Free Webcast This Thursday: The Use of the DecisionTools Suite in Biotechnology Project and Portfolio Decision Making

Monday, August 30, 2010 by DMUU Training Team
Vertex Pharmaceuticals, Inc. is a global biotechnology company based out of Cambridge, MA. The Company's strategy is to commercialize its products both independently and in collaboration with major pharmaceutical companies. Vertex's product pipeline is focused on viral diseases, cystic fibrosis, inflammation, autoimmune diseases, cancer, and pain.

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

Vertex’s strategy and analytics group within the corporate finance division seeks to provide the senior management with dynamic revenue and profit forecasting methodology that helps to identify types of drugs that should be developed given a finite amount of cash and resources. A traditional financial view allows the user to identify scenarios and potential outcomes, but lacks the ability to show the range of potential values within each and every outcome. Vertex’s team uses the DecisonTools Suite to establish the average outcome, the variability of outcomes and to pressure-test risk and uncertainty of a particular scenario throughout the decision process.

Vertex’s team built a complex financial risk analysis model using @RISK to enhance its portfolio process. Monte Carlo simulation and optimization are used to analyze and optimize project and portfolio decisions, given short and long-term corporate strategy. @RISK is also frequently used throughout the business development process: simulating across multiple sales forecasts provides BD team with a range of potential outcomes, making it easy to pinpoint a particular scenario on a curve, along with its probability and value. TopRank turns the sensitivity analysis into a quick and seamless exercise, answering multiple what-if questions within minutes. Franchise and program leaders can now see a dollar effect of their program being delayed or advanced, adding supplementary indications to the development plan and even addressing the price uncertainties all at the same time. The simple interface of PrecisionTree along with tornado chart outputs makes it easy to explain the effect and importance of a particular assumption / decision to an audience with no finance background.

As the company continues to grow, adding more drugs and collaborations to its development pipeline, we will see in this free live webcast how the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.

» Register now (FREE)
» View archived webcasts
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Oops! Didn’t see that coming! Part 2

Tuesday, June 15, 2010 by Steve Hunt

Guest blogger David Roy Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Dave also has a quick survey for your input on structuring DFSS training. brings us the second installment of his four-part blog. Dave comes to us from SSPI,

 

--Steve Hunt

 
Oops! Didn’t see that coming! Part 2

We’d like to ask for your guidance by completing a short marketing survey to help SSPI structure our training in a way that is most useful to our community. This 8 question survey should take less than 5 minutes, and is anonymous. Your opinions are greatly appreciated.

As a continuation from the May blog, we are now covering the “Identify” phase of the ICOV framework of a rigorous new design process.

This phase is important because it establishes the framework for the concept, establishes the level of rigor required for the project management process, estimates the development cost, collects the Customer and Business requirements and the criteria for success.

 

The level of project management needs to be flexible and scalable depending on the Level of Effort (cost) and the Level of Innovation (risk) of the new concept.

 

Surely a project that will take a month to develop and has been done elsewhere requires less rigor that a concept that will take 3 years to develop and represents a brand new invention which has never been done before.

 

The I phase consists of two Tollgates during which an objective steering committee will decide whether to refine the work in the current phase, proceed or cancel the project. 

 

Tollgate 1 Exit Criteria are:

o     Decision To Collect The Voice Of The Customer To Define Customer Needs, Wants And Delights

o     Verification adequate funding is available to define Customer Needs

o     Identification of the Tollgate Keepers1 leader & the appropriate staff

 

Tollgate 2 Exit Criteria is successful demonstration of:

o     Assessment of market opportunity

o     Command a reasonable price or be affordable

o     Commitment to development of the Conceptual Designs

o     Verification adequate funding is available to develop the Conceptual Design

o     Identification of the Gate Keepers leader (gate approver) & the appropriate staff

o     Continue flow down of CTSs to Functional Requirements

Click to Enlarge 

Formal tools which can be used in this phase are Market/Customer research tools, Product Roadmaps, Process Roadmaps, Technology Roadmaps, Multigenerational plans, Quality Functional Deployment (House of Quality).

 

Market/Customer research tools may include Customer Relationship Management (CRM) Data, Surveys, Focus Groups, Conjoint Analysis and Kano Model Analysis.

 

The next blog will cover the Conceptualize phase

 

 

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. Dave’s experience includes Product and Transactional so his examples are of interest to all. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

» Part 1
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(Data) Cleanliness Is Next To Godliness

Monday, June 7, 2010 by Steve Hunt

I’m pleased to welcome Palisade Six Sigma Partner Edward Biernat of Consulting with Impact as featured guest blogger. As well as running a successful consultancy, Ed is a noted Six Sigma educator and author.

 

--Steve Hunt

 

 

(Data) Cleanliness Is Next To Godliness

 

I recently had dinner with Eric Alden, a Master Black Belt for Xerox corporation.  Eric had just gotten back from the American Society for Quality’s  (ASQ) headquarters in Milwaukee where he was one of 200 Master Black Belts worldwide that generated the questions for the upcoming ASQ Master Black Belt certification examination (more on that in an upcoming post).  Eric had also recently completed a mini-course for the local ASQ chapter on data integrity.  We shared some war stories and came up with some common threads regarding data integrity.

 

1.       Just because it is a number doesn’t mean it is worth anything.  People get enamored with tons of data from process instrumentation, shop floor collection sources or Excel spreadsheets.  There seems to be a false security with this pile of data, and managers often look to the Black Belt to ‘sort it out’, because with all that data, the answer is in there somewhere.  Many a belt has crashed on the rocky reefs of bad data, often after tons of time and effort (and credibility) were wasted generating false answers.

2.       GIGO.  The Garbage In – Garbage Out philosophy of computing applies especially to existing corporate databases.  Here a few recent examples of GIGO.

a.       A belt wanted to analyze the specific timing of events in shop floor process and had tons of data from the process instrumentation that had times down to the fraction of a second.  After lengthy analysis, they found a significant difference between two shifts and forced the lesser shift to adopt the sequence of the more uniform shift.  After introducing costly production problems and actually hurting the overall process, the sensors were found to be faulty and the overall process subject to human manipulation to generate the ‘pretty charts’ that everyone expected.

b.      Office areas are not immune.  Something as simple as a checksheet to gather data to analyze when a particular computer error occurred can be in question, especially when the clerk fills in the times at the end of the shift from memory rather than logging the event as it occurs.

3.       Good data in bad spreadsheets.  Even if you get good data, having an inexperienced person setting up the spreadsheet can cause problems.  It is analogous to a person using a word processing software and making a table using spaces and tabs.  It looks like a great table until you have to manipulate it.  Then it falls apart.  Problems like merged cells, subtotals, random formula inserted in cells, etc. can make a Belt weep and cause significant errors in the resulting analyses.

4.       Useless manipulation.  Often a big issue is that management wants data sliced a certain way for no good reason.  This sometimes leads to the proliferation of additional spreadsheets or databases that needlessly add to complexity.  (Note: If you have an ERP system like Oracle or SAP, USE IT!  They are designed to house data and protect its integrity.  Plus the data entry screens typically allow for better and more accurate entry.  Few things are more wasteful than entering everything in the ERP system then re-entering it into a spreadsheet to appease a manager’s inability to adapt and change.)

 

What are some tactics for resolving these issues?

1.       On a macro level, start ensuring that the data that your company is collecting is sound data as part of the preparation for a Six Sigma launch, or a part of plain old good business.  Bad data slows down or stops a Six Sigma project dead in its tracks, changing it from getting something done to fixing the data. 

a.       Know catalog your data databases, including the extra ones (Excel, Access) that are usually relied upon but undocumented.

b.      Prioritize the data sources by synchronizing them with your Six Sigma launch sequencing. 

c.       Sample the data to insure its usefulness.  If it is bad, fix it.  This will give teams better data to start off with and will allow time for that data to accumulate for analysis.

2.       For specific projects, conduct a Measurement System Analysis (MSA) on you data sources (This tool is often used in the Measure phase of the DMAIC model).  We often think of MSA’s when it comes to physical measurements.  It is just as critical in the ‘softer’ data. 

a.       Pull the correct sample size.  In StatTools, under  Statistical Inference there is a Sample Size Selection tool that can be used to pull the correct amount of data needed for the analysis.

b.      Pull your data randomly and follow the trail to the actual entry point.  That may mean watching how individuals enter data, probing for special circumstances, etc.

c.       In your analysis, look for random factors such as vacation fill-ins.  Both Eric and I both had several experiences where one person was filling in for someone who is out sick or on vacation and, usually do to inadequate training, varies from the expected process.

3.       Pivot Tables are our friends.  Start today upgrading the skill sets of the people that do the actual data entry and first level analysis.  Train them in how to use tools like Picot Tables that slice the data but leave the actual spreadsheet intact.  The fewer merged cells, etc. that we fight with, the better.

4.       Managers – Trust your Belt.  If they say the data is bad, it probably is.  No matter how much you want an answer today, you may not be able to get one.  The good news is that some processes can be modeled using @RISK to begin improvement that is directionally correct while waiting for the data to compile.  Then the better data can be used to either update or replace the early model.

5.       Go hunting.  Find extraneous datasets and merge them / kill them.  The fewer that are out there, the more likely you will be able to ensure the integrity of those that remain.

 

Remember that data analysis is a funnel.  Tons of data leads to bunches of information which then can help us make some decisions.  Throwing bad data into the system is similar to throwing bad tomatoes into the food distribution system.  The end results can be pretty messy and difficult to clean up. 

 

Also, don’t miss Ed Biernat’s free live webcast DMAIC and Using a Non-Intuition Approach, Thursday, 11AM Eastern Time.

 

Sign up here:

https://palisade.webex.com/palisade/onstage/g.php?d=719996370&t=a

 

 

BIO:

 

Edward Biernat is the president of Consulting With Impact, Ltd., a training, coaching, and consultancy located in Canandaigua, NY that he founded in 1998.

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Oops! Didn’t see that coming!

Wednesday, May 12, 2010 by Steve Hunt

We are pleased to introduce you to consultant and trainer David Roy, our first guest blogger in my blog. Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. David’s blog will be the first in a series, and this initial entry also has a quick survey at the end for your input on structuring DFSS training.

--Steve Hunt

 
 

Oops! Didn’t see that coming!

 

How often do we hear these words after we have made a change to product, service or process?

 

We frequently solve one problem only to discover a new problem; or the solution we selected didn’t really resolve the problem.

 

There are many reasons for these surprises. Problem Solving sometimes addresses the symptoms and not the root cause. Useful solutions often have compromising harmful effects that we did not consider.

 

You may now be thinking; “Wow, if everything we do is going to turn out bad let’s not change anything.”   The reality is that change is inevitable. Whether driven by rising customer expectations, innovative new technologies or even variation in inputs over time; change will occur.

 

Managing the design and implementation of these changes requires a more formal methodology than the prominent “Launch and Learn” method.

 

The sophistication of the methodology will vary depending on the magnitude of the risks associated with the change. If we are problem solving for variation in a standard process and trying to regain control simple tools such as Cause and Effect diagram and Failure Mode Effects Analysis and Standard Work may be all that is required.

 

When we start to explore reducing variation or introducing new technologies or process then we need to bring on a Design For Six Sigma (DFSS) methodology which incorporates elements such as Change Management, Robust Design, Reliability, Modeling & Simulation and Piloting & Prototyping.

 

Over the next 4 blogs we will cover the four phases of a DFSS project under the framework of I-dentify, C-onceptualize, O-ptimize, and V-erify or ICOV for short.

We will give a high level look at the steps within these phases and the tools used to reduce the risk of the change and un-intended consequences.

 

On another note, if you are able, we’d like to ask for your guidance by completing a short marketing survey to help SSPI structure our training in a way that is most useful to our community. This 8 question survey should take less than 5 minutes, and is anonymous. Your opinions are greatly appreciated.

http://www.surveymonkey.com/s.aspx?sm=2aQk8QF1eLB5MFQJC1pUXA_3d_3d. 

 

BIO:

 

David Roy is an integral part of the Six Sigma community. He taught GE’s Jack Welch and entire staff Six Sigma, as well as served as Senior Vice President of Textron Six Sigma. He is a Certified GE Master Black Belt, was instrumental in developing GE’s DMADV (DFSS) methodology, and has taught 3 waves of DFSS Black Belts. Dave’s experience includes Product and Transactional so his examples are of interest to all. David holds an BS in Mechanical Engineering from The University of New Hampshire. He is also the co-author “Services Design for Six Sigma – A Roadmap for Excellence”

 

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June 2010 - Worldwide Training Schedule

Tuesday, May 11, 2010 by DMUU Training Team
Palisade Training services show you how to apply @RISK and the DecisionTools Suite to real-life problems, maximizing your software investment. All seminars include free step-by-step books and multimedia training CDs that include dozens of example models.

North America
London
Brasil
Latin-America
Asia-Pacific
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Put More Science into Cost Risk Analysis

Tuesday, May 4, 2010 by DMUU Training Team
At the 2010 Palisade Risk Conference in London, John Zhao of Statoil used a mock cost estimate contingency model to demonstrate how @RISK simulation functions can yield a more realistic project contingency through integrated qualitative risk assessment and quantitative risk analysis.

While future oil prices may be hard to predict due to low manageability, it is absolutely possible to scientifically forecast the sizes of risks that companies are willing to take, and such risks may include the probabilistic volumes of newly discovered reserves, the probability of meeting a project development schedule, chances of project cost overruns, and the likelihood of eroding entire project profitability. To achieve these goals, @RISK has lent a helping hand to business analysts for easier operation of complicated mathematical modelling.

Statoil, an international oil company, takes risk management seriously and has applied Monte Carlo simulation techniques in core and support businesses using @RISK. Such applications not only include the solo use of individual applications, but integrated combinations from drilling, reserve estimation, and well completion to cost and schedule controls at project execution. Besides the widespread uses of the software, Zhao discussed a specific application of @RISK to convincingly simulate required capital project contingency  in detail.

A simplistic line-item ranging exercise using @RISK Monte Carlo simulation is no longer adequate to derive large capital project contingency, as empirical data confirmed that many disastrous cost overrunning projects were lack of contingency to cover the covert risks. In order to show management a complete risk picture on a project, both systemic risks (which empirical history has indicated a likelihood of occurring), and specific risks (which have discrete probabilistic characteristics), should be included in the overall project risk analysis. Therefore the combination of continuous PDF for project cost estimates, and discrete PDF for project risk registers, may prevail and provide management with a more convincing project cost contingency.

John Zhao is Quality and Risk Manager at StatoilHydro Canada Limited. He has 22 years project management experience in the petrochemical industry. He has authored many papers and made numerous presentations worldwide on the subject of risk and contingency management. In the past 10 years, John has developed his expertise in cost engineering and risk analysis for large downstream and oilsands upstream projects across Canada. His extensive knowledge in construction project qualitative risk assessment process has made him an expert on the subject in North America; his proprietary Monte Carlo model using @RISK is a popular tool for project contingency and escalation simulation. The quantitative model that John has built has integrated @RISK with PrecisionTree to help corporations conduct risk-based strategic decision-making.

» View the complete abstract and PDF presentation of "Put More Science into Cost Risk Analysis"
» Read Zhao's whitepaper, "Put More Science into Quantitative Risk Analysis"

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Free Webcast this Thursday: Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

Monday, May 3, 2010 by DMUU Training Team
The value of quantitative science projects too often remains unrealized for would-be consumers. Despite flawless analyses, sophisticated reports and dazzling presentations, the message goes unheeded by those who could most benefit: If only they understood how to operationalize the results. The clarity with which quantitative scientists view the practical application of results is often paralleled only by their inability to generate that same clarity in their customers. The result is that good management science is at best ignored and worst, misunderstood (and misapplied). This free live webcast describes steps we as quantitative scientists can take to foster understanding, generate novel insights and stimulate actionable results with our clients, as well as demonstrates some of the tools we use - including @RISK.

» Register now (FREE) 
» View archived webcasts
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Robust Risk Analysis for the Time/Expertise Poor – Part 2

Thursday, April 15, 2010 by DMUU Training Team
In my last blog I introduced the idea of a customised risk analysis solution to problems commonly faced in project risk management, especially cost estimation. Of course this idea is not uniquely applicable to project costs, but this paradigm is the simplest to explore, and that’s what I’m about to do.

Picture a risk register in a worksheet that has been created at a macro level to encapsulate most (all?) of the risks your projects may face. For any given project only a subset of these will be relevant – what is the best way to get these risks into a risk model on the next worksheet? By pressing a button of course! It is almost trivial to write code that picks up all selected risks and places them and the relevant data fields in the model worksheet. Sure beats manually copying and pasting individual line items and the transcribing errors that follow.

The next problem is utilising the workshopped parameters (likelihood of event, three-point estimates for severity etc.) in a logical way to be referenced by appropriate @RISK functions. Once a model structure has been agreed upon a macro button can place @RISK distributions where they ought to go, either logically due to the paradigm (using RiskBinomial, for example) or via a drop-down selection for dollar impact (RiskPert or RiskGamma, say). My clients have been especially thankful when I limit their choice of distribution and provide a simple flow-chart to follow to make this very decision. Reducing the propensity for arguments in risk workshops is worth its weight in gold; if we can assume that reducing this risk ‘weighs’ plenty!

Similarly one or two instances of the simulation settings are likely to satisfy all requirements, so these too can be activated by macro buttons. In this way a user can’t run a ‘poor’ simulation thus creating spurious results. The simulation output that is required can be placed into a report template attached to the model template and generated using yet another simply-labelled macro button. In this way there will be consistent reporting across the organisation allowing decision makers to become familiar and comfortable with simulation results they might otherwise ignore or be unaware of.

A risk model created by this process may not be the theoretically optimal one, but it will be valid and in context with its intended use. It will certainly be easy to use! The results will be consistent and should satisfy management’s desires as well as regulatory requirements.
The project cost estimation is but one example, and the above possibilities are far from the only ones imaginable. Additional complexity or alternate needs would be just as easily met simply with different code essentially without any practical limits. You don’t need to be an expert in Monte Carlo techniques and software to run robust, credible risk analyses. All you need is a risk analysis consultant who macro-controls the cumbersome and probabilistic elements, some appropriate simulation options and reporting procedures. Ask for me by name!

» Robust Risk Analysis for the Time/Expertise Poor – Part 1

Rishi Prabhakar
Trainer/Consultant
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Robust Risk Analysis for the Time/Expertise Poor – Part 1

Tuesday, April 13, 2010 by DMUU Training Team
I have recently spoken to several clients whom have all came to the same conclusion about the risk analysis solution they think is most appropriate. They don’t want to do it, and I have no problem with that!

Of course that’s not precisely true. The benefits of Monte Carlo techniques in risk analysis are quite well understood and there is plenty of buy-in from businesses in the Australasian region. The trouble these businesses face (particularly in the realm of project cost estimation) is that the specific process of quantifying their risks for stochastic analysis and the ensuing simulation is not well understood and the means to ameliorate this appears to be beyond their reach. The modelling and simulation components of the project risk management process are not given adequate resources to be performed well, and certainly not to the extent that they provide the most useful information.

It is the case that many companies do not employ dedicated quantitative analysts. This means they have to rely upon some (maybe one) person in the team who has a non-zero quantity of experience and possibly training with risk simulation software to create a valid and credible stochastic model. This person is also not likely to be given enough time to do said task, thus the model inevitably suffers. It is my experience that most models – and all project cost estimation models – can be improved or actually need to be fixed.

So the corporate mind is willing, but the flesh is weak. How can this be addressed? No amount of additional training will suddenly allow you to overcome your time and resource constraints. Perhaps you can’t get the budget for training anyway or don’t want to master risk analysis software when it’s not really core to your role? The solution is one that I personally endorse (and provide!) as a risk analysis consultant – custom Excel programming.

VBA for Excel is a fairly simple language to learn, yet very powerful tool for automating repetitive or sometimes complex spreadsheet tasks. A customised solution involves writing VBA code to perform the tasks we’d rather not do ourselves in the risk analysis model. The “we” here refers to companies that find themselves in the situations previously described whereby they are incapable of creating and operating these models, not necessarily though any fault of their own. In my next blog I’ll examine some modelling problems/requirements and how they might be dealt with effectively using customisation.

Rishi Prabhakar
Trainer/Consultant
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Making Optimal Choices, or Just Making Choices? Part 3

Friday, March 26, 2010 by DMUU Training Team
Part 2 of this blog ended with me very quickly stating that the MotoGP tipping comp optimiser was identical in structure to a portfolio optimisation problem, where the portfolio could contain stock or other assets, or even projects. Let’s look at this in a little more detail as I’m sure you’re reading this to find how to optimise your own decisions rather than wondering how I went in the tipping competition!

In my model there was a fixed budget (though less could be spent if desired) to spend on riders, with the aim of maximising their total points haul. In the real world you may have a total budget of say $100m to invest in a range of projects perhaps many hundreds of millions of dollars in total value each of which have certain expected returns. At its simplest this decision evaluation will find the most (expected) profitable portfolio of the projects. This is an inclusion/exclusion grouping model, but it is very simple to optimise assets with a continuous level e.g. the amount of money invested in various shares etc. Another real example I have seen when working with an investment company here in Australia was a model whose goal was simply to find the portfolio mix that came closest to the total allowable spend without exceeding it.

Further realism can be included by using constraints should there be the need. A resource constraint may mean there has to be a limit to the number of projects that can be run simultaneously. There may also be a minimum number of projects determined by management as a mitigation strategy. Such constraints are very simple to employ using Evolver and add value to the decision analysis without the need to provide specific risk analysis/Monte Carlo simulation information for the model.

A slightly more sophisticated method of turning an optimisation into a useful portfolio risk management tool where uncertainty hasn’t been specifically modelled is to estimate the possible downside of each asset and include it in the calculation of the portfolio’s ‘score’. The Evolver software comes standard with over twenty example spreadsheets for your educational pleasure, of which “Portfolio Mix.xls” gives one method for doing just this.
In the next (and final) instalment of the Making Optimal Choices blog I will explore the idea that not all optimisations no matter how mathematically correct will produce the same results in good time, and that elegant modelling should always be the goal prior to firing up Evolver.

And so you know, I came second in the competition. Next year I’m hoping to go one better!

» Making Optimal Choices, Part 1
» Making Optimal Choices, Part 2

Rishi Prabhakar
Trainer/Consultant
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New Approaches to Risk and Decision Analysis

Wednesday, March 17, 2010 by DMUU Training Team


Risk analysis and decision-making tools are relevant to most organisations, in most industries around the world.  This is demonstrated by the speaker line-up at this year's European User Conference, an event at which we believe it is important to bring together customers from a wide range of market sectors.

We are holding 'New Approaches to Risk and Decision Analysis' at the Institute of Directors in central London on 14th and 15th April 2010.  As with previous years, the programme aims to provide everyone attending with practical advice to enhance the decision-making capabilities of their organisation.  Customer presentations, which offer insight into a wide variety of  business applications of risk and decision analysis, include:
  • CapGemini: Faldo's folly or Monty's Carlo – The Ryder Cup and Monte Carlo simulation
  • DTU Transport: New approaches to transport project assessment; reference scenario forecasting and quantitative risk analysis
  • Georg-August University Research: Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer
  • Graz University of Technology: Calculation of construction costs for building projects – application of the Monte Carlo method
  • Halcrow: Risk-based water distribution rehabilitation planning – impact modelling and estimation
  • Pricewaterhouse Coopers: PricewaterhouseCoopers and Palisade: an overview
  • Noven: Use of Monte Carlo simulations for risk management in pharmaceuticals
  • SLR Consulting: Risk sharing in waste management projects - @RISK and sensitivity analysis
  • Statoil: Put more science into cost risk analysis
  • Unilever: Succeeding in DecisionTools Suite 5 rollout – Unilever's story
We will also look at the recently-launched language versions of @RISK and DecisionTools Suite, which are now available in French, German, Spanish, Portuguese and Japanese.  Software training sessions will provide delegates with practical knowledge to ensure they can optimise their use of the tools and implement business best practise and methodologies.

With over 100 delegates from around the world attending, the event is also a good opportunity to network and knowledge-share with risk professionals from around the world.

» Complete programme schedule, more information on each presentation,
   and registration details


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Quantitative risk assessment under utilised for infrastructure projects

Friday, March 12, 2010 by DMUU Training Team
Why is it that most of the high profile projects managed by the government in the UK all ultimately become beset by problems? A number of projects jump to mind – the Millennium Dome, Wembley Stadium and currently the NHS IT. All three have been plagued by developmental delays and financial mismanagement.

Recently, yet another worthy, but ambitious project has been announced – the North-South high speed rail line to connect London to Scotland. One wonders if the government undertakes detailed quantitative project risk analysis for its infrastructure initiatives?

A good example to highlight in this context is ENGCOMP, a Saskatchewan-based engineering consulting firm that has worked with the Canadian Department of National Defence (DND) to help define budgets for the fourth phase of construction of its Fleet Maintenance Facility at Canadian Forces Base Esquimalt in Victoria, British Columbia. Using @RISK, a Monte Carlo simulation tool, ENGCOMP helped the DND define and secure budget approval from the Federal Government’s Treasury Board. The consultancy firm was able to estimate the impact of the variability and uncertainties pertaining to risks, costs and scheduling. This assessment enabled it to estimate the project risk budget or the risk reserve and schedule contingency, which were both factored in when defining the total project cost of the infrastructure project.

The fact is, in the world of business, risk is inherent and unavoidable. Whilst one cannot completely control risk, one can certainly help reduce uncertainty, greatly increasing the chances of project success. For instance, a key finding of the project risk analysis conducted by ENGCOMP was that, taking into account all the risk and uncertainties on the project, there is an 85 per cent chance that the Fleet Maintenance Facility project will be completed in January 2014. A fairly positive result for the DND, given the scale and complexity of this project in question.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis
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Adopting a healthy approach to risk

Tuesday, December 29, 2009 by DMUU Training Team
Having talked in previous posts as to why it’s important, and today how accessible it is for any size of organisation to adopt a healthy approach to risk, I’ll now take you through my top ten tips on how you can maximize your risk management programme:

1. Get buy-in
Risk management is not an optional extra. It is a business critical tool that is an asset and an integral part of the project. The company culture must be developed to embrace QRM (quantitative risk management) and DMU (decision making under uncertainty) in order that everyone understands their benefits and therefore accepts the need for them.

2. Get budget
Business tools cost money, but managing risk is an investment - not an overhead – and must be regarded as such. Allocating resource and making it a formal business process should be seen as an insurance policy.  Not only will it help organisations make better decisions that will save them money in the long term but, by identifying potential risks and adverse events, it can protect them against unexpected costs in the future.

3. Get words
As with any organisational change, it is essential that everyone is clear on the new processes. Therefore a common risk language – or 'glossary' – needs to be developed to avoid misunderstanding and to ensure a consistent approach to QRM and DMU.

4. Get numbers
Qualitative assessment is essential, but numbers are more powerful – for example the percentage chance of meeting a deadline or budget. Monte Carlo simulation random sampling provides the margin of error for a venture and is a good way to illustrate the consequences of different courses of action. Risk management experts must ensure everyone understands these figures, and accepts them.

5. Get structure
Managing risk in order to make better-informed decisions requires an appropriate organisational structure. Individuals and groups need clearly defined roles, and must then each take responsibility for their own area of expertise.

6. Get lateral
Every organisation has risks that it deals with on a daily basis and which must therefore be factored in to the decision-making model. However, no enterprise operates in isolation, so other external variables must be included. For example, even a small rise in fuel costs could have a major effect on revenues if raw materials need transporting long distances.

7. Get perspective
Political, cultural and social risk factors can be explored by involving all stakeholders.  Investing time and money in consultation and research ensures that businesses have a clear idea of the complete environment in which they operate, and therefore minimise the chances of products and services failing.

8. Get reporting
Risks, and the management of them, must be reviewed regularly – and the programme amended if necessary. This requires a regular reporting process, in which risks are clearly identified and prioritised.

9. Get with it
Being risk aware does not mean being risk averse. Businesses should guard against rigidly adhering to 'the way we've always done it' approach, instead keeping up-to-date, learning new tricks and not being afraid to be bold.  Although risky on the surface, these tactics prevent being left behind – much of the potentially uncertainty can also be removed with QRM and DMU.

And finally…

10. Get it documented
Back up the commitment to a thorough QRM and DMU programme with documentation. This validates the budget and buy-in requested at the start. And it’s good for business – organisations this thorough are guaranteed a competitive edge.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis
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Making Risk & Decision analysis accessible to all

Friday, December 18, 2009 by DMUU Training Team
It’s clear that the financial crisis has exposed a number of failings in the practice of risk management. In my last post I talked about the relevance risk analysis and the disciplines of ‘quantitative risk management’ (QRM) and ‘decision making under uncertainty’ (DMU) are to all sizes of organisations, be it large or small. 

However, how accessible are these disciplines to the average size business across the globe today?

With the need to make more informed decisions more pressing by the day, thankfully QRM and DMU and now far more accessible than ever before.  Traditionally systems tended to be expensive, enterprise-based applications targeted at large companies who were prepared to spend considerable time, money and human resources.  The result was an all-singing, all-dancing product which often ended up underused due to confusion on the part of the very employees who were supposed to make it work.

Steady increases in computer processing have given the desktops of today as much power as the high-end servers of a few years ago, meaning that risk analysis and management is now an achievable goal for businesses of all sizes.  Palisades @RISK and Decision Tools Suite software are such desktop risk and decision analysis tools – working within Microsoft Excel and therefore being accessible to a large number of users.

‘Monte Carlo Simulation’, a technique originally conceived by scientists working to develop the atomic bomb as part of the Manhattan Project, is an inherent part of @RISK, a cornerstone of the Suite.  It enables users to introduce uncertainty into their previously static spreadsheets, which lets them look at things in a probabilistic, rather than a deterministic way.  In layman’s terms, this means that rather than companies and individuals making decisions based on estimates or best guesses, they can see all the potential outcomes to a venture – and how likely these scenarios are to occur.

For many companies this significantly improves the decision-making process.  Firstly it requires a change in the methodology of employees responsible for assessing risks and opportunities and secondly for the first time employees have a tool which allows them to communicate their recommendations to management or colleagues in a transparent and standardised way.  Equally, being able to look at scheduling risk in a probabilistic and quantitative sense allows for the allocation of labour and resources in a way which minimises slack and wastage whilst maximizing ROI.

So, it would seem that the new ‘risk management’ language that is starting to develop in the workplace and being taught to a new generation of managers on MBA courses should be welcomed.  With the accessibility of the technology available to assist them, we need to make sure that organisations do more than just pay lip service to QRM and DMU if they are to reap the rewards.

In my next blog I’ll be giving you the my top ten tips to adopting a health approach to risk, that will help businesses of all sizes maximize their risk management programmes.

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis
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Free Webcast This Thursday: “Integrated Project Risk Analysis - Structuring the Model Effectively”

Monday, November 30, 2009 by DMUU Training Team
On Thursday, December 3, 11am-Noon ET, Jay O’Connor will present a free Live Webcast about project risk management.

A project risk analysis is only as good as the model that was used to prepare it. It is critical that the model be constructed to reflect the risks specifically associated with the project. The model must be able to accurately reflect the risks associated with schedule, quantities, cost and the residual unmitigated risk items from the qualitative risk analysis. The model should also take into account the interrelationships and dependencies of these items.
This webcast will address these issues and present examples of how results can vary based on the level of detail used in preparing the risk analysis, and will include the use of @RISK, and @RISK for Project.

Palisade is pleased to host Jay O’Connor’s presentation. With over 25 years of experience in the areas of estimating, planning and quantitative risk analysis for international projects, Jay understands the complexities that are associated with identifying and assessing project risks. His experience includes both the owner’s and contractor’s side of engineering and construction projects. He has worked in the upstream and downstream oil and gas industry sectors and the pulp and paper sector. His career has taken him to the United Kingdom, Japan, Indonesia, Malaysia, Singapore and Australia.

» Register now (FREE)  
» View archived webcasts

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Allocating Contingencies to Risk Events that were identified in a Risk Register

Friday, October 30, 2009 by DMUU Training Team
In a previous blog, I presented a very simple way to allocate contingencies to uncertain cost elements in the project risk management process. However, that methodology works well when there are not risk events that affect a cost element or a group of cost elements.
A risk event is described by two elements: the probability of occurrence and the conditional impact to the project given its occurrence. For example, we have a risk that describes the possibility of a new regulation. If it occurs, it will increment the cost of group of cost elements by a minimum of 10%, most likely 15%, and a maximum 20%. If the risk does not occur, no impact will be observed. Using a Discrete and a PERT distribution, we can model such risk such as:



When sampling from this distribution approximately only 20% of the time will generate a multiplier with a minimum of 1.1, most likely 1.5 and a maximum of 1.2; in 80% of instances the multiplier will be 1. That means that only 20% of the time the risk will increment the cost of selected cost elements by the multiplier previously described as show in the figure below:



In addition to risk events in our cost risk analysis models, we often use distributions that describe cost uncertainties. These distributions model ranges are mostly in a different order of magnitude. Therefore, the variance will also be in a even greater order of magnitude. For example, the cost of Item 3 modeled using a 3-point estimate (i.e., min 100,000, ML 120,000, and max 150,000) has a variance of   87,698,412.70), while the variance of the risk event is 0.0036. 

If we are to distribute the contingency using the % of contribution of the variance method, the risk event that we just modeled will be ignored even though we know that such risk event has an impact that we cannot dismiss. Given this practical scenario, the method of variance contribution will not work appropriately.

As an alternative, we can use a tornado diagram that results from @RISK’s sensitivity analysis. Here we can use the regression coefficients to understand what risk events or uncertainties are affecting the total cost in a more drastic way. In the case that you also incorporated events that represent an opportunity to reduce cost, you will observe that the coefficient is negative; in your allocation calculations you should not consider negative coefficients.

In the figure below you can observe the Regression Tornado. Here risk events and uncertainties are represented in a scale that goes from 0 to +/-1:



Knowing the regression coefficient of each input that affects the total cost in a negative way, we can construct a table and obtain a normalized percent that can be used to distribute contingency. If for example, we have a contingency of $100,000, it can be distributed to each input proportionally to the regression coefficient as shown below.



Some risk management experts do not distribute the entire amount of the calculated contingency. It is common practice to distribute only a percentage of it (i.e., 70%). The remaining amount will be used as a reserve that will handle unidentified risks.

Javier Ordóñez, Ph.D
Director of Custom Solutions
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Allocating Contingencies to Uncertain Cost Elements in a Cost Risk Analysis Model

Tuesday, October 20, 2009 by DMUU Training Team
In a previous entry to this blog I discussed how to assess the contingency required in a cost risk analysis study. The next step is to allocate the calculated contingency to uncertain cost elements that drive the variation in the total cost of the project. In this way, the contingency can be better managed and controlled throughout the life of a project.

While reviewing literature on this topic, I found a practical way to do this. This methodology uses the percentage contribution of each uncertain variable (usually 3 point estimate distributions) to the variance of the resulting distribution of the total cost.

To apply this method, we need to report the variance of each input distribution and the variance of the end result. In case that input distributions are independent from each other, we can just add up individual variances to estimate the variance of the total. However, this is hardly the case since correlation between input variables is expected in cost models.

@RISK allows reporting statistics from an input distribution without running a simulation as well as statistics that describe an output. These functions are from part of the @RISK functions library: Statistic Functions> Theoretical and Statistic Functions>Simulation Results, respectively. These functions can be accessed using the fx icon from the @RISK toolbar. 

To report the variance of input distributions we can use the RiskTheoVariance and for the output RiskVariance. The construction of the allocation model is shown below.



In the project risk management model above, it can be observed that the % Contribution to the Variance of the Total Cost is calculated as a proportion of the input variance to the total variance. Once these percentages are determined we can use them to allocate the management contingency to each cost element. It can be also observed that the engineering allowance is also calculated, and the decision maker now has criteria to manage and control contingencies.

Javier Ordóñez, Ph.D
Director of Custom Solutions
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Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

Monday, October 19, 2009 by DMUU Training Team
Michael A. Kubica is Founder and President of Applied Quantitative Sciences, Inc. He has over 18 years' experience within the healthcare industry, and has been providing quantitative sciences consultancy since 1999. Michael has extensive experience in providing quantitative decision support solutions for leading pharmaceutical, medical device/diagnostics, and biotechnology companies, addressing a wide range of business issues. Prior to establishing AQS, Michael held the position of Vice President, Operations for Magellan Health Services. During his career Michael has also held positions of Director of Quality Management, Regional Director of Business Operations & Finance, and Hospital Administrator. Throughout his career, Michael employed sophisticated quantitative methods to forecast performance, streamline operations, and improve quality. Michael has an MBA and Master’s of Science in psychology. He serves as Adjunct Professor of Research Design and Statistical Analysis at St. Thomas University in Miami, FL. Applied Quantitative Sciences, Inc. (AQS) is a consultancy specializing in assisting medical device, pharmaceutical and biotechnology companies make decisions under conditions of complexity and uncertainty. They are a market leader in providing simulation and optimization models which are used by industry leaders for the purposes of forecasting, new technology valuation, business and strategic planning, supply chain management, and resource planning.

Mr. Kubica will present a case study later this week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training, 21 - 22 October at the Hyatt Regency in Jersey City (10 minutes by PATH from Manhattan's Financial District).

See the abstract for his case study below, and see the full schedule for the Conference here.

Targeted Analyses and Compelling Communication: A Formula for Successful Value Creation in Management Science

The value of quantitative science projects too often remains unrealized for would-be consumers. Despite flawless analyses, sophisticated reports and dazzling presentations, the message goes unheeded by those who could most benefit: If only they understood how to operationalize the results. The clarity with which quantitative scientists view the practical application of results is often paralleled only by their inability to generate that same clarity in their customers. The result is that good management science is at best ignored and worst, misunderstood (and misapplied). This workshop describes steps we as quantitative scientists can take to foster understanding, generate novel insights and stimulate actionable results with our clients. 

This Week: October 21-22 in NYC

Building on the success of last year’s record-breaking event, the conference will offer a wide range of software training, model building, and real-world case study sessions. Last year, the event drew over 150 practitioners and decision-makers from a broad spectrum of industries. The @RISK and DecisionTools software tracks were more popular than ever. This year, we’re expanding software training with sessions that let you walk through examples and try the tools directly. This will enable you to take some new tips back to the office. Please join us in October for a great opportunity to learn and connect with colleagues.
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Contingency Calculation in Cost Risk Analysis

Tuesday, October 13, 2009 by DMUU Training Team
When performing a cost risk analysis study, one of the key results is the amount of extra monetary resources that is to be added to the project cost baseline to guarantee that the budget is not exceeded at a certain confidence level. Good project risk management strategies must take this into account.

After defining the uncertain variables and risk events that affect the cost performance of the project, we can run a Monte Carlo simulation with @RISK to find out what the range of the total project cost is.  Simulation results can help us to explain the risk exposure that we have in the total cost of the project. The most popular statistics are the mean (average cost), the most likely cost, and the 10th and 90th percentiles.




To determine the contingency to be allocated to the project, we need to define what confidence level we would like to achieve: The higher the contingency level, the larger amount of contingency needed. For example, in the figure above, we are reporting the total cost of the project. Here we can observe that we are showing the 85th percentile that corresponds to a total cost of $7.8M (right delimiter).  We can say that there is only a 15% chance that we will exceed $7.8M, or alternatively, we have an 85% chance that the total cost will be less than or equal to $7.8M.  In the same figure we can also see that the 90th percentile of the total project cost is $8.02M.  We can say then that in order to increase our confidence level from 85% to 90%, we will need to add $220,000 to the total cost.

The calculation of the contingency is then accomplished by using the base cost estimate (BE) before the risk analysis was implemented, and the expected cost (EC) of the simulated results.

Some practitioners separate the contingency into two components: engineering allowance, and management contingency.

Engineering allowance (EA) is the difference between the expected cost and the base estimate:

EA = EC – BE

Management contingency (MC) is calculated using the difference between the cost at certain confidence lever (Cp) and the base estimate:

MC = Cp – EC

In our example, our BE = $6.5M; therefore, engineering allowance EA = EC – BE = 6.86M – 6.5M = $0.36M. 

For the calculation of management contingency, we use a confidence level of 85% so Cp(85%) = $7.8M; therefore, MC = Cp – EC = 7.80M – 6.86M = $0.94M.

In many situations, the suggested contingency might be excessive, so the need for a mitigation study is necessary. We can use the sensitivity analysis tool in @RISK to detect the key drivers affecting our total cost. This is valuable information so that we can concentrate our efforts in reducing the impact of risk events and uncertainties to the total cost. Below, we see a tornado graph with the most important drivers. The analyst will then explore the appropriate mitigation strategies and assess their implementation cost. A second simulation can be run to assess the effectiveness of the proposed mitigation plan, and compare the pre-mitigated and post-mitigated cost distributions.




In following blog posts, I will explain how to distribute the assessed contingency to cost elements and identified risk events in project risk management models.

Javier Ordóñez, Ph.D
Director of Custom Solutions
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Introduction to DecisionTools Suite 5.5 Products: Software training at the NYC Conference

Monday, October 12, 2009 by DMUU Training Team
Palisade is gathering trainers from our New York and London offices to present software training seminars next week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training. The conference is set to take place on 21 - 22 October at the Hyatt Regency in Jersey City, 10 minutes by PATH from Manhattan's Financial District.

Take this convenient and inexpensive opportunity to learn from Palisade’s trainers and software developers. Learn how to use the elements of the new DecisionTools Suite 5.5 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. See how each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — can be used to solve practical problems in the real-world.

The conference also features case studies demonstrating how to use @RISK and DecisionTools Suite, from risk management experts in the fields of finance, healthcare and pharmaceuticals, energy, oil and gas, DFSS and Six Sigma, project management, operations management, manufacturing, and more.

See the full schedule for the Conference here.

Next Week: October 21-22 in NYC

Building on the success of last year’s record-breaking event, the conference will offer a wide range of software training, model building, and real-world case study sessions. Last year, the event drew over 150 practitioners and decision-makers from a broad spectrum of industries. The @RISK and DecisionTools software tracks were more popular than ever. This year, we’re expanding software training with sessions that let you walk through examples and try the tools directly. This will enable you to take some new tips back to the office. Please join us in October for a great opportunity to learn and connect with colleagues.
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