Apr 8 2014
Preventing Errors in Food Delivery by Natural Mapping at Benihana
Restaurant waiters who deliver food to tables of five or more customers rarely remember who ordered what, and have to ask.
Most restaurants still use paper order forms, and the most common are not much help, because they tell the waiter what was ordered at each table, but not which customer ordered it. The row of titles on the top, with “APPT- SOUP/SAL-…” is intended as a series of column headers to record each customer’s choice in each category.
Some form suppliers, like National Checking with their WaitRpad, have addressed this problem by providing table maps at the top of the form. These sketches include the following:
- The shape of the table.
- Where the waiter is to stand when delivering food.
- A clockwise numbered position for each customer.
The waiter arrives with a tray carrying the dishes laid out clockwise to match the customer positions. National Checking posted the following video to highlight the advantages of this form:
Benihana, however, goes one step further and takes advantage of the special characteristics of their service. It is a chain of Japanese restaurants in the US, with a single 8-seat table layout and a chef at each table cooking on a hot plate in front of the customers, from ingredients in a cart. The work done away from the table is limited to kitting the ingredients to match the customers’ orders.
The order form is a map of the table, which is possible only because the tables are all identical, and the form can be filled out with abbreviations because the orders are all for full-course meals: “DIA” for “Diablo,” “SM” for “Splash-and-Meadow,” etc., with a few options, such as fried rice versus steamed rice.
While this is effective at ensuring that customers receive exactly what they ordered, it is not mistake-proofing/poka-yoke. It does not physically prevent mistakes, nor does it have a mechanism to signal any error that may happen. A disorganized chef could still get it wrong, and customers could confuse any chef by switching seats.
It is instead an application of the usability engineering principle of natural mapping. An order form that is a map of the table makes it easy for the chef to know which dishes to give to which customers and thereby reduces the likelihood of errors. Mistake-proofing would be better, if someone could find a way to do it.
Aug 8 2014
The meaning(s) of “random”
In this sense, a side-loading truck provides random access to its load, while a back-loading truck provides sequential access.
While these uses of random are common, they have nothing to do with probability or statistics, and it’s no problem as long as the context is clear. In discussion of quality management or production control, on the other hand, randomness is connected with the application of models from probability and statistics, and misunderstanding it as a technical term leads to mistakes.
In factories, the only example I ever saw of Control Charts used as recommended in the literature was in a ceramics plant that was firing thin rectangular plates for use as electronic substrates in batches of 5,000 in a tunnel kiln. They took dimensional measurements on plates prior to firing, as a control on the stamping machine used to cut them, and they made adjustments to the machine settings if control limits were crossed. They did not measure every one of the 5,000 plates on a wagon. The operator explained to us that he took measurements on a “random sample.”
“And how do you take random samples?” I asked.
“Oh! I just pick here and there,” the operator said, pointing to a kiln wagon.
That was the end of the conversation. One of the first things I remember learning when studying statistics was that picking “here and there” did not generate a random sample. A random sample is one in which every unit in the population has an equal probability of being selected, and it doesn’t happen with humans acting arbitrarily.
A common human pattern, for example, is to refrain from picking two neighboring units in succession. A true random sampler does not know where the previous pick took place and selects the unit next to it with the same probability as any other. This is done by having a system select a location based on a random number generator, and direct the operator to it.
This meaning of the word “random” does not carry over to other uses even in probability theory. A mistake that is frequently encountered in discussions of quality is the idea that a random variable is one for which all values are equally likely. What makes a variable random is that probabilities can be attached to values or sets of values in some fashion; it does not have to be uniform. One value can have a 90% probability while all other values share the remaining 10%, and it is still a random variable.
When you say of a phenomenon that it is random, technically, it means that it is amenable to modeling using probability theory. Some real phenomena do not need it, because they are deterministic: you insert the key into the lock and it opens, or you turn on a kettle and you have boiling water. Based on your input, you know what the outcome will be. There is no need to consider multiple outcomes and assign them probabilities.
There are other phenomena that vary so much, or on which you know so little, that you can’t use probability theory. They are called by a variety of names; I use uncertain. Earthquakes, financial crises, or wars can be generically expected to happen but cannot be specifically predicted. You apply earthquake engineering to construction in Japan or California, but you don’t leave Fukushima or San Francisco based on a prediction that an earthquake will hit tomorrow, because no one knows how to make such a prediction.
Between the two extremes of deterministic and uncertain phenomena is the domain of randomness, where you can apply probabilistic models to estimate the most likely outcome, predict a range of outcomes, or detect when a system has shifted. It includes fluctuations in the critical dimensions of a product or in its daily demand.
The boundaries between the deterministic, random and uncertain domains are fuzzy. Which perspective you apply to a particular phenomenon is a judgement call, and depends on your needs. According to Nate Silver, over the past 20 years, daily weather has transitioned from uncertain to random, and forecasters could give you accurate probabilities that it will rain today. On the air, they overstate the probability of rain, because a wrong rain forecast elicits fewer viewer complaints than a wrong fair weather forecast. In manufacturing, the length of a rod is deterministic from the assembler’s point of view but random from the perspective of an engineer trying to improve the capability of a cutting machine.
This categorization suggests that that a phenomenon that is almost deterministic is, in some way, “less random” than one that is near uncertainty. But we need a metric of randomness to give a meaning to an expression like “less random.” Shannon’s entropy does the job. It is not defined for every probabilistic model but, where you can calculate it, it works. It is zero for a deterministic phenomenon, and rises to a maximum where all outcomes are equally likely. This brings us back to random sampling. We could more accurately call it “maximum randomness sampling” or “maximum entropy sampling,” but it would take too long.
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By Michel Baudin • Data science, Technology • 2 • Tags: Quality, Quality Assurance, Randomness