Next Generation S&OP

20 Journal of Business Forecasting | Fall 2010
E X E C U T I V E S U M M A R Y | Although the Sales & Operations Planning process presently lies at the center of our business,
businesses have yet to take full advantage of it. This article describes what is missing in our process; in particular, how to deal with
uncertainty, complexity, and risk using scenario planning through probabilistic planning, predictive analytics, and“what if”simulation.
GREGORY L. SCHLEGEL | Mr. Schlegel is VP Business Development for SherTrack LLC, professor of supply
chain at Lehigh University, and Past President of the APICS organization. He has served as a supply chain executive
for several Fortune 100 companies, including IBM.
Next Generation of
S&OP: Scenario Planning
with Predictive Analytics
& Digital Modeling
By Gregory L. Schlegel and Peter Murray
PETER MURRAY | Mr. Murray is Global Supply Chain Development & Innovation Leader of E. I. du Pont de
Nemours and Company and has implemented demand management across DuPont’s 14 strategic business
units. He is currently on the APICS board of directors.
hose who have been in the
operations/supply chain management
arena for a good portion of their
professional careers know very well that
the Sales & Operations Planning (S&OP)
process lies at the center of our business
because it is where disparate functional
areas convene to gain consensus. In
the best practice we integrate plans
from functions across the business and
operationalize corporate or business
strategy and policies for execution.
However, despite significant investment
over the last 30 years, the S&OP process
continues to be an opportunity for
improvement for many organizations,
especially for manufacturing-centered
businesses.
As a business planning process, S&OP is
used to balance the supply of products
with the demand in congruence with
T

Journal of Business Forecasting | Fall 2010 21
business strategies and the product
and business portfolio. Manufacturing-
centered organizations implement the
S&OP process with the expectation that
the process will drive improved customer
service, lower inventories, support more
effective use of production assets, and
facilitate better communication among
the corporate stakeholders involved in
the process. One of the major drivers is
the recognition that the demand side
of the business is often not process
driven, nor does it tap far enough into
end user / consumer decision making
and consumption information. That
information can be analyzed more
effectively and brought back to the S&OP
process with significantly reduced timing
to address both market drivers and
reducethebullwhipeffectofdemandand
supply gyrations. This has been especially
evident in the global downturn during
2008-2009, and even now as economies
and consumers are recovering.
The S&OP process normally spans several
business planning horizons. These
horizons include Strategic Planning (1-5
years in the future), Tactical Planning
(normally covering up to 18 or 24 months
ahead), and Operational Execution
(including anywhere from 30 to 45 days
ahead).
Normally, the process is a series of
preplanning sessions, which promote
teamwork as well as involvement and
communication among executives and
mid-level operational management.
Preparation for the monthly S&OP
meeting involves the so-called “heavy
lifting” of the S&OP process, including
Demand Planning/Management and
SupplyandCapacityPlanning.Datainbest
practice are aggregated and collected
in a shared repository for analysis and
review. Managers from all connected and
integrated disciplines meet to assess risk,
develop alternative plans, and identify
actions and resource plans required to
reach consensus on what is best for the
business, not just for their functional
areas. The process typically involves
monthly executive level meeting—the
S&OP meeting—where future demand
and supply plans are rationalized, and
specific inventory targets, corporate
goals, and financial targets are reviewed
and validated. Participants typically
include representatives from Sales,
Marketing, Operations, Supply Chain,
R&D, Finance, and Management. Many of
us use the term“Journey”when we speak
about the S&OP process and for a good
reason. Over time, there has emerged
an S&OP Maturity Model developed by
Aberdeen Group. The model profiles
the stages and definition or complexity
of the stages within the S&OP
process. The duration of each stage
is approximately 18 to 24 months,
according to Aberdeen, provided there
is the correct amount of management
focus, funding, and skill sets.
To further reinforce the journey of the
S&OP process moving forward, AMR
Research has provided the community
with the following salient statistics on
the process. Companies that utilize this
collaborative process:
• Can see a 5% to 10% improvement in
forecast accuracy;
• Can see a 1% to 5% improvement in
supply chain costs; and
• Have to work on this process anywhere
from 9 to 15 months to see any initial
improvement.
AMR Research also identified some
problems with the current S&OP process
including:
• Ithidesthecriticaldetails(i.e.,variability
in product mix);
• Its aggregate planning process lacks
the comprehensive understanding of
the production feasibility constraints;
• It tends to treat all the customers
alike and thus ignores the important
factors, such as the cost of customer
service and the impact of operational
strategies on profitability;
• It causes unanticipated deviations
during live operation because of
uncertainty and variability in the S&OP
plan; and
• It neither quantifies/qualifies risk nor
supports the scenario planning with
any quantitative or statistical rigor.
THE 21ST
CENTURY
“SUPPLY CHAIN”:
NEW NORM
Despite numerous troubling endogenous
issues, there are three exogenous factors
that significantly impact the S&OP
process: Uncertainty, complexity, and risk.
Uncertainty: The demand uncertainty
has been with us all along, but so far
we’ve not done well in reducing it. Over
the years we’ve focused on improving
forecast accuracy, leveraging demand
management/forecasting tools to do the
statistical “heavy lifting,” and leveraging
methods such as CPFR (Collaborative
PlanningForecasting&Replenishment)to
minimizedemandvariationsanddemand
surprises.We have also reduced variability
in our demand management/forecasting
by applying Six Sigma to our internal
processes and driving those processes
that are aligned with the voice of a
customer. Despite all these, our average
MAPE (Mean Absolute Percent Error) at
the SKU level is still approximately 35%

or greater, according to the IBF study. In
dealing with uncertainty, there is much to
gain by applying the methods described
above. The key is using them only
where they add value to the business.
If your business has not achieved at
least average benchmark performance
in reducing uncertainty, it is worth
pursuing it. If you are aiming at reaching
the average benchmark performance of
your industry, it is important to recognize
that uncertainty will be reduced though
not completely eliminated. Depending
on your position in global value chains
and the complexity of your business,
the amount of uncertainty could be
significant.
Inthearenaofuncertainty,twoprofessors
from the University of Ghent wrote a very
compelling paper in 2002 that profiled
sources of uncertainty in the supply
chain. They highlighted 13 sources
of uncertainty across three planning
horizons, and categorized them as Low,
Medium, and High. (See Figure 1)
One take away from this study is that,
among all the risk factors, the major one
stems from our Tactical Planning Horizon,
which is part of our S&OP process!
Complexity: We’d like to start with the
findings from an AMR Research survey
on complexity. It has found that the
supply network of companies with
an average sales of $1 billion manage
15 internal manufacturing sites and
38 contract manufacturing providers
across five supply chains in at least
two continents. The takeaway here is
that complexity is an integral part of
the S&OP process. In fact, it’s basically
what our S&OP process attempts to
deal with every month—balancing the
network’s supply with demand. We also
have another thread that contributes
to complexity in our S&OP process—
product proliferation. We supply chain
professionals are very good at getting
product out through our chains and
launching new products. However, we
are not very good in “Culling the Herd,”
i.e., rationalizing the product offerings
on a regular basis. It’s just not in our
genes! On that note, we want to share
a couple of key findings from a very
recent survey on product portfolio
management conducted by Appleseed
Partners & OpenSky Research:
• 56% of the surveyed companies say
that one of the top three pain points is
that they have too many products for
their resources;
• 49% are risk-averse and thus are less
likely to make bets on new product
innovations;
• 45% manage product portfolios on a
spreadsheet;
• 37%haveportfoliodeliveryprojections
that are very inaccurate; and
• Only 11% of the companies constantly
monitor/or stop underperforming
products.
Here are the key takeaways:
• More time is spent on collecting data
and less on analysis;
• More than 50% of the companies are
operating with too many products;
and
• A method needs to be developed and
then leveraged to drive improvement
of the S&OP process.
In the last few years, the Performance
Management Group, which has been a
sourceforSCOR(Supply-ChainOperations
Reference-model), has begun to capture
theimpactofcomplexityinsupplychains.
Their data show the following:
• Complexity is related to the number of
supply chains, not just the number of
SKUs, and
• Advanced practitioners can model
complexity and take actions to address
them.
Risk: Finally, we conclude our discussion
with risk. We’d like to start with the
definition of risk and decisions under
risk as set forth by the Association
for Operations Management (APICS).
According to APICS, risk analysis is a
review of uncertainty associated with
research, development, and production
of products, services, and/or projects.
Exchange rates OOO OO
Supplier Lead-times O OOO O
Supplier Quality OO O
Manufacturing yield OO OO
Transportation times OO OO O
Stochastic costs O OOO OO
Political environment OO
Customs regulations O OO OOO
Available capacity OO OO O
Subcontractor availability OOO OO
Information delays OOO OO
Stochastic demand O OOO OO
Price fluctuations O OOO O
Van Landeghen, H. Vanmaele/Journal of Management, 2002
Sources of Uncertainty Operational Tactical Strategic
0-45 Days 1-18 Months 1-5 Years
Sources of Uncertainty Operational Tactical Strategic
0-45 Days 1-18 Months 1-5 Years
O Low | OO Medium | OOO High
Figure 1 | Sources of Uncertainty in the Supply Chain

Here, a decision maker considers several
future possibilities and then estimates
the probabilities of each occurrence. So,
we ask ourselves this: Haven’t we always
included risk within our S&OP process?
The quick answer is yes. However, our
professional, academic, and consultative
observations over 30 years tell us that
our use has been mostly implicit, though
rarely explicit.
So let’s start with explicit risk. Accenture
did a nice job profiling explicit risk during
their 2009 Global Risk Management
Study.They surveyed over 260 CFOs, chief
risk officers, and other executives, in over
21 countries and asked questions about
risk management.The key findings were:
• 85%ofthemsaidtheyneedtooverhaul
their approach to risk management;
• 40% said they have already increased
or will increase their investment to
enhance their risk-management
capabilities;
• 41%statedthattheirrisk-management
costs have increased by at least 25% in
the past three years; and
• Only 27% of them said that their risk-
management function has involved to
a great extent in objective-setting and
performance management.
TOP OF MIND
ISSUES
Top of mind issues that keep leaders
awake at night are:
• Ineffective integration of risk, return,
and capital issues in decision-making;
• Lack of alignment between the
company’s strategies and its risk
appetite;
• Inadequate availability of timely risk,
finance, and business data; and
• Ambiguous risk responsibilities
between corporate and business units.
Despite this surprising “Status Quo”
assessment related to risk management
around the globe, there are companies
that have incorporated the risk parameter
intheirS&OPprocess.BayerCropSciences
is one such company. According to Curtis
Brewer, Director of Consumer Forecasting
and S&OP Lead for Bayer Crop Sciences,
integration of risk management into
the process allows the business to get a
better feel for potential changes and the
impact they may have on the business.
He further adds that risk may be as minor
as competitive price shifting or as major
as potential labor strike, but it must be
considered and accounted for in the
S&OP process.
To round up our brief profile of risk, let’s
moverightintoourtacticalhorizon,which
is supported by the S&OP process, and
discuss briefly various risk issues that are
continually prevalent within the “S&OP
Comfort Zone.”Back in 2004, Sunil Chopra
& ManMohan S. Sodhi put together
a very comprehensive profile of risks
that impact the S&OP and supply chain
landscape. These risks are described in
their article titled, Managing Risk to Avoid
Supply-Chain Breakdown. Those risks
are information delay, supply disruption,
systems, forecasts, intellectual property,
procurement, receivables, inventory,
and capacity. They further developed
definitions of these risks along with their
causes and mitigation strategies to deal
withthem.Withthatsaid,wefeelwedon’t
need to dwell further on the explicit risk
within our supply chains and our existing
S&OP process. Instead, we should move
forward with our discussion and outline
our approach for mitigating the elements
of uncertainty, complexity, and risk within
the S&OP process.
THE NEXT
GENERATION OF
S&OP VISION
AMR Research has been talking all
along about the complexity of the 21st
Century supply chain, and sometimes
during the discussion they talk about
Probabilistic Planning. Stochastic
Demand Management and Dynamic
Figure 2 | Nex Gen S&OP Scenario PlanningVision
Probabilistic Simulation
continued on page 28

Inventory Planning support this planning
process. How do these new approaches
impact S&OP Operational Excellence?
Let’s first get grounded with definitions.
Stochastic models, according to the
APICS dictionary, are models where
uncertainty is explicitly considered in
the analysis. Probabilistic models are
statistical procedures that represent the
uncertainty of demand by a set of certain
outcomes(i.e.,byprobabilitydistribution),
and suggest inventory management
strategies under probabilistic demand.
Are these methodologies new? Certainly
not! Academia, pharmaceutical firms,
medical companies,Wall Street, insurance
companies, and banks have been using
themtoevaluateandmitigateriskforover
50 years. Are they new to supply chain?
You bet they are. Stochastic Optimization
(SO)methodsareoptimizationalgorithms
that incorporate probabilistic (random)
elements, either in the problem data
(objective function, constraints, etc.) or
in the algorithm itself through random
parameter values. This concept contrasts
with familiar Deterministic Optimization
methods where the values of the
objective function are assumed to be
exact and the computation is completely
determined by the values sampled or
observed. Such methods include linear
programming, integer programming,
simplex method, time series analysis, and
regression models.
With that said, let’s discuss the what, why,
and how of our new Next Generation of
S&OP Scenario Planning process. What
does our Scenario Planning process look
like? It starts with building a flow model
of the enterprise. We then populate the
model of the enterprise with base case
data from an ERP System, identifying
the historic stochastic behavior and
uncertainty of all relevant factors.
These include elements such as lead
times, capacities, demand, production,
inventory, and more. We then begin to
develop the scenarios and their potential
probability of occurrences, which are
then just about ready for an iteration of
the model. Before we do that, we need
to define the Design of Experiment (DOE)
relative to each variable in the model in
terms of its ranges to insure statistically
significant outcomes. We then run
the discrete-event simulations across
the entire enterprise and operational
variables and begin to review the
outcomes and their significance. The
outcomes will normally take the shape
of histograms with confidence intervals,
probabilities of occurrence, and more.
The iterations will continue until the
outcomes are considered statistically
significant via the DOE, and results are
within ranges of the DOE. At that point,
the outcomes will be prioritized based on
their probabilities of occurrence. The final
step here is to develop a Risk Response
Plan for the scenarios deemed critical to
the enterprise covering the tactical S&OP
horizon. (See Figure 2)
Why use the probabilistic approach to
scenario planning? Let’s take a quick
pictorial view of two familiar approaches—
Deterministic and Probabilistic (or
Stochastic). (See Figure 3) If we view our
graph with supply chain costs on the Y
axis along with a“best value”and“optimal
value,” and view range of uncertainty for
our variables within the scenarios on the
X axis, we can see that our deterministic
approachattemptstoprovideuswithone
optimal solution and a very narrow band
with little or no influence of uncertainty.
With our probabilistic approach that
incorporates risk, uncertainty, and
probabilities, we have a near-optimal
solution that remains valid across a
broader range of variable values at a
predictable cost. In scenario planning,
this is much more appealing and can be
profiled very concisely for review—much
like the weather forecasters when they
track and forecast the path of hurricanes.
In hurricane parlance, they call it “the
Cone of Confidence.”And in our scenario-
planningenvironment, thisConecontains
ourprobabilitiesofoccurrence.Withthese
outcomes, we can then move into our
last phase of scenario planning utilizing
predictive analytics, which can help to
develop the Risk Response Plan.
The Risk Response Plan approach is not
new. The oil and gas industry has been
Figure 3 | Deterministic Planning vs Probabilistic Planning

using it for more than 30 years, especially
those companies that operate offshore
rigsandplatformsinhostileenvironments
subjecttosevereweatherandoperational
risks. Other industries, such as insurance,
banking, and hedge funds have been
leveraging the probabilistic approach
to support risk management in their
own S&OP forums for years. Within
the enterprise-wide Risk Management
context, there is an established “Process.”
We dig into this process in much more
detail at Lehigh University’s MBA course
in Risk Management, where we focus on
profiling the Risk Response Plan elements
in this manner.
The Risk Response Plan normally has four
elements:
1) Identification of known risks
• Description about the nature of risks
(ERM framework)
• Their causes
• Their likelihood of occurrence, driven
by probability distributions and
discrete-even simulation
• The cost of each risk, if possible
2) Identification of owners of risks
• What disciplines, who, and where
(strategy, operations, finance,
compliance, etc.)
• Everyone’s existing roles and
responsibilities
3) Articulation of risk responses
• The plans—what will one do and
what tactics will be deployed?
• Who is responsible and what would
be their roles?
• Cost/benefit relationships, if possible
4) Articulation of the measures of
successful mitigation
• What are the KPIs?
• How will we know we’ve succeeded?
And finally, we would like to conclude
with the question that many might be
wrestling with: Why spend time on Risk
Management and Risk Response Plans
within the S&OP process? Here are some
of the benefits:
1. Gain a competitive edge by identifying
andmanaginguncertainty,complexity,
and global risk;
2. Maintain a solid corporate reputation
and brand throughout the globe;
3. Obtain/maintain solid financial ratings
with rating agencies and positive
analyst commentary as a publicly held
company; and
4. Reduce the cost of capital through
diligent risk mitigation tactics and
management of enterprise-wide risk
strategies with Risk Response Plans.
In our next segment, we will glimpse at
the Scenario Planning, Risk Management,
and Risk Response Plan visions of DuPont,
which has been exercising S&OP for
several years.
VISIONARIES
AND EXEMPLARS
At DuPont, we are yielding many benefits
to the bottom line across several divisions
through experience during the downturn
and foundational work on S&OP and
Demand and Supply Management over
several years, and by using techniques
such as Scenario Planning, Risk
Management, and Actionable Supply
Chain Plans based on understanding
demand uncertainty.
DuPont has begun to apply advanced
S&OP concepts through the “DuPont
Integrated Business Management”
process. The approach is based on the
ideas advanced by Oliver Wight, Inc.,
whichfocusonIntegratedPlanningacross
functions and are linked through a five-
step managing process run on a monthly
basis. DuPont has been featured in APICS,
Fortune, and AMR Research articles
where our cash generation process and
supply chain performance during both
the downturn and upturn are discussed.
That said, DuPont is moving forward
and attempting to bring Scenario
Planning to an actionable level across the
organization. Scenarios are used when
best practices for creating demand plans
and reaching consensus do not meet
business objectives or where significant
uncertainty exists. This is mainly due to
significantly varying future outcomes
because of major economic, competitive,
regulatory changes, and natural disasters
that exceed even the Failure Mode Effect
Analysis (FMEA) conditions of normal
operations. DuPont has started using
Demand Management Scenarios as well
as a “Scenario Viewer” tool managed by
a combined demand and supply team
within the S&OP process. Part of the
change in the planning approach is the
recognitionthatthehistoricmanagement
of variations of plans within a range of
outcomes of one plan is not adequate in
many cases. The next generation process
shows the evolution in the approach that
deals with different possible outcomes or
“what if” future conditions. This process
comprises several demand scenarios
coupled with the probabilities of plans
and assumptions along with inflection or
“trigger points” for each scenario. These
“scenarios” are for completely distinct
situations similar to what we experienced
in the 2008-2009 downturn, the “new
reality” of running with less value chain
inventory today as demand returns.

A specific process is emerging that
will improve upon ranges or statistical
limits, forecast accuracy, measures of
aggregate levels, and assumptions of
confidence levels. This Demand Scenario
process has taken on the step-by-step
approach for leveraging probabilities of
occurrence, identifying trigger points,
moving into the consensus planning,
creating scenarios across the enterprise,
and processing the information through
the S&OP meetings. At this time the
outcomes of this early process can be
considered a rudimentary set of Risk
Response and Opportunity Plans, but
much more work is required. One key fact
is that businesses now recognize that
this type of process applies to advanced
S&OP processes, those with a strong
foundation in fundamental processes
for communication, alignment to
business models, high quality timely
data, and trust among team members
at all organizational levels. The value of
this type of approach, especially when
linked to “automation” and “facilitation”
tools and systems, is that it helps create
planning scenarios that are actionable
and executable—not just academic
exercises.
NEXT
GENERATION OF
S&OP ROADMAP
TO SUCCESS
We would like to leave you with a
couple of threads supporting a roadmap
going forward if and when you deem it
appropriate to exercise Scenario Planning
to mitigate and manage uncertainty,
complexity, and risk within your S&OP
process.
1) Continue to implement basic Best-in-
Class elements of S&OP and move the
journey forward through each stage.
2) Begin the process of incorporating risk
mitigation and management tactics
into your S&OP process.
3) Begin developing Risk models and
support Scenario Planning with
Probabilistic Predictive Analytics.
4) Integrate the ERM (Enterprise-wide
Risk Management) framework into
your S&OP process that includes
the very actionable Risk Response
Plan.
Enjoy your S&OP journey. (info@ibf.org)
REFERENCES
1. Landeghem, H. Van and Vanmaele, H. |
“Robust Planning: A New Paradigm for
Demand Chain Planning.” Journal of
Operations Management. Volume 20. Issue
6. November 2002, pp. 769-783.
2. AMR Supply Chain Network Survey, 2006.
(www.amrresearch.com)
3. Appleseed Partners & OpenSky Research
Survey, 2009.
4. Chopra, Sunil and ManMohan S. Sodhi. |
“Managing Risk to Avoid Supply-Chain
Breakdown.” MIT Sloan Management
Review. Fall 2004, pp. 53-61.
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