Redefining Demand

Management

Supply Chain Insights LLC Copyright © 2016, p. 2

Presenters

Lora Cecere,

Founder of

Supply Chain

Insights

Gerrott

Faulkingham,

Business

Development,

ToolsGroup

Bryan Semple,

FCILT | VP

Healthcare,

ToolsGroup

Supply Chain Insights LLC Copyright © 2016, p. 3

Demand Error and Uncertainty Growing

Supply Chain Insights LLC Copyright © 2016, p. 4

The Long Tail is Growing

Supply Chain Insights LLC Copyright © 2016, p. 5

Probabilistic Approaches

Supply Chain Insights LLC Copyright © 2016, p. 6

Impact of Localized Assortment

Supply Chain Insights LLC Copyright © 2016, p. 7

Demand Management Success Is Like a Flip of a Coin

Supply Chain Insights LLC Copyright © 2016, p. 8

Satisfaction with Demand Planning is Low

Supply Chain Insights LLC Copyright © 2016, p. 9

Satisfaction

Supply Chain Insights LLC Copyright © 2016, p. 10

Data

Inputs

Engines Demand Plan

Outputs

Align Engines with Outcomes

Planning Master Data

Supply Chain Insights LLC Copyright © 2016, p. 11

Companies Make the Mistake of

Trying to Get Precise on

Imprecise Numbers.

Instead, they need to manage

demand flows.

Supply Chain Insights LLC Copyright © 2016, p. 12

• A pattern caused by order frequency, order quantity or batch size.

• A type of demand: trade promotion, new product launch, seasonal

consumption.

• A product build to execute a supply chain strategy.

The longer the tail, the more skewed the distribution.

Life for a supply chain planner is not as easy as it used to be.

What Is a Demand Flow?

Supply Chain Insights LLC Copyright © 2016, p. 13

Business Pain

Supply Chain Insights LLC Copyright © 2016, p. 14

Because of Issues Most Companies Use Spreadsheets

Twitter Hashtag: #SCIWebinar

Supply Chain Insights LLC Copyright © 2016, p. 15

Summary

• Demand flows through the supply

chain. It is a river.

• Outside-in processes, reduce

demand latency.

• Engines should be aligned with

flows.

• The fit of the engine is a more

significant factor to user

satisfaction than purchase from

the same vendor.

• Test and Learn. Focus on

outcomes.

AcelityRestoring People’s Lives

Product brands

ADAPTIC™Non-Adhering Dressings

TIELLE™ Silicone Border TIELLE™HydropolymerTIELLE™ Non-Adhesive with LIQUALOCK™Technology

V.A.C.ULTA™ Therapy Unit with V.A.C. VERAFLO™ Therapy

CELLUTOME™ Epidermal Harvesting System

PROMOGRAN™ Collagen / ORC Dressings

ABTHERA™ OpenAbdomen Negative Pressure Therapy with SENSAT.R.A.C.™ Dressing

PREVENA™ Incision Management System

ACTIV.A.C.™ Therapy

SILVERCEL™ Antimicrobial Alginate Dressings with Silver

SNAP™ Therapy System

Development and commercialization of innovative healing solutions, including negative pressure wound therapy, negative pressure surgical management, and epidermal harvesting, specializing in advanced devices and advanced wound dressings.

Focus

BIOSORB™ Gelling Fiber Dressing

17

Advanced Wound Therapeutics

Support Dillon, MT San Antonio, TX Charlotte, NC Budapest, Hungary

Business centers San Antonio, TX Gatwick, UK

Manufacturing Athlone, Ireland Gargrave, UK Peer, Belgium

Technology centers San Antonio, TX Ferndown, UK Gargrave, UK

Our Global FootprintWith 5,000 global employees, Acelity offers products in more than 80 countries supported by world-class sales and service organizations around the globe.

©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.18

Activities in Gargrave• Product development• Production / Sterilisation• Distribution

Our planning challenges

19

Dynamic Demand Flows

Fast moving and long tail products

Mature, volatile and emerging markets

Continual innovation and NPI

Difficult to capture market intelligence

Long timescale to deliver monthly forecast

No real input to inventory and production plan to meet service levels

Lack of consensus forecast

Poor planner productivity due to time spent on data manipulation

Planning team second guessing commercial input

Cumbersome data capture and reporting tools

Limited Tools and Systems

Demand Volatility

©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.

Our solution

STATISTICAL FORECAST

FORECASTING COLLABORATION

SALES

NPI REGULATORY

FINANCE

CONSENSUS FORECAST

TARGET SERVICE LEVELS

INVENTORY OPTIMISATION

SAP APO/SNPHistorical Demand

Forecast & Safety Stock Levels for each location

©2017 KCI Licensing, Inc., and/or Systagenix Wound Management, Limited. All rights reserved.6

ToolsGroup SO99+ DP/Fulfillment

Results & Benefits

21

AcelityRestoring People’s Lives

Traditional Forecast Methods

1. Are adequate at handling fast moving items

2. Do not leverage existing data

3. Cannot take advantage of additional data streams/external inputs

Traditional Forecast Methods

1. Are adequate at handling fast moving items

2. Do not leverage existing data

3. Cannot take advantage of additional data streams/external inputs

Normal Distribution

SKU/L

Sale

s V

olu

me

Anything But Normal

The “Long Tail” is Growing

Volatility (COV)

WMAPE

Forecast Error %

0 .5 1.0 1.5 2.0 2.5

20

30

40

50

60

70

80+

Opportunity

Demand Modeling

Traditional Forecasting

Tail Items

Risk

Forecast Error is a Difficult Problem to Manage

What is Demand Modeling

0

1

2

3

4

5

Demand

Demand Modeling is the Science of Calculating Probabilities or

Ranges of How Demand Could Occur

What is Demand Modeling

0

1

2

3

4

5

Demand Forecast

Demand Modeling is the Science of Calculating Probabilities or

Ranges of How Demand Could Occur

What is Demand Modeling

0

1

2

3

4

5

Demand Forecast

Demand Modeling is the Science of Calculating Probabilities or

Ranges of How Demand Could Occur

What is Demand Modeling

0

1

2

3

4

5

Demand Forecast Demand modeling

understands there is

inherent uncertainty

associated with future

demand whether that SKU

is a fast mover or a slow

mover

Demand Modeling is the Science of Calculating Probabilities or

Ranges of How Demand Could Occur

Traditional Forecast Methods

1. Are adequate at handling fast moving items

2. Do not leverage existing data

3. Cannot take advantage of additional data streams/external inputs

Point of Sale Data

Daily Ship-To

Daily Ship-From

Weekly Shipments

Monthly Shipments by Customer

Leveraged using the “traditional” approach

Detail lost in “traditional” approach

Data Leveraged: Traditional vs. Probabilistic

Why Demand Details Matter

Same aggregate

historical salesSKU: A

SKU: B

Traditional

Why Demand Details Matter

Same aggregate

historical sales

Same forecast

result

SKU: A

SKU: B

Traditional

Why Demand Details Matter

SKU: A

SKU: B

Same aggregate

historical sales

Traditional

Different detailed

ordering pattern

Probabilistic

Same forecast

result

Why Demand Details Matter

SKU: A

SKU: B

Same aggregate

historical sales

Vastly different

forecast certainty

Traditional

Different detailed

ordering pattern

Probabilistic

Same forecast

result

Traditional Forecast Methods

1. Are adequate at handling fast moving items

2. Do not leverage existing data

3. Cannot take advantage of additional data streams/external inputs

Probabilistic Forecast

Trend, Seasonality, Calendars and

Daily Sale Patterns

Market Intelligence

3

6

7

Trade Promotion

Media Event Effect4

Special Actions and Events

5 New Product Introduction

THE DAILY BASELINE

De

ma

nd

In

sig

ht In

cre

asin

g

1

2

STOCHASTIC MODELING

MACHINE

LEARNING

DEMAND SHAPING

PLANNER

“Layers” of Demand Modeling

Stocking Program Growth

Program…

0%

(% Change YOY)

0

20

40

60

80

100

120

Planning Hours

Before After

Wayfair Results

Traditional Forecast Methods

1. Are adequate at handling fast moving items

2. Do not leverage existing data

3. Cannot take advantage of additional data streams/external inputs

Why are companies still using the same traditional forecasting methods which have been around for

decades to solve the business problems of today?