To ensure a product development process in the CE environment to run. “Concurrent engineering is the extent to which product and process designs are.

1. The Concurrent Design Process In Apple
2. Concurrent Design Group
3. Types Of Concurrent Design Approach

From Wikibooks, the open-content textbookscollection

Introduction

Throughout the design of a part or system of parts, there is aprocess that engineers will follow. Depending on what they aredesigning and what the concentration is on, the specific processesthat they go through can be vastly different. This section attemptsto capture many different concepts of the design process and putthem in one place.

Although there are many differences between some designprocesses, here is a brief overview of what should happen: Thefirst step in the design process is to define the design. Thismeans writing down everything that you are working towards andcoming up with a brief, dense summary of what the design is.Normally, a customer has to express a need in order for a productto be designed. Communication with the customer can come directly,from marketing research, or some other source. It can be beneficialto both the customer and the engineer if a direct line ofcommunication is set up. Once customer requirements are laid out,then they are translated into engineering requirements. Theserequirements are then used to come up with initial concept ideas.Usually many concepts are conceived (sometimes hundreds or eventhousands), but are then narrowed down based off of which designsare the most feasible. A few concepts are then chosen to beprototyped and tested. Based off of testing they are improved and afinal design is chosen. Once a design is chosen, manufacturing canbegin and the customer receives the finished product.

DesignProcesses

(Chris Fagan)

The design process for concurrent engineering can vary quite abit depending on the size and nature of the project. However, mostapproaches follow a similar structure outlined below:

• Define customer requirements
• Define engineering requirements
• Conceive design solutions
  • Customer Requirements
  • Come up with multiple designs/ideas
• Approval
  • Funding
  • Happens throughout process
• Develop prototypes
  • Develop/optimize few ideas from original concepts
• Approval
• Implement design

Interview with a Designer

by Chris Cookston

Examples of DesignProcesses

(Chris Fagan)

• Design Processes that have a history of success
  • Focusing on processes that have been shown to work
  • Difference between singular produced parts and mass produced
    • Automotive vs. Aeronautics vs. NASA JPL for example

SMAD

Space Mission Analysis and Design By James R. Wertz and Wiley J.Larson

This is a book that is a great basis for space mission design,but also for Complex System Design.

Toyota

(Karl Jensen) (Adam Aschenbach)

Toyota is listed specifically in this wikibook because Toyotahas been an originator of many design techniques that are in usetoday.

One such example is lean manufacturing. Lean manufacturing isthe idea of getting more value with less work, and was derived fromthe Toyota Production System (TPS). More information can be foundin the lean manufacturing wiki:

A few things that set Toyota apart from other manufacturersinclude:

• Toyota considers a broader range of possible designs anddelays certain decisions longer than other automotive companies do,yet has what may be the fastest and most efficient vehicledevelopment cycles in the industry.

• Set based concurrent engineering begins by broadly consideringsets of possible solutions and gradually narrowing the set ofpossibilities to converge on a final solution.

• This makes finding better or the best solutions morelikely.

• The above figure depicts the way that most American carcompanies went about design. The figure represents a point basedserial engineering approach with quickly decides on one solutionthat will work. Once this solution is found the solution isoptimized to find a better solution but not necessarily the bestsolution.

• The above figure depicts the way that Toyota designs it’sproducts. The process described shows a set based concurrentengineering approach. This approach involves creating a large poolof ideas that groups can communicate about. This large group ofideas is eventually narrowed down to the final solution which isusually the best solution. Using this system of concurrentengineering allows a robust solution to be found without much needfor optimization. Instead of optimizing a design many designs maybe prototyped and evaluated to determine which is best^[1].

Design Under changingrequirements

(Chris Fagan)

It has been estimated that 35 percent of product developmentdelays are a direct result of changes to the product definitionsthroughout the design process ^[2]. Withnumerous groups working on the same project the requirements cancontinually change. Steps toward a design process that wouldfacilitate change was outlined in 6 steps ^[3]. Design whenthe system requirements are changing are quoted below ^[4].

1. Establish and foster open communication between customers anddesign engineers. This includes communication within a designteam.

2. Develop and explicitly write down design requirements as soonas possible. It is important to identify requirements for componentinterfaces and other possible unspoken product specifications.Analyze the list for completeness and to seek out missingrequirements.

3. Examine the list of requirements to identify whichrequirements are likely to change and which are stable. In theearly stages of design spend more time on the enduringcomponents.

4. Predict future customer needs and requirement changes. Makeallowances for changes and create flexibility in components toaccommodate future changes.

5. Use an iterative approach to product development. Quickturnover of designs and prototypes provides a method for testingrequirements and discovering unanticipated requirements.

6. Build flexibility into a design by selecting productarchitectures that tolerate changing requirements. This can beachieved by over-designing components to meet future needs,particularly in components that are likely to change. These stepshave been established during the ongoing design of the Bug IDproject and have been helpful in preventing lost time due to designchanges. However, the system was not looked at as a complexsystem.

Concurrent Design Tools

(Chris Fagan)

Concurrent engineering projects bring many disciplines togetherfor a single design process. Tools have been developed to helpdesigners with different knowledge and background work together onsingle designs.

SystemDesign with SysML

(Chris Fagan)

SysML is a graphical language that uses a modeler to aid in thecreation of diagrams. The basic idea of SysML is to create diagramsthat capture information and actions of a system. SysML then linksthem together allowing the model information to be used to help“specify, design, analyze and verify systems” ^[5]. Each of thenine diagrams in SysML represents a specific part of the system.The diagram types in SysML are outlined in the paragraphs below^[6]:

1. Requirement Diagrams represent the requirements and theirinteractions in the system.

2. Activity Diagrams illustrate the behavior of the system,which is dependent on the inputs and outputs of the system.

3. Sequence Diagrams also portray behavior of the system, butthis time in terms of messages between parts.

4. State Machine Diagrams represent behavior when there aretransitions between entities.

5. Use Case Diagrams render system functionality in terms ofexternal actors. The external actors use the entities in thediagram to complete specific goals.

6. Block Definition Diagrams represent structural components,composition and classification of the system.

7. Internal Block Diagrams shows the different blocks of thesystems and the connections between the blocks.

8. Parametric Diagrams contain property constraints, such asequations, that aid engineering analysis.

9. Package Diagrams shows how the model is organized. It usespackages and relates them together to represent the model.

Together these linked diagrams model the entire systemcontaining elements within the physical environment such as people,facilities, hardware, software, and data ^[7]. Thediagrams are implemented across disciplines to show the flow ofdata through the design.

Limitations and Benefits ofSysML

(Farzaneh Farhangmehr)

For an efficient system modeling, the system languages should beunified. To aim this goal, Unified Modeling Language (UML)presentsa standard way to write system models including conceptual things(for example system functions, software components etc.); Howeverit has some limitations, for example because of its software bias,UML can not describe relationships between complex system composedof both hardware and software. The Systems Modeling Language(SysML) is an extension of UML structures by several improvementstending to address these limitations. It attempts to provide amodeling language for complex systems that also includenon-software components (i.e. hardware, information, etc). Based onthe general definition: “SysML is a general-purpose graphicalmodeling language for specifying, analyzing, designing andverifying complex systems hat may include hardware, software,information, personnel, procedures, and facilities.” ^[8]

SysML presents a general purpose modeling language for complexsystems applications. It can cover complex systems with a broadrange of diverse domains (especially hardware and software) byfacilitating the integration between systems and software. Itallows the design team to determine how the system interacts withits environments in addition to understand how different parts ofthe system interact with each others. In addition, since SysML is asmaller language rather than UML (because it reducessoftware-centric limitations of UML), SysML is easier tounderstand, more flexible and easier to be expressed. Furthermore,its requirement diagram provides a technique for requirementmanagement. Finally, since SysML diagrams describe allocationsbetween behavior, structure and constraints (In general itsallocation of function to form especially deployment of software ona hardware platform ^[9]), it can reduce the cost of thedesign.

Trade Studies with BayesianTheory

(Chris Fagan)

Any complex design task will require some trade off ofperformance, cost or risk. The results of these trade offs can beseen in a trade study, which are used to find configurations thatbest meet the requirements ^[10]. Thesetrade studies often have a large amount of uncertainty because ofthe limited knowledge of the design when the study is produced.Bayesian models can be used to support these trade studies and toshow the probability of certain events.

A Bayesian model has three elements ^[11]:

1. A set of beliefs about the world

2. A set of decision alternatives

3. A preference over the possible outcomes of action

Although these Bayesian models may contain information that isinconsistent or incomplete, they are especially good at situationsthat are characterized by uncertainty and risk. In these casesBayesian models will suggest the best choice to pursue given themodel statement ^[12]. This suggestion comes frommodeling the decision making during the design process. There is asignificant advantage in the design process just by structuring theproblem. The Bayesian model provides analytical support to thedecision making process ^[13]. Bayesian models can address^[14]:

• Targeting uncertainty
• Evaluating uncertainty
• Importance of uncertainty
• Mix of qualitative and quantitative criteria
• Fusion of multiple team member evaluations
• Determining what to do next to ensure the best possibledecision is being made

Overall, applying Bayesian models to trade studies clarifiesdecisions and provides direction.

ARL Trade SpaceVisualizer (ATSV)

(Farzaneh Farhangmehr)

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In the early stages of design, one of the main goals of decisionmakers is to generate trade studies of possible design options thatcan meet design requirements and select the best one. Intraditional multi-objective optimization techniques,decision-makers have to quantify their preferences a priori. To aimthis goal, a Pareto Frontier of non-dominated solutions has beendefined so that decision makers can evaluate preferences. However,sometimes decision makers cannot successfully determine theirpreferences a priori. To address this problem, ARL Trade SpaceVisualizer (ATSV) ^[15] has been developed to allowexploration of a trade space. The goal of ATSV is generally toprovide a technique for populating a multi-objective trade spaceusing differential evolution ^[16] based onthe reduced (in aspect of dimension) subset of the objectives. As aresult, instead of a single solution, ATSV provides unconstraineddimensions and it allows decision makers to analyze interactions ofdesign variables.

Limitations and Benefits of ARL Trade Space Visualizer(ATSV)

Design by shopping which was introduced by Rick Balling ^[17] in 1998 enables a posterioriarticulation of preferences by allowing decision makers to view avariety of feasible designs and then selecting a preference aftervisualizing the trade space for an optimal design based on thispreference. ATSV by multi-dimensional data visualization (Glyphplots, Histogram plots, Parallel coordinates, Scatter matrices,Brushing, Linked views) displays multiple plots simultaneously andinteractively applies preferences. So, instead of a singlesolution, ATSV provides unconstrained dimensions and it allowsdecision makers to analyze interactions of design variables. Inaddition, this exploration provides additional information aboutother areas of the trade space which may affect decision makers’preferences. Furthermore, the automation used in ATSV brings animplementation to shopping paradigm by analyzing a large number ofdesigns in a short period of time. In spite of these benefits, as anew technique, ATSV needs to be matured in some areas. One of thisarea is to develope faster graphical interfaces; understand theimpact of problem size and complexity on user performance and limitthe size of the problem based on results of theses findings ^[18]. Furthermore, future worksshould be done to ptovide ATSV with techniques of group decisionmakings. Finally, like most of available design methods, ATSV failswith respect to provide techniques of visualizing risk anduncertainties associated with systems.

CollaborativeEngineering

(Chris Fagan)

Intelligent Negotiation Mechanism states that because differentdisciplines have different goals and knowledge, it is unavoidablethat conflict will happen in the design process. Manymultidisciplinary design projects require engineers to work ondesigns simultaneously, which can lead to confusion due to gaps incommunication among engineers. Thus, it is important to design awork flow to keep the design process from being delayed ^[19].

Efforts have been made to find solutions to this issue and makeconcurrent engineering more effective. Similar to shopping for adesign in ATSV, it was found that a visual representation for themanagement of the project could be beneficial. This firstculminated in Gantt charts; a basic planning tool. These Ganttcharts were only able to display information inputted from a user.As a result, this output was only as good as the information put inand if there were changes midstream the program may or may not beable to accurately predict a change^[20].

There still was a desire for a program that could bridge the gapbetween engineering design and project management. ConcurrentSimultaneous Engineering Resource View (ConSERV) is aknowledge-based project and was built with the idea that there is arelationship between design and project management. ConSERV's aimis to provide a visual representation of engineering designactivities being done concurrently ^[21]. Figure 4shows a graphical representation of this idea ^[22].

The ConSERV concept essentially applies a project managementapproach to the design process. The ConSERV software serves as adecision support system, providing schedule reminders and keepingtrack of progress. This allows the project manager to oversee allaspects of a multidisciplinary project with ease. The ConSERVmethodology can be implemented in 5 stages ^[23]:

1. Identify the project and parameters

2. Identify the main risk elements

3. Identify the most appropriate management tools

4. Establish the team and the project execution plan

5. Apply the ConSERV concept

Although ConSERV is an entire software package that applies allthese design strategies simultaneously, the concepts can be appliedwith traditional project management software and organization.

ProductData Management

Product data management or PDM is a very important part of anydesign. PDM is a software of some type that controls and tracksdata for different designs. This can save time in money incompanies where data can sometimes be lost when no PDM is used. Onesuch software tool is Team Center. Team Center allows people fromall of the world to be working on one system design while keepingall the data on a central server. It also acts as a overseer forsensitive files such as CAD files, allowing only certain users tomodify the part and preventing any lost data.

More on PDM can be read at the wiki here: PDM Wiki

DFX(Design for X)

DFX is the process of designing of a specific trait. This caninclude design for manufacturing, safety, performance, marketing,environment, cost and flexibility.

DesignFor Manufacturing

(Adam Aschenbach)

What isit?

Designing for manufacturability is a principle that engineersuse to design parts that can be easily manufactured. DFM is usuallya principle that concentrates on reducing the cost of partproduction. Designing for manufacturability should start at thebeginning of product development and should be improved uponthroughout the design process. Engineering should work withmanufacturing and other functional groups to design parts that canbe easily manufactured.

Why useit?

By thinking about the manufacturability of parts from thebeginning of a project, engineers and manufacturing can worktogether to create cost effective parts that satisfy both groups.Parts that are designed for manufacturability and assemblygenerally cost less to produce than parts that are not designedwith these considerations. Since cost reduction is one of the maingoals of DFM, DFM is a principle known and used at mostcompanies.

DFM Guidelines and CommonPractices

Examples ofDesigning for Manufacturability

•Using the same screws throughout a product to reduce the numberof tools on the assembly line, the number of tool changes whenmachining, and the number of different screws that are held ininventory

•Creating access holes for assembly workers that allow for easyinstallation of screws in tight spaces

•Reducing the number of parts within an assembly so there isless time spent assembling

•Leaving enough space between parts to allow for toolclearance

Design forSafety

(Adam Aschenbach)

Designing for safety is used when the safety of the people whouse and are around the parts is most important. Designing spentnuclear fuel transport vessels is a perfect example of designingfor safety. The transport vessels for spent nuclear fuel have to beextremely rigid and have to be able to take a massive amount ofpunishment. For example the vessel has to be able to withstand a40” drop onto a 6” diameter steel spike. The vessel must also beable to withstand a drop test from 30’ onto an unyielding surface.Both of these tests are to show the safety of the transport vessel.In general, designing for safety does not consider cost as one ofthe design parameters because it often costs a lot of money to makea part or system safe.

Designfor Performance

(Karl Jensen)

Design for Performance is a specialized area. There are manyaspects of DFP that are different from other areas of design. Inthis case, the design is all about getting the most performance outof the product as possible. Often times, little care is given tocost and manufacturability. Some examples of where this applies isthe racing industry (automotive and otherwise), space missions, andsome military projects (such as fighter jets). In order to get thebest performance out of a design it is important to model andsimulate the product before it is built. The modeling andsimulation can be a complex process, but can save time and moneylater in the design process, as well as produce the bestperformance out of the design. Through simulation combined withDoE, trends can be found between design variables that can be usedto achieve maximum performance. Complex system design is one ideathat can be applied well to DFP. By looking at the product as awhole, complex interactions between large systems can be found.

(Blake Giles)

The following diagram depicts a typical design process gearedtoward the design of high performance complex systems such asairplanes and space vehicles.

Step 1: Define the overall goal of the mission and itsobjectives through qualification. This statement should be referredto throughout the design cycle to ensure that the mission needs arebeing met^[24].

Step 2: Quantify how well the mission objectives should be metto allow for success. These should be high level performancemetrics of system attributes. For example, in the design of anautomobile, these metrics would be acceleration, cornering, fueleconomy, etc. These metrics are certainly subject to changethroughout the design cycle and should be revisited.

Step 3: Define and characterize concepts that will meet missionobjectives. This brainstorming activity should enumerate thepossibilities of several different concepts that could potentiallylead to mission success.

Step 4: Define alternate mission elements or missionarchitectures to meet the requirements of the mission concept.Architectures are the high level descriptions of the physicalsystems and sub-systems that carry out functions to accomplish themission concepts.

Step 5: Identify the principle cost and performance drivers.These are the architectures which have a relatively high impact onsystem cost and performance. By identifying these drivers earlywill allow the design team to balance performance and cost.

Step 6: Characterization of mission architectures means todefine the sub-systems of the vehicle including weight, power, andcost. Here mathematical models can be applied to describesub-system performance.

Step 7: Evaluate quantitative requirements and identify criticalrequirements

Step 8: Quantify how well requirements and broad objectives arebeing met relative to cost and architectural choices.

Step 9: A baseline design is a single consistent definition ofthe system which meets most or all of the mission objectives. Aconsistent system definition is a single set of values for all ofthe system parameters which fit together.

Step 10: Translate broad objectives and constraints intowell-defined system requirements.

Step 11: Translate system requirements into component levelrequirements.

Design forMarketing

(Adam Aschenbach)

Design for marketing is a way to design parts and systems sothat they will be marketable. Some products may have greatfunctionality but they may never sell in large quantities becausethey are not marketable. An example of design for marketing waspresented in class. When Professor Burke worked at HP, marketingcame to the engineers and said that they wanted the printcartridges to fit into beveled card board boxes. Although theseboxes may have been slightly harder to produce and assemble theywere used as a way to differentiate HP print cartridges from alltheir competitors. This made it easy for customers to tell whichcartridges they needed for their HP printers and the boxes had moreappeal due to their unique shape.

PPP

3P is a product and process design tool or methodology. Thethree P's represent product, preparation, process. Derived fromlean manufacturing, 3P is a 'Design For Manufacturability' approachin which large consideration of the product or process design istargeted at reducing waste in the manufacturing process. Wherecontinual improvement, also derived from lean, aims to iterativelyimprove the manufacturing process in small low impact incrementsover time, 3P allows engineers to redesign a process from scratchand seek better performing solutions. The type of overhaul ishigher performing but requires more resources and capital.

The goal of a 3P team is to meet customer requirements whileusing the least amount of resources and realizing a quick time toimplementation. A cross-functional team is selected to representmultiple aspects of the product or design. In a multi-day process,the team will review customer requirements and brainstorm severalsolution possibilities. These solutions are evaluated for merit andthree are selected for prototyping. Physical representations arebuilt to better understand the qualifications of the solutions,finally one is selected for implementation.

Failure Modes and EffectsAnalysis

(Adam Aschenbach)

What isit?

FMEA is a tool that can be used to determine possible failuremodes for a product and the severity of those failures. Usually alist of parts within a system is created and then failure modes foreach part or sub assembly are found through group brainstorming.Once a list of failure modes is determined, each failure mode isgiven a score for severity (S), occurrence (O), and detection (D).Once these numbers are determined (based on a scale of 1 to 10),the risk priority can be found. Below is a site that gives onedescription of what the ratings 1-10 may pertain to for severity,occurrence, and detection.

FMEA ScoreDefinitions

The risk priority is found by multiplying S x O x D = Risk. Ingeneral, a higher risk number means that the specified failure modeshould have a higher design priority than a failure mode with alower risk number. At the end of an FMEA meeting, tasks are usuallyassigned to people that are present. These tasks pertain tounknowns discovered while participating in the FMEA. By assigningtasks to specific people there is a higher probability that thetasks will be completed and that the document will be updated withcorrect information.

Is FMEA a concurrentengineering tool?

The people involved in FMEA sessions usually come from manydifferent disciplines. There are usually people present fromengineering, customer service, marketing, manufacturing, as well asmanagers. All of these people have to work together to determinepossible failure modes for parts. These people also need todetermine the scores associated with each failure mode. People fromcustomer service may have knowledge about past products and can usetheir knowledge to give scores for occurrence. Someone fromcustomer service may also be able to brainstorm possible failuremodes for parts similar to ones used in the past. People frommanufacturing may be able to provide information about howdetectable a certain failure mode is because they have worked onthe assembly lines or have experience in failure detection. Bybringing in a diverse group of people together early in the designphases, more problems can be identified and properly scored.

Why useit?

Many companies from small engineering firms to largeorganizations such as NASA use FMEA as a tool to minimize some ofthe risks associated with introducing a new product. FMEA can beused as a design tool to help prevent failures and improve productrobustness. A well written FMEA document can also be used forreference when designing new products.

Whenshould it be used?

The Concurrent Design Process In Apple

An FMEA document should be started at the onset of a project.The document should be updated any time there is a significantrevision to the design, if new regulations are implemented, ifcustomer feedback indicates a problem, and if there are any changesmade to the operating conditions.

Advantages

•If possible failure modes can be realized and eliminated earlyin the design process, the resulting product will be more robustand fewer changes will need to be made after the product isreleased.

•Cooperating with different functional groups can bring upfailure modes that engineering alone may not have seen.

•Creates a document that can be referenced for future projectsthat may use the same parts or practices.

Limitations

•It is unlikely that all of the failure modes will be realizedeven if significant time is spent in FMEA meetings.

•FMEA is dependent on the knowledge of those involved in theprocess. If the people involved do not have experience with pastproducts or previous failure modes, the document may not be asstrong as it could be.

•Risk numbers may be misleading and ultimately judgment must beused to determine what the highest risk failure modes are.

Collaborativedecision making within optimization domain

(Farzaneh Farhangmehr)

Decision making during the design process has three main steps:options identification; expectation determination of each optionand finally expression of values. Following the complexity ofmultidisciplinary systems, the design process of such systems ismostly based on concurrent design team. For sure, collaborativedecision making has its own challenges. The collaborativeoptimization strategy was first proposed in 1994 by Balling andSobieszczanski-Sobieski ^[25] and Kroo et al ^[26]. Two years later, in 1996,Renaud and Tappeta ^[27] extended it for multi-objectiveoptimization for non-hierarchic decisions. In recent years manyresearches have been conducted to address challenges ofCollaborative Decision-Based Design for eliminating communicationsbarrios of design team during design lifecycle. Agent-baseddecision network ^[28], Multi-Agent architecture forcollaboration ^[29] and decision-based designframework for collaborative optimization ^[30][31] are examples of theseapproaches. (Also see decision-based software development: designand maintenance by Chris wild et al ^[32]. In spiteof differences, all these methods should be able to meetrequirements of making decisions by considering these facts:

- decisions might have different sources and disciplines;

- they might be in conflict with each others due to differentcriteria;

- decision makers might be individuals or groups;

- decisions might be made sequentially or concurrently;

- designers make decisions based on personal experiences

- information might be uncertain and fuzzy.

Decision making in multidisciplinary complex systems is to selectoptions that maximize the objective function while optimizationmethods (as automated decision making) minimize the number of timesan objective function is evaluated. Optimization techniques, inaspect of decision making, are applied for selecting the mostpreferred design options from the set of alternatives withoutevaluating all possible alternatives in details. As a result,optimization techniques increase the speed of design byautomation.

As mentions above, decision making has three main elements: optionsidentification; expectation determination of each option andfinally expression of values. The optimization problems, which aremaximization or minimization of the objective function while allconstraints are satisfied, can be modeled as decision making tool.In this context, the option space can be modeled as a set ofpossible values of x in the feasible area; the expectation ismodeled as F(x) and the preference is modeled by maximization orminimization. In this process, optimizer aims to maximize theexpected VN-M utility of the profit or net revenue. This figureshows the basic architecture of collaborative optimizationdeveloped by Barun et al ^[33].

Risk and UncertaintyManagement

(Farzaneh Farhangmehr)

Risk andUncertainty

The main duty of design teams during the design process anddevelopment of engineering systems is to make optimal decisions inuncertain environments. Their decisions should satisfy limitationsdue to constraints associated with systems. One of theselimitations is risk that might lead to failure or suboptimalperformance of systems. On the other hand, uncertainties associatedwith decisions have significant effects on critical factors andassumptions underlying each decision and having no plan formanaging these uncertainties increase costs of design and decisionmaking by changing resources (market, time, etc).

According to concerns of diverse fields, including design,engineering analysis, policy making, etc., there are severaldefinitions for the term of “uncertainty, such as: “acharacteristic of a stochastic process that describes thedispersion of its outcome over a certain domain”^[34] or “The lack of certainty, Astate of having limited knowledge where it is impossible to exactlydescribe existing state or future outcome, more than one possibleoutcome” . ^[35]“ and so risk is defined as 'astate of uncertainty where some possible outcomes have an undesiredeffect of significant loss'^[35].

Riskmanagement

In literature generally refers to the act or practice ofcontrolling risk ^[36]. Based on this definition, riskmanagement includes the processes of planning, identification,analysis, mitigating and monitoring of risks ”^[37]. TheInternational Organization for Standardization identifies thefollowing principles of risk management: ^[38][39]

• Risk management should create value.
• Risk management should be an integral part of organizationalprocesses.
• Risk management should be part of decision making.
• Risk management should explicitly address uncertainty.
• Risk management should be systematic and structured.
• Risk management should be based on the best availableinformation.
• Risk management should be tailored.
• Risk management should take into account human factors.
• Risk management should be transparent and inclusive.
• Risk management should be dynamic, iterative and responsive tochange.
• Risk management should be capable of continual improvement andenhancement.

According to the standard ISO/DIS 31000 'Risk management --Principles and guidelines on implementation' ^[38], the process of riskmanagement consists of several steps as follows ^[39]:

1. Identification of risk in a selected domain ofinterest
2. Planning the remainder of the process.
3. Mapping out the following:
   • the social scope of risk management
   • the identity and objectives of stakeholders
   • the basis upon which risks will be evaluated, constraints.
4. Defining a framework for the activity and anagenda for identification.
5. Developing an analysis of risks involved inthe process.
6. Mitigation of risks using availabletechnological, human and organizational resources.

-Risk planning:

The first step of risk management is providing the design teamwith the list of possible risk identification methods, assessmenttechniques and mitigation tools and then allocating resources tothem by balancing resources (cost, time, etc.) against their valueto the project.

- Risk identification: The taxonomy applied inNASA Systems Engineering Handbook Management Issues in SystemsEngineering [37] categorizes risks into organizational, management,acquisition, supportability, political, and programmatic risks.Applying previously examined risk templates, conducting expertinterviews, reviewing the similar lessons learned documents ofsimilar projects, analyzing the potential failure modes and theirpropagation by applying FMEA, FMECA, Fault Tree Analysis, etc.

- Risk Mitigation and Monitoring:

• Do nothing and accept the risk: Future risk informationgathering and assessments

• Share the risk with a co-participant: Share it with aninternational partner or contractor (i.e. Incentive contracts,warranties, etc.)

• Take preventive action to reduce the risk: Additional planning(i.e. additional testing subsystems and systems, designing inredundancy, off-the shelf hardware, etc.)

• Plan for contingent action: Additional planning

Uncertainty Management

The evaluation of sources, magnitude, and mitigation of risksassociated with systems has become a concern of designers duringthe design process and life cycle of complex systems. They shouldunderstand attitudes toward risks; know where more information isneeded, and possible consequences of inevitable decisions should bemade during the design process. However; inevitable uncertaintiesassociated with systems change critical factors and assumptionsunderlying these decisions and might either increase costs ofdesign or explore new opportunities.

Uncertainty Classification^[40]

Uncertainty can be due to lack of knowledge (refers to Epistemicor Knowledge uncertainty) or due to randomness in nature(refers toAleatory, Variability Random or Stochastic uncertainty). One sourceof uncertainty, ambiguity uncertainty [6-8], results fromincomplete or unclear definitions, faulty expressions or poorcommunication. Model uncertainty includes uncertainties associatedwith using a process model or a mathematical model. Modeluncertainty might be a result of mathematical errors, programmingerrors, and statistical uncertainty. Mathematical errors includeapproximation errors and numerical errors, where approximationerrors are due to deficiencies in models for physical processes andnumerical errors result from finite precision arithmetic [9].Programming errors are errors caused by hardware/software [10-13],such as bugs in software/hardware, errors in codes, inaccurateapplied algorithms, etc. Finally, statistical uncertainty comesfrom extrapolating data to select a statistical model or providemore extreme estimates [14]. Uncertainties associated with thebehavior of individuals in design teams (designers, engineers,etc.), organizations, and customers are called behavioraluncertainty. Behavioral uncertainty arises from four sources: Humanerrors, decision uncertainty, volitional uncertainty and dynamicuncertainty. Volitional uncertainty refers to unpredictabledecisions of subjects during the stages of design [14]. Humanerrors [15-16] are uncertainties due to individuals’ mistakes.Decision uncertainty is when decision makers have a set of possibledecisions and just one should be selected. The fourth major sourceof behavioral uncertainty, dynamic uncertainty, is when changes inthe organization or individuals’ variables or unanticipated events(e.g., economic or social changes) contribute to a change in designparameters that had been determined initially. Dynamic uncertaintyalso includes uncertainties resulted from degrees of beliefs whereonly subjective judgment is possible. Finally, uncertaintiesassociated with the inherent nature of processes are called NaturalRandomness. This type of uncertainty is irreducible and decisionmakers are not be able to control it in the design process.

This figure shows uncertainty classification provided by CACTUS^[41]

Uncertainty assessment^[40]

Attempts to quantify uncertainty during the design process havebeen published, but most focus on the quantitative aspects ofuncertainty only [17]. These technical methods have to becomplemented with qualitative methods, including expert judgments.While there have been attempts to accomplish this in various fields[18-21], methods to incorporate both types of uncertainties in adesign process are not addressed. Uncertainty assessment methodsgenerally are divided into four major approaches based on theircharacteristics in analyzing data and representing the outputs:

A probabilistic approach is based on characterizing theprobabilistic behavior of uncertainties in the model including arange of methods to quantify uncertainties in the model output withrespect to the random variables of model inputs. These methodsallow decision makers to study the impact of uncertainties indesign variables on the probabilistic characteristics of the model.Probabilistic behavior may be represented in different ways. One ofthe basic representations is the estimation of the mean value andstandard deviation. Although this representation is the mostcommonly used result of the probability methods, it cannot provideus with a clear understanding of the probabilistic characteristicsof uncertainties. Another representation of probabilistic behavioris the probability density function (PDF) and the cumulativedistribution functions (CDF), which provide the data that isnecessary for analyzing the probabilistic characteristics of themodel. Although the classic statistical assessment approachesclarify the type and level of risk by assessing associateduncertainties, they cannot take past information into account. Toaddress this problem, a Bayesian approach offers a wide range ofmethodologies based on Bayesian probability theory, assuming theposterior probability of an event is proportional to its priorprobability [22-24]. The Bayesian logic can also be used to modeldegrees of beliefs. The role of a Bayesian model for assessingdegrees of beliefs is more important in large-scalemultidisciplinary systems. Simulation methods analyze the model bygenerating random numbers and then observing changes in the output.In other words, a simulation approach is a statistical techniqueclarifying the uncertainties that should be considered to reach thedesirable result. Simulation methods are generally applied when aproblem cannot be solved analytically or there is no assumption onprobability distributions or correlations of the input variables.The most commonly used simulation-based methodology is the MonteCarlo Simulation (MCS) [25, 26]. MCS includes a large number ofrepetitions, generally between hundreds and thousands. Simulationmethods can be used on their own or in combination with othermethods. Methods which incorporate both qualitative andquantitative uncertainties are placed in the fourth category asqualitative approaches. One example is NUSAP [18], which stands for“Numeral, Unit, Spread, Assessment and Pedigree”, where the firstthree categories are quantitative measures and the two nextcategories are qualitative quantifiers which might be applied incombination of other assessment methods such as Monte Carlo andsensitivity analysis. Some other methods, such as ACCORD® [31],which are based on the Bayesian theory, can be considered asassessment techniques that combine both types.

Uncertaintymitigating and diagnosing methods^[40]

Although being familiar with sources of uncertainty andmethodologies for assessing them are critical, one challengeremains: how can we handle and mitigate the effects of theseuncertainties in the systems? In addition, how can we diagnose thembefore it is too late and they get out of control? To answer thesequestions, let's provide methodologies for uncertainty diagnosisand mitigation:

Uncertainties due to programming errors can be diagnosed by thosewho have committed them. Since programming errors may occur duringinput preparation, module design/coding and compilation stages[27], it can be reduced by better communication, software qualityassurance methods [28, 29], debugging computer codes and redundantexecutive protocols. Applying higher precision hardware andsoftware can mitigate the effect of mathematical uncertaintiesassociated with the model due to numerical errors resulting fromfinite precision arithmetic. In addition it reduces the effect ofstatistical uncertainties. Statistical uncertainty also can bemitigated by selecting the best data sample in terms of both sizeand the similarity to the model. Similar to the statisticaluncertainty, approximation uncertainty is minimized when the bestmodel with acceptable range of errors and the best assumption forvariables, boundaries, etc., is selected. Simulation approachesmight be applied to generate the best model. Ambiguity uncertaintyis naturally associated with human behavior; however it can bereduced by clear definitions, linguistic conventions or fuzzy setstheory [7], [30]. Volitional Uncertainty which results fromunpredictable decisions especially in multidisciplinary design isdiagnosed by other organizations or individuals and is mitigated byhiring better contractors, consultants and labor [9, 14]. AlthoughHuman errors and individuals’ mistakes are inevitable in thesystem, they might be diagnosed and mitigated by applying humanfactors criteria such as inspection, self checking, externalchecking, etc. When only subjective judgments are possible theeffect of dynamic uncertainty can be mitigated by applying Bayesianapproach [22-24]. In addition this type of uncertainty can bereduced by applying design optimization methods to minimize theeffect of changes in variables or unanticipated events whichcontribute changes to design parameters. Such as dynamicuncertainty, design optimization is useful for reducing the effectof decision uncertainty when a set of possible decisions areavailable. Methods based on Bayesian decision theory (such asACCORD® [31]) also can be used to help decision makers to make moreinformed choices. Sensitivity analysis [32-33] and robust design[34-35] are also helpful by determining which variables should becontrolled to improve the performance of the model.

ProductLifecycle

Wikipedia gives a good definition of product lifecycle. Product Lifecycle

References

1. ↑ Sobek II, DurwardK., Allen C. Ward, and Jeffrey K. Liker. 'Toyota's Principles ofSet-Based Concurrent Engineering.' Sloan Management Review:67-83.
2. ↑ Ullman, D. G.,“The Mechanical Design Process,” Third Edition, McGraw-Hill, NewYork, 2002.
3. ↑ Peterson, C.,“Product Innovation for Interdisciplinary Design Under ChangingRequirements: Mechanical
4. ↑ Peterson, C.,“Product Innovation for Interdisciplinary Design Under ChangingRequirements: Mechanical
5. ↑ Friedenthal, S.,A. Moore, R. Steiner, “A practical Guide to SysML: The SystemsModeling Language,” Chapter 3, 2008.
6. ↑ Friedenthal, S.,A. Moore, R. Steiner, “A practical Guide to SysML: The SystemsModeling Language,” Chapter 3, 2008.
7. ↑ Vanderperren, Y.,W. Dehaene, “UML 2 and SysML: Approach to Deal with Complexity inSoC/NoC Design,” Proceedings of the Design, Automation and Test inEurope Conference and Exhibition, 2006.
8. ↑ SanfordFriedenthal, Alan Moore and Rick Steiner, “A practical guide toSysML: The System Modeling Language”, ISBN: 978-0123743794.
9. ↑ 6- Mattew Hause,“An overview of the OMG Systems Modeling Language (SysML)”,Embedded Computing Design, August 2007. http://www.embedded-computing.com/pdfs/ARTiSAN.Aug07.pdf
10. ↑ Ullman, D., B.Spiegel, “Trade Studies with Uncertain Information,” SixteenthAnnual International Symposium
11. ↑ D’Ambrosio, B.,“Bayesian Methods for Collaborative Decision-Making,” RobustDecisions Inc., Corvallis, OR.
12. ↑ D’Ambrosio, B.,“Bayesian Methods for Collaborative Decision-Making,” RobustDecisions Inc., Corvallis, OR.
13. ↑ D’Ambrosio, B.,“Bayesian Methods for Collaborative Decision-Making,” RobustDecisions Inc., Corvallis, OR.
14. ↑ Ullman, D., B.Spiegel, “Trade Studies with Uncertain Information,” SixteenthAnnual International Symposium
15. ↑ Gary M. Stump,Timothy W. Simpson, Mike Yukish and John J. O’Hara, “Trade SpaceExploration of Satellite Datasets Using a Design by ShoppingParadigm”, 0-7803-8155-6/04/$17.00© 2004 IEEE.
16. ↑ R. Storn and K.Price, “Differential evolution – a simple and efficient adaptivescheme for global optimization over continuous spaces”. TechnicalReport TR-95-012, ICSI, 1995.
17. ↑ R. Balling.“Design by shopping: A new paradigm”, In Proceedings of the ThirdWorld Congress of Structural and multidisciplinary Optimization(WCSMO-3), volume 1, pages 295–297, Buffalo, NY, 1999
18. ↑ Timothy W.Simpson, Parameshwaran S. Iyer, Link Rotherrock, Mary Frecker,Russel R. Barton and Kimberly A. Barrem, “Metamodel-DrivenInterfaces for Engineering Design: Impact of delay and problem sizeon user performance”, American Institute of Aeronautics andAstronautics.
19. ↑ Liang, C., J.Guodong, “Product modeling for multidisciplinary collaborativedesign,” Int. J. Adv. Manuf. Technol., Vol. 30, 2006, pp.589-600.
20. ↑ Liang, C., J.Guodong, “Product modeling for multidisciplinary collaborativedesign,” Int. J. Adv. Manuf. Technol., Vol. 30, 2006, pp.589-600.
21. ↑ Conroy, G., H.Soltan, “ConSERV, a methodology for managing multi-disciplinaryengineering design projects,” International Journal of ProjectManagement, Vol 15, No. 2, 199, pp.121-132.
22. ↑ Conroy, G., H.Soltan, “ConSERV, a methodology for managing multi-disciplinaryengineering design projects,” International Journal of ProjectManagement, Vol 15, No. 2, 199, pp.121-132.
23. ↑ Conroy, G., H.Soltan, “ConSERV, a methodology for managing multi-disciplinaryengineering design projects,” International Journal of ProjectManagement, Vol 15, No. 2, 199, pp.121-132.
24. ↑ Test
25. ↑ Balling, R. J.,and Sobieszczanski-Sobieski, J., 1994, ‘‘Optimization of CoupledSystems: A Critical Overview of Approaches,’’ AIAA-94-4330-CPProceedings of the 5th AIAA/NASA/USAF/ISSMO Symposium onMultidisciplinary Analysis and Optimization, Panama City, Florida,September.
26. ↑ 138. Kroo, I.,Altus, S., Braun, R., Gage, P., Sobieski, I., 1994,‘‘Multidisciplinary Optimization Methods for Aircraft PreliminaryDesign,’’ AIAA-96-4018, Proceeding of the 5th AIAA/NASA/USAF/ISSMOSymposium on Multidisciplinary Analysis and Optimization, PanamaCity, Florida, September
27. ↑ Tappeta, R. V.,and Renaud, J. E., 1997, ‘‘Multiobjective Collaborative Optimizatio“, ASME J. Mech. Des., 119, No. 3, pp. 403–411
28. ↑ Mohammad RezaDanesh, and Yan Jin, “AND: An agent-based decision network forconcurrent design and manufacturing”, Proceedings of the 1999 ASMEDesign Engineering Technical Conferences, Sep 12-15, 1999, LasVegas, Nevada
29. ↑ Zhijun Rong,Peigen Li, Xinyu Shao, Zhijun Rong and Kuisheng Chen, “GroupDecision-based Collaborative Design,” Proceedings of the 6th WorldCongress on Intelligent Control, and Automation, June 21 - 23,2006, Dalian, China.
30. ↑ Xiaoyu.Gu ,“Decision-Based Collaborative Optimization” journal of Mechanicaldesign Vol 124, No1 PP 1-13, March 2002
31. ↑ X. Gu and J.E.Renaud, “decision-based collaborative optimization”, 8th ASCESpecialty conference on Probabilistic Mechanics and structuralReliability, PMC2000-217
32. ↑ Henk JanWassenaar and Wei Chen, “An Approach to Decision-Based Design WithDiscrete Choice Analysis for Demand Modeling,” Journal ofMechanical Design, SEPTEMBER 2003, Vol. 125
33. ↑ 140. Braun, R.D., Kroo, I. M., and Moore, A. A., 1996, ‘‘Use of the CollaborativeOptimization Architecture for Launch Vehicle Design,’’AIAA-94-4325-CP, Proceedings of the 6th AIAA/NASA/USAF/ISSMOSymposium on Multidisciplinary Analysis and Optimization, Bellevue,WA, September
34. ↑ Tumer Y, I., etal. An Information-Exchange Tool for Capturing and CommunicatingDecisions during Early-Phase Design and Concept Evaluation. in ASMEInternational Mechanical Engineering Congress and Exposition. 2005.Orlando, FL: ASME
35. ↑ ^abDouglas Hubbard, How to Measure Anything: Finding the Value ofIntangibles in Business, John Wiley & Sons, 2007
36. ↑ AFMCP 63-101,Acquisition Risk Management Guide, USAF Material Command, Sept.1993USAF Material Command, Sept. 1993.
37. ↑ NASA SystemsEngineering Handbook. SP-610S, June 1995
38. ↑ ^abTemplate:Cite article
39. ↑ ^abhttp://en.wikipedia.org/wiki/Risk_management
40. ↑ ^abcFarhangmehr, F., Tumer Y. I., “Optimal Risk-Based Integrated DEsign(ORBID) for multidisciplinary complex system”, Submitted toICED09
41. ↑Farhangmehr, F. and Tumer, I. Y., Capturing, Assessing andCommunication Tool for Uncertainty Simulation (CACTUS), Proceedingsof ASME International Mechanical Engineering Congress &Exposition (IMECE 2008), Boston, MA

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