200 • Supply Chain Risk Management: An Emerging Discipline
scenario planning and what- if discussions to take place inside the S&OP
process. e end result was better mitigation of supply chain risks. Over
time, inventory was reduced by almost half and service levels improved
dramatically through scenario planning and subsequent risk response
plans to mitigate risks, if and when they appeared.
Combining probabilistic tools and techniques with formal risk assess-
ment methodologies can, as demonstrated by Huntsman, eectively iden-
tify, assess, mitigate, and manage risk while improving the bottom line.
CONCLUDING THOUGHTS
Most supply chain professionals reside in a comfort zone that is popu-
lated with deterministic models and approaches. As mentioned through-
out this chapter, these models have served a clear purpose over the last
50years or so, and they will continue to enjoy widespread use and rene-
ment. However, these tools were developed in an era where SCRM was
not even an aerthought. Consequently they fail to consider the supply
chain uncertainty that has become a way of life. If we can sum up the
basic premise of this chapter in one phrase, it is that deterministic think-
ing must give way to probabilistic thinking. is will lead to the develop-
ment of new approaches that emphasize uncertainty. It will also lead to the
extension of existing tools and techniques where uncertainty is explicitly
considered in the analysis.
Summary of Key Points
• Stochastic/ probabilistic methods and models have been around for
more than 50years. Yet, supply chain management professionals are
now just understanding that these tools and techniques can be lev-
eraged to mitigate and manage risk because they overtly take into
account uncertainty.
• Many of the stochastic/ probabilistic tools and techniques support
what- if scenario planning approaches to evaluate uncertainty, com-
plexity, and risk within global supply chains.
• Emerging stochastic/ probabilistic modeling tools are being com-
bined with lean/ Six Sigma techniques such as DMAIC as a data-
driven, fact- based framework and also design of experiments to
Using Probabilistic Models to Understand Risk • 201
ensure that model outcomes are statistically signicant and provide
sensitivity curves that explain the cause- and- eect relationships
within model scenarios.
• To manage risks in complex supply chains, leading companies will
combine risk response plans with digital modeling to provide pow-
erful risk management frameworks.
• Many best- in- class companies that practice elements of SCRM are
also leveraging additional lean/ Six Sigma methods such as FMEA to
identify, codify, classify, and force- rank risks throughout their sup-
ply chains and processes.
• Several leading companies in SCRM are also injecting stochastic/
probabilistic tools and techniques along with lean/ Six Sigma methods
into their normal S&OP processes to not only assist in supply/ demand
balancing, but also to develop a risk assessment process to make
informed decisions about their supply chains that take into account
uncertainty, complexity, and risk.
• e methodology of leveraging digital modeling, probabilistic tools,
discrete- event simulation, and risk assessment is a powerful envi-
ronment to evaluate operational alternatives without experimenting
on customers.
ENDNOTES
1. Accessed from http://www.apics.org/ industry- content- research/ publications/ apics-
dictionary.
2. Arntzen, Bruce, PhD Director Global Scale Risk Initative, Massachusetts Institute of
Technology. “MIT Scale Survey Presentation.” APICS International Conference 2009.
3. Accessed from http://www.apics.org/ industry- content- research/ publications/ apics-
dictionary.
4. Murray, Peter. CIRM, “Next Generation of S&OP: Scenario Planning with Predictive
Analytics and Digital Modeling.” Journal of Business Forecasting, 29, 3 (Fall 2010):
20–31.
5. Baxendell, Richard. “Coupling Lean/ Six Sigma DMAIC Methodology with Digital
Modeling/ Discrete- Event Simulation and DOE to Drive Protable Manufacturing
Response.” IQPC International Conference, April 2008.
6. Van Landeghem, Hendrik, and Hendrik Vanmaele. University of Ghent, Belgium,
“Robust Planning: A New Paradigm for Demand Chain Planning.” Journal of
Operations Management, 20 (2002): 769–783.
7. Supply Chain Council SCOR Model, Accessed from www.Supply- Chain.org.
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