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205420 crystal ball case studies
1.
Crystal Ball Case
studies Success & failures using simulation models and Crystal Ball and what you can learn from them Huybert Groenendaal, PhD, MBA Managing Partner EpiX Analytics www.epixanalytics.com 2013 © EpiX Analytics LLC
2.
Agenda • What is
Crystal Ball and three main ways of how it improves decision-making • Case studies: • Creating value from data to decisions (Technology & Services) • Focus on the decision, not on the tool (Pharmaceutical) • Best practices of building Crystal Ball capacity (Oil & Gas) • Conclusions and next steps 2013 © EpiX Analytics LLC
3.
About EpiX Analytics •
Specialized simulation modeling consulting, training and research firm • Objective = improve decision making under uncertainty ‘From data to decisions’ • Wide range of fields: • Pharmaceuticals • Mining • Manufacturing • Transportation • Insurance • Financial industry • Health / Food safety • Energy, oil & gas • Government • Many others…. 2013 © EpiX Analytics LLC
4.
• Four marbles
in bag, two black and two red • Investment of $5 • Two marbles are drawn: • Two blacks you’ll win $25 • One black, one red, you’ll win $10 • Two reds, you’ll have to pay me another $5 Investment opportunity 2013 © EpiX Analytics LLC
5.
Who invests and
why? Vote: 1. No, it’s nap time… zzzzz… 2. No (I expect to lose money) 3. Yes, (I expect to make money) 4. Yes, I always play when I can… 2013 © EpiX Analytics LLC
6.
Decision trees can
help… Invest $5 Do not play 2 blacks 2 red $25 $-5 $0 1 red, 1 black $10 ? ? ? 2013 © EpiX Analytics LLC
7.
What is the
probability of two hearts? There are four possibilities: 1. Black, Black 2. Black, red 3. Red, black 4. Red, red 2013 © EpiX Analytics LLC
8.
Probability of drawing
two black marbles is ¼: Vote: 1. Yes 2. No 3. Not enough information 2013 © EpiX Analytics LLC
9.
Probability tree… First =
red Second = red First = black 1/3 Second = black2/4 2/4 2/3 1/3 1/3 1/6 1/6 1/3 2/3 Second = red Second = black 2013 © EpiX Analytics LLC
10.
Invest -$5 Do not
play 2 heart 2 spades $25 $-5 $0 Expected Value = 1/6 * $25 + 2/3 * $10 + 1/6 * -$5 = $10 $10 $5 1 heart, 1 spade $10 1/6 2/3 1/6 Updating the decision tree… 2013 © EpiX Analytics LLC
11.
Let’s do the
poll again….invest? Poll: 1. No 2. Yes 3. Still not enough info 2013 © EpiX Analytics LLC
12.
Lesson’s learned • A
decision is about something in the future • Typically, future is uncertain • Uncertainty is combination of scenarios (possibilities) and probabilities • Don’t trust your intuition regarding uncertainty • For situations that are harder (i.e. almost all real-life decisions) than the example we just considered, Crystal Ball allows us to take into account uncertainties 2013 © EpiX Analytics LLC
13.
Crystal Ball’s main
three uses Three main decision questions: 1. Valuations of investments with uncertain return; 2. How much uncertainty/risk is there? 3. What is the optimal decision? 2013 © EpiX Analytics LLC
14.
1. Valuations • Transparent
way to address risk and uncertainty in valuation model (eNPV) • Provides consistent (and disciplined) way to measure value of investment with uncertainty outcomes (e.g. R&D, Business Development, M&A, etc.) • Risk often has negative connotation, but Crystal Ball can help change discussion about uncertainty to create value and managing the upside potential First main use of Crystal Ball 2013 © EpiX Analytics LLC
15.
Background: Fortune 500 pharma
company Improve prioritization and financial performance & achieve a risk-balanced pipeline Challenge: No valuation or uncertainty/risk analysis tool available to evaluate pipeline Approach: Identify key risks and variables in R&D process Designed Crystal Ball (template) Model Pharmaceutical client: Portfolio Valuation, Strategy and Prioritization Improvement Result: • Better prioritization of projects and improved risk- balanced pipeline • Crystal Ball critical part of every project valuation 2013 © EpiX Analytics LLC
16.
$10 $12 $14
$16 $18 $20 $22 $24 $26 $28 $30 COGS[-] Success launch [+] Fixed costs [-] Marketing[+] Price trends[+] Competitor reaction[+] Yield [+] Market size [+] eNPV (value ofproject in US$ million) Importance of uncertaintyof parameters to projectvalue (eNPV) Uncertainty drivers Quantifying uncertainty helps management focus on those uncertainties that have the greatest impact on value Market size greatest uncertainty driver of value, can change value from $13M to $28M COGS not important uncertainty driver of value First main use of Crystal Ball 2013 © EpiX Analytics LLC
17.
2. What is
realistic? How confident are we about our numbers? • ‘Traditional’ budgets, sales forecasts, project costs, project schedules don’t quantitatively take into account uncertainty • Uncertainty often ignored or alternatively base, low and high scenarios determined • Crystal Ball provides way of including expectations and their uncertainties into estimates; • Provides a “bandwidth” where likely the future will fall; • Gives management insight into how likely it is that they will reach their goals/sales/budget/costs; • Focuses on main risk drivers, instead of a wash-list of many risks/uncertainties Second main use of Crystal Ball 2013 © EpiX Analytics LLC
18.
Background: Fortune 500 pipeline
and midstream company High CAPEX rate of new pipelines Challenge: Traditional cost estimation had proven inaccurate, not accounting for risk Approach: Used Crystal Ball for cost estimation and project valuation Set up internal Crystal Ball team, trained senior management Natural Gas pipeline project cost estimation Result: • Understanding of uncertainty in project costs and schedules and financial results • No projects approved without Crystal Ball / Monte Carlo analysis of costs and key financial metrics 2013 © EpiX Analytics LLC
19.
3. What is
optimal decision? • After understanding how much risk there is, question is what is optimal? • With Decision Optimizer (part of Crystal Ball), can support optimal decisions that include uncertainty • For example: • Optimal R&D and project Portfolio: What are the right projects to pursue? • Optimal inventory: What is the best amount of inventory? • Optimal sales force territories: What are the optimal territories? • Optimal staffing levels: What is the optimal hiring/firing? Third main use of Crystal Ball 2013 © EpiX Analytics LLC
20.
Background: Fortune 500 chemical
company Investments in R&D with different risk profiles Challenge: What is strategically the right portfolio of R&D projects that balances rewards and risk Approach: Used Crystal Ball for evaluating uncertainties around R&D Set up internal Crystal Ball team, trained senior management R&D of large chemical company Result: • Crystal Ball analysis helped senior management understand risk-return profiles of different R&D strategies and portfolios • Decision Optimizer helped understand optimal R&D project portfolio R&D Project 1 R&D Project 2 R&D Project 3 R&D Project 4 R&D Project 5 R&D Project 6 R&D Project 7 R&D Project 8 R&D Project 9 R&DProject 10 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Expectedreturn Risk around financial returns Risks-returncharts R&D projects (size of bubble proportional to investment amount) 2013 © EpiX Analytics LLC
21.
Example Crystal Ball
applications 2013 © EpiX Analytics LLC Inventory optimization ReliabilitySales commission estimation Pricing decisions R&D portfolio forecasting Schedule risk estimation Financial analysis (NPV, IRR etc.) Sales forecasting Private equity Budgeting Strategic decision-making Business development Hedging and insurance Price forecasting Value of information Hiring decisions Safety
22.
Agenda • What is
Crystal Ball and three main ways of how it improves decision-making • Case studies: • Creating value from data to decisions (Technology & Services) • Focus on the decision, not on the tool (Pharmaceutical) • Best practices of building Crystal Ball capacity (Oil & Gas) • Conclusions and next steps 2013 © EpiX Analytics LLC
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Background: Fortune 500 Industrial
equipment company Selling equipment and providing related services (set-up, maintenance, repair etc.) Challenge: Given that it takes 1 year to train service engineers, how many to hire? Approach: Analysis of historical data, understanding patterns, trends, uncertainties etc. Used Crystal Ball (see next slide) Case study 1: Data to decisions 2013 © EpiX Analytics LLC
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Overall result: • Company
saved $2 - $3 million in the first year of using the model • Company now uses Crystal Ball and Decision Optimizer in a number of other applications Case study 1: Data to decisions Crystal Ball Model Sales forecast Historical service demands Equipment in use Example result: Need to hire 3 engineers to be 80% confident to meet demand or 4 to be 94% confident 2013 © EpiX Analytics LLC
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Background: Fortune 500 pharmaceutical
firm Large number of products in the R&D pipeline, different stages and risks/returns Decisions about R&D funding critically important for future Challenge: How to improve prioritization and achieve a risk-balanced pipeline? How to develop accurate and useful Crystal Ball model to support decisions around this? Case study 2: Focus on the decision 2013 © EpiX Analytics LLC
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Case study 2:
Focus on the decision Crystal Ball Model Develop Crystal Ball model Failure: Crystal Ball model was overly complex, too laborious for analysts, didn’t have buy-in and results weren’t understood Crystal Ball Model Develop Crystal Ball model Success: Crystal Ball model now integral part of company’s prioritization and decisions around R&D portfolio Management Marketing R&D Legal Operations Sales 2013 © EpiX Analytics LLC
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Background: Many industries ranging
from oil & gas and pharmaceuticals to manufacturing and insurance Challenge: Often only ‘pockets’ of an organization use Crystal Ball How to best build more capacity and skills in the use of Crystal Ball throughout the organization? How to build understanding by senior management for Monte Carlo simulation and Crystal Ball and create demand for it? Case study 3: Building CB capacity 2013 © EpiX Analytics LLC
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Typical successful approach: •
Develop & maintain a core-group of experts and users. This group can be ‘called upon’ by other groups • Develop process of peer-review with group of other Crystal Ball users. Quality control is very important! • For decisions that are made regularly (e.g. product launches within pharmaceutical company) create Crystal Ball template model(s) • Knowledge about Crystal Ball and Monte Carlo outside this group important too, but not everyone within organization needs to know how to develop and run Crystal Ball model; Case study 3: Building CB capacity 2013 © EpiX Analytics LLC
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Approach for management: •
Educate management! • Increase internal demand with a few highly visible projects • Involve management in Monte Carlo / Crystal Ball based analysis early on in analysis/projects (not only at final presentation) • To management, don’t focus on the model (or on complex statistical jargon) but focus on the analysis relevant to decisions Also for presentation, use peer-review! Case study 3: Building CB capacity 2013 © EpiX Analytics LLC
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Summary • Future is
uncertain, so don’t ignore uncertainty in your analysis and decisions! • Crystal Ball allows us to combine risks and uncertainty to support a wide range of decisions • Range of visual graphics for clear communication • Thinking in probabilistic terms!! 2013 © EpiX Analytics LLC
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Thanks for your
time! Dr. Huybert Groenendaal Managing Partner EpiX Analytics Huybert@epixanalytics.com 2013 © EpiX Analytics LLC
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