss are ground level or narrow benchmarks, usually impacted by only one or two variables, as opposed broader benchmarks such as cost per unit shipped, which are affected by all the variables of all the productivities for all the tasks required to receive, store, replenish, pick and ship an item, as well as the wage rate, facility operating costs, rent and other fixed overhead.  Those broader benchmarks are no less subject to misapplication, but it is much easier to point out the pros and cons of benchmark application when discussing a straightforward or narrow benchmark—one like case picking rates.

First let’s agree on a unit of measure for our benchmark rate—cases picked per hour.  In the benchmark examples listed above let’s assume the operation being evaluated is picking 100 cases per labor hour, and the benchmark rate that’s been published by the industry or other esteemed expert (either as an average rate from many surveyed operations, or as  singular point of comparison to another operation) is 200 cases per hour.  A well intentioned manager, upon learning another seemingly similar operation, is picking 100 more cases per labor hour would be remiss if they didn’t ask why their operation was not equaling that performance.  But let’s examine the operational characteristics around those rates and understand if perhaps not only should they be different, but why, in particular cases, the 100 cases per hour rate might actually represent a more efficient operation than the 200 cases per hour rate.

SKU count.  It’s very simple math.  The more SKUs an operation must pick from, all other things being equal, the longer the pick path will be.  If one operation has 200 SKUs and another has 2,000, there is the potential that the latter’s pick path will be 10 times longer than that former.   Given that half of pick labor is travel along the pick path, it is understandable easily be seen how pick rate would suffer from a higher SKU count.

Cases picked per order line (order profile).  Assuming the cases per line are still significantly less than a pallet, an operation that must pick one case per order line will be less productive than an operation that picks three cases per order line.   Again all other things being equal, more ‘stops’ in a pick path to assemble an order translates to lower productivity.

Case characteristics consistency.  An operation with very similar case sizes, or only a few different case sizes (i.e. a soft drink distributor) will pick cases to an order pallet more quickly than one with very varied case sizes. This is also a further complexity sometimes driven by a high SKU count when compared to a low SKU count.   The more SKUs an operation must support the more likely that those cases will differ in their size, weight and levels of fragility.  It is much easier to build a stable mixed case pallet when the cases are of similar size than when they are dramatically different in size, weight or even fragility.  The light bulbs do not last long if picked before the crowbars and the care necessary to ensure they do last reduces the productivity with which they can be picked

Overall Volume. It is not so much the volume itself that might make one operation more productive than another (although there is something to be said for economies of scale) but an operation with enough volume to justify capital investment in mechanization or automation, will surely have a great productivity level, but that does not mean the mechanized or automated solution is the right answer for the lower volume operation, since there may not be anywhere near enough labor for the mechanization to reduce to pay for the mechanization itself.

Though not exhaustive, the factors above all can contribute to a particular metric having very different productivity levels, and there being nothing wrong with those differences.  The level of similarity between the operations used to develop a benchmark, and the operation to which the benchmark is compared, will determine if it represents valuable insight that can allow an operation to work toward world class capability, or if that particular benchmark for your operation is nothing more than what the other kids are doing.
—Bryan Jensen, St. Onge Company

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