Tocico 2010 toc_retail-myths_and_truths-v1.1

1© 2009 TOCICO. All rights reserved.
TOCICO 2010 Conference
TOC in Retail:
Myths and
Truths
Presented By: Humberto R. Baptista / Goldratt Schools // VectisPresented By: Humberto R. Baptista / Goldratt Schools // Vectis
Date:Date: 21/June/201021/June/2010

2
© 2010 TOCICO. All rights reserved.
AgendaAgenda
• A simple inconsistency
• The reality in Retail
− Types of retail
− Main differences
• TOC Solution for Retail
− Assumptions x reality
− Challenges
− Logistical issues
− Additional elements
• How to go about selling and implementing it?

3
A Simple InconsistencyA Simple Inconsistency
IF
• The TOC Solution for Retail (TOC Distribution) is so
powerful
THEN
• We should have a significant number of TOC
implementations in Retail
• So: why don’t we see it? Possibilities:
− It’s hidden
− It wasn’t implemented
− It was implemented and failed

4
REALITY IN RETAILREALITY IN RETAIL
“Reality is that which, when you stop believing in it, doesn't
go away”

5
Slice of realitySlice of reality
Successful
Retailer
Protect and
grow sales
Control
Costs
Increase
stock
Decrease
stock
The stock is
unbalanced
Many items
have high
stocks
Many items
have low
stocks
The focus is
on stock
quantity
Pressure to
increase
stocks
Resupply from
WH to Stores
does not help I
turns
Stores usually
have a store
warehouse
In-store resuply
does not help I
turns
Stock
accuracy
degrades
over time
Store stock
accuracy is
awful
Sometimes
resupply follows
consumption data
“Consumption”
based resupply
does not help I
turns
Pressure to
decrease
stocks
Success
jeopardized
Sales are
hurt (margin
and volume)
Costs
uncontrolled
Inventory is done infrequentlyPurchases do
not help I turns

6
Types of retailTypes of retail
• Classifications often confuse
• Criteria: a classification is only useful if the groups it
generates behave significantly different
A few classes under this criteria:
• Service: self of non-self service retailers
• Average number of items purchased*
• Integration with supply chain

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Retail Margins – a closerRetail Margins – a closer
looklook
• A supermarket sells many SKU’s per sale (ticket) – say
20.
• 7% stockouts (not true, but let’s assume it)
• Is it worth to implement the TOC solution here?
• (remember: a supermarket has ~2% profit on sales)
• What is the impact on the consumer experience?
• In other words: what is the “frustration frequency”?
• And financially is it worth it?

8
looklook
• A quick look:
• * means conservative, maybe very conservative
− Think of turns and margins on high runners, impulse buys, etc.
• It’s a 67% increase in absolute Profit, and 50% increase
in profitability

9
looklook
• And what about higher margin retailers (like
department stores or apparel)?
• Lower items per purchase (ticket), higher profitability
• And higher stockouts
• And even worse: less statistical basis for forecasts
• Therefore also very good results
• See my presentation on TOCICO 2007: The finance of
TOC Distribution for more on the financial aspect

10
Grids – batches by otherGrids – batches by other
namename
• On the topic of integration with suppliers comes an
interesting and perverse tidbit:
• Grids
• I.e. purchase a set of SKUs in multiples
• Ex: Converse All Star grid:
Size Color White Black Blue
36 2 2 2
38 4 4 4
40 4 4 4
42 2 4 2
Purchases do
not help I turns

11
TheThe αβγαβγ curvecurve
• The most famous is the ABC curve, although it may
help focus efforts/resources it does not give a good
view of the damage of pushing
• Introducing the αβγ curve:
Type Definition Problem(s) Impact
Alpha (α)
Sells well in (almost)
ALL stores
Stockouts Lost
sales
Beta (β)
Sells well in some stores
and poorly on others
Stockouts and excesses
of the same SKU
Lost
margins
& Sales
Gamma (γ)
Sells poorly in (amost)
ALL stores
Overstock everywhere
(blockages)
Lost
margins
Resupply from WH to Stores
does not help I turns

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TheThe αβγαβγ curve’s impactcurve’s impact
Type Sales profile Mark-Up Push Mark-up Pull
Alpha Well on all stores Full Full
Beta
Well on some stores Full Full
Poorly on some stores Discounted Almost full
Gamma Poorly on all stores Discounted Discounted
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Impact
Margin
increase
22%
% products 30%
Profit
increase 6,6%

13
TheThe αβγαβγ curve – Additionalcurve – Additional
ImpactsImpacts
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On Gammas:
•Sold faster (outlets)
•Lower purchase quantities
(specific purchasing
agreements)
On Gammas:
•Sold faster (outlets)
•Lower purchase quantities
(specific purchasing
agreements)
On Alphas:
•More volume
•Possibility of using
increased prices
(elasticity)
On Alphas:
•More volume
•Possibility of using
increased prices
(elasticity)
Type Sales profile Mark-Up Push Mark-up Pull
Alpha Well on all stores Full Full
Beta
Well on some stores Full Full
Poorly on some stores Discounted Almost full
Gamma Poorly on all stores Discounted Discounted

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The long tailThe long tail
• The long tail
− The tail of the curve: stock targets x average sales
− It is substantially long
• Quantization (discrete/integer quantities)
• A real life example
• Importance of the tail
• Interaction with inaccuracy

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Long Tail and QuantizationLong Tail and Quantization
15
1
StockLevels
Average Sales
Should we put 1 (a significant excess)Should we put 1 (a significant excess)
or 0 pieces (stockout) of each skuor 0 pieces (stockout) of each sku
here?here?
Should we put 1 (a significant excess)Should we put 1 (a significant excess)
or 0 pieces (stockout) of each skuor 0 pieces (stockout) of each sku
here?here?

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A real curve (DepartmentA real curve (Department
Store)Store)
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The tail is huge. It is hard to even seeThe tail is huge. It is hard to even see
how many SKUs sell more than 1/Dayhow many SKUs sell more than 1/Day
The tail is huge. It is hard to even seeThe tail is huge. It is hard to even see
how many SKUs sell more than 1/Dayhow many SKUs sell more than 1/Day

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A real curve (DepartmentA real curve (Department
Store)Store)
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Only 40 out of 48,860 SKUs have onOnly 40 out of 48,860 SKUs have on
average more than 1 unit sold/dayaverage more than 1 unit sold/day
Only 40 out of 48,860 SKUs have onOnly 40 out of 48,860 SKUs have on
average more than 1 unit sold/dayaverage more than 1 unit sold/day

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A real curve – ImportanceA real curve – Importance
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64% of sales are
of targets up to 3
64% of sales are
of targets up to 3

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• Let’s combine the long tail (which carries very few
units/SKU) with a large inaccuracy:
The long tail is
very inaccurate
The long tail & InaccuracyThe long tail & Inaccuracy
Store stock
accuracy is
awful
There is a
long tail
The long tail
represents
significant sales
The long tail is
composed of very
low targets
Very low targets are
more affected by
inaccuracy
Significant sales are lost
(even when trying to
replenish to demand)
In many SKUs actual
stock is higher than
what’s reported
In many SKUs actual
stock is lower than
what’s reported
“Consumption” based resupply

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The store operationThe store operation
• Different modes of operation:
− Self-service (supermarkets, dept stores, apparel etc.)
− Serviced (Prescription drugs, eletro-electronic, shoes, etc.)
Self Service
• Mostly uncontrolled and unmapped
• No significant structure to find things in the store WH
• Therefore:
• Store stockouts ≠ consumer PoV stockouts = shop
floor (sales area) stockouts
− Mis-supply: stock in the store WH is not in the store floor
In-store resuply does not
help I turns

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The store operation -The store operation -
ExampleExample
• Let’s check a real example:
Store # of SKUs Inaccuracy
Store
Stockouts
Sales Area
Stockouts
Large 48,407 42% 36% 49%
Medium 41,446 42% 35% 53%
Small 37,018 31% 27% 58%
TOTAL 126,871 39% 33% 53%
1.5% are promotion SKUs
waiting a release date
1.5% are promotion SKUs
waiting a release date
20% Stockouts caused by in-store mis-supply?
18.5% are SKUs stocked out in the
shop floor, but exist in the shop WH
18.5% are SKUs stocked out in the
shop floor, but exist in the shop WH
In-store resuply does not
help I turns

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The Warehouse operationThe Warehouse operation
• It’s a FACTORY!
• Some things are harder to see, i.e. stock piling up in
front of a work center (work centers move).
• Volume mentality leads to doing things ASAP
• This is not good because it releases excess WIP and
with efficiency mindset lead to mixing priorities
• The DDP is not good (seldom really measured)
• And the lead time is significant
Resupply from WH to Stores does
not help I turns

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The Warehouse operation -The Warehouse operation -
ExampleExample
• For instance, the lead time on a resupply that should
be done in 1 day (below we have days late to fulfill):
Resupply from WH to Stores does
not help I turns

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Bottom LineBottom Line
• Push is about volume (quantity)
• It is hugely different than pull (quality)
What are the behavioral implications?
The focus is on
stock quantity

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• One type of clouds is of chronic Do x Don’t where
one side is used extensively (Do) and the other side
is evoked occasionally (Don’t) and without process
and/or structure.
• In this case the evaporation is vulnerable to vices
(inertia with of without logic):
• “Chum Kiu”- Seek/Break the Bridge
AA
CC
BB
Don’tDon’t
DoDo
(Chronic Do x Don’t clouds)(Chronic Do x Don’t clouds)
AA
CC
BB
InjectionInjectionInjectionInjection
Do HabitDo HabitDo HabitDo Habit Do HabitDo HabitDo HabitDo Habit
AA
CC
BB
ReinforcedReinforced
InjectionInjection
ReinforcedReinforced
InjectionInjection

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TOC SOLUTION INTOC SOLUTION IN
RETAILRETAIL
“If you find yourself in a situation where you can’t find a way
to achieve the full target, increase the target…”

27
Solution – AdditionalSolution – Additional
ElementsElements
- Switch from ASAP to ALAP
- Life cycle management
- Switch from ASAP to ALAP
- Life cycle management

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Solution – DBMSolution – DBM
• So, we’ve got a enormous number of buffers
(targets) of size = 1
• DBM does not work on these, or does it?
• Problem: 1 = 0% buffer consumption (totally green),
0 = 100% buffer consumption (totally red or black)
• A solution: use consecutive sales (in relation to the
replenishment time) to trigger buffer increases
− Note: Symphony from Inherent Simplicity already
implements this.
• Open point: DBM does not tell (nor should it) when
to make the buffer = 0

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Solution –Solution –
Promotions/SeasonalityPromotions/Seasonality
• When we have these types of consumption peaks, in
most cases little intervention is needed
• In small intensity and/or medium to long duration:
DBM handles it
• When it does not we still have to check the duration
of the replenishment time from the WH to the Stores
− When the duration is larger than the replenishment time
then it may suffice to increase buffer targets in the WH and
the stores will consume more naturaly
• Else:

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• Increasing stores’ buffers has some problems
− Space to store the added quantities
− Time to manipulate the added quantities (wide variety of
small quantities)
− We may amplify the quantization error significantly (big
problem)
− Let’s see this point graphically

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• We have the following:
Quantized buffer targetsQuantized buffer targets
(notice the long tail of 1’s)(notice the long tail of 1’s)
Quantized buffer targetsQuantized buffer targets
(notice the long tail of 1’s)(notice the long tail of 1’s)
The actual buffer target numberThe actual buffer target number
calculated (many times non integer)calculated (many times non integer)
The actual buffer target numberThe actual buffer target number
calculated (many times non integer)calculated (many times non integer)

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• Comes a season that’ll double consumption:
If we could assess the curve we wouldIf we could assess the curve we would
have this new targets (notice howhave this new targets (notice how
many remain on 1)many remain on 1)
If we could assess the curve we wouldIf we could assess the curve we would
have this new targets (notice howhave this new targets (notice how
many remain on 1)many remain on 1)
If we were to recalculate the buffersIf we were to recalculate the buffers
this would be the curve, but somethis would be the curve, but some
time has passed so…time has passed so…
If we were to recalculate the buffersIf we were to recalculate the buffers
this would be the curve, but somethis would be the curve, but some
time has passed so…time has passed so…

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• If we use the buffer (integer) values to recalculate:
All this light blue area are roundup errorsAll this light blue area are roundup errors
(due to the long tail their impact is quite(due to the long tail their impact is quite
big)big)
All this light blue area are roundup errorsAll this light blue area are roundup errors
(due to the long tail their impact is quite(due to the long tail their impact is quite
big)big)
Here we have the “correct” valuesHere we have the “correct” valuesHere we have the “correct” valuesHere we have the “correct” values

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• Solution to increase (at least two):
− Keep the non-integer values of buffers and update them
via DBM (and derive the proper supply targets rounding
them)
− Estimate the rate of sales and discover the point where we
should not increase the buffers of size 1
• Other problems:
If the store cannot hold more, we can alter the logistical
delivery:
− Increase frequency: same buffers cover more demand (and
variability)
− Set up temporary “warehouses” (containers or similar
setups)

35
Solution – In-store RessuplySolution – In-store Ressuply
• In a volume/quantity mindset (push) the reception of
products follows this path (for all SKUs):
• In a value/quality mindset (pull) the reception must
be different, something like:
• And during the day (between shipments) whatever is
sold and is in the store WH should be moved quickly

36
Solution – AccuracySolution – Accuracy
• The solution is straightforward: hunt down the causes
of errors and eradicate them. Simple, right?
• No: there are significant problems
− To act on all stock (even simple operations) in a reasonably
sized chain takes a significant time (and sometimes money)
− And there is no reliable mechanism to detect, prioritize and
control errors
− And some errors are unavoidable (theft, for instance)
• Are we doomed to the hard and long path?
• No: this is one of the cases where attacking
symptoms is the path to discover and correct the
cause(s)

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• What is a good indicator of data inaccuracy in stock?
• What when detected UNQUESTIONABLY tells us that
we have an error?
Negative stocks
• And (test this) they hold a significant correlation with
actual errors in their respective product groups,
• For instance:

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Sum of negative
stocks per Product
Group (PG)
Actual stock inaccuracy gauged
in a full store balance
Each color represents a major department (5 such
departments)
The area of the disk is the total stock of the PG
There is a very strongThere is a very strong
correlation betweencorrelation between
negatives and inaccuracynegatives and inaccuracy
There is a very strongThere is a very strong
correlation betweencorrelation between
negatives and inaccuracynegatives and inaccuracy

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SELLING ANDSELLING AND
IMPLEMENTINGIMPLEMENTING
“Are we there yet, daddy?”

40
We have a problemWe have a problem
• In most cases a pilot is a step in the buy in process:
SuccessfulSuccessful
PilotPilot
HaveHave
organizationorganization
embrace itembrace it
Generate theGenerate the
mostmost
commitmentcommitment
Set objectiveSet objective
lowlow
Set objectiveSet objective
highhigh
High (ambitious) objective focuses the organization (other
projects are subordinated or dropped) and galvanizes action
(increasing morale)
High (ambitious) objective focuses the organization (other
projects are subordinated or dropped) and galvanizes action
(increasing morale)
Low objective relates with past experience and is easier to accept
by members of the organization. (Results are proportional to
efforts/risks)
Low objective relates with past experience and is easier to accept
by members of the organization. (Results are proportional to
efforts/risks)
Obs: a low objective also turns into a self-fulfilling prophecy (D’ !-> B)

41
We have a problemWe have a problem
Besides the cloud above, we have:
A pilot is
implemented
The pilot requires
significant effort
and attention
The pilot suffers
from lack of
attention and effort
The pilot
underdelivers
A pilot (even in a
limited scope) is
complicated
Management time is
the constraint
Errors go
uncorrected and
opportunities
unexploited
• Stepping in two boats at once is
not a good idea (bad multitasking)
• Inertia fights against the new
boat, i.e. when in doubt people
revert to “old” and “proven” ways
• In retail the number of variables
(SKUs, sales events, transactions
etc.) is huge
• When piloting a change people
won’t commit fully because the
change isn’t guaranteed
(unavoidable)
• The temptation of adding to the
pilot (to achieve more) is very high
• Stepping in two boats at once is
not a good idea (bad multitasking)
• Inertia fights against the new
boat, i.e. when in doubt people
revert to “old” and “proven” ways
• In retail the number of variables
(SKUs, sales events, transactions
etc.) is huge
• When piloting a change people
won’t commit fully because the
change isn’t guaranteed
(unavoidable)
• The temptation of adding to the
pilot (to achieve more) is very high
The buy-in process
is compromised
The pilot is just
another project

42
Solution criteriaSolution criteria
• We need a solution that will
− Generate the most commitmentGenerate the most commitment
− Have organization embrace itHave organization embrace it
− Is easy and requires little or not special attention toIs easy and requires little or not special attention to
managemanage
− Does not conflict with current systems/processesDoes not conflict with current systems/processes
− Have results that are accepted by the organizationHave results that are accepted by the organization
− Set a high ambitious targetSet a high ambitious target
• The C -> D’ is the best target, and the erroneousThe C -> D’ is the best target, and the erroneous
assumption is:assumption is:
• ““Results are proportional to efforts/risk”Results are proportional to efforts/risk”

43
Direction of the solutionDirection of the solution
So we need two elements in the solution:
• Consensus
− Geared to generate logical (qualitative) acceptance
− Will also generate agreement to proceed with:
• Expectation (ambition) alignment
− A specific kind of pilot (small, fast, easy, decisive)
− Geared to generate quantitative acceptance (expectation
alignment)

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Micro PilotsMicro Pilots
• Satisfies the second step:
• Take a small number of representative SKUs (less
than 100) in a few stores (the more diverse, the
better), pick some other stores as the control group
• MANUALLY control these for:
− Accuracy (i.e. full count daily)
− In-shop replenishment & display (dedicated people and
control)
− WH resupply (manual separation and shipping)
− Collecting extra stock from other stores to insure
availability on the WH
− Etc.
• And compare with control group in the same period

45
Thank YouThank You
• Comments, questions?

46
About HumbertoAbout Humberto
• Husband and father changing
the world one person at a time
• Scientist seeking to apply
science to people’s endeavors
• Hunter of hidden assumptions
• Teacher, student and
colleague of students
• Believer of values over tools
• Partner in crime at Goldratt
Schools (and Group)
humberto.baptista@goldrattgroup.com
www.goldrattschools.org
humberto@vectis-solutions.com
www.vectis-solutions.com

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