## Getting SH** Done

Tonight’s thought exercise is about capacity… what is the limit of what we can and cannot get done in a finite amount of time.  Being me (#Datanerd), I see an equation.  W=E x T
Lets call it W: “work”. I don’t mean in a physics sense necessarily, but in the amount of work accomplished. What you need to accomplish.
Next, lets say E: “effort” this is the amount of effort used.  Effort can be at a desk building a model, sorting data, or digging a hole.
Last, T: “Time” This is a finite amount.
So my equation is W= E x T… to finish a task (the work) is to expend the effort over a time period.

Where am I going with this one? I don’t feel like I have enough time in the day to accomplish all the “work” I need to get done.  How do I, That Planning Guy, find an analytics solution to the age-old problem?  Look at the 3 components.
First- the needed outcome, the work.  This is a hard one to shift, since it is rarely defined by self. My report is due Friday, my hole needs to be dug by noon, I need to capture a lot of food for dinner (thinking caveman times, not knocking over a Whole Foods)  I have to achieve the work by the time required.  For this exercise, let’s say there are no extensions.  Deadlines are just that.

That leaves either or both of the variables time and effort.  But time can’t be controlled, only added to or subtracted from.  If you dig 1 foot per hour, and you dig for 3 hours, you have a 3 foot hole.  If you need it in 2 hours, you have to change the E – which means either work very very hard (dig faster, type faster, build model faster, chase the deer down quicker, whatever) Or you have to change the ‘technology’ to change the effort, the other E being efficiency.

Efficiency can be either a better technique, or better tools.  What if you have a better shovel? Can you dig deeper, faster? Or a jackhammer.  Or a backhoe.  What used to be a 3 hour event is now a 15 min event…so YOu now need to dig 12 holes in the 3 hours.  Because the ‘new normal’, the expectations, just changed. Same in modeling, what if you have a better tool-kit, pre-made model, better solutions, better programs.  What we used to do in Excel, maybe now we do in a new BI tool, or R, or Python.  Or we re-puirpose a model we already had.  Or a better rifle for hunting.

In the 20’s all commerce had to be local because people had to get to work, to stores, to home as the limit of their ‘distance’ to travel.  When cars became common, people could move further from these locations. Same in trains, and later planes.  I went to a 3 hour meeting 1500 miles away last year, and ate both breakfast and dinner at home.  I can’t imagine 50 years ago people even dreaming that.

Henry Ford dreamed up and made the assembly line because he needed to create cars (W) in less time (T): Increase the E.

So the question comes, until we have better tools, in order to achieve better results all we can do is put in more time.  Or, force the advent of better efficiencies.  Build, buy, create, dream up. But whichever route achieves the W, the new normal is that is in fact achievable

Is necessity the really mother of invention, or does increasing technology/productivity change the expectations, forcing invention, or better still innovation?  Figure out how to do 12 hours work in 9 hours.

I am,
-That Planning Guy
PS- Sorry its been so long, I will try to get more pen to paper, just need to find better efficiencies.

## Demand Forecasting and Weight Loss

Asked: “Can you project jacket orders for this fall/winter”? SURE, says That Planning Guy. Innocuous enough request, right? We all know from Replenishment 101 (click here) that Need = Demand () – OH – OO.  We have no on order, as we are well in pre-season.  We do have carryover product, so we just need to know demand, right?  We have years of history, myriads of like-items, plenty of data points to draw conclusions from.  OK, #datanerds:  GO!

Slight detour in the story, but I swear it will come back around…I am actively working on losing weight.  Trying to be ‘That Skinny Planning Guy’, yeah right.  I go to the gym every day, I monitor what I eat fanatically, and I understand the very simple math of calories in – calories out = calories deficit; 3500 calorie deficit= 1 lb lost fat! WOO! Easy!  Simply burn more than you consume, and every 3500 deficit, the scale rewards you. What can be easier? So— if I eat 2000 calories and burn 3000 calories every day, I should lose 1 pound every 3.5 days, or 2 lbs a week.  Just like clockwork.  Right?
First, let’s validate the dataset.  Calories in. For lunch I had a sandwich which said 400 calories on the label, and a salad which I estimated about 300.  But is 700 EXACT???? Not even close.  The 400 calorie sandwich had no mayo . I took the cheese off.  I added mustard.   I am not weighing a salad. I have no idea exactly to the gram how much dressing. So 700 is an estimate- at best.  My apple has 80 calories.   Do I have to eat all of it? Core and all? I am not a horse! What if it’s a really big apple???  The right side, calories out, is even more vague.  I went to the gym.  Regular readers won’t be surprised that I have a heart rate monitor and an app on the phone that tracks calories EXACTLY (#gymnerd) with an  EKG accurate HR monitor, age, weight, height, sex = calories burned.  Precise? I doubt it.  My resting (basal) metabolism burns 100 calories an hour- so says an article on Wikipedia, so that has to be right. At the gym I burned 500 calories in the hour. HARD work. Is that incremental? Or inclusive? And if incremental, is the 100/hour even close or just a big estimate again based on age, weight height, etc.
The point of all this is that none of this is an exact science.  But over an extended period of time, the math should be CLOSE. Will I lose EXACTLY 2 pounds this week? Doubt it.  Will I lose 20 over 10 weeks if I keep this up diligently? VERY likely. Time makes fluctuations over a curve smoother.

What does that have to do with a jacket projection?  Short term precision is nearly impossible, but being right over the time period is what matters most.
We need to have jackets on the floor when it gets colder.  It’s March now, so predicting when the weather in Vegas will get cooler is easy. Should occur sometime between late August and ‘it won’t get cold at all this year’. We had a 45 degree swing in daily high this week! If I could predict the weather with precision, you’d be reading ‘That Clairvoyant Guy’ website right now.  All I can tell is that it’s going to get hot, soon.

Factor #2- predict the weather for WHERE? We sell a good amount of jackets in February and March because it is cold where people are from, not necessarily here.  It is still snowing on some of you right now.  In Vegas, our customer base is truly the entire world- so predict the weather globally.

Factor #3- internal weather.  When it’s cold out or hot out, inside your glorious resort is perfectly comfortable.  People can come for a visit at several wonderful resorts and really never go outside at all.  Wake up to room service breakfast.  Go work out at gym. Have a Spa treatment, massage. Go have lunch.  Take in a little afternoon shopping, an afternoon nap.  Have an early supper and see an award-winning show.  Then do a little gaming after the show before heading to bed.  All that without ever leaving the building.  That’s a pretty perfect Vegas day! But, If 74 degrees is a little chilly for your tastes, you may need a sweater or a light coat- even though its July and 117 out.

Factor #4: Sizing.  Last year, we sold jackets in a perfect 1-2-2-2-1 bell curve, S-2XL.  But is that because that’s what we had on the floor?  If we had no mediums, that effects sales of L. So what we bought = what we sold.  Should we look at size sales for the first few weeks of selling? But was that indicative of the full season? Maybe we should follow S/O rate by style/color/size and of course by location, as the demographics in each location are so widely different.

Simple request, once you factor in global weather, internal thermostat strategy by resort, consumer demand, and the actual size of the potential customers.  And we didn’t even talk about color.

None of this is an exact science, but if we factor in all the ‘knowns’ and make educated, data-driven assumptions on the unknowns, time will smooth out the curve, and we will sell a lot of jackets.

-THAT Planning Guy