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?