Theory of Constraints Handbook - James Cox Iii [417]
With this information and knowing that the actual touch time of a job was measured in minutes, we were confident that the cycle time through the shop should go from the 2 weeks or more now to just a few days. We anticipated that we should be able to improve this shop to 99+ percent DDP and the time it takes an order to flow through the shop should be dramatically reduced. Our estimate was two to three days.11
Next, we turned our attention to the industry and how the industry sells custom labels. Now, if you’ve ever bought anything printed you know that the lower the price per piece that you want, then the more you will need to buy. If you only want one or a few pieces, then your price per piece will be very high. The printing industry uses a price per quantity curve like the one shown in Fig. 22-2.
In addition to a price per quantity curve, it was also standard practice for this label printer and its competitors to allow customers to spread the quantity across all their different labels. Therefore, if a customer needed 100 different labels they could spread the volume across all 100.
Next, we looked at the impact that the 2-week lead time with 90 percent DDP along with the industry practices have on the label company’s customers. In other words, what negative effects for our customers are we causing because of our capabilities and how we sell?
In the case of this custom label printer, we selected a representative customer to understand the cause-and-effect relationship between how we sell and the impact it has on our customers. We selected a coffee roaster that purchased about 100 different labels and who, when they looked at that price per quantity curve, decided to purchase 6 months worth of labels at a time. Labels are relatively small and inexpensive, so holding 6 months of inventory was common.
FIGURE 22-2 Price-quantity curve
To finalize the order of six-months worth of labels, the coffee roaster needs to determine how to spread the quantity across the 100 labels. How many French Roast, Columbian Roast, and French Vanillas were going to sell in each size bag? To do that they had to forecast out 6 months how many of each label they were going to need, which meant they had to guess:
how much coffee all of us were going to buy;
in what flavor; and
in which size bag.
Now, if you only know one thing about a forecast, what do you know? That it’s wrong! The only question is just how much is it wrong and in which direction?
Our practices cause our customers to have to forecast. What are the negatives that our customers would be experiencing from a wrong forecast? This is easy to check. I went into the customer service department of the label company and asked the customer service people two questions:
1. Do you ever get frantic calls from customers who have stocked out of labels? They said, “Yes, we get those calls all the time.” What’s “all the time”? They indicated they were getting 2 to 3 of those calls a week!
2. Does the opposite also happen? Do you have customers who typically order 6 months worth of labels, but it’s been over 6 months since some of the labels have been reordered? Customer Service responded, “Yes, that also happens. In fact, the coffee roaster you were just asking us about called last week. They were frantic because they were out of Columbian Roast labels for their one-pound bags. And while we had them on the phone we asked them if they also wanted to order French Roast labels because it had been over 9 months.” They responded, “We have enough French Roast labels for our grandchildren, so just send the Columbian Roast!” The customer explained that their lines had gone down when they ran out of labels and they needed them ASAP to fulfill an order. They asked the label company to ship them overnight.
Our practices force our customers to forecast. The forecast ends up being wrong in one direction or the other. If the forecast