A distributor needs to hire six hourly people across four locations. But:
“We can’t find acceptable candidates for our normal starting wage. We don’t want to hire new people at a higher rate than our veterans. And, we don’t want to hire flakes who can’t pass our drug test. What do we do?”
Continue reading 142. Hiring Hourly People SolutionsBig players (Amazon, Walmart, Home Depot, etc.) are combining many technologies to create the onrushing Cloud Omnichannels to which B2B brands must respond.
Amazon’s (AMZ) “doorstep-back-to-producers” value-channel is all in the cloud. It incorporates technologies like AI, machine learning, robots, and cobots. And, AMZ has already hugely pre-invested in exploiting 5G bandwidth, instant-startup-clone brands, info-videos with watch-me reward points, last-mile-uber-metro-delivery platforms, voice commerce, drones, fintech, blockchain, etc.
Continue reading 140. Channel-Model Innovations AheadAmazon’s analytics can identity SKUs on which they “Can’t Realize a Profit” (CRaP). Even with their improving, world’s best fulfillment and last-mile delivery costs, they can’t cover costs on items lower than $15 per pick/order.
AMZ’s fixes to date?
Continue reading 135. Fix Your CRaP Items and Their Root CausesLollapalooza Effect: (A Charlie Munger term) When multiple cognitive biases reinforce one another within a group, irrational beliefs will take over.
When distributors create a Cost-to-ServE (CTS) model to estimate and rank customers by net-profitability, there are typically two, customer-group shocks:
Continue reading 134. The Small-Customer, Small-Order “Lollapalooza Effect”Charlie Munger, renowned investor, advises: “To become wise you’ve got to have a latticework of models in your head”.
Research proves our evolutionary brains are riddled with “cognitive biases”. Good for species survival, but bad for innovating service value.
We don’t know, what we don’t know. And, thanks to “confirmation bias”, we prefer to listen to people who share our data-free beliefs. Willful ignorance is common; we humans struggle to cope with too much math reality
Because models are simplifications of reality, they are flawed with blind spots. But, not as flawed as our thinking. And, multiple models can offset the others’ blind spots. Let’s look at some fill-rate models.
GMROI= Warehouse Gross Margin Dollars (divided by) average Inventory Investment.
TURN X EARN (GM%)
Having best fill-rates for all types of customers is tough. But, what if you have historically strong sales and fill-rates for a peculiar niche of customers? Or, do you want to target a specific niche? Then, model what the niche buys and beef up those SKUs.Best fill-rates will both retain and win more niche customers. Increased fill-rates also boosts average Gross-Margin-Dollars per order and employee which in turn cranks profit-dollars per employee.
Financial ratios for inventory don’t see any of my under-linings! Get a Cost-To-Serve model at the line/SKU model to win.
Financial KPIs urge: “try harder, be cost efficient”. Good stuff. But, what are your analytics to achieve unique service-value capabilities which win and keep big, targeted, net-profitable accounts that average higher margin dollars per order than their average service-cost per order? And, do they keep and motivate best-service-ethic people to:
This note is one of 130 blogs posted at www.merrifieldact2.com. A big underlying theme for these postcards has been “inventing analytics to better measure and manage true causes of Profit-Power”.
I’ve rotated through sub-topics (or lenses) including:
Would an E-book that re-sorts my blogs into sub-topics with additional comments and discussion questions be useful? If YES, let me know: [email protected]