Technology revolution is making decision sciences more complex than ever before. Here is a short 9 slide deck to look at the latest and greatest tools at your disposal

1. CEO’S GUIDE TO SOUND DECISION MAKING
IN TWENTY FIRST CENTURY

2. HOWGOODAREYOURDECISIONS?
Scenarios and war gaming
Low High
50% 55% 60% 65% 70% 75% 80% 85% 90%
Intuitive
Intuitive
Folklore Based
MainlyJudgementalMainlyAnalytical
Fact Based
Historical
Forecasting
Simulation
Scenarios
Big Data
Hypothesis
Driven
Key Elements of
Methodology Example
Pros Cons
• Absorb information
• Assess situation
* Watch result
• No paralysis by
analysis
• Saves times
• Can lead to wrong
decisions
• Can lead to
overconfidence
• Military officers,
pilots and ship
captains
Forecasting
Key Elements of
Methodology
Example
Pros
Cons
• Dig data
• Make a judgment
about future action
• Reason out success
and failures
• Reasonably good
for tactical decision
• Notoriously
inaccurate
• Over-reliance on
forecasts is
common
• Demand forecasts
• Suitable for other
tactical planning
Historical Supply Demand
Analysis
Key Elements of
Methodology Example
Pros
Cons
• Dig data
• Make a judgment
about
future action
• Quick way of
considering the
key facts
• Very few markets
are stable enough
for historical analysis
to be sufficient for
decision making
• Most economic
analysis
Key Elements of
Methodology
Example
Pros
Cons
• List all PARTS
(Players, Added
value,
Rules,Tactics and
Scope) of the
business situation
• List alternative
actionsand their
payoffs
• Think about the
unthinkable
• Can be very time
consuming
• Consensus is
unlikely
• Actual war gaming
exercises
Simulation
Big Data
Key Elements of
Methodology
Key Elements of
Methodology
Example
Example
Pros
Pros
Cons
Cons
• Take historical data
• Use forecasting algo-
rithms
• Create a probability
distribution
• Assess risk reward
trade-offs
• Decide on a future
course of action
• Excellent tool to
model uncertainty • Choose the right
data
• Build models that
predict
• Transform your
company’s capa
bilities
• Information is too
hard to interpret
• Need for accuracy
• VAR modeling
• Holistic
• Business-Focused
• Battle to fetch
relevant information
• Update can mis-
match real figures
• Each and every
piece of data your
organization has
stored till now.
Hypothesis strategy
Key Elements of
Methodology
Example
Pros
Cons
• Hypothesis
generation
• Collect data
• Conduct analysis
• Fast, actionable,
and reliable
• Fact based
• Good mix of
analytical rigor and
intuitive mastery
• Usually take longer
than intuitive
decision making.
• Scientific studies
• Top-tier consulting
• Effective leaders
RightBrain
Left Brain
TYPEOF
DECISION
MAKING
SOPHISTICATION
Probability that the decision will be deemed a sound decision in retrospect
SENIORMANAGERSAREPAIDTOMAKEGOODDECISIONS-THEBESTDECISIONSCOMBINEANALYTICSANDINTUITION
(c) Global Supply Chain Group, 2016
www.globalscgroup.com

3. PURE INTUITION - A BIT BETTER THAN 50/50
KeyElementsof
Methodology
PROS
Example
CONS
Absorb as much information on
the issue as is available
Assess situation in light of your
extensive experience
Decide on the potential course of action,
commit whole heartedly to it
Can easily lead to wrong decisions (several key mili-
tary decisions of many wars were proven to be wrong
in retrospect)
Can easily lead to overconfidence; situation may have
changed without the decision maker’s knowledge
Watch for early results
Change direction as necessary
No paralysis by
analysis
Saves time,
especially in situations
where time is of the
essence
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(c) Global Supply Chain Group, 2016
Military officers, pilots and ship captains
are trained for rapid decision making
Fire fighters and other rescue teams
Suitable for most situations where the
decision maker has faced exactly the same
decision criteria several times
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4. HISTORICAL SUPPLY DEMAND ANALYSIS
KeyElementsof
Methodology
PROS
Example
CONS
(c) Global Supply Chain Group, 2016 www.globalscgroup.com
Dig out
historical supply
demand data
Make a judgment
about future action
based on data
In the next period
reason out success
and failures, and do
the same all over
again
Quick way of
considering the key
facts
Useful for static
and stable markets
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02
Most economic analysis is based on historical
demand-supply analysis
Very few markets are stable enough for
historical analysis to be sufficient for
decision-making

5. FORECASTING - ALWAYS BLAMED FOR BEING WRONG
(c) Global Supply Chain Group, 2016 www.globalscgroup.com
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KeyElementsof
Methodology
PROS
Example
CONS
Take historical data
Use one or more for
forecasting algorithms to
forecast the future based on
historical data
Make judgment about future
course of action based on
forecasts
In the next period reason out
success and failures and do
the same all over again
Demand forecasts for most goods and
services are the bases for most sales and
operations planning
More forecasts are notoriously inaccurate.
If someone could accurately forecast the
future they could make a killing on the
stock marke
Over-reliance on forecasts and inadequate
use of associated statistical information is
a common problem in business
Suitable for several other tactical planning
situations
Reasonably good for tactical decision
making
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6. (c) Global Supply Chain Group, 2016 www.globalscgroup.com
KeyElementsof
Methodology
PROS
Example
CONS
SIMULATIONS - A GOOD WAY TO TEST SOLUTIONS
Take historical
data
Use one or more
forecasting
algorithms to
forecast the
future data
Create a probability
distribution of future data
based on forecasting
algorithms or uncertainty in
the input
Decide on a future course of
action offering the most
attractive risk reward trade-
off
Repeat the cycle in the next
period
Use probability distribution
function to assess risk reward
trade-offs for future courses
of actions
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Value at risk mode-
ling to measure and
manage risk of
portfolio manage-
ment situation rang-
ing from banks,
trading and com-
modities to shipping
Modeling of
uncertainty in
any business sitution
can be done by sim-
ulations-such as
demand planning
operations planning,
production planning
etc
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Most decision-making is under uncertain-
ty. Simulation are excellent tool to
model uncertainty
Sometimes the information is too hard to interpret or
act upon model uncertainty
As with all modeling, the results reflect the inputs. The
dictum GIGO (garbage in garbage out) equally applies here
Results may not be conclusive enough to be
actionable
Allow the decision maker to gauge the
probability of various possible outcomes
and from these make a trade off between
the reward they seek and commensurate
risks
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7. (c) Global Supply Chain Group, 2016 www.globalscgroup.com
KeyElementsof
Methodology
PROS
Example
CONS
Forces the decision-maker to think about
the unthinkable, and plan for it
List of main courses of action
and their probabilistic payoffs
based on forecasts
List potential responses of
other players to each of these
courses of action and their
respective payoffs
Choose the option that gives
the best-expected payoffs
Construct a decision tree
which allow the decision
maker to think forward and
reason backwards
Actual war gaming
exercises
use scenarios planning
extensively
Long term strategic
planning
frequently entails extensive
use of scenario planningy
Game theory applications
have ranged from frequency
spectrum auctions to public
policy planning and
negotiable in political and
commercial world
Make an effect to think about
various possible scenarios
related to all PARTS (Players,
Added value, Rules, Tactics
and Scope) of the business
situation
No matter how hard one thinks it is impossible to make a
list of everything that you don’t know
Can be very time consuming
Consensus may not emerge even after protected
discussions
Very powerful tool to analyze systematically
several steps ahead
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SCENARIOS AND WAR GAMING - THINK FORWARD AND REASON BACKWARDS
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8. BIG DATA - MAKING DATA SING
(c) Global Supply Chain Group, 2016 www.globalscgroup.com
KeyElementsof
Methodology
Example
PROS CONS
Choose the right data and get the necessary IT
support
Each and every piece of
data your organization has
stored till now
The big data is in extended
use in the field of medicine
and healthcare
The real disadvantage of ‘big’ is the inherent risk
of failure
It frequently allows decision makers to slip into a
wait-and-see mind set
Battle to fetch relevant information
Update can mismatch real figures
Holistic
To set a foundation for long-
term success, companies
need a big-picture view
Business-Focused
Strategic planning for big data
should be business-led with
IT leadership fully engaged to
inform the process.
Flexible
Strategies must account for
incremental value creation
and a evolutionary process
overall.
Structural and Scalable
Think beyond the pilot to en-
sure big data strategies can
be fully executed.
The high demand of the nat-
ural sources on this earth is
challenging the high volume
Build models that predict and optimize busi-
ness outcomes
Transform your company’s capabilities-Develop
business-relevant analytics that can be put to use
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9. (c) Global Supply Chain Group, 2016 www.globalscgroup.com
KeyElementsof
Methodology PROS
Example
CONS
Universally deployed in scientific studies
and universities
In commercial world, applications can be
seen at top-tier consulting companies
and their clients
Collect necessary and
sufficient data to prove or
disprove the hypothesi
Fast, actionable,
and reliable
Good mix of
analytical rigor-
and
intuitive mastery
Fact based
Robust, well documented
methodology
No jumping to
conclusions (that
might prove to be
erroneous in the
hindsight
No churning the
ocean with endless
data collection and
analysis
Brainstorm for hypothesis
generation on key business
issue
Conduct necessary and
sufficient analysis to prove
or disprove the hypothesis
Prepare detailed
implementation plans
including timeframes,
milestones, responsibilities,
and tracing mechanisms
Decide on a future course of
action based on results of
previous steps,
Implement and track
Usually take longer than intuitive decision making. May
not be suitable when there is no time-e.g. insolvent
companies
Most effective decision makers use this
approach in disguise
STRATEGIC THRUST-HYPOTHESIS TESTING (ARISTOTELIAN APPROACH)
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