DFSS Training

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

As companies make changes to or introduce new Products, Processes or Services, we observe a wide spectrum of methodology; from well defined process with trained resources, effective tools and excellent results - to no process, ad hoc application of tools, and frequent cycles of “Launch and Learn.”

Where does your methodology rank?

In this free live webcast, we will provide a framework for assessing the Process, People, Tools and Results for premier attributes in the New Product, Process, Services Introduction Methodology.

This is the conclusion of Dave Roy’s guest blog, we hope you have found them informative. Again, Dave comes to us from SSPI, Six Sigma Professionals, Inc., and taught Jack Welch and his entire staff their Six Sigma Green Belt training. Also, look for Dave’s free live webcast on August 19^th, Assessing your New Product, Process or Service Introduction Methodology: Is yours premier? Does it enable Six Sigma performance?

Oops! Didn’t see that coming! Part 4
 

As a continuation from the July blog, we are now concluding with the “Optimize” and “Validate” phases of the ICOV (Identify-Conceptualize-Optimize-Validate) framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

These phases are important because it allows time and methodology to optimize the design, develop all of the detailed documentation, verify performance and capability under operating conditions and manage an orderly transition to the new state.

The Optimize phase consists of a single stage (Design Optimization) and associated Tollgate 5 to validate successful completion of the requirements. 

The Design Optimization stage involves completing all of the detailed design documentation, building Prototypes of the design, simulating/analyzing Process Capability, preparing all Control Plans and updating the Design and Process Scorecards.

Tollgate 5 Exit Criteria:

o    Agreement that functionality and performance meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Validate stage.

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Simulation, Change Management, Control Plans, Reliability and Robustness.

The Validate phase consists of two stages (Verification and Launch Readiness) and associated Tollgates (6 and 7) to validate successful completion of the requirements. 

The Verification stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

Tollgate 6 Exit Criteria:

o    Agreement that functionality and performance from the pilot meet the customers’ and business requirements under the intended operating conditions.

o    Approval to proceed with the Launch Readiness stage.

Formal tools which can be used in this phase are Design Scorecard, Process Management, Mistake Proofing, Change Management, Control Plans, Statistical Process Control (SPC), and Confidence Analysis.

The Launch Readiness stage involves developing Pilot plans, Piloting the new design and process and analyzing and making adjustments to achieve the desired functionality and performance under operating conditions.

Tollgate 7 Exit Criteria:

o    Agreement that transition plans and training plans have been developed and are executable.

o    Approval to proceed with the Production stage.

Formal tools which can be used in this phase are Transition Plans, Training Plans, Process management, Change Management and Control Plans.

Following the ICOV model we have now used a formal methodology that allows us to validate performance at progressive economical stages and have improved the ability to detect unknown risks thus avoiding the Oops! Didn’t see that coming!. It should be mentioned that the methodology should be flexible and scalable to adjust for level of invention and risk. A brand new invention (Research & Development) that has never been deployed in similar conditions is much different than implementing a known solution (Application Engineering) under new conditions.

 » Part 1
 » Part 2
 » Part 3
 

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. 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”

We are pleased to welcome back to my blog consultant and trainer David Roy from Six Sigma Professionals, Inc.

Oops! Didn’t see that coming! Part 3
 

As a continuation from the June blog, we are now covering the “Conceptualize” phase of the ICOV framework of a rigorous new design process as explained in “Services Design for Six Sigma – A Roadmap for Excellence”.

This phase is important because it conceives, evaluates and selects good design solutions through robust process methodology which ensures alignment to the customer and the business needs.

Many design solutions skip this phase and become typically named as “Launch and Learn”.

The Conceptualize phase consists of two stages and associated Tollgates to validate successful completion of the requirements. 

The Concept Development stage involves translating Customer requirements into solution free Functional requirements, developing the System Level Conceptual Design, generating Concepts for required functions, Concept selection and translation of the Functional Requirements to Design Parameters.

An example of a Functional Requirement for a Customer Want of “Speedy Service” could be “Speed of Service” and a Design Parameter could be “Waiting Time”

Tollgate 3 Exit Criteria:

• Assessment that the Conceptual Development Plan & Cost will satisfy the customer base
• A Decision the design represents an economic opportunity (if appropriate)
• Verification adequate funding will be available to perform Preliminary Design
• Identification of the Tollgate Keeper & the appropriate staff
• An action plan to continue flow-down of the design Functional Requirements

The Preliminary Design stage involves creating the design documentation and configuration management, performing design analysis and testing, translating the Design Parameters into Process Variables and formulating the Production strategy.

An example of further mapping the Design Parameter of “Waiting Time” to a Process Variable could be “Number of Phone Lines”

Tollgate 4 Exit Criteria:

• Acceptance of the selected Solution/Design
• Agreement the Design is likely to satisfy all Design Requirements
• Agreement to proceed with the next stage of the selected Solution/Design
• An action plan to finish the flow-down of the design Functional Requirements to design parameters and process variables

Formal tools which can be used in this phase are QFD, TRIZ/Axiomatic design, Measurement System Analysis (MSA), Failure Mode effect Analysis (FMEA), Design scorecard, Process mapping, Process management, Pugh Concept Selection, Robust Design, Design Scorecards, Design for X and Design reviews.

The next and final blog will cover the Optimize and Validate phases.

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. David holds a 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
 » Part 2

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

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”

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.

We are pleased to introduce you to consultant and trainer Sandi Claudell, today’s featured guest blogger. Sandi is CEO of MindSpring Coaching, and has been a valued Palisade Six Sigma Partner for quite some time. She is a Six Sigma Master Black Belt (Motorola), and is a Lean Master (Toyota Motors - Japan) among other notable achievements.

--Steve Hunt

Part 1: The Platform Disaster

Much has been said about the disastrous BP oil spill in New Orleans. If we use the theory of probability and reliability then have too many different companies responsible for a very complex construction and operation added to the chance of failure.

There is probably a cultural issue at work where each entity wanted to give the other what they wanted to hear rather than the truth. (For historic and recent examples: NASA Challenger and recent Toyota Prius problems). When we lose sight of quality and reliability of parts, construction, maintenance, testing under ALL conditions rather than the obvious few, etc. then we run high risks of failure. When you build 100+ wells and avoided disasters  . . . perhaps people fool themselves into thinking there never WILL be a disaster. They don’t look at a model that demonstrates the longer you go without such an event (given the input factors of how each element can and will fail) the closer you come to the event we all want to avoid.

They may or may not have used an integrated Systems Design  . . . not simply an engineering system but the system on how individuals work together, communicate with each other, act as a conforming unit or a more self-directed autonomous unit looking for and generating solutions outside the box. A team that is innovative and willing to look at all the possibilities and create a breakthrough design that was / is more mistake proof.

If they had used DFSS (Design for Six Sigma) then their designs would be more robust taking into consideration all the necessary safety precautions for human life as well as immediate response to a potential failure. As part of DFSS we use a statistical tool call Design of Experiments (Strategy of Formulations, Central Composites, etc.) where we can try very complex interactions (factors) with minimal effort / cost and maximum statistical accuracy. DoE creates prediction equations that allow us to model and ask questions of what would happen under different conditions. More importantly we can look at many different quality metrics (responses, outcomes, etc.) with the same experimental trial. If we replicate the test then we can even forecast what elements cause variation (very hard to detect in highly complex systems without the use of statistics).

If they had used an FMEA (Failure Mode Effect Analysis  . . . a tool used in Six Sigma) then they could have anticipated failures and put error proofing devices in place to detect and/or respond to potential faults BEFORE it is irreversible. If we add a Monte Carlo simulation to potential working conditions then the model forecasts probability plots and identifies key factors that will be critical to success or failure.

Perhaps they did indeed use a Monte Carlo using Crystal Ball. It is a good product but if they used Palisade’s @RISK and added some of the other tools provided by Palisade such as RISK Optimizer, Neural Tools, etc. then they could have analyzed the system in other dimensions besides a simple Monte Carlo, thus uncovering weaknesses BEFORE designing and/or building the platform and well.

Part 2: Capping the well head

In Lean there is a whole discipline called “Error Proofing Devices”. As part of the design effort we need to create first and foremost safety and other devices that prevent the error from occurring in the first place. If that line of defense fails then there should be devices built into the process designed to cap the well if your error proofing fails. If that line of defense fails then there should be a disaster response plan created and practiced and tested to ensure that the spill is repaired immediately.

Part 3: Treating the resulting spill

Again, Design of Experiments could test different materials, chemicals and methods to find the right combination to contain or otherwise manage the resulting oil spill. Trying one chemical only may be the age old definition of madness . . . trying the same thing over and over again expecting different results. Again, a robust design of experiments could aid in the process of finding a solution that is most effective and with multiple tests on the same samples ensure that is it the most safe for the environment and the population most directly in the path of the oil spill. These tests are ideally run years before such a spill however, doing something now is better than simply standing by and watching it happen.

Last but not least:

Management (Executives down to line managers) should have coaches. Coaches who can speak to the culture, the systems design, the tools and methods used in Lean Six Sigma and who can verify data analysis and help with the accurate interpretation of the data. These coaches should be independent . . . not a full time employee of the corporation as they are more likely to speak the truth and highlight risks as well as opportunities.

Now BP and all the other entities may have done some of what I mentioned above. But I would assume they must have left out one or more of the listed items or we wouldn’t be looking at the oil traveling into the wetlands around New Orleans right now. Hindsight is always brilliant but we can learn from our mistakes. We can create better cultures, systems, error proofing devices, Experimental Designs etc.

BIO:  

Sandi Claudell is CEO of MindSpring Coaching. She is a Master Black Belt in Six Sigma, a Lean Master and has worked as a consultant for many companies to initiate worldwide improvements. For more information or to contact Sandi please visit http://www.mindspringcoaching.com/.

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”

• 1-2 June 2010, Calgary, AB
  Decision-Making and Quantitative Risk Analysis using @RISK
• 1-3 June 2010, Calgary, AB
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite
• 29-30 June 2010, Philadelphia, PA
  Decision-Making and Quantitative Risk Analysis using @RISK

• 8 June 2010, London
  Quantifying the impact of a Project Risk Register using Monte Carlo simulation and @RISK for Project
• 9-11 June 2010, Brussels
  Risk Modelling, Uncertainty Analysis and Decision-Making using @RISK and The DecisionTools Suite
• 14-15 Jun 2010, London (near Heathrow)
  Decision-Making and Quantitative Risk Analysis using @RISK

• 9-10 de junho 2010,  São Paulo, Brasil
  Risk Analysis for Project Management for Users of @RISK for Project (in English)
• 28-30 de junho 2010,  Rio de Janeiro, Brasil
  Avaliação do Risco para Usuários Iniciantes/Intermediários (em português)

• 1 al 3 de junio del 2010,Mexico D.F
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)
• 2 al 4 de junio del 2010,Santiago, Chile
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)

• 2-3 June 2010, Perth
  Decision-Making and Quantitative Risk Analysis using @RISK

• 6-7 April 2010, Washington, DC
  Decision-Making and Quantitative Risk Analysis using @RISK
• 6-8 April 2010, Washington, DC
  Decision-Making & Quantitative Risk Analysis using the DecisionTools Suite

• 20-22 Apr 2010, London (near Heathrow)
  Risk Modelling, Uncertainty Analysis and Decision-Making using @RISK and The DecisionTools Suite

• 19-20 de abril 2010,  Vitória, Brasil
  Avaliação do Risco em Projetos para Usuários Iniciantes/Intermediários (em português)
• 26-28 de abril 2010,  Belo Horizonte
  Avaliação do Risco para Usuários Iniciantes/Intermediários (em português)

• 21 al 23 de abril del 2010,  Guayaquil, Ecuador
  Análisis de Riesgo y Decisión para Aplicaciones en Finanzas y Banca utilizando @RISK y el DecisionsTool Suite
• 26 al 28 de abril del 2010,Buenos Aires, Argentina
  Evaluación de Riesgo para Usuarios Principiantes/Intermedios (en español)

• 7-8 April 2010, Sydney
  Decision-Making and Quantitative Risk Analysis using @RISK
• 7-9 April 2010, Sydney
  Decision-Making and Quantitative Risk Analysis using the DecisionTools Suite
• 9 April 2010, Sydney
  Decision-Making and Quantitative Risk Analysis using the DecisionTools Suite

• 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