Modern TOC Distribution or Everything you wanted to know

1© 2014 TOCICO. All rights reserved.

TOCICO 2014 Webinar

Modern TOC Distributionor

Everything you wanted to know about TOC Distribution, but

were afraid to askPresented By: Humberto R. BaptistaDate: August 15th 2014


TOCICO 2014 Webinar

Contents

• TOC Distribution essentials• The main variable of the TOC Distribution solution• Supplying• Dynamic Buffer Management• Seasonalities• Mix and Category Management


TOCICO 2014 Webinar

TOC Distribution essentials

• TOC Distribution is a solution for distribution environments* that have the following Core Cloud

* Environments where clients (demand points) tolerance time is smaller than the Supply Time (from supply points)

B D

Manage Well

Protect Cash

Decrease Stocks

Increase Stocks

Protect Sales


TOCICO 2014 Webinar

TOC Distribution: quick overview

Until the end consumer has bought no one in the supply chain has sold

Buffer Size = optimistic demand within the RLT factored by supply Murphy

Agg

rega

tion

Buf

fer M

anag

emen

t Decide: Hold

(MTA) or Order (MTO)

Stocks

Stocks

Push

Pull

Supply Chain goods flow

Sale -> Resupply

Forecast -> Resupply


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• Injections (from TOC Insights):

11• Establish the plant (central) warehouse.

22 • At each place and for each product establish the inventory target according to the formula.

33 • Move to "Order daily - Replenish periodically".

44 • Monitor the inventory targets according to the zones.

55 • Re-examine policies of make-to-stock, make-to-order.

66 • Educate sub-systems to monitor execution using Dollar-Days measurements.

And if it is not a plant and/or there is no internal supply chain?

All products? Everywhere? What numbers on the formula?

What about batches? And how frequent “periodically” be?

What actions should be done? And What about peaks?

How? What guides this? And where in the supply chain?

Necessary? Ever done?


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What is really left Outside:• Mix management (sort of)• Lifecycle management• Data integrity• Pricing• (Visual Merchandizing)• Expiration (uneven SKUs)• Multi sourcing• “Use & Return” stock

See my presentation on TOCICO 2010 (Retail TOC: Myths and Truths)

See Rickkets: Reaching The Goal and other discussions by Dr. James Holt


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• Recognize the impact of the accumulation of stock in the different links of the supply chain

• Definition of distribution environment:

• Leading to:

The main variable

Supply

Demand

Supply Time

Tolerance Time

Supply

Demand

Supply Time

Tolerance Time

Stock Supply Time

So we are talking about TIME! (every

TOC logistical solution is about

time! Check it out!)


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Main Variable

Replenishment time is long

Suppliers aren’t reliable

Forecasts are Inaccurate

We suffer with supplier’s

unreliability

We suffer with unbalanced stocks &

shifts in demand

Successful Distribution Protect

CashDecrease

Stocks

Increase Stocks

Protect Sales


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Setup – Buffers (what, where & how much)

How Much• Buffer sizing formula…• What is “Optimistic demand?” How can we gauge it from

current data?• Best solution (less distorted):• Average speed of sales = sales/days with stock:

• Formula precision required?

1 00 1 00 1 00 00 00Sales

Stock 3 2 2 1 1 00 00 00

Traditional (more distorted):Average Sales = 3/8 = 0.375/day

Suggestion (less distorted):Average Speed of Sales = 2/4 = 0.5/dayDays


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What/Where• What mix should be protected? This is not usually clear

(why?)• Lifetime x Turns (weight of the tail)

− Sales cycle: imagine a 8 units grid (8 sizes x 1 unit/ size) that sells on an average sales speed of 0.1units/day => typical sales cycle = 80 days. (optimistic since different sizes of the same product sell at different speeds)

• Active mix within the internal supply chain vs the sales points

− Different treatment for Central Warehouse, Regional Warehouse and Point of Sales

• Budget(/Space) Constraint(s)


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Demand Curve Conceptually

Buffer sizes (integer numbers)

Demand speed curve

Should we hold these?


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What / Where@Supplier Product CWH RWH POS

Avail.

RLT <= Life Cycle

SKU Buffers[Macro P]

SKU Buffers[Micro P]

OR

FMs: SKU Buffers

SMs: MTO/Pass Tru

SKU Buffers*

RLT >> Life Cycle

FMs: SKU BuffersSMs: Cat Buffers

Not Avail. NA NA

* Taking care of the 1 size buffer problem on the tail


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Too much variety/mix/assortment

• Usually retail suffers with too much assortment(except when there is a variety constraint in the supply chain)

• A good rule of thumb: 50% of the existing variety on the POS or less if this will still insufficient to reduce overall stock on the POS AFTER the balancing of stocks

• Careful and forceful consideration of tail liquidationschemes


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Is variability equal throughout the chain?

How many consumption points do you have?


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Store 1

Backroom

Quiz: Aggregation or not?

• Question: if we’re moving from 1 store to 2 stores should we use aggregation? Why?

• And if the products sold are usually slow movers?

New ProductsResupply of FMs

( )Backroom+ “CWH”

(FMs)

Supplier(s)

Sales Area

Store 2

New Products

Supplier(s)

Backroom

Sales Area

Res

uppl

y@

RLT

Replenishment@TLP

FMs


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• Difference on speed: order of magnitude between average and fast and slow movers

Fast & Slow Movers – Some real data

Type Speed (un/day)Fast Runner 2.87Average 0.27Slow Mover 0.10


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Virtual CWH (RWH?)

WH1 WH2

Imports (1)External Suppliers (2)

Internal Supplier (3)(factory)

CWH for (1) and (2)

RWH for (3)

CWH for (3)

RWH for (1) and (2)

POS POS POS POS POS POS… …

Replenishment


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FlashBack!

See my presentation on TOCICO 2010 (Retail TOC: Myths and Truths)

Also: lost sales due to sales area and cash BLOCKAGE!


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Setup – Balancing Stock

• After Buffers are set what do we do with significant (> 2x buffer) excesses? -> Back to WHs!

• Not a nicety! (not explicitly in the classic injections also)• Close to end of life products x far from end of life products

− Even close to the end of life should do the balance -> the impact on sales outweigh the cost of liquidation (by far, check it).

• Impact for the transition period− Reducing of stocks (and possibly mix) on the POS− Increasing availability of β (Betas)− Reducing damage of γ (Gammas) (very significant!)− Focusing sourcing on α (Alphas)!

Immediate Sales Impact (SLT)

Fast Sales Impact

Strong Sales Impact

AND MARGINS!!!


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Impacts

• Notice the progression:

Units Sales (outflows)

Money Sales (Income)

Margin Generation

(Throughput)

TURNS(Throughput/In

vestment)

Can be assessed by Speed of Sales x Average Sales (easier)

Can be assessed by α β γanalysis (harder)

Impa

ct


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Supplying

• Above or within the buffers (or what do I do with my batches)?

− Impact on Priorities− What to do with batches− Potential negatives

• Careful with grid batches (one of the worst kinds)− Definition of grid batch: multiple SKUs in one batch


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Batches: above or withinB

uffe

r siz

e

TimeRELEASEPOINT

Releasing batches WITHIN the buffer: wait until the stock level + minimum batch is below the buffer size.

Releasing batches ABOVE the buffer: wait until the stock level is below the buffer size.

Buf

fer s

ize

TimeRELEASEPOINT


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Effects

Within− Less WIP (at first)− No portion of WIP above the

target (buffer size)− Priorities are more

aggressive− DBM pushes buffer size up

(batch size is a variability)− Seasonalities might built too

much/little inventory− Only works well if the batch

is less than a zone

Above− More WIP (at first)− Priorities are less aggressive− DBM must deal with buffers

above green (cyan)− Seasonalities are OK− Works well with any batch

size− “Relax” the pressure to

reduce batches


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Example for 1 SKU (pull, no DBR yet)

0

50

100

150

200

250

300

-50

0

50

100

150

200

250

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

MIN

MA

XM

INM

AX

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

TOC

1 3 5 7 9111315171921232527293133353739414345474951535557596163656769717375777981838587899193959799101103105107109111113115117119121123125127129131133135137139141143145147149

EstoqueinicialWIP

P.Rep/Alvo

Before (MinMax):Stock: 103Losses: 10%Turns: 2.7

After (Pull):Stock: 54Losses: 2%Turns: 5.4


TOCICO 2014 Webinar

Supplying

• Priorities− On hand (measures the execution vis a vis the goal)− In Transit (measures the execution of the supply)− Evaluation matrix and expected behaviors


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Uniform priorities!

• Priorities valid throughout the supply chain:

Color: red, high priority

Color : red, high priority

Color: yellow, normal priority

Color: green low low priority

On Hand Stock

Transit Stock

WIP

New PO to release


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Buy or buy & expedite?

• CWH buffer:

On hand

PO 1

PO 2

PO 3

On Hand

PO 1

PO 2

PO 3


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Focusing MTA Matrix

On Hand + WIP Buffer Status (virtual Pipe)

Blue Green+Yellow Red+Black

On

Han

d B

uffe

rSTa

tus Blue

if WIP = 0: OKelse: Orders to be

canceled/Adjusted Down

Green+Yellow

OK(do nothing!) Release Orders

Red+Black Expedite Release Orders and Expedite

Release Orders Urgently and

Expedite

Quick tip: List # of SKU Locations in each cell and below list # of SKU Locations with active Orders outstanding (WIP).


TOCICO 2014 Webinar

Focusing MTA Matrix

On Hand + WIP Buffer Status


On

Han

d B

uffe

rSTa

tus Blue



Green+Yellow OK Release Orders



Expedite

Any # of SKU Locations here =Order Releasing Problems


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Focusing MTA Matrix



On

Han

d B

uffe

rSTa

tus Blue






Expedite

Any significant # of SKU Locations here =Priority following Problems


TOCICO 2014 Webinar

Focusing MTA Matrix



On

Han

d B

uffe

rSTa

tus Blue






Expedite

Any significant # of SKU Locations here with WIP >0 =Excess Releasing Problems


TOCICO 2014 Webinar

Focusing MTA Matrix



On

Han

d B

uffe

rSTa

tus Blue






Expedite

Focus on Priorities - Focus on Release


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Focusing MTA Matrix

• Real case example:

73-16 SKUs=57 urgent SKUs not released

61-18 SKUs=43 regular SKUs not released

73 urgent SKUs released with insuficient quantities... 116 urgent SKUs

to expedite

58 SKUs in production = Excesses to be trimmed


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Exceptions of DBM

• “Reserved” or segregated stock• “MTO” consumption• Auto DBM during seasonality changes• Contractual limits (min/max)• Coupled SKUs:

− One SKU substitutes an old (obsolete) SKU− One active SKU may be used for another active SKU

− Symmetric (both ways)− Asymmetric (only one way)

• SKU Equality (assumption)• “Recyclable” stock


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Supplying

• Frequency x transportation lead time− Different variables, which (or both) should be used?

Buffer Size = optimistic demand within the RLT factored by supply Murphy

Transportation Lead Time

Transportation Frequency


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Dynamic Buffer Management

• Classic DBM and its limitations• Implementing DBM

− Too much green, too much red− Exceptions to automatic DBM− Very Small Buffers− Exception handling

• Fast movers• POOGI?


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Implementing DBM (Too Much Green)

Buffer is reduced when consecutive penetration area > limit

Last green penetration

Consecutive penetration area

Coolling PeriodCoolling Period


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Implementing DBM (Too Much Red)

Buffer increases when consecutive penetration area > limit

Last red penetration

Consecutive Penetration Area Coolling PeriodCoolling Period


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Small Buffers

• How to increase 1 unit buffers?• (decrease to 1 unit is easy)• TMR = # consecutive days of consumption adjusted for RLT• (consecutive = adjusted for replenishment)


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Exception Management

• When there are problems in supply DBM cannot be allowed to work automatically all the time.

• Careful with the scalability cloud:

• To evaporate it there must be some Exception Management:− MAIN GOAL: allow maximum scalability!− IO: fully automated DBM− IO: robust way of handling exceptions

Successful Distribution Avoid

distortions Manual DBM

Full Auto DBMScale Well


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Fast Movers and DBM

• Speed of change for DBM is big, but on events that change structurally the product distribution (like activating buffers in more consumption points, etc.) an additional change may be in order

• Also the RLT limits how fast FM Buffers can grow, therefore:

− Expediting actions should be always cultivated− Resting period should never be tied to an specific RLT (but to a

zone change)− Additional change (upon detection of a new FM) may be necessary,

but can be treated as an exception: an aggressive consumption triggers TMR, and leaves an exception, to be reviewed, presenting the possibility of further increase


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POOGI on Distribution

• Is there? Is it DBM? No.• Can we use the statistic of red penetrations?• And green penetration?• What is the assumption behind gathering red penetration

instances and attacking the most frequent ones?• The instances are coming from a same system (i.e. can be

grouped)• Problem with DBM: each target (buffer size) change

modifies the system (transition period) and therefore is not a good source of statistics.


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POOGI on Distribution – A proposal

• In an stable situation any given red penetration is statistically significant and may lead to sources of variability we can trim (internal) or mitigate (external)

Stable: good source of instances to

aggregate statistically

Transition: inadequate source of instances to aggregate statistically


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Seasonalities

• A Sudden change in demand that is known in advance (exception to regular DBM)

− Usually > 33% increase/RLT

• Types of seasonalities− Within the noise: <=33% / RLT− Normal: above− Super-seasonality: above to a degree that many links in the supply

chain cannot supply enough to meet the demand

• How to prepare (MTA/MTO) and supply during event− Raising buffers (gradually) is a nice solution, other is to raise the

“reserve” stock effectively raising the black level (more sensitive to changes in demand during change, but not very useful)


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Seasonality

• Should we multiply all buffers by a factor?− What about end-of-life products?− Treat Fast Movers (FMs) as we do Slow Movers (SMs)?− Error on the 1 size buffers

− Vast majority of 1 size buffers have sales speed so slow even a very strong seasonality should NOT change the buffer size.

• And what about transient Bottlenecks (or better: overloaded CCRs)?

− Some simple actions alleviate this: increasing frequency of shipments, establishing temporary WHs, pre-loading of some FMs.


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Seasonality

• 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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Seasonality

• Only applied on FMs or buffers > 1

Here apply the seasonality adjustment

All this light blue of errors are saved by NOT applying seasonality here


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Mix and Category Management

• Buy-once products (above)• How to buy and resupply on the various points of the chain

(above)• Buffer Management and DBM

− Also for category buffers (where different SKUs are in the same buffer)

• Dealing with very slow products in not so long life cycles− How to cut the tail even further and increase the freshness at the

same time

• (Fashion and freshness myths and truths)


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Demand Curve

• 2 parts• Fallacy of average• ABC or αβγ? (more on my TOCICO 2010 presentation)

FAST MOVERS

SLOW MOVERS


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Demand Curve

• 2 parts• Fast Movers: 3-5% -> 50% sales, Tail: rest.• Still want to average?• TIP: each part is reasonably “linear”, use this.

FAST MOVERS

SLOW MOVERS

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


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0.0

2.0

4.0

6.0

8.0

10.0

12.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

Quant(Y)

Y

1 unit size Stock/Buffer

• And the looooooooooooooooooooooong tail


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Tail Thinning

• Challenge the 1 unit buffer/tail problem by tail rotation (ex: 2 stores, 2 NP groups):

NP1NP2

Store 1Store 2

FM1FM2 SM1SM2

SM1 SM2 FM1FM2FM2FM1

• This reduces significantly the buffer size for NP in the WHs and in the Stores and still allows for the effective discovery of FMs.

TIM

E

= move to other store (rotate)

= preserve and promote


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Freshness

• Usually seen as an aid to generating more traffic (impact on elevating the constraint)

• More important: the gateway for more FMs!

NPs

FMs

SMs NPs

FMs

SMs NPs

FMs

SMs NPs

FMs

SMs NPs

…

(not to scale)Time


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NPD and Fashion and Freshness…

• What for?• Elevation is the obvious answer. Exploitation (margin

maintenance) is less obvious, but also and answer.• BUT: under TOC mode of operation: these mechanisms are

ideal to:• Sell (Exploit) more and more Fast Movers (at full margins)• Substitute more and more Slow Movers (Subordinate)


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NPD and Fashion and Freshness…

• To do so:

NPs

FMs

SMs NPs

FMs

SMs NPs

FMs

SMs NPs

FMs

SMs NPs

…

Re-purchasing of FMs, challenging transportation lead times, shortening production (supply) lead times

Purchasing the minimum quantities, challenging (grid) batches, tail thinning, store space buffers, liquidation (outlet) mechanisms


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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• Strong believer of values over tools• Practicing atheist and skeptic

[email protected]

Documents

Modern TOC Distribution or Everything you wanted to know