ceo's guide to sound decision making in 21st century

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

HOW GOOD ARE YOUR DECISIONS?

ScenarioS and war gaming

Low High50% 55% 60% 65% 70% 75% 80% 85% 90%

intuitive

Intuitive

Folklore Based

Mai

nly

Judg

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Anal

ytic

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Fact Based

Historical

Forecasting

Simulation

Scenarios

Big Data

HypothesisDriven

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

ForecaStingKey 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


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


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


Example

ExamplePros

Pros

Cons

Cons

• Take historical data • Use forecasting algorithms • 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 StrategyKey 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

Right Brain

Left Brain

TYPE OF DECISION MAKING

SOPHISTICATIONProbability that the decision will be deemed a sound decision in retrospect

SENIOR MANAGERS ARE PAID TO MAKE GOOD DECISIONS - THE BEST DECISIONS COMBINE ANALYTICS AND INTUITION(c) Global Supply Chain Group, 2016

www.globalscgroup.com

PURE INTUITION - A BIT BETTER THAN 50/50


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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www.globalscgroup.com

HISTORICAL SUPPLY DEMAND ANALYSIS


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 periodreason 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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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

FORECASTING - ALWAYS BLAMED FOR BEING WRONG


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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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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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0406 05

03 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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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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BIG DATA - MAKING DATA SING



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

HolisticTo 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.

FlexibleStrategies 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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Key Elements of 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-

andintuitive 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 thisapproach in disguise

STRATEGIC THRUST- HYPOTHESIS TESTING (ARISTOTELIAN APPROACH)

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ceo's guide to sound decision making in 21st century

Leadership & Management