Time in the day

If you don’t plan your time,  someone else will. 
This old adage is certainly true for Data people (I like that phrase to cover everyone from analyst to planner,  allocater, Data Scientists,  whatever your taste  may be)
If you leave time ‘open’  someone else will fill it with things they need.  
Is this the best use of time?  Whose priority is more important to accomplish?  If it’s  not your priority,  why are you spending your time?
Time is not replenishable.

Disney Dynamic Pricing?

If anyone hasn’t heard yet, the new Disney pricing strategy started today. I have to say I am shocked more people haven’t been shocked by this. imagine, pricing being reflective of demand? Who’s next, airlines, hotels, movie theaters?  All already doing. Restaurants? Do you think the lunch portion is REALLY that different?

The questions is, who isn’t doing some degree of dynamic pricing.  Everyone should.    Demand, in its simplest, is what a customer will pay.

Applaud their innovation.
Forbes article here: LINK

Predictive vs Prescriptive Analytics

A lot of people read the article I had in RIS news last year. If not, HERE:


(Link updated, 2018!)

I wanted to talk a little further about the idea of prescriptive analytics. As a few people know, I like to bet sports. REALLY like to. (Yeah, I live in Nevada, so its legal. Don’t judge. )

If you’re really good at what you do, then you are willing to put your money where your math is. So this football season, I built a NFL betting modeling system. Without telling too much details, as that may be a whole different story line later, it was a fun experiment.

First, gather the data: Build the history. Data without enough enough data points is too inconclusive. In week 6, I added in the predictive engine, and started picking bets. All told, against the spread for the year, I was +7%. Many lessons learned, many theories tested, but suffice to say I was pretty happy by the end of the season as I picked more winners than losers. Many things came clearly into the light – visualization. How often to use teasers, how often to pick Over/Under. But my best lesson was that the people (Customers? Guests? Shoppers? Apply your own business here) are willing to overpay for a favorite. The Panthers were an exception, and very predictable. But most other ‘favorite’ picks were overpriced. Hmmm. As I believe I understand the concept of para-mutual, and how sports betting is a balance of money, so I cant blame the house for rigging the system: Blame the consumers.

Point? Predictive analytics was effective. However, where I didn’t end up with a new car, new house, lovely beach condo on Maui was in the prescriptive piece. I need to refine this to tell me what actions to take based on the prediction. Yeah, the Pats covered a lot – but whats the bet amount? Planning the actions is what separates a moral win from a real win.
Analytics drives business.

PS- Dead wrong on Superbowl. What makes analyics awesome? Its not perfect. If it was, this would be boring. The best model wins the most, but not all. Betamax was great technology. Creative Lab’s MP3 player was awesome in 2000. And we all know how that ended up.

-THAT Planning Guy

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DRF 2016 logo

The 2016 PRI Digital Retail Forum: How In-store Technology is Redefining Retail takes place from 9 a.m. – 5 p.m. on March 15, the day before the Digital Signage Expo trade show opens at the Las Vegas Convention Center.

PRI’s all-new Digital Retail Forum will focus on how changing consumer shopping preferences are causing retailers to transform the way they do business. Much of this change is supported by customer-facing technologies, from digital signs and equipping employees with smart phones and tablets, to those that are required to support an omni-channel business model. Opportunities increase through the use of Wi-Fi to understand customer behavior and RFID tags to locate merchandise, as well as the growth of big data analytics



Replenishment 101


Do we over-think replenishment? It’s really a simple formula to keep in stock.

Need = (weekly demand  X  (weeks to cover + lead time) + safety stock) – OH-On Order

Where OH is current position, on order is stock that will arrive in the demand period. (less than the lead time of next order, I assume. If not, the prior order already left us in a bad position)

Is it the ‘demand’ that trips us up? The ‘Art’ or the science? If you sell 10 a week now,  will you sell 13 in the future? Or 8? Which is better, too little stock or too much? So many questions to this simple piece of the equation. And there are lots of factors.  And lead time is all to often ignored or under calculated.  Lead time is not the “vendor says 4 weeks”; it is how long from the point of PO writing to store shelf.

Safety stock?  That’s your insurance against miscalculation or ROS changes.

Lets do some math!

I have 1000 units currently in inventory, and I sell 200/week, and have 500 already on order (again, due in time),  and a 6 week lead time. I would like to cover sales for next 12 weeks… my math is:

Demand = (200x(12+6)),  3600 Units.

Need = 3600-1500. Order 2100 to cover.  OH of 1000 will last 5 weeks. On order of 500 is 2.5 more weeks.  So on week 6 when the new order is due,  position is still 300, so 1.5 WOS,  a little close.

My feel is safety stock should be demand X 1/2 lead time, but only on initial replenishment order.  Factoring  this in,  the above order would go to 2700.

Going forward, assuming no change to ROS,  all future orders = # weeks in replenishment cycle X demand,  in perpetuity.  Run replenishment every 4 weeks? Order = 4 X 200, order 800. That has a carry of below table:


Owning 11-14 Weeks on hand for an item with a 6 week true lead time is about as close as I want to be, with 4 more weeks always on order. That should cover any minor supply-chain issues.

Simple?  So why do so many retailers struggle with this? Which variable are we getting wrong, lead time or demand?

Lets make stock outs a thing of the past.

– THAT Planning Guy