I am always looking for a simple, fun exercise to break up class for a few minutes, and find that shuffling cards is perfect for Chapter 10’s topic of Time Studies.
The equations average observed time, normal time, and standard time are very straightforward (see Equations 10-1,10-2, and 10-3 in the text). But some bright students usually inquire as to where the Performance Rating needed in Equation 10-2 comes from. So I bring as many decks of playing cards as I can find to class that day.
Explaining that there are some tasks for which the Normal time is already known and established, I recompute the equation as 
Performance rating factor=Normal time/Average observed time. Then I bring out the decks of cards and group the students into teams that measure how fast each person can deal the deck into a very neat bridge hands (ie, 4 piles of 13 cards per pile). Each student repeats the task 2-3 times, while team members time her, then compute the average. Each person takes a turn being timed.
The Standard time for this task turns out to be 30 seconds , and I point out that the dealing activity resembles other production tasks involving manual dexterity. Each person’s Performance rating is then computed (30 sec./average time) and recorded by the team on the board next to that student’s name. Students have fun seeing who has the highest and lowest Performance ratings for this type of job skill.
Years ago, I was in the Operations Analysis department at a casino hotel in Atlantic City, New Jersey. (This was before computers were used in the casino’s “pit” area, which is the various groups of table games). One of my jobs was to help compute the amount of a “comp” we would give to each player. A “Comp” is something for free, like a dinner for two in one of our restaurants. We wanted to estimate the amount of money we won, and then give the players a percentage of it back in comps. Today, of course, this is all done through a point system, but we used work measurement to calculate it.
For example, we wanted to determine how much, on average, we would win from a player per hour. Now, there are many types of games in a Casino, but we chose BlackJack, because we estimated that that was the WORST game for the house. In fact, our win percentage, playing against a customer who knew what he was doing (not a card counter, but just a good player) was only about 1%.
We studied our BlackJack dealers by sitting up in the cross walks above the tables, concealed by mirrors. We counted dozens of different dealers, and discovered that a good dealer could deal 99 hands per hour. (We defined a “hand” as a round of cards, so that each player was dealt 99 hands per hour, regardless of how many people were actually sitting at he table.) Some of our dealers were a little faster, and some were slower.
At 99 hands per hour, and a 1% margin for the house, we calculated that, at our worst game, playing against our best players, we would win an average of one more hand per hour than the players. If a player was making an average bet of $25/hand (Our target market in those days) then we assumed we could make $25/hour from that player.
Our Pit Bosses’ job was to watch our players, record their average bet, and the length of time they played. This gave us a “worst case” estimated win from the player. For example, if someone bet an average of $25/hand, and played for four hours (again, our target) we estimated our worst-case win as $100. It didn’t matter to us if they actually won or lost, we simply wanted to reward them for playing. We then instructed the Pit Bosses to give that player a certain percentage of the win in comps. Today’s point systems all work the same way, except that computers track playing time.
All this was based on observable work measurement tools. So, the next time you are in a casino, and you are losing the family’s inheritance, cheer up. You are just part of a work measurement observation.