Demand Sensing - What's New in 2019, How Are Organisations Leveraging It

CONFIDENTIAL | © 2018 E2open, LLC. All rights reserved.
Anupam Aishwarya
Demand Sensing
What’s new in 2019 how are organizations
leveraging it

© 2018 E2open, LLC. All rights reserved. CONFIDENTIAL | 2
Anupam Aishwarya
Director, Customer Solutions, E2open
Anupam.Aishwarya@e2open.com
8+ years at E2open
13+ years in Supply Chain Planning, and, Enterprise Software
Domain Expert in Demand Sensing and Forecasting, Advanced
Analytics, AI and Machine Learning
Some key customer engagements: Kimberly Clark, Procter &
Gamble, Unilever, Akzonobel, Shell Lubricants
▪ MBA, Carnegie Mellon University, USA
▪ Bachelor of Technology, Computer Science, Indian
Institute of Technology, Kharagpur

Topics for
Today’s Talk
1.Why traditional planning and forecasting techniques
are reaching their limits, and, what can help
2.Key evolutions in Demand Forecasting and
Demand Sensing
3.Customer Case Study – Creating value through
Demand Automation and Augmentation
4.Where to start your journey?

“
Yuval Noah Harari
21 Lessons for the 21st Century
Data will eclipse both land and machinery
as the most important asset
Simply having information won’t offer a competitive
edge; knowing what to do with it will
“
Bill Gates
Gates Notes - Book Review, Dec 2018

“Everything we do as a company runs
on an estimate: financial planning,
supply planning, excess inventory or
lack thereof… in short, our wins-losses
are all based on an estimate.”

Demand Planning in the New World

What Is The Current State of Demand Planning?
Business Change
What It Means for
Demand Prediction
SKU proliferation,
Network Complexity
Higher workload
Increasing channel
Complexity
Shorter customer order
lead times
Decreased sales per item
Pressure on headcount
Increased competitive
activity
Shorter product lifecycles
More volatility and higher
Workload
More volatility and higher
safety stock
More volatility
No increase in planners
More volatility
Traditional models are less
useful

Traditional
Forecasting
cannot keep
up
Extra Effort from Planners Keeps Demand
Planning Accuracy Essentially Flat
Forecast Error is
• 40% higher for new
items
• 100% higher for
items in the tail
• 50% higher for
seasonal items
*Measured at Item / DC / Week Level
Source: E2open Forecast Benchmark Study, >$250B in annual sales

Because demand is not just about trend and seasonality
Why Doesn’t Demand Planning Work Better?
UnemploymentFTSE 100 Index
We live in an increasingly Volatile, Uncertain, Complex and
Ambiguous world (VUCA)
Interest Rates

Traditional Demand Forecasting uses the past to
predict the future
Volume
2019
50% Accuracy Item/Loc level
2017
2018

1. Demand plans are created monthly/weekly
• Most processes involve multiple parties
2. The real world gets in the way
• Customers create unexpectedly high or low
orders
• Inventory not as expected
• Uncommunicated promotions
• Events and holidays
3. Replenishment planning cycles are typically
shorter than demand planning cycles
What does this mean for the Supply Chain?

How can planners possibly make sense of all this data?
NOISY
• Data is not perfect
• Traditional time-series methods are too noisy at daily level
Data Snapshot from an existing customer
Is this Planning model Scalable?
• Forecast Every day?
• For every item?
• At every Location and Channel?

Traditional Planning Approaches Can’t
Solve these Problems…
Historical Time-series Based Forecasting
Stand-Alone Planning
Increase Number of Planners

What is needed to be able to predict Demand
Better?

Demand originates outside the 4 walls of the Enterprise
Within The EnterpriseSupply Networks
Supply
Planning
Demand
Planning
S&OP / IBP
Demand Networks
VMI
Social
Sentiment
Customer / Channel
POS & Analytics
Customer/Channel
Collaboration
TRADITIONAL
INSIDE-OUT
VMI
VMI
ASNs
Orders
Reseller
Dist
Dist
Retailer
Value-Optimized Response

How can Technology help?
1. Intelligence to understand all buying signals and predict using them
2. To capture buying signals in all channels and make them “decision
grade”

Early 21st Century Revolutions
Artificial intelligence
Decision grade data
Computing power
Create the
most accurate
signal over
each horizon
for each
product,
location and
channel
DemandSensing

A Brief History of Forecasting Mathematics
Charles C. Holt 1959
Contribution to
forecasting
Holt-Winters, a.k.a., triple
exponential smoothing
Institution Carnegie Mellon
Jean Baptiste
Joseph Fourier
1822
Contribution to
forecasting
Fourier series
Institutions École Normale
École Polytechnique

2002
Sensing Demand for better “Operational” Forecasts
Every Item
Every Day
Distribution
Requirements
Planning
Touchless

2008
Extending the Value using “Multi-Enterprise Data”
Every Item
Every Day
Distribution
Requirements
Planning
Touchless

2013
Demand Sensing for “Tactical and Strategic” Horizons
Traditional Statistical Forecasting based way of working
Reinventing Demand Planning - Advanced AI based way of working

2016
Creating and Understanding the “Buying Signals”
Acquire sales and inventory
data from channels
Normalize, validate,
and enrich the data
received from channel
partners
Analyze and optimize sales performance, inventory, coverage and
capacity utilization across the multi-tiered channel partners
Gain decision-grade
data

Shipment/Order Data
Demand Planning Results
Promotions and Events
Demand Sensing in 2019
Point of Sale Data
Channel Inventory
Social Sentiments
Population Demographics
Web Traffic
One Single DS Forecast
…
• Every Input Data Series contributes
differently to the predictability of the
customer shipments across the fc horizon
and different business groups
• Let the engine determine the predictors.
Humans decide preferred outcomes.
• Pattern Analytics
• Predictability Analytics
• Segmentation Analytics
• For All Businesses
Demand Signal Inputs
Machine Self
Learning

The Future:
From Sensing Demand to
Shaping Demand
Channel
Composition
Channel
Behaviour
Channel Knowledge
Omni
Channel Data
Management
Incentives,
Funding &
Engagement
Partner
Assessment
& Enablement

It’s all about “Proximity to Consumption”
Better and quicker understanding of Independent Variability
DATA ALGORITHMS CONVERGENCE

Case Study

P&G presentation at the 2010 E2open
Customer Conference

Business 1
Business 2
Orders
DP Fcst
Store Inv
DC Inv
Point of Sale
DC Ship to Store
P&G Shipments
Source – P&G presentation
at the E2open Users’ Conference
What is predictive in one business is not for another!

Impact of Point of Sale and Downstream Data
(H1N1 Based Demand Surge)
During this 3 week period MAPE was reduced 47%
Source – P&G presentation at the E2open User Conference
During this 4 week period MAPE was reduced 44%
All Brands ResultsPilot Customer Results

Decision Automation Use Case
Process Automation Cycle
Demand Automation
How automation of demand processes has transformed P&G’s results
1 Process Assistance
Small programs to
support planners &
streamline tasks
Some productivity
savings & results
2 Process Replacement
Full automation of
processes with
automated algorithms
Step-change in
productivity & results
Bimodal Approach at P&G
Capture
Savings
Isolate &
Measure
Identify
Opportunity
Automate

Demand Automation: Best Forecast for Any Horizon
Demand Planning Process
Long-term
Create Stat
Forecast
Apply Biz.
Intelligence
Replace with
Demand Sensing
(100% automation)
DP
Forecast
Apply
Intelligence
Executional
Forecast to
DRP
Negative Value-add
Manually Intensive
^
Near-term
• Positive FVA
• Accuracy up 30-40%
• Free planners for
strategic activities
Replace with Long-term
Demand Sensing
(100% automation)
Negative Value-add
Manually Intensive
^
• Positive FVA
• Accuracy up 10%+
• Free planners for
strategic activities

P&G – John Parsons
• Embed video
Demand Automation
How automation of demand processes has transformed P&G’s results”
2016 Leaders Forum
https://www.dropbox.com/sh/lbwvvik7a1ys3cu/AABlldOZnnPz8jNNTYfz7i43a?dl=0
John Parsons, Global Demand Planning Business Expert at Procter & Gamble

Customers Are Receiving Actual Benefit
• Cut forecast error by more than 40%
• Decreased safety stock by more than 30%
• Reduced finished goods inventory by $100s million
• ROI: 185%
• Payback: 0.5 years
• Annual benefit: $7M
• Service: 2% points @ 10M Euro/point
• Cut short-term forecast error 40%
• Produced the right product mix, decreased costs
and better served customers
• Cut forecast error by 50%
• Decreased safety stock 20%
• Improved order fill 5%
• Saved $20 million in inventory
General
Mills

Summary and Takeaways

Forecast Accuracy Drives Shareholder Value

Forecasting Systems are driven by demand signals
Traditional Systems use only a fraction of available data
Forecasting
Accuracy
HistoricalMonths/Week
HistoricalDetailed
Orders
Events+Holidays
PhaseIn/Out
NewProduct/EndofLife
PromotionsSalesadjustments
Data Collection - Level of effort
Internal Data External Data
Tier1–Sellout,channelinventory,forecasts
Tier2–N–Sellout,channelinventory,forecasts
Unstructureddata-Weather,Social,etc.…..
50%
70%
80%

Demand Sensing Improves over traditional methods for
all time horizons
From 45%- 30% to 20%
6-12 Wks
Order and
Shipment
information
POS,
Customer
Fcst,
Inventory
Seasonality
and trend
Using pattern
recognition
New Product
introduction
Using Pattern
recognition
Blended statistical method
By lag
15% to 25%
Up to 12 month
Weather
Price
Market growth
People
demographics
2 4 6 8 10 12 14 …. 22 26 30 … 48 50 52
Lead
Time
Relative
Fcst Error
Reduction
7 30% to 55%
14 25% to 45%
21 20% to 30%
28 18% to 28%
…..
……
…..
28 wks 15% to 25%
36 wks 12% to 27%
52 wks 10% to 25%
Promotions
Using Pattern
recognition

CONFIDENTIAL | 39© 2018 E2open, LLC. All rights reserved.
Key Takeaways
1. Forecast Accuracy drives Business Value.
2. Demand Sensing provides step change in
results - incremental gains are not enough
o You have enough data to start ! Time to Value is
typically <6 months.
3. Better forecasts are the starting point
o Don’t ignore how better forecasts will be used by the
supply chain!
Talk to us and your peers who have
undertaken this journey

Visit us at our booth in the Palm Court outside and get
your copy on a memory drive.
Find all about E2open>>
www.e2open.com
Interested in the Presentation Slides?

Your Business – Networked,
Harmonized, Optimized, Live.
www.e2open.com

Demand Sensing - What's New in 2019, How Are Organisations Leveraging It

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à Demand Sensing - What's New in 2019, How Are Organisations Leveraging It

Similaire à Demand Sensing - What's New in 2019, How Are Organisations Leveraging It (20)

Plus de SCL HUB

Plus de SCL HUB (20)

Dernier

Dernier (20)

Demand Sensing - What's New in 2019, How Are Organisations Leveraging It