Look beyond the data, and beyond our fingertips
Readers of Plant Engineering are nice enough to share the inner workings of their operations with us, so I thought this month, I’d return the favor. Here’s a brief bit of technical data on the magazine industry:
The typeface you’re reading here is called Times New Roman, and it is 10 points in size, a point being one of those arcane measurements that we just kind of accept as real and don’t think about too much. We also don’t think too much about the names each of the typefaces get, such as Times New Roman, which we like because we think it’s very readable, even for those of us who need good light and a sturdy pair of glasses to read anything these days. But as anyone who has done any word processing over the years can tell you, there are all kinds of typefaces available for print.
For example, here’s the word “manufacturing” in 18 points, in both Times New Roman and another typeface called Garamond:
If you look closely, you’ll see that the same word in the same point size is just slightly smaller and thinner in Garamond than it is in Times New Roman. You almost wouldn’t notice, unless it was exactly what you were looking for.
It’s what a 14-year-old from Pittsburgh named Suvir Mirchandani was looking for when he did a science project about how much his school district could save on printing costs if they switched from Times New Roman to Garamond. The savings for his school district were based on using less paper because the font would take up less space, and less ink toner, because the lighter face required less ink. (It was noted in his research that the cost of ink is almost double the cost per ounce of Chanel No. 5, but that’s a problem no one is likely to solve anytime soon.)
Mirchandani figured out the school district might save $21,000 if it adopted this change in its printing practices. He then extrapolated those results to estimate what the federal government might save in printing costs if it adopted this strategy.
Once the story of his exploits hit the newswires, there was the initial wave of excitement and awe at a 14-year-old discovering something so simple that made such sense. Then there was the inevitable backlash that suggested that maybe the savings were really not as spectacular as first estimated. Let’s suffice it to say, however, that Mirchandani’s simple idea will save money.
What we really ought to concentrate on is the fundamental idea here: Little things mean a lot. Look back at that example above about the differences in the two fonts. Let’s assume for a moment that the difference is not in font size, but is a difference of a penny for every word. This magazine produces roughly 25,000 words every month. That’s $2,500 in savings every month.
Now take that to your own operation. If you could save a penny on every item you produce by making one improvement, then it’s a dime on 10 of them. Now multiply that by everything you make. Look how quickly little things can add up.
We measure all our processes and collect all this data, but unless we analyze it all, it’s just measurement and data. Figuring out how to use that data to affect change is the key. But not all change is affected by data. Sometimes you simply have to look more closely for that minute piece of information that will make your process better.
And just as multiple improvements save multiple dollars, multiple people looking for those improvements increase your chances of finding it. I recall one plant I visited where the plant manager replaced all the light bulbs in the plant with high-efficiency bulbs, savings $68,000 in energy costs.
Then he took the next step, which in my mind was far more important. He told his staff what he’d done, and what had been saved. He told them that the savings roughly amounted to two jobs. He then challenged his employees to see what other savings they could find elsewhere in the plant, and that no savings would be too small.
Suddenly it wasn’t one manager making one decisions to affect one line item. It was 300 people looking for ways to save money. The goal was to reduce costs, not cut productivity or output. And the ideas started coming.
We have all this information literally at our fingertips. That data is amazing and useful. We need to mine it and manage it and act on it. But very often the answer is beyond our fingertips, and we have to look there as well.