toc in retail: myths and truths...© 2009 tocico. all rights reserved. 1 tocico 2010 conference toc...

1© 2009 TOCICO. All rights reserved.

TOCICO 2010 Conference

TOC in Retail:

Myths and Truths

Presented By: Humberto R. Baptista / Goldratt Schools // Vectis

Date: June/2010

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© 2010 TOCICO. All rights reserved.


Agenda

• 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?

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A 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 RETAIL

“Reality is that which, when you stop believing in it, doesn't go away”

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Slice 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

Purchases do 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 infrequently

6



Types 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

7



Retail Margins – a closer look

• 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?

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• A quick look:

• * means conservative, maybe very conservative

− Think of turns on high runners, impulse buys, etc.

• It’s a 67% increase in absolute Profit, and 50% increase in profitability

Retailer Numbers Now % Then %

Sales 1000 100% 1070 100%*

TVC 800 80% 856 80%*

T 200 20% 214 20%*

OE 180 18% 180 17%

Profit 20 2% 34 3%

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• 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

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Grids – batches by other name

• 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

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The αβγ curve

• 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

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The αβγ curve’s impact

Type Sales profile Mark-Up Push Mark-up Pull

Alpha Well on all stores Full Full

BetaWell on some stores Full Full

Poorly on some stores Discounted Almost full

Gamma Poorly on all stores Discounted Discounted

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EXAMPLE Push Pull

% Betas 60%

% Slow Betas 30%

TVC 100 100

Markup 200% -> 80% 200%

Price 180 300

Margin (T) 44% 66%

Impact

Margin

increase22%

% products 30%

Profit

increase6,6%

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The αβγ curve – Additional Impacts

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On Gammas:•Sold faster (outlets)•Lower purchase quantities (specific purchasing agreements)

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

BetaWell 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 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 Quantization

15

1

Sto

ck

Le

ve

ls

Average Sales

Should we put 1 (a significant excess) or 0 pieces (stockout) of each sku here?

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A real curve (Department Store)

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The tail is huge. It is hard to even see how many SKUs sell more than 1/Day

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A real curve (Department Store)

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Only 40 out of 48,860 SKUs have on average more than 1 unit sold/day

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A real curve – Importance

18

64% of sales are of targets up to 3

Min

imu

m

targ

ets

are

1 o

r 2

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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 & 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

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The 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

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The store operation - Example

• 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%

18.5%

1.5

% 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

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The 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

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The Warehouse operation - Example

• For instance, the lead time on a resupply that should be done in 1 day (below we have days late to fulfill):

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Bottom Line

• Push is about volume (quantity)

• It is hugely different than pull (quality)

What are the behavioral implications?

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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

A

C

B

Don’t

Do

(Chronic Do x Don’t clouds)

A

C

B

Injection

Do Habit Do Habit

A

C

B

ReinforcedInjection

26



TOC SOLUTION IN RETAIL

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

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Solution – Additional ElementsS

upplie

rs Eliminate grids

Focus on small batches

Information flow

Avoid “easy” cost savings

Ware

houses Are not

Are: Aggregation factories!

Micro picking

Sto

res Accuracy

In-store resupply

Display

- Switch from ASAP to ALAP- Life cycle management

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Solution – 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 – Promotions/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 targets (notice the long tail of 1’s)

The actual buffer target number calculated (many times non integer)

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• Comes a season that’ll double consumption:

If we could assess the curve we would have this new targets (notice how many remain on 1)

If we were to recalculate the buffers this would be the curve, but some time has passed so…

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• If we use the buffer (integer) values to recalculate:

All this light blue area are roundup errors (due to the long tail their impact is quite big)

Here 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)

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Solution – 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

Unload & Unpack

[Process]Store in the Store WH

Ressuply to the floor

Unload & UnpackSeparate

Promotion/Display SKUs

[Process]Ressuply to the

floor

Take whatever doesn’t fit to the

warehouse

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Solution – 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 strong correlation between negatives and inaccuracy

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SELLING AND

IMPLEMENTING

“Are we there yet, daddy?”

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We have a problem

• In most cases a pilot is a step in the buy in process:

Successful Pilot

Have organization embrace it

Generate the most

commitment

Set objective low

Set objective high

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)

Obs: a low objective also turns into a self-fulfilling prophecy (D’ !-> B)

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We 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

The buy-in process is compromised

The pilot is just another project

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Solution criteria

• We need a solution that will

− Generate the most commitment

− Have organization embrace it

− Is easy and requires little or not special attention to manage

− Does not conflict with current systems/processes

− Have results that are accepted by the organization

− Set a high ambitious target

• The C -> D’ is the best target, and the erroneous assumption is:

• “Results are proportional to efforts/risk”

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Direction 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 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

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Thank You

• Comments, questions?

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About 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)

[email protected]

www.goldrattschools.org

[email protected]

www.vectis-solutions.com

mailto:[email protected]

http://www.goldrattschools.org




http://www.vectis-solutions.com



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