S&OP Analytics: Crystal Ball or Ball and Chain

1
Your S&OP Analytics:
Crystal Ball or Ball and Chain?
Bob Ferrari, Executive Editor- Supply Chain Matters Blog
Managing Director- The Ferrari Consulting and Research
Group LLC
August 13, 2013

Copyright 2013 The Ferrari Consulting and Research Group LLC
Key Messages
Slide 2
S&OP needs to move toward more
timely decisions and better prediction
of events and business outcomes
The Planets of Business Needs and
Information Technology Are Aligning
Toward More Leveraged Use of
Predictive Analytics as the Enabler
Predictive
Analytics
Constraint
Modeling
Best Outcomes for
Your Business
=+

Copyright 2013 The Ferrari Consulting and Research Group LLC Slide 3
Outline
 Snapshot of today’s global supply chains and
S&OP mechanisms
 Explore what are predictive analytics capabilities
 Reflect on todays S&OP challenges and needs
 Converging forces (Business, Supply Chain, IT)
 Implications and Takeaways
 Recommendations

Copyright 2013 The Ferrari Consulting and Research Group LLC 4
End-to-End Supply Chain
4
Supplier CustomerSuppliers’
Supplier
Source
Internal or External Internal or External
Your Company
Return
Deliver MakeSource
Return
Plan
Deliver
Return
Source
Return
MakeSource
Return
Plan
Deliver
Return
DeliverMake
Plan
Return Return
Customers
’
Customer
Physical, Information, and Financial Flow

5
Today’s Supply Chains
• Complex, global-based value-chains
• Segmented-Open-Closed-Vertically
Integrated- Outsourced
• Servicing more demanding customers:
• Increased product complexity
• Technology-enabled online consumer
• Shorter product development cycles
• Multiple years of cost-cutting, lean and demand-
driven initiatives
• Explosion in data- drowning in data
• Dramatic increases in supply chain disruption
and risk events

The New Normal
Slide 6
Risk Complexity Unpredictability
Balancing Efficiency – Agility - Responsiveness
In a 7 Day by 24 Continuous Cycle

S&OP As Originally Launched
 Aligning supply and demand in a
series of process steps
 Achieving one-number consensus
 Context of the financial plan
 Sequential in nature with lots of data
collection and analysis
 Operations-led
 Execution planning horizons
 Push to senior management for
needed decisions
Slide 7
Demand
Analysis
Supply
Analysis
S&OP
Meeting
Financial Goals & Metrics
Business Metrics
Sales & Marketing Forecasts
Inventory Plans
Forecasts and Order Rates
Supply Plans
New Product Plans
Source: Dick Ling, Andy Coldrick Original Creators of S&OP
Constant Analysis of Data

S&OP As Matured in Today’s Supply Chain
Environments
 Broad cross-functional involvement and
collaboration
 External stakeholders and trading
partners
 Senior management sponsored and
need for process to support more
integrated business planning
 Longer planning horizons (tactical and
strategic)
 Outside-in orientation
 Orchestrate implementation of desired
business outcomes
Slide 8
Portfolio of Products and Services
Suppliers
Trading Partners
Customers
Sales & Marketing
Managing
Supply
Managing
Customer
Demand
Activity
Portfolio
Demand
Supply
Integrative
Reconciliation
Executive S&OP
Goal: A Decision Support Process for End-to-End Supply Chain
Requirements

Previous Steelwedge Webinar- Tom Wallace
 Wallace’s takeaway messages regarding
today’s evolution of S&OP:
 Continues to iterate important inflection points
 Needs to integrate both financial and operational
planning
 S&OP scope is both strategic and tactical
 Needs to enhance agility through near-term tactical
and medium and long-term strategic plans.
 Days of blood, sweat and Excel are over
Slide 9

Current Process Challenges
 Multiple research surveys indicate S&OP processes
have stalled
 Too much time invested in assembly and analysis of data
 Process lacks ability to synchronize planning and fulfillment
information
 Desire and/or need to move beyond a monthly cycle
 More integrative tools beyond spreadsheets / ad-hoc analysis
 Too much time required in background analysis projects vs.
ability to make informed decisions at an S&OP meeting
 Latency and inconsistency in S&OP decision-making leads to
senior management frustration
 Desire by participants to move toward extensible integrated
business planning and response
Slide 10

Time for Polling Question
 What is the single most significant
challenge hindering your firm’s S&OP
process?
A. Too much time required to gather and analyze
data to come-up with meaningful insights
B. Cannot keep-up with the current pace of business
C. Process needs to become more predictive to
business outcomes
D. Ability to analyze information and trends during
the actual S&OP meetings
Slide 11

What are Predictive
Analytics Capabilities
Slide 12

Standard Reports
Ad Hoc Reports
Query / Drill Down
Alerts
Statistical Analysis
Randomized Testing
Predictive Modeling
Optimization “What’s the best that can happen?”
“What will happen next?”
“What happens if we try this?”
“Why is this happening?”
“What actions are needed?”
“What exactly is the problem?”
“How many, how often, where?”
“What happened?”
Predictive and
Prescriptive
Analytics
Descriptive
Analytics
(the “what”)
Degree of
Intelligence
Source: Analytics At Work, Thomas H. Davenport, Babson College
Analytics Defined- Tom Davenport

Analytics Within Supply Chain Processes
 Deployed in supply chain process for quite
some time
 Supply chain planning and optimization
 Transportation scheduling and routing
 Factory and production process scheduling
 Multi-echelon inventory optimization/ network
design
 New advances in information technology,
now open new opportunities and awareness
 Dramatically accelerated clock speed of
business and added complexity and risk
Slide 14

Analytics and Big Data
An Analytically-Driven Process
 “Big-Data” term often over-hyped and used to convey
all sorts of concepts
 Organizations are literally drowning in data but lack
meaningful insights
 Analytics is essentially how to process, analyze and
visualize vast arrays of information in more
innovative ways
 Analytics-enabled decision-making in speed, quality
and context
 Both a qualitative and quantitative mindset:
 What do we know vs. what do we think we know
 Requirement of augmented or new skills
Slide 15

Analytics Applied to Key S&OP Decisions
 Sensing market changes ahead of the competition
 Defining the most appropriate segmentation of supply
chain(s)
 Determining most profitable product and/or customer
segments and how to best respond.
 Identifying new opportunities for responsiveness,
reducing inventory or decreasing costs
Ability to simulate or compare different response
scenarios to market opportunities and/or supply chain
non-alignment quicker than industry competitors
Slide 16

One Other Polling Question
 What is your organization’s current
awareness of predictive capabilities?
A. We have no such capabilities at this time
B. We are beginning to explore use
C. We need further understanding to the
benefits of S&OP
D. We have a staged deployment plan for use
Slide 17

Converging Forces in
Business, Supply Chain and IT
Slide 18

C-Suite Concerns and Objectives
Slide 19
“ As a group, CEO’s are investing more in customer insights than any
other functional area. They are seeking a better understanding of
individual customer needs and improved responsiveness”
2012 IBM Global Chief Executive Officer Study of over 1700 CEO’s Across 18 industries
Top CFO and Finance priorities over next 2-3 years:
73 percent cite lowering costs and increasing productivity
61 percent cite investing in analytics, planning, budgeting/forecasting
44 percent cite needs for upgrading organizational skills
2013 Accenture CFO Survey of 1250 Senior Finance Executives from Multiple Industries
Top Cited Concerns:
• Our inability to predict future performance in the new normal of
permanent market volatility (70 percent)
• Worried about the resilience of their supply chains (80 percent)
March 2012 Accenture Outlook: Preparing for the Unpredictable

Strong Evidence of Business Benefit
Slide 21
“Two-thirds of respondents report that use of information
(big data) and analytics is creating competitive advantage
for their organizations.”
Compares with 37 percent citing benefit two year ago- a 70
percent increase in two years
Source: Analytics: The real-world use of big data
IBM Institute for Business Value and Said Business School
2012 survey of over 1100 professionals across 26 industries

IT Technology Convergence
 From OLTP to in-memory database and highly
scalable OLAP
 Sequential processing to MPP
 Data attached to the application to de-coupled
streaming databases
 Data aligned to business constraints, insights
and decision-making support
 Centralized data warehouse to bundled
analytical appliances
 Centralized to distributed data management,
ownership and control of data
Slide 22

Supply Chain Process Convergence
 More rapid and volatile business cycles are motivating many
manufacturers and retailers to re-look at sequential planning and
execution processes
 Converge planning with sensing of orders, changed
events, disruptions, market opportunities
 Augment planning with analytics, and shift emphasis
toward more predictive capabilities
 True decision support fueled by embedded analytics
Slide 23
The Holy Grail of
Supply Chain
Insights-Enabled
Execution

Implication of These Converged Forces
 The ability to extract insights at sophistication levels not easily
achieved in the past
 From historic sequential to more predictive and near real-time
planning and fulfillment information analysis
 Leverage the advancements in in-memory computing,
visualization, business intelligence convergence
 Incorporate both structured and unstructured information
 Cloud computing and composite apps allow S&OP teams to fill-in
process gaps
 Sense and respond capabilities to orchestrate desired business
outcomes
 Supplemental mobile and social enabled tools to accommodate
virtual mobility and time-critical needs
Slide 24

An S&OP Information Architecture Blueprint
Slide 25
What-If
Scenario
Management
Enhanced
Collaboration
Analytics-
Based
Decision
SupportSimulation
S&OP Information Utility (Streaming Data)
Management Dashboard (Sense and Respond)
Business Activity Monitor and Management Platform
In-Memory Enabled
Faster Planning and Decision Support Cycles
Demand
and
Supply
Planning
Data
Leading
Indicator
Data
Execution
and
Fulfillment
Data
Unstructured
Data
Demand &
Financial
Sensing
Data
Best
Optimized
Business
Outcomes
Continuous or
Ad-Hoc
Analysis and
Review
Exception
and
Response
Based
Planning
Insertion of
Additional
Analysis or
Predictive
“Apps”

Implications and Takeaways
Slide 26

Implications and Takeaways
 Clock speed of business change has dramatically increased
 Extended and global supply chain now constantly challenged
with complexity / variability / volatility and risk
 Organizations and S&OP teams drowning in data but lacking
important insights
 Sequential planning and execution processes no longer
keeping-up with business change is reflected in S&OP
process frustration
 Senior management converging on need for improved
business responsiveness and predictive decision-making
 Technology convergence will open new opportunities in
supporting more predictive decision-support
Slide 27

A Revised Goal
 Extensible Integrated Business Planning
 Making timely, more well informed and insightful decisions
based on information insights and business goal context
 Single information and intelligence-based repository that
supports decision-making within the S&OP process (equates
to original Reconciliation Process)
 Abilities to sense and respond to market opportunities and/or
operational risk
 Anticipate or shape market, product demand, supply or
fulfillment changes quicker than competitors
 Simulate or compare various business scenario options
 Orchestrate desired business outcomes
Slide 28

Select Examples of Predictive Analytics- Case Study 1
Slide 29
Global distributor and wholesaler of pharmaceutical drugs and
medicines servicing hospitals, pharmacies and healthcare providers.
A predictive capability that synthesizes market intelligence from customers,
manufacturers and other sources to identify significant or unusual upticks
in orders for specific pharmaceuticals that could trigger a broader market
shortage. Can also identify potential areas of abuse.
If supply shortage occurs or is expected, an automatic allocation system is
recommended and initiated that allocates limited supplies of that drug to
customers based on a set of pre-defined parameters.
If network-wide shortage is predicted, it can trigger alerts to procurement
supply team to augment inventory of specific likely drug alternatives that
customers will likely substitute.

Select Examples of Predictive Analytics- Case Study 2
Slide 30
A global wired and wireless network infrastructure equipment provider
Within a year of implementing analytics capability, the company could
determine in near real-time, when a product was not selling as expected,
and trigger demand shaping recommendations for other similar products.
Alternatively, if product sales trend higher than forecast, a quick analysis
of likely sales growth based on available sales forecasts triggers a review
of contract manufacturing material and capacity planning options to
determine timing options to boost production both short and long-term.

Leverage Power of Newer Technology Tools
o Data attached to an application to de-coupled
streaming planning and execution data
feeding an S&OP information utility
o OLTP central data warehouse to in-memory,
highly engineered, OLAP “apps”
o Data aligned to business constraints, insights
and predictive decision-support capabilities
o More emphasis on data visualization /
simulation and planning scenario tools
o Augmenting existing IT investments with
more cloud-based predictive and business
intelligence capabilities
Slide 32
Leverage Current Converging Forces of Advanced Technology

Plan for Impact on Existing People and Process
 Recognize the growing awareness of
the power of a predictive analytics
driven process
 Be cognizant that predictive analytics
capabilities are still evolving
 Encourage the receptiveness of S&OP
participants to analytical concepts
 Understand requirement for
augmented skills
Slide 33

Other Recommendations
 The goal is enhancing S&OP decision-making vs. that of
technology and tools
 S&OP team must take ownership- IT partnership and support
 Begin in small, managed scope and expand
 Tailor the process, data sources and analytics tools to
support specified opportunities based on S&OP priorities
 Include the change management and skills impacts in your
milestones. Consider centralization of capability
 Critical to strive for high data quality and well understood
information taxonomy
 Not a rip and replace of existing systems but rather an
insertion of augmented capability
Slide 34

Final Thoughts
Slide 35
A vibrant S&OP process supporting
timely decisions and better prediction
of events and business outcomes
Seize the opportunity of the alignment
of business, supply chain and IT
planets toward more leveraged use of
predictive analytics
From Key Performance Indicators (KPI’s)
To Key Responsive Indicators (KRR)

Thank You
Contact Information: Bob Ferrari
Email: bferrari@supply-chain-matters.com
Twitter: @Bob_Ferrari
Web Site: http://www.theferrarigroup.com

Copyright 2013 The Ferrari Consulting and Research Group LLC Slide 38
Introduction
• Independent supply chain and B2B
industry analyst- previous leadership
roles at AMR Research and IDC
• Multiple years of supply chain
business planning, operational and IT
systems implementation experience
• Recognized supply chain strategic and
tactical thought leader, technology
market influencer
• Consultant in supply chain strategy,
business process, and information
technology deployment
• Founder and Executive Editor Supply
Chain Matters blog
Top Ten SCM Blogs Top 25 Most Influential
SCM Blogs
Logistics Degree.net

© 2013 Steelwedge Software, Inc. Confidential. 1
Single Line of Sight: Plan, Perform, Profit
Planning Analytics – Big Data Driven S&OP
Nari Viswanathan

< Control and visibility
> Risks
> Business change
> Collapse: planning/execution
12 months=70%
Growth in Data
Bigger Demand/Supply Network =
Bigger Data

Explosion of Data
Requires Advanced S&OP TechnologyDatavolume/complexity
Business Value
20-
100X
2-
10X
X
Big Data Powered S&OP:
Measures units/dollars, links departments/ time
horizons and
delivers
multiple
supply
scenarios
Advanced S&OP:
Measures units/dollars
links
supply,
demand &
finance
Traditional
S&OP:
Measures units

Characteristics of S&OP Datasets
• Classic Big Data problem
– High volume, velocity and
variability
• Steelwedge Integrated
Business Planning
manages
– Millions of planning data
elements
– 1000’s of business rules
– Deep & complex
hierarchies

Volume Velocity Variability
Vertical, horizontal and
multi-dimensional
aggregation and
disaggregation in real-time
Reflects real world
complexity
Harmonizes
Heterogeneous Data
(from different formats,
structures & semantics)
S&OP Big Data Model
1000
Customers
100M to 1 B
planning
relations
10000
orders
per day
1-10M
calculations
per minute
100 new
items per
day
1000 new
customer-
product
combinations
per week

Planning Versus Analytics
• Planning
– Forward looking
– Suited for simulation and
mathematical calculations
– Planner roles typically use
– OLTP technology
• Analytics
– Backward looking
– Suited for metrics and
aggregation
– Execs and business
analysts
– OLAP technology
Ideal solution marries the best of both these worlds

Planning Analytics
Scalability & Performance
Benefits
• Higher User & Data Scalability
• Higher Performance at Scale
• Faster Report Creation
• Superior End-user Self-service
• Dedicated Reporting Data base
Operational
Data base
(OLTP)
S&OPInsight
S&OP Insight
High Performance with High Scalability
Operational
Data base
S&OP Insight (OLAP)

#1: To High Resolution S&OP
The Hierarchy of Supply Chain Metrics
Source: Gartner

#2: Extra-Ordinary Scenarios

#3: Better Global Visibility

S&OP Apps – Planning Analytics For Offline Processes
Set S&OP
Parameters
& Drive
Process
S&OP
Enhanced
Data
Inventory
Segmentation
Stocking
Strategy
Demand
Policy
Demand
Pattern
Segmentation
Inventory
Segmentation
Service
Level
Planning
Automate and
Standardize
Offline
Orphaned
Processes
Customer
Developed
Solutions
Range
Based
Forecasting
In collaboration
with Aztral
& Blake Johnson
Range
Forecast

S&OP Analytics: Crystal Ball or Ball and Chain

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