More and more companies and government agencies are aware of the value that analytics can bring them. With the availability of Excel doing your own bit of analytics is easy. I agree, Excel is helpful, but like any other tool in inexperienced hands, things can go wrong easily, especially when averages come to play. It is also an area where I come across the flaw of averages a lot. To give you an example, picture a cross dock operation. Trucks will arrive at a certain moment in time. The trucks will be unloaded; the material in the truck will be processed (like sorting or repacking) and loaded on trucks again leaving towards the next destination. The question that the cross dock manager has is how many floor workers he needs to process all the goods passing thru in such a way that all trucks can leave on time again. He start’s up Excel and puts in the scheduled arrival and departure times of the trucks. That was the easy part, although he knows that the trucks from the north arrive on average 10 minutes late. Next question to answer is how much time is required to offload or load a truck. Actually there are two questions; first the volume on the truck is required, next to it the number of items a floor worker can take out, sort and put into the next truck per hour. The cross dock manager does some fact finding and comes up with an average volume per truck and an average productivity for the floor workers. Using the averages he calculates the amount of floor workers required, easy.
After a few weeks the cross dock manager is not that satisfied. His customers complain that the trucks are leaving to late half the time, suggesting that that there are not enough floor workers. The manager has no clue on what is wrong. When he goes out to verify the numbers he used to calculate the number of floor workers, he finds the same averages on volume and productivity. Surely he has become a victim of the flaw of averages. To see why, for simplicity assume that all the trucks arrive at the same time and leave at the same time. Also assume a sort window (the time between trucks arriving and departing) of 1 hour. Also assume that the volume in the trucks is fixed at 100 items. What is left is the productivity of the workers. Assume that a worker can on average unload, sort and load 10 items per hour. Then, on average, 50 workers are required to process the freight of 5 trucks.
As you can see, the required number of floor workers runs from 40 up to 75. The average number required workers is still 50, but the variation in productivity results in shortages nearly half the time, resulting in unsatisfied customers (and the manager’s headache). To help him a little assuring that in 95% of time he has enough floor workers, he should hire 60 workers. I left out the actual arrival times of the trucks and the actual number of items on the trucks, these will complicate the analysis, but with the use of Monte Carlo analysis these dynamics can be modelled with ease. So instead of using just an average, use all the information you have. This will lead to better plans, and fewer headaches.
Nothing wrong with being an average floor worker, but plans based on average assumptions are wrong on average!