Reading time ( words)
Where is our industry going? The beginning of a new year provides a good opportunity to look into the crystal ball and foretell what we think will be. Using general cause and effect observations of the past, can we predict products and processes that will be developed in the near future?
In science, we call it extrapolation: establishing a relationship between variables that form data points (statistical inference) and using the relationship or function to predict an occurrence outside the known observation range.
In a sense, this is what Professor Marvel did in the Wizard of Oz when Dorothy asked him to tell her the future. He sees a young girl running away from home with a picture of an older woman (Auntie Em) in her basket and combines this with the danger of the forthcoming tornado and says, “I see a woman who is very worried about someone she loves very much.” Professor Marvel infers a conclusion from a series of observations.
We can create a predictive model with causes and effects based on independent and dependent variables. However, the model may have to be very complex and statistically based. Remember the butterfly effect, where a tiny action like a butterfly’s fluttering wings in Singapore is attributed to causing a hurricane in Florida.
Starting with a set of initial conditions we apply the most likely changes to the independent variables (What we allegedly have control over, like climate change) and calculate the effect on the dependent variables—like the seas rising. Does the model converge or diverge? The dirty little secret is that when we know the conclusions we want, we can rig the model to predict the desirable results. How? We keep changing the model’s relationships to agree with the model’s results (the actual observations), that in turn, become the new set of initial conditions. Creating a predictive model for weather systems produces similar problems—where small perturbations cause significant changes in results.
It is chaos theory that helps lead us to the conclusion that perhaps there is nothing absolutely improbable or absolutely probable (i.e., either a zero or a one). We sit in a conference room listening to the boss bloviate on how we must perform better as a company this year. At that moment, how many of us think about the fact that we can’t predict absolutely where each gas molecule that makes up the air in the room will move in the next microsecond? There is a finite probability that after a while they will all move into a corner and you will suffocate—based on some of the bosses I have had, there are worse outcomes. However, it is a very, very small probability—but not zero!
In 1933, H.G. Wells wrote a story titled “The Shape of Things to Come.” This work of science fiction tells the history of the world from 1933 to 2106. It is taken from the notes of a Dr. Philip Raven, who meticulously compiled his dreams of a future history book he was reading. It predicts World War II that officially started in 1939 with the invasion of Poland.
This account of a future history is one of many utopian and dystopian views of where we as a species are ultimately headed. In this case Wells foresees an optimistic ending.
However, our purpose here is not to predict human destiny, but simply to predict the shape of things to come in the high tech electronic product assembly business.
What are the general drivers of change in this business, and what specific new product and process types do we see that are a result of those drivers?
1. Reduced product cost
2. New disruptive technologies and applications
3. Time to market
To read this entire article, which appeared in the February 2017 issue of SMT Magazine, click here.