Benchmark — Don't Imitate

Benchmarking may be the sincerest form of flattery, but it's only effective when comparing apples to apples — or applications to applications.

“But mom, all the other kids are doing it!”

“If all the other kids jumped off a bridge, would you?”

That universal child/parent exchange illustrates one of the weaknesses of benchmarking. What other children did never justified duplicating their actions. In fact, it was the weakest argument one could give a supervising adult when seeking permission for some activity. Why then should it be applied to benchmarking in the 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, hoping to better understand how our own operations perform. Being confronted with a productivity rate from another operation that is significantly higher than the same functional rate your operation is achieving 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.

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.

The most important question to ask when benchmarking is not, “How can I duplicate or surpass that performance?” but “Does that operation's benchmark apply to my operation?” Answering that question will keep you from wasting time chasing after performance benchmarks that are completely irrelevant to your operation. It will also allow you to focus on benchmarks that are bona-fide targets for your operation and should be pursued to elevate it to higher levels of performance.

Realistic Targets

Distribution center benchmarks are most commonly metrics around productivity, but we also frequently focus on throughput, accuracy, storage density and even shrink percentages. The simplest benchmarks are ground level or narrow, usually impacted by only one or two variables, as opposed to 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. Wage rate, facility operating costs, rent and other fixed overhead are also factored in.

It is much easier to point out the pros and cons of a narrow benchmark — one like case picking rates. Take cases picked per hour, for example. 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 another esteemed expert 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. We might want to understand not only why the rates should be different but also 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

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, the latter's pick path may be 10 times longer than the former's. Given that half of pick labor involves travel along the pick path, it is easy to understand 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 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 widely varied case sizes. This is an additional 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 those cases will differ in 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. Light bulbs do not last long in a pallet load if picked before the crowbars. The care necessary to ensure they do last reduces the productivity with which they can be picked.

Overall Volume

An operation with enough volume to justify capital investment in mechanization or automation will surely have a great productivity level. That does not mean the mechanized or automated solution is the right answer for a lower volume operation, since there may not be anywhere near enough labor for the mechanization to reduce to pay for the mechanization itself.

A particular metric can have very different productivity levels from facility to facility — and there's nothing wrong with that. The level of similarity between one operation and another will determine if studying operation A's benchmarks will result in valuable insights for operation B. Put another way, you can either work toward world class capabilities or just do what the other kids are doing.

Bryan Jensen has 25 years of experience in retail and wholesale distribution, transportation and logistics and is a vice president and principal with St. Onge Co., York, Pa. St. Onge Co. is a material handling and manufacturing consulting firm specializing in the planning, engineering and implementation of advanced material handling, information and control systems supporting logistics, manufacturing and distribution since 1983.

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