I think people are too happy to dismiss “old school” in favor of new technologies. Old vs New should not be the singular criteria but rather, appropriateness given the situation. This being one of my pet theories, I was delighted to find (via Treehugger) that there is an active traditional knowledge inventory of older technologies being redeployed toward sustainable ends. At the forefront is Low Tech Magazine; consider this counter intuitive argument that bicycles are faster than cars (circa 1978):
The model American male devotes more than 1,600 hours a year to his car. He sits in it while it goes and while it stands idling. He parks it and searches for it. He earns the money to put down on it and to meet the monthly installments. He works to pay for gasoline, tolls, insurance, taxes, and tickets. He spends four of his sixteen waking hours on the road or gathering his resources for it. The model American puts in 1,600 hours to get 7,500 miles: less than five miles per hour… With his much lower salary, the Chinese acquires his durable bicycle in a fraction of the working hours an American devotes to the purchase of his obsolescent car. The cost of public utilities needed to facilitate bicycle traffic versus the price of an infrastructure tailored to high speeds is proportionately even less than the price differential of the vehicles used in the two systems.
Caveats abound, no need to argue the point but it is interesting to think about. Shortly after I read this, I found notice of a new exciting technology courtesy of BBC News. An Estonian firm has engineered a fitting robot with the purported intention of spurring online clothing sales.
“Only 7% of all clothing sales in the world happen online,” says Heikki Haldre, chief executive of Fits.me. “And the reason is that there’s no way to really try clothes on before you buy”.
Although it is irrefutable you can’t try on clothes virtually, it is not a given that a robot could provide this virtual function. I argue a better approach involves analyzing assumptions of the nature of sales and how to maximize them. Which is not to say the technology isn’t shiny (there’s a video, very very cool) but appropriateness is key. My approach would be to analyze who is buying clothes online, why they do it, which brands they’re buying and which they are not. For the record, it’s women aged 35-55 buying clothes online. It would be more useful (old school) to discover which vendors are better at capturing this market to reproduce its effects.
A fitting robot is not going to solve enough problems for enough people to be cost effective. First, the solution relies on a robot having volume and pattern measures of myriad styles across manufacturers embedded in its database. Most manufacturers don’t have that information internally so how could it be supplied to a third party (assuming they even would)? Why would manufacturers go through the considerable expense and time of gathering and supplying data when styles change every season?
You could argue a fitting robot would be great for a perennial style such as the Levi’s 501 but it is not so simple. Because each of Levi’s contractors is charged with making their own pattern, Levi’s would need data sets from multiple facilities they have in each country (Argentina plant 1, 2, and 3, lather, rinse and repeat for Brazil, Mexico, Turkey etc). The customer is only served if the jeans displayed, exactly matched the country of origin and supplier code from the care label to cause the given data set to be loaded into the robot’s working memory -and for said exact pair to be shipped to the customer. It is such a heady challenge as to be insurmountable.
An old school person would look for ways to increase Levi’s 501 sales by (arguably) more cost effective means such as standardization. If Levi’s used one pattern for all of their 501 production (destined for US consumers), their fit would be far more predictable. If their fit were more predictable across contractors, a customer wouldn’t need to try the jeans on. They could buy it online and be done with it. But no, if you want a well fitting jean, you are compelled to go to a store and try it on.
Technology is great to solve problems but it will never be a cost effective or appropriate application if its primary use is to compensate for inefficiencies or mistakes in our processes. If your refrigerator stinks, you can use some kind of high tech deodorant to mask the odor but the better solution is old school -clean the fridge. New technologies should be used to solve complex problems, not relatively simpler ones of our own making or within our control to mitigate. If your sizing is consistent and predictable, more people are going to buy it online even though buying online is a trade off. A consumer is trading the cost of their time in exchange for less uncertainty and a trouble free transaction.
Summary: the difference between old school vs shiny new technologies is that old school seeks to reclaim that which we once knew to solve problems that were never solved when newer technologies were lain over them. Both are useful and have their place provided the problem has been analyzed so you know you are trying to solve the appropriate one. It is less a matter of having the right answers than it is having the right questions.