Category Archives: LIPA

144. Baseball’s Adoption of Analytics v. Distributors

Red Sox ‘02 v The Orioles ‘19  

Analytics have swept through Major League Baseball (MLB) over the past 17 years. The Oakland A’s got first analytical results. But, the Boston Red Sox were the first to go big in 2002. John Henry, the new owner, was a believer. He had gotten rich by trading commodities with his own invented analytics.   

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140. Channel-Model Innovations Ahead

BIG CHANGES BY 2020+?

Big 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.      

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136. SELL WIN-WIN, LOWER PRICING (?)

PRICING EFFICIENCY ISN’T PRICING EFFECTIVENESS

Practice good-pricing hygiene. Don’t underprice SKUs or customers if they will continue to (happily) buy from you at higher prices. But, consider also the positive trade-off of lower prices in exchange for larger average order-size buying. What are your general buying and selling incentives for increasing order size?

CASE STUDY ON ORDER-SIZE ECONOMIES

For 2018, a $100MM contractor-supply distributor had roughly 4000 active accounts. More facts:   

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135. Fix Your CRaP Items and Their Root Causes

AMAZON’S WAR ON CRaP

Amazon’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?  

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134. The Small-Customer, Small-Order “Lollapalooza Effect”

Lollapalooza Effect: (A Charlie Munger term) When multiple cognitive biases reinforce one another within a group, irrational beliefs will take over.   

THE PROBLEM WITH SMALL CUSTOMERS WITH UNPROFITABLE, AVERAGE-ORDER SIZES

When distributors create a Cost-to-ServE (CTS) model to estimate and rank customers by net-profitability, there are typically two, customer-group shocks:

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132. Multiple Models for Fill-Rate Economics

Charlie Munger, renowned investor, advises: “To become wise you’ve got to have a latticework of models in your head”

WHY MULTIPLE MODELS?

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.

THE DOMINANT FINANCIAL LENS

  • Inventory is your biggest asset, so turn it faster for a better ROI. 
  • Improve two related financial ratios:

GMROI= Warehouse Gross Margin Dollars (divided by) average Inventory Investment.

TURN X EARN (GM%)

  • But, slimming inventory reduces fill-rates. What’s the optimal target fill-rate percentage that balances declining service-value to customers with increasing ROI?
  • Graph inventory investment vs. fill-rates. Find the sharp bend in the graph where diminishing returns set in. In a classic, hardgoods-distributor case, at 92% fill-rates inventory would need to double to improve fill-rates to 95%? So, target all SKUs for 92%?   
  • And, fill-rates increase with: knowledgeable substitutions; inter-branch transfers; and back-ordering, non-urgently needed stock outs. 

COST-TO-SERVE LENS AT THE LINE-ITEM/SKU LEVEL

  • Completing orders with inter-branch transfers and back-orders creates significant operational activity expense. Fatter inventory improves: fill-rates; transactional costs for both distributor and customers; and productivity of your people. All positive trade-offs.
  • About 5% of SKUs are super net-profitable. Why not target those for especially high fill-rates?
  • Another 5% of SKUs are very: popular yet unprofitable small-dollar-picks. Target higher fill-rates, but also pursue a blend of other profit-improving moves for these SKUs.

HIGHER FILL-RATES FOR TARGET CUSTOMER NICHES 

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.

CONCLUSION

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.