Move Past Spreadsheets with Modern Requirements Management

Are you still using Microsoft Office to capture, manage, analyze, and trace all your requirements? Products and systems increase in complexity every day. You need a requirements management tool that can properly handle large complex projects.

When you use spreadsheets for requirements management you increase your time to market Even worse not using a modern requirements management solution can result in a higher risk of product failure.  A CIO report found that “as many as 71 percent of software projects that fail do so because of poor requirements management.”  Poor requirements management occurs when teams use antiquated RM tools that do not have the needed traceability, collaboration, and quality analysis features.

Traceability needs to happen through the entire process. It’s much simpler to get full project traceability if you can map your process in the same place you create your requirements. That’s why more and more companies are looking at robust solutions for their requirements management. Solutions like Innoslate, that have built in collaboration features, traceability, test processes, system processes, and more. Innoslate has the benefit of being a full lifecycle tool. You can start with requirements management, develop a process for the product and system, and then verify and validate that the process meets the requirements.

Modern requirements tools should be able to  trace between requirements and other classes and get reports such as the RTM, RSM, and RVM. In Innoslate, you can actually use the Test Center feature as well, and you can even trace the requirements to your verification actions (Test Case) and create a complete RVTM.

Another major problem with using spreadsheets is that teams can barely communicate with each other. It becomes difficult to keep files updated. Files are often shared between people using the same tools, but cross sharing isn’t really possible. Large teams with large complex requirements need to be able to communicate effectively. Cloud RM tools provide the ability to collaborate and keep information accurate. You can also look for on-premise solutions that offer your team collaboration, but still meet your security needs. Innoslate offers the ability to work collaboratively throughout the entire project. With Innoslate you can communicate quickly via chat and comments and keep a record of your conversation. Version control allows team members to work together on the same requirements document saving you time and reducing errors.

Of course, with all these collaboration features you need strong program management controls. A program manager can see every change made to a requirement and the team member that made the change. He or she can then revert back to older versions. Baselining allows you to see changes throughout the entire history of the document. With permissions, you can determine which team members can have owner, read/write, or read/only privileges to your project. Branching and forking provides even stricter controls, allowing the program manager to split off certain sections of a project to different groups. From there the program manager can decide which changes to accept back into the main project.

Spreadsheets were not specifically designed for capturing, managing, and analyzing requirements. Microsoft Office’s spell check was built to help maintain proper grammar and spelling. However, Innoslate has a quality analysis feature that can look for mistakes specific to requirements. Writing multiple requirements into one can make verification impossible or writing requirements that aren’t specific enough. These mistakes are costly and can result in poor requirements management. Innoslate can improve your entire requirements document by finding these mistakes for you.

It’s important to find a modern solution that can allow you to move past spreadsheets with traceability, collaboration, and quality analysis.

Watch “Move Past Spreadsheets with Modern Requirements Management” webinar.

10 Most Important Requirements Capture and Management Rules

Requirement documenting plays an important role in systems engineering. Writing high quality requirements can not only save millions of dollars, but lives. No matter how experienced you are it’s important to remind yourself of requirement writing rules and techniques.

  1.  Know Your Stakeholders

    The first and most important commandment of writing requirements is to know your stakeholders. Understand what common knowledge they have. Make sure you are all on the same page. Understand what each group of stakeholder’s priorities is and their objectives. You do not want each group to develop their own priorities and objectives separately. Separate priorities and objective result in a time consuming and expensive review process with lots of conflicts. Collaborative software that allows for continuous reviewing will help you keep up with all the stakeholders needs. You never want to give them a completely finished product and then ask for review (although that is common practice).

  2. Remember the CONOPs

    Most of you will probably not forgo the Concept of Operations (CONOPS), since it is such a valuable artifact. The CONOPS will be something that all the stakeholders understand and collaborate on together.  In this step you basically create stories that will consider different scenarios and needs. From there you will have a better understanding of where to start with your requirements. The CONOPS will help you write quality requirements by finding all the assumptions. It will help evaluate the ‘what if’ scenarios, make testing easier, and formulate your needs into the requirements.

  3.  Understand What is Really Needed

    First of all, there is a huge difference between want and need. Will the system work without a particular requirement? If you answered yes, then you can probably omit that requirement. A common mistake systems engineers make is listing possible solutions to needs rather than the actual needs. If your need is an efficient way to communicate, don’t specify cell phones, since there are many other forms of communications that may be more feasible, less expensive, or effective.  List what is actually needed; don’t list possible solutions to the needs.

  4. Be Specific (Give actual numbers. Don’t leave room for assumptions.)

    Leaving room for assumptions is leaving room for error. If you are not careful with the language you choose you could end up making costly assumptions. Using words such as minimize, maximize, etc., and/or, more efficient, forces the stakeholders to assume. Don’t let the stakeholders assume how much you want to minimize.

    • etc. can mean so many things
    • and/or causes the reader to guess whether its ‘and’ or ‘or’
    • min. max. don’t just say minimize expansion, say minimize expansion to 300
    • don’t just say quick, say how quick
    • give actual numbers
  5. Do Not Be Too Specific

    The only mistake worse than not being specific enough is over specifying. You want to be specific, but not too specific. Carefully review your requirements before baselining. During this review delete any unnecessary specifics.

    Allow scope with your numbers. If a requirement is good enough at expanding 300% +/- 10%, then give that option. Have any numbers be based on the results of analyses, not just someone’s “engineering judgment.”

  6. Give Requirements Not Instructions

    Understand what is needed and create requirements from those needs. This is why Commandment #1 is so important. If you understand your stakeholders needs writing requirements and not instructions becomes an easier task. It might be tempting to just writing instructions, but that is not what requirements are for. Requirements should provide enough information to allow the builder to provide the most cost-effective solution to the problem.

  7.  Use the Words ‘Shall’, ‘Should’, and ‘Will’

    The industry’s standard word usage for a requirement is “shall”, a goal is “should”, and a statement is “will”. If you do not use these standard word choices you will confuse other stakeholders.

  8. Include a Rationale

    A rationale justifies the inclusion of a specific requirement. Attach a rationale to each requirement by explaining the need for the requirement. The rationale provides reviewers and implementers with additional information on the intent of the requirements, thus avoiding confusion down the line.

  9. Use Proper Grammar

    You will prevent a lot of costly mistakes due to confusion if you use proper grammar. For example, run on sentences will result in two requirements appearing to be one. One technique to improve grammar is to use bullet points first and then construct sentences out of them.

  10. Use a Standard

    Use a standard to ensure consistency. Three common standards are MIL-STD-490, IEEE, and ISO. You should choose one that is right for your industry.

    MIL-STD-490: The United States Military Standard establishes the format and content for the United States Department of Defense’s objectives. It can be useful in other areas as well.

    IEEE: The Institute of Electrical and Electronics Engineers Standards Association develops the IEEE standards. Unlike the MIL-STDs, the IEEE reaches a broad range of industries, including transportation, healthcare, information technology, power, energy, and much more.

    ISO: The International Organization for Standardization develops standards for business to optimize productivity and minimize costs.

Why We Built Innoslate – About Us

As systems engineers who had been using modeling and requirements tools for decades, we kept running into the same problem: we needed a solution that spans the entire system lifecycle. Effective requirements analysis and management requires not only capturing and managing the requirements provided by the customer, but also the analysis and decomposition of those requirements into specifications for the buying and building of the system components.

Requirement Analysis includes modeling the processes and procedures related to the requirements and simulating those processes with realistic constraints in resources, bandwidth, latencies, and many other factors. And once we have a baselined set of requirements, we have to not only “maintain” them (as they constantly change over the lifecycle), but we also have to verify that the resulting components and systems meet the requirements at every level of composition (component, subsystem, system, system of systems, etc.).

We realized what most systems engineers needed was a requirements tool, a modeling tool, a simulation tool, a verification tool, a risk tool, a program management tool, and a document management tool. None of the existing software solutions had native integration for all these different tools. The International Council on Systems Engineering (INCOSE) provides a tools interoperability working group, which includes representatives from all the major tool vendors. Unfortunately, without native integration, users experience missing data points and a drain on time and resources to integrate. It also became unreasonably expensive to purchase so many different tools and to manually integrate and manage each one.  

SPEC Innovations built Innoslate to be the all-in-one solution for systems engineer and program managers. We also wanted engineers to start analyzing overall quality of the entire project early in the lifecycle. That’s why Innoslate provides analytical capabilities like Innoslate’s Requirements Quality Checker and Intelligence View. These two internal features identify problems with the requirements and the overall model, quickly, so they can be fixed early in the process, thus saving time and money. Not only that, you get the scalability and collaboration features you need to work not only small projects, but also very large ones.

PLM Moving to the Cloud

Why Product Lifecycle Management Is Moving to the Cloud

“The cloud” means many things to many people. It’s a common misconception that the cloud is the Internet itself. They think that all the information they put on the cloud can be easily “hacked,” so they see this as a very public thing. But for those who work in cloud computing, they see it as a means to deliver safe, secure services to more people, at a lower cost. You can share computer resources, including CPU power, memory, and storage. This sharing or “on-demand” use of computer resources means that you can pay less for those resource, than when you have provisioned them on your own.

To take advantage of this resource sharing you must use applications that take this new environment into account. Just using a client-server or desktop tool with a “web front end” does not work well. The application programmers must re-architect their code to take advantage of this new capability and at the same time deal with the problems, such as latency, since now you must pass data between the servers where the data is stored and the web browser on the client machine you are using. Those servers may be down the hall or a few miles away, so there can be substantial delays in data transmission.

Scalability

Most desktop/client-server tools assume very little latency, so they grab a lot of information at a time and put it into local memory. That’s fine when you are close to the data, but in cloud computing the servers could be anywhere in the world or at least across the continent. So, when people try to use a desktop tool in this new environment, they begin to breakdown quickly in terms of response time. Another way to say this is that these tools do not scale to meet the growing needs. But the whole idea of cloud computing is to allow the application to scale to meet the needs.

Collaboration

Cloud computing also enables world-wide collaboration. So now the need to scale becomes critical, as more and more people are working together and capturing/generating more and more information. A “web-based” tool must be designed to process more information locally, including visualization of the data. Otherwise, we are back to central computing, where you had a dumb terminal connected to a computer often far away. I can still remember how slow the response was when that occurred. Even though the “bandwidth” has grown to Gigabits per second, we are trying to move Terabytes of information.

PLM on the Cloud

So, what does all this have to do with Product Lifecycle Management (PLM)? PLM today requires a large amount of data, analytical tools to transform data into information, and personnel who collaborate to create the products. Clearly, PLM would benefit the most from this new cloud computing environment. But where are the cloud computing products for this market? Legacy tool makers are reticent to re-architect 100s of thousands of lines of code. Such an effort would take years and be very expensive only to compete with themselves during the transition. So, most have created some “web front-end” to provide limited access to the information that exists in the client-server or (worst case) desktop product.

Innoslate® is the rare exception in the PLM marketplace. Innoslate was designed from scratch as a cloud computing tool. The database backend persists the data, while the web front end visualizes and performs the necessary analyses, including complex discrete event and Monte Carlo simulations. Innoslate support all areas of PLM, from Systems Engineering, to Program Management, to Product Design, to Process Management, to Data Management, and more. All this in one simple, collaborative, scalable, and easy to use tool. Check out www.innoslate.com for details.

Overview of DoDAF with Innoslate Webinar

It’s that time again. Dr. Steve Dam will be hosting one of our most popular webinars, “Overview of DoDAF with Innoslate.” Make sure to register for our latest webinar on DoDAF 2.0, on Thursday, August 24th at 2:30 pm EST.

Register here

Your webinar host, Dr. Steve Dam will provide you with an in-depth overview of the DoDAF 2.0 using the systems engineering software, Innoslate. Dr. Dam, the President and Founder of SPEC Innovations, participated in the development of DoDAF. He recently published “DoDAF 2.02 – A Guide to Applying System Engineering to Develop Integrated, Executable Architectures.” The presenter will provide a live tool demonstration of Innoslate with a questions and answer session to follow. 

What will be covered?

  • Clear understanding of the DoDAF
  • Knowledge of what will make a good methodology
  • Applicable use of the DoDAF Dashboard in Innoslate
  • Overview of DM2 Concepts
  • Export to the Physical Exchange Specification

When? Thursday, August 24th at 2:30 pm EST

Where? https://register.gotowebinar.com/register/4963997416370276098

What is Model-Based Systems Engineering?

System engineering is the discipline of engineering that endeavors to perfect systems. As such, systems engineering is a kind of meta-engineering that can be applied across all complex team-based disciplines. The idea of systems engineering is to enhance the performance of human systems. It has more to do with the engineering of team performance, engineering meetings or political agreements. It is not quite a social science, but it is the science of perfecting human outcomes.

The International Council on Systems Engineering (INCOSE) has been the organizing body for systems engineering programs. The field of systems engineering has been emerging as a discipline with its own unique training and advanced degree programs. As of 2009, there are some 80 United States institutions offering undergraduate and graduate programs in systems engineering.

Systems-centric programs treat systems engineering as a separate discipline with a specific focus on a separately developed body of the theory and practice. Domain-centric programs are imbedded within conventional engineering fields. All programs are designed to develop the capability of managing and overseeing large scale engineering projects.

The field of systems engineering has developed its own unique set of tools and methodologies that have less to do with the physics and mathematics of the hardware of the project and more to do with the process of bringing the elements of the project together. Modeling software, refers to modeling the process of creation, not so much to models of what is being created. These tools enable members of project team to better collaborate and plan the process of creating a complex finished product, such as a space station or a skyscraper.

Innoslate software has developed as an all inclusive baseline tool for modeling and managing the system of engineering projects. It includes full collaboration systems that allow all team members to work from the same information base, to contribute new information and to have access to what has been accumulated. It has a rich vocabulary of diagramming and flow-charting media to illustrate, change, and embellish the engineering process over time. It has modalities to provide clear feedback about the growth of the project, where it has achieved goals and where it lags behind.

Reposted from SPEC Innovation with permission.

 

How to Keep MBSE from Becoming Just a Buzzword (or Is It Too Late?)

The term “Model-Based Systems Engineering” or “MBSE” has been around for nearly a decade. We see the term in requests for proposals, marketing materials, social media, conferences and many other places in the systems engineering community and even in the general public. Clearly, MBSE has become an important part of systems engineering, but has it also become the definition of a buzzword? First, take a look at the definition of a buzzword.

buzz·word

[buhz-wurd]

NOUN

  1. a word or phrase, often sounding authoritative or technical, that is a vogue term in a particular profession, field of study, popular culture, etc.

Source: Dictionary.com

So, it definitely sounds authoritative, as it comes from the “International Council on Systems Engineering” (INCOSE). It sounds technical, using “Model-Based” and “Systems Engineering.” And clearly, it’s “in vogue,” from its appearance everywhere.

 

What the definition of a buzzword doesn’t seem to provide is the way a buzzword has a negative context or as Dilbert put it:

       

 

What this means is that a buzzword is used by people who don’t really know what it means. I’m sure we have all heard many people use it without any idea of what it means. So, what does MBSE really mean?

 

Well to understand its real meaning, we need to review the definition of MBSE from INCOSE:

 “Model-based systems engineering (MBSE) is the formalized application of modeling to support system requirements, design, analysis, verification and validation, beginning in the conceptual design phase and continuing throughout development and later life cycle phases.” – INCOSE

 

As systems engineers, the first thing we want to do is decompose this rather long sentence. It can be broken down into two parts:

  • Modeling (formalized application); and
  • Lifecycle (system requirements, design, analysis, verification and validation).

 

The formalized application of modeling means that we create models of the system using a “standard.” We know that there are a number of formal and informal standards, which are applied in many different ways. The standard most are familiar with is SysML since it is a profile of UML. SysML focuses on communicating with the software community primarily. The Lifecycle Modeling Language (LML) open standard (www.lifecyclemodeling.org), covers the second part of the definition better, as its name implies. It also addresses the program management aspects of systems engineering (risk, cost, schedule, etc.), none of which is really addressed by SysML.

 

But we have been creating drawings, which are a type of model, since well before anyone called the discipline systems engineering. So, what makes this term different from classic systems engineering?

 

The key difference is the type of modeling we use when we talk about MBSE. We mean the development of “computable models.” Computable models are models based on data (usually in a standard ontology, like the one LML provides) that can be visualized in standard ways (again using any drawing standard, which both SysML and LML provide). These models can also be tested to determine their validity and to make sure we don’t introduce errors in logic or problems related to dynamic constraints (i.e., lack of resources, bandwidth, latencies, etc.). This testing also includes checking the models against general rules of quality, such as “all function names should start with a verb.” The tools for this kind of testing today include simulation (e.g., discrete event, Monte Carlo) and natural language processing (NLP).

 

Having models that can be tested and testing them is a clear way to make MBSE real and not a buzzword. Therefore, to implement MBSE you need a tool or set of tools to conduct this testing.

 

When considering a “MBSE” tool, you will hear claims from almost all of the tool vendors that they are one. To distinguish between those who deliver on the promise of MBSE and those who are treating it as a buzzword, just ask the following questions:

 

  1. Are your diagrams essentially drawings or are they automatically generated from the data?
  2. If I make a change to one piece of data in the database is that automatically updated in all the other visualizations of that piece of data, including the diagrams?
  3. Can I execute the models using strong simulation techniques?
  4. Do those simulation techniques include discrete event and Monte Carlo?
  5. Do the simulations take into account resource, latency, and bandwidth constraints?
  6. Does your tool test the entire model against common standards of good practice (heuristics)?
  7. Does your tool support the entire lifecycle (system requirements, design, analysis, verification and validation) in a seamless, integrated fashion?

 

If you ask all these questions, you will find a limited set of tools that can even come close to keeping MBSE from just being a buzzword. So, it’s essential that you carefully evaluate these tools to make sure they provide the support you need to become more productive and produce higher quality products. To see a tool that does meet all these needs check out www.innoslate.com.

Quick Guide to Innoslate’s Ontology

Innoslate uses the Lifecycle Modeling Language (LML) ontology as the basis for the tool’s database schema. For those new to the word “ontology,” it’s simply the set of classes and relationships between them that form the basis for capturing the information needed. We look at this in a simple Entity-Relationship-Attribute (ERA) form. This formulation has a simple parallel to the way we look at most languages: entities represent nouns; relationships represent verbs; attributes on the entity represent adjectives; and attributes on relationships represent adverbs.

LML contains twelve (12) entity classes and eight (8) subclasses. They represent the basic elements of information needed to describe almost any system. The figure below shows how they can be grouped to create the models needed for this description.

Most of these entity classes have various ways to visualize the information, which are commonly called models or diagrams. The benefit of producing the visualizations using this ontology means that when you create one model, other models that use the same information will automatically have that information available.

All these entities are linked to one another through the relationships. The primary relationships are shown below.

 

This language takes a little getting used to, like any other language. For example, you might be used to referring to something functional as a Function or Activity. These are both “types” of Actions in LML and implemented as labels in Innoslate. Similarly, you may be used to using different relationship names for parents and children for different entity classes. However, by using the same verbs for the parent-child relationships you can avoid confusion in having to remember all the different verbs.

You still might need other ontological additions. LML was meant to be the “80% solution.” You should look very closely at the ontology, as often you only need to add types (labels) or an attribute here and there. Hopefully, you will rarely need to add new classes and relationships. If you do add new classes, try to do so as subclasses to existing ones, so that you inherit the diagrams as well. For example, when the Innoslate development team added the new Test Center, they decided they needed to extend the Action class. This enables the TestCase class to inherit the Action class and other functional diagrams, as well as the status, duration, and other attributes that were important.

Hopefully, you can see the benefits of using LML as the basis for Innoslate’s schema. It was designed to be:

  • Broad (covers the entire lifecycle – technical and programmatic)
  • Ontology-based (enables translation from LML to other languages and back)
  • All the capabilities of SysML (with LML v1.1 extensions) and DoDAF
  • Simple structure
  • Useful for stakeholders across the entire lifecycle

For more information, see www.lifeyclemodeling.org and visit the Help Center at help.innoslate.com.

Great Read “Enhancing MBSE with LML”

Review on “Enhancing Model-Based Systems Engineering with the Lifecycle Modeling Language” by Dr. Warren Vaneman

Dr. Vaneman’s paper on “Enhancing Model-Based Systems Engineering with the Lifecycle Modeling Language” provides a compelling justification for the need of a simpler, yet more complete, language, which integrates systems engineering with program management to support the entire systems lifecycle. It shows that the current LML standard 1.1 includes all the key features of the Systems Modeling Language (SysML), and thus can be used by people who practice systems engineering to generate the complete SysML diagram set.

This paper expresses the key goals of LML: “1) to be easy to understand; to be easy to extend; to support both the functional and object-oriented (O-O) approaches within the same design; 4) to be a language that can be understood by most system stakeholders, not just systems engineers; 5) To support the entire system’s lifecycle – cradle to grave; and 6) to support both evolutionary and revolutionary system changes to system plans and designs over the lifespan of the system.”

Dr. Vaneman covers three themes in the rest of the paper: 1) overview of legacy modeling and in introduction to LML; 2) comparison of SysML and LML using eight MBSE effectiveness measures; and 3) the potential to use LML as an ontology for SysML. Of particular interest was the comparison of SysML to LML. The major problem with SysML is the lack of an ontology, which makes it less expressive and precise. SysML seems to have problems in usability as well, due to the complexity of the diagraming notations.

Although a preliminary mapping of LML to SysML was done as part of the first release of the standard, the 1.1 version only had to be slightly modified to more fully visualize all the SysML diagrams. Only two new entity classes were defined (Equation and Port). Equation was developed to support the Parametric Diagram, which diagrams equations, and the Port, which is a subclass of Asset, was essential for a couple of the physical modeling diagrams.

I heartily agree with Dr. Vaneman’s conclusion that “LML provides a means to improve how we model system functionality to ensure functions are embedded in the design at the proper points and captured as part of the functional and physical requirements needed for design and test.”

You can read Warren Vaneman’s paper here:

IEEE MBSE Paper- Vaneman

More resources:

-LML website

Quick Guide to Innoslate’s Ontology

 

10 Qualities That Make a Good Systems Engineer

All systems engineers should have an understanding of basic concepts and a strong technical background, but these qualities go beyond just the necessities. From 40+ years of experience, I have found that a good systems engineer must have the following 10 qualities.

#1 Patience and Perseverance

To create a complicated system, an engineer must have a lot of patience and perseverance. The more complex the system the longer and more tedious a project it becomes. An engineer cannot figure out everything at once. It takes time to see the big picture, to look for all the small details. You will test and test and still find errors. You have to have patience to know that it takes time and determination to keep going after hundreds of failed attempts.

#2 Ability to Know When You Are Done

A good systems engineer wants their project to be flawless, but often it’s too easy to fall into a perfectionist trap. You tell yourself, “One more change and it will be perfect.” However, doing this may mean you never complete your project and all that hard work will become obsolete. The best engineers know when their system is good enough and when the system needs a little more re-engineering.

#3 An Analytical Brain

Most engineers are naturally analytical, which is probably why they were attracted to the field in the first place. From the moment they could talk, they were the ones that continually asked questions and analyzed the world around them. A good systems engineer can go one step further than just analyzing and look for solutions to the problems and questions they analyze.

#4 Knowledge of Systems Engineering Software Tool(s)

In this day and age all systems engineers should have some experience with tools. Most colleges, especially grad school level, use systems engineering software tools. These tools allow you to create complex systems. They help you organize your information and develop documentation and reports at a much quicker pace and with higher accuracy. They can also help you analyze your information better. Even though you should already be a pro at analyzing, using a tool can help your organize the information in a way that makes analyzing faster and easier. Tools can make you into a better systems engineer. Tools, such as Innoslate®, are capable of improving you as a systems engineer.

#5 Strong Organizational Skills

You need organizational skills in order to handle the amount of information that a systems engineer deals with on a regular basis. It is important to organize well, so you are able to track status and history accurately and create documents and reports that are understandable. Although a tool can greatly improve the way you organize, you still need to understand organizational concepts.

#6 Ability to See the Small Picture

One of the greatest qualities a systems engineer can have is to be detailed oriented. You should be able to look at the small picture and see that all the details are thoroughly reviewed and that no errors occur. You need to be detail oriented type of person. Much of what we do is planning. Just like if you are an event planner, you have to make sure all the details are just right to make the ultimate goal (the event) a success.

#7 Ability to See the Big Picture

The overall system needs to be looked at just as much as the small details that make up the system. You need to make sure that the goal of the entire system is kept in mind throughout the planning. A good systems engineer needs to be able to determine future needs as well. They must have vision (I talk about this in my upcoming book on LML) and be detail oriented, but still be able to see the big picture.

#8 Well Rounded Background

A bad systems engineer knows systems engineering concepts and definitions like the back of his hand, but knows nothing else. A good systems engineering tries to be knowledgeable in other subjects relating to their field. A great systems engineer understands the importance of being well-rounded. A well rounded background will help a systems engineer analyze and find potential issues better than anyone else.

#9 Communication Skills

Unfortunately, English is not a high priority for many engineering colleges. Systems engineers need to communicate well. They need to be able to communicate to non-engineers. Communication skills take time and practice to perfect. If you are a systems engineer and you know that communication is not a strong skill of yours, make the effort to improve.

#10 Ability to Lead, Follow and Work Well in a Team

At some point in your career you will have led, followed, and worked in a team. The best systems engineers know how to do all three well. A good leader knows how to follow and work together with others. A leader understands what his or her team needs to know and understand. The inability to do all three can be detrimental to a project. Systems engineers, more often than not, do extremely important work and need a good leader and a good team to follow.

 

It takes a lot of time to develop all these qualities. I know I did not have all of them when I began my career. Don’t let this discourage you, but make it a goal to obtain each one of these qualities.

If you think you have these qualities, join our team.

Reposted from SPEC Innovations with permission.