“Everything should be as simple as it can be, but not simpler” apocryphal quote attributed to Einstein.
“Among competing hypotheses, the one with the fewest assumptions should be selected.” Occam’s Razor.
We have all heard variations of the above quotes. I always strive to make my solutions as simple as possible so a non-technical audience can digest the findings. The following example is based (loosely!) on a real life analysis.
Choc Bars Inc (CBI) produces high end chocolate bars for a small but loyal customer base. CBI prides itself on producing top quality chocolate but like any company, it wishes to keep production costs low. The new CFO Mr McMoney believes that there are substantial economies of scale if each machine produces as many bars as possible. The engineers are skeptical that trying to get too much out of the machines could lead to higher maintenance costs. The company founder, Madame Chocolat, does not wholly disagree but she is more concerned that choco bar quality remains high.
I collected data from 100 CBI machines and surveyed customers too. I plotted cost against production to see if McMoney’s economies of scale theory played out in practice. I also layered on the customer survey data so Mme Chocolat could see if customers were satisfied with the choco bar quality.
There are clear opportunities for savings if production can be increased in some machines but there are diminishing returns apparent too once production increases beyond about 70ish bars per day. In this case all parties agreed that production could be increased in some machines without affecting product quality too much. Mme Chocolat also noted that machines producing the most bars per day led to lower quality products.
There was a board meeting coming up at the end of the quarter. McMoney wanted to present projected savings but there was some concern that board members are unfamiliar with loess models and this may distract from the findings. A loess model is basically a locally weighted regression model and this gif gives a wonderful visual explanation. To allay concerns, I built a tool with the same data which allowed the user to adjust where they felt the optimum point was and simple linear models were fit either side of the selected optimum.
The interactive tool gave the decision makers greater control and allowed them to interact with the data. McMoney had a rough rule-of-thumb for savings projections. Mme Chocolat pushed for a lower production target of 68 bars per day for each machine – beyond that point, quality is reduced. Of course this is a dummy example but it is inspired by a real life scenario. For example the data could be teaching costs, student/teacher ratio and student satisfaction – the numbers would change but the principle would be the same.