bt supply chain - gestión de multiproyectos /...

© British Telecommunications plc

BT Supply Chain

Brian Dooley FCILT, CDDP, CDDL

Senior Supply Chain Planning Manager

BT’s Demand Driven Journey


A bit about us …


Agenda

• Our journey so far

• The case for change

• Selling the solution

• Results, learnings & next steps


Our starting point

Our transformation journey so far…


• Inventory management and supply chain planning decentralised

• Separate teams within each Line of Business

• No Pan BT standard approach or reporting


Our starting point

September 2014 transformation

programme launched



• Design new operating model for Pan-BT inventory management

• Create Inventory Management Centre (IMC) – responsibility for inventory planning and execution moved to BT Supply Chain

• Develop insightful inventory reporting


Our starting point


programme launched

The first 18 months …



• Completed design and development of new operating model

• Demand forecasting process

• Safety stock calculation

• Demand & supply balancing through S&OP

• Design and re-organisation of IMC team

• Implementation of new IM processes across the lines of business


Our starting point


programme launched


Achievements to December 2015 …



• Stable and standardised processes and controls in place

• Reduced inventory holding by 15%

• Systems in place for demand forecasting & inventory optimisation

• Introduction of formal S&OP process


• Forecast accuracy a challenge, particularly at SKU level

• Inventory glide path from DRP not being achieved

• Service issues & expedite costs from none forecast demand

Our starting point


programme launched


Achievements to December 2015 …



Where do we go next?


The Case for Change

In a word – FORECAST ACCURACY

Manifested in two key areas; OTIF (availability) and inventory glide path

“Can we add an extra line to the glide path to account for how wrong we think the forecast will be”


29%17% 13% 17% 19% 22% 26% 22% 27% 26% 23% 19%

29%

22%22%

33% 27%33% 24%

21%25% 28%

24% 32%

42%61% 65%

50% 54%44% 50% 56%

48% 45% 53% 49%

0%

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FC Error SKU Mix

SKUS <20% Err SKUS 20%-50% Err SKUS > 50% Err

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mb

er o

f SK

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Forecast Error

Rolling 3 Month FC err %

Half the items have a forecast error in excess of 50%

At an aggregate level there is a strong negative bias (over forecasting)

There is a broad spread of both under and over forecast items

Our push to forecast model results in a bi-modal inventory distribution of either too much or too little stock

The Case for Change – Example Forecast Accuracy Results for one PortfolioAggregate Demand vs Forecast

Forecast Demand

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Distribution of SKU’s by Buffer Zone


So What’s the Solution?

Improve forecast accuracy……..

“If I had asked people what they wanted, they would have said faster horses”

Henry Ford

But…….

“Trying to predict the future is like trying to drive down a country road at night with no lights while looking out

the back window”Peter F Drucker


Answer – Become Demand Driven

January 2016 strategic decision to adopt a demand driven approach:

Enabling us to de-risk the supply chain and address the challenge of demand forecasting particularly at SKU level

At first glance this approach made sound sense – de-risks the supply chain and removes dependency on detailed demand forecasts

Big question ‘How do we sell this to the rest of the business’

The change challenge:

Small core team educated in DDMRP (CDDP course)

Built a ‘proof of concept’ model to test the theory and simulate comparison with traditional approach

Introduced concept and outcome of simulations to main line of businesses making up majority of inventory holdings - all agreed to extend ‘proof of concept’ on larger portfolio of products

Potential benefits quantified, supply chain risks assessed and senior stakeholder support obtained for implementation

Developed an in house Excel DDMRP model to quickly roll out the proof of concept & maintain momentum


A Simple Model to Explain the Concept

Demand

Forecast

BT Finished

GoodsIn Transit

Supplier

Finished

Goods

Supplier

Manufacturing

Supplier Raw

Materials

100% Coupled and Dependant on Forecast – Forecast Error Propagated Through the Supply Chain

Actual

Customer

OrdersIn Transit

Supplier

Finished

Goods

Supplier

Manufacturing

Supplier Raw

Materials

BT

Finished

Goods

DDMRP

Buffer

Driven by Real Demand and Independent of Forecast

PU

SH

PU

LL

Buffer sized using:

- Business forecast + planned

events

- Product lead time

- Demand variability

Sized & positioned to cope with

the demand we expect to

happen (have forecasted to

happen) – reviewed monthly as

part of S&OP

Maximum stock exposure is limited to filling the

buffer to top of green, after that all

replenishment orders are driven by real demand

Key Messaging

Supply chain ‘de-risked’ from forecast accuracy

Strong positive impact on cash flow and ROCE

Higher service levels and shortened customer response times

Overall inventory reduction typically between 20%-30% across LoBs

Release of supply chain capacity by not producing, buying and storing the wrong stuff

Cost reduction – lower planning effort with improved outcomes – more stable signals to suppliers – lower expedite costs

Results achieved


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Inve

nto

ry V

alu

e (£

)

Actual vs DDMRP Simulation – 6 month Inventory Evolution

Actual – over forecast items Actual – under forecast items DDMRP – over forecast items DDMRP – over forecast items

Actual – Total items DDMRP – Total items

Simulation model built using 6 months history of actual daily orders

Two groups of SKUs from the same family modelled that had been historically over and under forecast but at the aggregate level had excellent accuracy

RESULTS• Stock is in balance and total inventory

is halved

• NOT Dependant on Forecast so decoupled from forecast error

• NO Service issues

• LESS Inventory

• STABLE load on factory

• NO costs of failure

Simulation - test the theory

Manufacturing Lead Time


DDMRP is not magic, assuming buffers are not

oversized then a significant increase in demand

will still result in a stock out

The buffer will react and recover but not within

the lead time

Still better than planning to too low a forecast

If the increase is known it can be accommodated

by flexing the buffers (ahead of lead time)

This flex does not have to be driven directly by

an item level detail forecast, buffer sizing can be

discussed as part of the S&OP process and

adjustments and/or simulations run accordingly

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Item A – 80% Demand Uplift October

RED YELLOW GREEN O/H Avail Stock

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Item A – 80% Demand Uplift October

RED YELLOW GREEN O/H Avail Stock

A New Role for Forecasting

Soft Benefits

• Better language to communicate

with none supply chain folk

• Greater willingness to share

business insight

• Less emotion when things go

wrong


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Red (£) Yellow (£) Green (£) Closing Inv (£) Actual Inv (£) Net Flow £

Actual Receipts from

existing POs to this point

44% reduction in

average on-hand

inventory

Buffers dynamically resized using an 8

week historical rolling ADU

Proof of concept simulation – Another example with dynamic buffer resizing


Count of SKUs 06

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OH Red 3 1 2 3 3 4 1 2 3 2 1 1 1 2

OH Yellow 4 6 4 11 6 6 7 6 4 7 8 5 6 9

OH Green 21 24 24 26 36 37 37 36 38 33 32 39 36 37

OH OTOG 86 83 84 74 69 67 69 70 69 72 74 70 72 67

Net Flow Red 1 0 0 0 0 0 0 0 1 1 0 0 0 0

Net Flow Yellow 3 4 3 29 15 6 7 8 13 12 15 9 11 17

Net Flow Green 36 39 52 40 53 64 63 64 58 57 56 59 60 56

Net Flow OTOG 74 71 59 45 46 44 44 42 42 44 44 47 44 42

P1

2

Q1

Q2

Q3

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-16

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r-1

6

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g-1

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v-1

6

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-17

DRP

DDMRP

DDMRP Projection

22% reduction in the number of SKUs with too high an on hand stock

43% reduction in SKUs over top of green – we are not ordering product we don’t need.

Clear priorities on which SKUs to expedite / ensure on time delivery

• For the first time since Q1 15/16 inventory has stopped increasing

• Demand has not increased, production has not reduced and we are in fact still pulling forward production

• DDMRP is ensuring we use capacity to produce what is actually required and bring the mix back under control

• S&OP is now focused on defining what supply chain capability we need in place to support the demand we are expecting rather than creating a master schedule of what we think (hope) we might need

Actual results from Excel model deployed in June 2016

Invento

ry V

alu

e (

£)

Inventory Glide Path


Index BT IC Desc ADU MOQ Order Cycle (days) + On Order -Qualified Sales Orders On Hand =Available Stock Recommended Order

1 Part1 601.08 8100 15 32,400 4,000 6,169 34,569 8100

2 Part2 58.69 6000 102 6,000 0 631 6,631 0

3 Part3 938.25 5030 15 45,270 0 10,542 55,812 15090

4 Part4 3417.14 8100 15 146,853 1,950 48,043 192,946 64800

5 Part5 347.85 8075 23 25,089 0 5,092 30,181 0

6 Part6 213.46 4840 23 10,502 1,500 4,491 13,493 0

7 Part7 8.63 12 15 415 13 194 596 0

8 Part8 2569.15 10100 15 125,193 2,500 67,041 189,734 0

9 Part9 190.77 10000 52 10,400 0 5,200 15,600 0

10 Part10 1670.35 4015 15 70,506 3,975 50,872 117,403 0

11 Part11 156.08 8100 52 8,100 790 4,951 12,261 0

12 Part12 6.66 50 15 250 10 221 461 0

13 Part13 711.98 3040 15 14,310 1,940 24,728 37,098 15200

14 Part14 10310.63 14050 15 315,961 13,635 360,075 662,401 0

DDMRP Excel Model - Clear Plan Priorities

Critical Items

• Expedite existing POs

• Call out any delays

• On Hand / ADU = Days of cover

Potentially at risk items

• Monitor

• Check days of cover vs PO due date

• Call out any delays

BT IC Desc On Hand ADU OH Cover PO NUMBER PO DATEQUANTITY ORDERED

QUANTITY DELIVERED NEED BY DATE

Part1 6,169 601 10 xxxxxxxxxxx 25/05/2016 8100 0 30/06/2016

xxxxxxxxxxx 29/06/2016 16200 0 31/07/2016

xxxxxxxxxxx 13/05/2016 8100 0 27/06/2016

Part2 631 59 11 xxxxxxxxxxx 07/06/2016 6000 0 19/07/2016


xxxxxxxxxxx 17/06/2016 15090 0 29/07/2016

xxxxxxxxxxx 29/06/2016 10060 0 31/07/2016

xxxxxxxxxxx 29/06/2016 5030 0 15/08/2016

Part4 48,043 3,417 14 xxxxxxxxxxx 07/06/2016 72900 7047 19/07/2016

xxxxxxxxxxx 29/06/2016 48600 0 31/07/2016

xxxxxxxxxxx 29/06/2016 32400 0 15/08/2016


xxxxxxxxxxx 17/06/2016 8075 0 29/07/2016

xxxxxxxxxxx 05/07/2016 16150 0 16/08/2016

Expedite

Confirm

delivery date


New Focus for S&OP -> DDS&OP

• Less time spent trying to predict the future

– Focus shifts to putting the capability in place to cope with the expected demand and its variability

• Focus is on managing the tails of the inventory distribution – too much / too little stock

• Move away from the goal of one number

– Scenarios and an integrated set of plans that support the business strategy

– Then focus on what is changing – the exceptions

• Short term horizon no longer looked at, focus is now on 3 months+

– DDMRP takes care of planning & execution in the short term

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Ideal Distribution

IdealToo

Much

Too

Little


DDMRP currently deployed via in-house solution

Implementation of software solution to underpin demand driven operating model

Work with key suppliers to extend DDMRP back into their supply chains

Align functional KPIs to support DDS&OP

Next Steps


Learnings

• Invest in education

– CDDP & CDDL

• Just do it

– Test it on a small subset of products in Excel

• Learn by doing

– As above, just start then shape, develop, and refine as you go – and then invest in technology

• DDMRP is disruptive

– Embrace the transformation and don’t be afraid to abandon current perceived wisdom

Rethink what your supply chain can deliver

bt supply chain - gestión de multiproyectos /...

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