on 22-Sep-2018 (Sat)

rom abaqus import * imports the abaqus module and creates references to aB puhlic~oots . ..defined by that module.

is for making t~e symbolic constants defined by the Abaqus Scripting Interface available to the script. What are symbolic constants? They are variables with a constant value (hence the term constant) that have been given a nanie that makes more sense to a user (hence the term symbolic) but have some meaning to Abaqus.

We use symbolic constants in the sc'ript. Look at the relevant lines in the script where the part is created. Notice the statement: beamPart=beamModel.Part(name='Beam', dimensionality=THREE_O, · type=OEFORMABLE_BODV) . Both THREE_D and DEFORMABLE_BODY are symbolic constants defmed in the abaqusConstants module.

The default model 1n Abaqus is always naf!led 'Model- I ', which is why when you open a new file you always see 'Model-1' in the model database tr ee on the left in the GUI.

mdb.models.changeKey(fromName='Model-1', toName='Cantilever Beam') changes the name of the model from the default of 'Model-}' to 'C antilever Beam'. c_hangeKeyO is a method of models which is in the model database, hence we refer to it usihg mdb.models.changeKeyO.

the models repository is a subclass of a dictionary object which keeps track of model objects. As explained before, a subclass means that it has the same properties and methods of the dictionary object along with a few more properties and methods, such as cbaogeKeyQ, that developers at SIMULIA decided to add in. The model name 'Model- I' is the key, while the value is a model object. The changeKeyO method which is not native to Python essentially allows us to change the key to 'Cantilever Beam' while referring to the same model object.

beamModel = mdb.models['Cantilever Beam') assigns our model, to the beamMOdel-variable. This is so that in future we do not have to keep referring to it as mdb.models('Cantilever Beam') but can in stead just call it beamModel. Look at the 'create the part' block and notice the statement beamProfileSketch = beamModel .ConstrainedSketch(name= 'Beam CS Profile', sheetSize = S) Don't worry about what it means just yet, I only want to point out that if we did not define the variable beamModel, then the same statement would have to be written as: beamProfileSketch = mdb.models(ccantilever Beam']. ConstrainedSketch (name='Beam CS Profile, sheetSize = S)

import sketch import part

Note that the statement can be written without the words 'name' and 'sheetSize' as: beamProfileSketch = beamModel.ConstrainedSketch('Beam CS Profile', 5)

The statement beamProfileSketch.rectangle(point1=(8.1,8.1), point2=(8.3J-8.1)) uses the rcctangleO method of the ConstrainedSketcb object to draw a rectangle on the sketch plane. The two parameters are the coordinates of the top left and bottom right corners of the rectangle.

import material This statement imports the material module into the script providing access to objects and methods related to materials.

The Material object is assigned to the variable beamMaterial.

beamMaterial.Density{table={(7872, ), )) This statement creates a Density object which specifies the density of the material by using the DensityO method. The Density object is defined in the material module, hence you do not use an 'import density' statement

Why a table? Well you might have a density that depends on temperature. In which case you would have a table in the form ((de nsity/ , temperature]), (density2,temperature2), (density3,temperature3)) and so on ...

A Region object is a type of object on which .you can apply an attribute.

lYou can use a Region object to define the geometry for a section assignment, or a load, or a boundary condition, or a mesh, basically it forms a link between the geometry and the applied attribute. A Region object can be a sequence of Cell objects. In fact it can be a sequence of quite a few other objects, including Node objects, Vertex objects, Edge objects and Face objects. In our script we are assigning a Cell object to it. But si.nce it needs to be a sequence of Cell objects, not just one Cell object that we are providing, we stick the comma at the end to make it a sequence

This statement assigns _ th~ rootAssembly . to - the variable beam.Assembly. The rootAssembly is an Assembly object. 1t is a member of the Model object. However you do not need to create it using any method, it is created by default when the Model object is created.

The FieldOutputRequest object is defined in the step module hence you do not use an import fieldOutputRequest statement.

Tbe Basics of Scripting- Cantilever Beam Example beamModel.HistoryOutputRequest(name='Default History Outputs', createStepName='Apply Load', variables•PRESELECT) This statement . creates a new HistoryOutputRequest object using the HistoryOutputRequest method. The first argument is the name of the object, which is its key in the historyOutputRequests repository.

Incremental reading is a learning technique that makes it possible to read thousands of articles at the same time

For, the record, the point should riot be shared by more than one face because then the method will return whichever face it first encounters and there is no way of knowing which one it will be.

When the face we pass as a parameter is set equal to sidelFaces which is the name argument, the normal of the region is the same as the normal of the face. Jf instead we had written side2Faces=top_face, the normal to our region would have pointed into the beam rather than out of it.

One of the reasons why companies struggle with the application of roadmapping is that there are many specific forms of roadmaps, which often have to be tailored to the specific needs of the firm and its business context.

Identifying disruptive technologies and surviving in disruptive markets is not easy but roadmaps can help

A key benefit of roadmapping is the sharing of knowledge and the development of a common vision of where the company is going, and this only comes about by sharing knowledge and making connections

Sectoral or multiorganization roadmapping promotes knowledge sharing and facilitates the development of a collective vision that can lead to action and collaboration

The books are on the table.

I want to hang a shelf on the wall.

I left my keys on the counter.

A strong analytics strategy relates business goals and use cases with how analytics will support employees and the business

Analytics (or data analytics ) can be defined as the systematic quantitative analysis of data or statistics to obtain meaningful information for better decision-making

analysis to understand the status quo that may reflect the result of their efforts to achieve certain objectives.

analytics to identify specific trends or patterns in the data under analysis so that they can predict or forecast the future outcomes or behaviors based on the past trends

hesitation is related to lack of training in the analytical tools, technologies, and processes

the analytics paradigm to break down the processes and systems in complex projects to predict their behavior and outcomes

Assessing feasibility

Managing data overload

Enhancing data visibility and control via focused dashboards

Analyzing project portfolios for project selection and prioritization: Project portfolio analysis is a useful application of analytics

project’s feasibility based on the organization’s project selection criteria, which might include, but not be limited to, the following factors: ■ Technical, economic, legal, political, capacity, and capability constraints ■ Cost-benefits analysis resulting in scoring based on various financial models such as: ■ Net present value (NPV) 1 ■ Return on investment (ROI) 2 ■ Payback period 3 ■ Breakeven analysis 4 ■ Resource requirements ■ Internal resources (only functional department resources, cross-functional resources, cross-organizational resources, or any combination of the preceding) ■ External resources ■ Both internal and external resources ■ Project complexity ■ Project risks ■ Training requirements

select the best course of action based on careful evaluation of various alternatives by analyzing the underlying tangible and intangible criteria is the only way a project manager can lead the project to achieve the stipulated objectives

Decision-making depends on multiple factors including knowledge, skills, tangibles, intangibles, pragmatism, and decision-making methodology

Time Is of the Essence Right and rational decisions made by a project manager in a timely 1 manner are critical for the progress of a project.

Lead by Example Leading by example is an important and effective skill of a project manager to motivate the project team.

Establish Credibility The ability of a project manager to be decisive and make things happen on the project helps her establish her credibility among the project team members as a strong leader

ptg16381985 30 Project Management Analytics Resolve Conflicts and Other Project Problems Projects typically have to deal with lots of uncertainty and involve multiple diverse stake- holders. They involve a variety of conflicts and project problems. The project manager’s responsibility ultimately is to handle and resolve those conflicts and problems effectively to keep the project on track to success.

Avoid Analysis Paralysis

ptg16381985 32 Project Management Analytics 7 Steps to Data-Driven Decision Making Step 5: Propose Potential Possible Solutions (Alternatives) Interpret data analysis results from Step 4 and propose potential possible solutions (alternatives); Define proper metrics. Step 6: Model analytics approach and analyze the proposed alternatives Use appropriate analytics such as predictive analytics, prescriptive analytics, or analytic hierarchy process to analyze proposed alternatives and to select the best alternative. Step 7: Make Decision Make smart informed decision by selecting the best alternative from all potential solution candidates (alternatives) based on the alternative analysis results from Step 6. Step 4: Analyze Data Analyze the collected data to find specific patterns, trends, outliers, and special causes. Step 3: Collect Data Collect data pertaining to the defined problem or situation. Step 2: Review Historical Records / Lessons Learned Check if the current problem occurred in the past. If yes, how was it resolved? If solution is already available, there is no need to re-invent the wheel. Even if the problem at hand did not occur in the past, find if any similar problems occurred in the past and how they were solved if they did occur. Leverage as much as possible to speed up the decision making process. Step 1: Define/Identify the Problem or Situation Clearly understand the problem - Wrong diagnosis means wrong treatment. Figure 2.2 Seven Steps to Data-Driven Decision-Making

Magnitude and complexity of the data: The higher the magnitude and complex- ity of the data, the more difficult and time consuming is the security, storage, and processing of the data

Sources of the data: Sources of data determine the type of data collected. Incor- rect sources means incorrect data and hence incorrect decisions, as discussed in the next section, “Garbage In, Garbage Out.

Quality of the data: Uniform data (attributes) pertaining to various alternatives must be compared to mimic apple-to-apple comparison for fair alternative analy- sis. Also, the quality of the data collected must be adequate to bring forth the true value of a given alternative

Personnel analytical skills: Inadequate analytical skills of data analyst personnel will certainly pose a challenge in ensuring the accuracy, quality, and efficiency of the analysis

Tools and technologies: The speed, accuracy, and quality of data collection, stor- age, analysis, and interpretation processes depend on the available tools and tech- nologies, particularly when the data in question is large and complex

Shelf-life of the data: Data collected that is not processed in a timely manner may become stale and no longer useful

six questions that should be asked to collect the good type and quality of data: ■ What was the source of your data? ■ How well do the sample data represent the population? ■ Does your data distribution include outliers? How did they affect the results? ■ What assumptions are behind your analysis? Might certain conditions render your assumptions and your model invalid? ■ Why did you decide on that particular analytical approach? What alternatives did you consider? ■ How likely is it that the independent variables are actually causing the changes in the dependent variable? Might other analyses establish causality more clearly?

seven Cs of stakeholder management: 1. Concern 2. Communicate 3. Contribute 4. Connect 5. Compound 6. Co-Create 7. Complete

Improve project stakeholder management: Analytics can help improve project stake- holder management by enabling a project manager to predict stakeholder responses to various project decisions. Project stakeholder management is both art and science—art because it depends partly on the individual skillset, approach, and personality of the individual project manager, and science because it is a highly data-driven process

Predict project schedule delays and cost overruns: Analytics can tell a project manager whether the project is on schedule and whether it’s under or over budget. Also, analytics can enable a project manager to predict the impact of various completion dates on the bottom line (project cost).

Project risk identification, rank- ing, and prioritization depend upon multiple factors, including at least the following: ■ Size and complexity of the project ■ Organization’s risk tolerance ■ Risk probability, impact, and horizon ■ Competency of the project or risk manager

Improvement projects typically involve four steps: 1. Understand the current situation. 2. Determine the desired (target) future situation. 3. Perform gap analysis (find the delta between the target and the current situations). 4. Make improvement decisions to address the gap.

Statistical approach comes in handy when dealing with project uncertainties because it includes tools and techniques that managers can deploy to interpret specific patterns in the data pertaining to the project management processes to predict the future more accurately.

Quantitative measure of a process, when that process is performed over and over, is likely to follow a certain frequency pattern of occurrence. In other words, there is a likelihood or probability of recurrence of the same quantitative measure in the long run. This likelihood or probability represents the uncertainty of recurrence of a certain quantitative value of the process.

Normal distribution is the result of the process of accumulation. Usually, the sum or average of the outcomes of various uncertainties constitutes an outcome whose prob- ability distribution is a normal distribution

Poisson Distribution This distribution can be used to count the number of successes or opportunities as a result of multiple tries within a certain time period

Uniform distribution arbitrarily determines a two-point estimate of the highest and low- est values (endpoints of a range) of a random variable. This simplest estimation method allows project managers to transform subjective data into probability distributions for better decision-making especially in risk management

A triangular distribution is called so because of its triangular shape. It is based on three underlying values: a (minimum value), b (maximum value), and c (peak value) and can be used estimate the minimum, maximum, and most likely values of the outcome. It is also called three-point estimation, which is ideal to estimate the cost and duration associ- ated with the project activities more accurately by considering the optimistic, pessimistic, and realistic values of the random variable (cost or duration)

DMAIC stands for the following stages of the Lean Six Sigma life cycle: D efine M easure A nalyze I mprove C ontrol

PDSA cycle follow (refer to Chapter 7 for detailed discussion on the PDSA cycle): ■ Plan: The development of the plan to carry out the cycle ■ Do: The execution of the plan and documentation of the observations ■ Study: The analysis of the observed and collected data during the execution of the PDSA plan ■ Act: The next steps based on the analysis results obtained during study

The AHP approach is a 360 o approach, which includes both subjective and objective criteria in decision-making. The key characteristic of this approach is that it uses pairwise comparisons 16 of all the possible factors of the complex problem at hand and evaluates their relative importance to the decision-making process.