Yes – DevSecOps Can be Done, and Done Well

Weeds picture

Rob Wedertz – VP DoD Programs

DevSecOps Diagram

Inarguably, the pace of change in the technology environment outpaces the program and acquisition oversight within the Department of Defense.  I don’t believe this is a controversial statement.  C-SPAN is riddled with testimony of senior ranking DOD officials asserting the same.  The National Defense Authorization Act (NDAA) is littered with language encouraging the Department to accelerate the adoption of rapid acquisition methodologies.  Nowhere is the delta between advanced technology capabilities and the Department’s ability to procure these capabilities more prevalent than in Software (i.e. Artificial Intelligence, Machine Learning, and Discrete Event Simulation).  And even more specifically, it is the incorporation of the development methodologies, for example DevSecOps, that often befuddles program managers, contracting officers, and even leadership, as this methodology is counter to acquisition guidelines and requirements oversight.

In an effort to close the delta, the Department has established bodies (e.g. DoD Enterprise DevSecOps Community of Practice – a Joint effort among DoD CIO, OUSD (A&S), and DISA; the Defense Innovation Board, the Joint Artificial Intelligence Center, and others) to “sanctify” best practices and is actively campaigning to align acquisition and procurement with best in breed enabling technologies and development methodologies.  Because we have been charged with designing, developing, and implementing the Joint Staff’s Global Force Management Decision Support Platform (ORION), we are actively “leaning out over our skis” to demonstrate that DevSecOps can and should be done.

As a software development company tasked to deliver leading edge technology-enabled decision support platforms to the Joint Staff, there is little more deflating than telling our platform leads that they cannot implement the best in breed capabilities (i.e. open-source software, enablers, architectures, etc.) because the product is evolving so quickly that we cannot introduce it into the Risk Management Framework accreditation sphere.

Fortunately for us, we were introduced to Defense Innovative Unit (DIU) (then with an “experimental” on the tail) early in the ORION development process. They were encouraged by our startup mentality developed in support of our commercial products and they encouraged our government oversight to think about things like; Minimally Viable Products (MVPs), continuous User Engagement, and leveraging modern technology and platforms.  During their assessment of the ORION Joint Platform (at the time known as the Joint Force Capabilities Catalog (JFCC) / Global Laydown Server (GLS)) DIU acknowledged that we were already accomplishing the things they suggested.  They passed as much to the Chairman of the Joint Chiefs of Staff and his support staff.  Achieving this level of maturity didn’t happen overnight.

We lived the painfully slow migration from “waterfall” acquisition and associated development practices to Agile, and are on the leading edge of DevSecOps.  In fact, as DoD CIO, OUSD (A&S), and DISA work through “sanctifying” the DoD Enterprise DevSecOps maturity model (via a Community of Practice), and the Defense Innovation Board awaits the response to their Software Acquisition and Practices (SWAP) study published in April of this year, we’re already demonstrating that the DevSecOps model works, can be implemented at no additional cost to the government, and perhaps most importantly, is scalable.  Case in point – when we began the ORION project, we were squarely in the “rapid prototyping” phase of development as the overarching requirements were being developed, and oversight was being codified.  The early days required rapid deliveries and constant engagements with users, all while adhering to information assurance requirements and cyber security.  (Note – we were (and are) deploying code to the SIPRNet, a production environment, every 2 weeks – functionality that is Beta, IOC, and FOC simultaneously.)  Achieving and sustaining this level of S/W development maturity is difficult and often requires a champion.

Advocacy is paramount.  It is not enough to be an innovative company with technical “chops”.  You MUST have a program sponsor that endorses the DevSecOps methodology and removes legacy critical barriers that prevent innovation at the speed required.  (It does not hurt that our advocacy was a shared understanding and endorsement from the sitting CJCS and the leadership of DIU.  That we were doing it was the result of technical leadership and guidance provided by our Joint Staff J35S Program Manager; that we are continuing to do it is the result of the senior leaders of the DOD acknowledging that is the way it SHOULD be done.  Early in the project, the J35 Deputy Director of Regional Operations, briefed the entirety of the Joint Staff (J-DIRs, Director, and Chairman) and the Deputy Secretary of Defense.  Paraphrasing his remarks, [sic] “these guys are pushing the envelope on s/w development.  They sprint, they fail, they recover, they deliver, they iterate – we win.”

Perhaps the lynchpin in achieving technical maturity in an oftentimes legacy environment is the simple acknowledgement that requirements WILL change.  When we started ORION, Globally Integrated Operations and Dynamic Force Employment were not yet established in policy.  Had we developed and delivered an application that was a reflection of solely the original requirements specifications, both the program and our platform would now be obsolete.  Fortunately we’ve been allowed to iterate throughout the software development lifecycle.  Continuous user feedback and rapid development cycles have facilitated relevance and viability that have ultimately enabled the Joint Staff to make Better Decisions, Faster.

Aligning the DevSecOps methodology with Scaled Agile Framework has additionally ensured that ProModel is permeating best practices not only across our DOD vertical but also in our COTS and Healthcare spaces as well.  Our collective roadmap is articulated in the Defense Innovation Board’s Software Acquisition and Practices (SWAP) study graphic below.  Our objective is to live in the “Do’s” and demonstrate that we can and should avoid the “Don’ts.  ORION is validation that it can be done.

Process Simulator 2019 SP3

Aaron Nelson Product Dev. Manager Process Simulator

Aaron Nelson Product Development Manager Process Simulator

We are excited to announce the release of Process Simulator 2019 Service Pack 3. This version of Process Simulator supports both 32 and 64-bit editions of Microsoft Visio 2016 and 2019. SP3 is for everyone. You will be prompted for an auto update when you sign into SP2 or earlier.

What’s New in SP3 ?

Ribbon Enhancements

  • Create and Install Package are now under More Tools within the Tools section of the ribbon.
  • Convert Diagram will now show on the right hand side of the tool bar only IF the file needs to be converted to a Process Simulator 2019 simulation model.

Image_PCS2019SP3_Ribbon

Image_PCS2019SP3_MORETOOLS

  • Model Elements buttons can be toggled on/off. You can select a model element and the button will stay selected and can be deselected to close that Element window.
  • Resource Grouping is now within the More Elements dropdown of the Model Elements section.

Properties Enhancements

  • Process Simulator’s Properties window has been a great addition to Process Simulator 2019’s User Interface. We have now grouped essential processing elements together.
  • We have also added indicator icons to show which sections in Properties have additional information. These indicator icons will show for LogicMulti EntitySetupDowntimes, and Notes. If there is nothing in the section you will not see the icon.

Image_PCS2019SP3_PROPERTIESACTIVITY

  • When you hover over Notes or Logic, the icon will display a preview of the contents of the section. Clicking the icon will now take you into the section as well.

Image_PCS2019SP3_PROPERTIESLOGIC

Model Compilation Speed Increase

Speed of Model Compilation after you select Simulate has been optimized. Benchmarking indicated a 125–135% improvement over Process Simulator 2019 SP2

How to Download SP3 ?

This product is available for download from the Solutions Café as well as our downloads site (just ask Support to send you a link).

If you have any questions, please contact ProModel Customer Support at 888-776-6633 or support@promodel.com.

Successful Implementation of FutureFlow Rx® at Seattle Children’s – A New Predictive Patient Flow Technology

Dan Hickman Avatar1

Dan Hickman ProModel CTO

I am jazzed about our FutureFlow Rx work with Seattle Children’s Hospital.  What a great group of people to collaborate with.  Check out the press release below and feel free to contact me if you’d like to learn more about this new product – dhickman@promodel.com

Thanks, Dan

Seattle Childrens Logo

Integrated Discrete Event Simulation Census Predictor, Developed by ProModel Corporation, Facilitates Inpatient Flow and Access Management

SEATTLE WA, May 14, 2019 – ProModel Corporation announced the successful implementation of a new predictive patient flow technology called FutureFlow Rx® at Seattle Children’s (SC).

Members of the Enterprise Analytics Community at Seattle Children’s will be presenting their use case at the Advanced Analytics for Children’s Hospitals Conference on June 5-6 at the Ann & Robert H. Lurie Children’s Hospital of Chicago. Seattle Children’s is a co-sponsor of this new analytics conference.

Seattle Children’s, like many other hospitals, faces challenges associated with capacity pressures and inpatient access. Demand for inpatient space often outstrips capacity, whether for physical or staffed beds.

SC leadership has established policy that scheduled elective surgeries must not be canceled due to capacity. Therefore, proactively managing inpatient capacity and staffing takes on even greater urgency in that context. In order to address this issue, they deployed a predictive-analytics solution for census estimation known as FutureFlow Rx®.

Seattle Children’s Quotes

“By providing accurate projections of staffing needs, FutureFlow Rx informs daily challenges faced by every hospital with timely and detailed information.” 

  • Susan Geiduschek, RN, DNP, Sr. Dir, Assoc. Chief Nurse, Seattle Children’s

 

“Integrating FutureFlow Rx into our Inpatient Access and Flow workstreams has really supported a smooth, functional, and most importantly effective process for proactively managing census during an especially challenging flu season. Diversions are down, and so is the general level of anxiety around decision-making. Fewer ad hoc huddles, more standard work with established guiderails and actions.”

  • Ruth McDonald, MD, Assoc. Chief Medical Officer, Seattle Children’s

 

Right Patient, Right Bed, Right Time

The functional goal of every hospital is to place the right patient in the right bed at the right time. Being the region’s only tertiary-care pediatric facility, it is paramount that SC maintain capacity to serve those children that cannot receive care elsewhere in the Pacific Northwest.

Thus, they must carefully maintain capacity and ensure as few diversions as possible. Combining the FutureFlow Rx® prediction with an application designed by SC Enterprise Analytics staff, they can accurately estimate their ability to manage daily and predicted census, allowing them to proactively make operational decisions that address challenges with foresight.

ProModel Quote

“We are very excited to be working with a hospital as visionary as Seattle Children’s.  They were willing to look beyond what existed today, to determine if a patient flow improvement platform could truly help increase safe inpatient access for their young patients”  Dan Hickman – CTO, ProModel Corporation

About ProModel Corporation

ProModel Corporation is a leading provider of simulation-based, predictive and prescriptive analytic decision support solutions. A Microsoft Gold Partner, ProModel specializes in custom and COTS (Commercial-Off-the-Shelf) software and services to help organizations optimize processes, policies and resource decisions to best align with their business strategy.

Founded in 1988, ProModel has tens of thousands of users of its software globally, focused across the Healthcare, Pharmaceutical, Government and Manufacturing & Supply Chain industries.

For additional information: ProModel Marketing 610-628-6842; healthcaresolutions@promodel.com

Predictive Variables, Artificial Neural Networks and Discrete Event Simulation in Manufacturing

Rebecca Santos

Rebecca Santos ProModel Tech Support Engineer

During my Master’s program at BYU, I worked with a team to complete my thesis.  The work turned into a published article by the International Journal of Modelling and Simulation!  Since I work for ProModel, it’s only appropriate that I share it with my fellow simulation enthusiasts.  Please let me know what you think by commenting below.

Discrete event simulation (DES) is a powerful tool that can help users make better decisions. Over the years tools such as ProModel and Process Simulator have been developed to simplify the application of simulation, decreasing the learning curve and increasing its use. One of the advantages of simulation is that it is able to create lots of data with valuable information. However, the data analysis process can be challenging and relevant information may not be fully analyzed.

Observing the opportunity to more fully learn from the data, we decided to use data mining algorithms to help in the data analysis process, and more than that, to guide the modeler to the variables that most impact the outcome of the system being modeled.

The data mining algorithm picked was Artificial Neural Networks (ANNs) which has been good at learning from the data and making accurate predictions, according to the scientific literature. We applied ANNs to the data generated by the simulation model and we were able to create ANN models that could predict the simulation model results. The ANN models created were then interpreted and through the interpretation it was possible to rank variables according to their impact on the output results. This makes it possible for decision makers to know how to prioritize and where to place their investments.

Here is the abstract from the article:

This research used a discrete event simulation to create data on a shipment receiving process instead of using historical records on the process. The simulation was used to create records with different inputs and operating conditions and the resulting overall elapsed time for the overall process. The resulting records were used to create a set of predictive artificial neural network models that predicted elapsed time based on the process characteristics. Then, the connection weight approach was used to determine the relative importance of the input variables. The connection weight approach was applied in three different steps:

(1) On all input variables to identify predictive and non-predictive inputs

(2) On all predictive inputs and

(3) After removal of a dominating predictive input.

This produced a clearer picture of the relative importance of input variables on the outcome variable than applying the connection weight approach once.

You are welcome to access the published article at the journal’s website or the full original paper at the ProModel website.

 

Process Simulator 2019 – Released!

Aaron Nelson Product Development Manager Process Simulator

Aaron Nelson Product Development Manager Process Simulator

As ProModel’s new Process Simulator Product Development Manager, I’m really excited to bring you my first blog post in this position.

We launched Process Simulator 2019, our Microsoft® Visio ® plug-in, earlier this year which now supports both 64 and 32 bit Visio 2016.  We have added many other great features to this redesigned product. Check out the highlights below and get the full details on the What’s New page.

Visio 2016 64 and 32-bit support

Dockable Windows – We have redesigned all element property windows and tables and made them dockable within the Visio application work-space. This allows you to keep them open and out of the way so your diagram view space will be clear for quicker modeling changes.

Image-pcs2019-dockable-windows

Find & Replace – Process Simulator now has its own find and replace capability, separate from Visio, which will search across its elements and objects only. It even automatically brings the shape into view and opens the associated property or field where the searched text is found.

Image-pcs2019-find-replace

Model Compile – When you run your model, it is checked for errors prior to simulation. In this release, you are presented with a list of all errors, which allows you to quickly navigate to and resolve the issues.

Image-pcs2019-model-compile

Referenced Hierarchical Models – Submodels now have the ability to be referenced in addition to their current capability of unique instances. This means that activities linking to the same submodel at different points in your diagram will send their entities to that exact submodel (not just unique copies of the submodel).

Activity Multi-Entity Table – We have added Multi-Entity functionality. This allows you to convert your existing models that use multi entity, and create new ones as well, without having to code the functionality using logic. In Shape Properties you can select Multi Entity and then Define.

In this initial release for our US and Canadian customers with current Maintenance and Support, you can get the download in the Solutions Café.  You can also access the Process Simulator 2019 What’s New webinar recording from the solutions cafe.  If you are not a current customer, please contact your Account Manager for details.

Process Simulator 2019 will be available to everyone later this year.  Please let me know if you have any feedback by leaving a comment below or contacting me directly. Thanks!  anelson@promodel.com

Univ. Texas Master of Public Health Program Teaches Lean Concepts With Process Simulator

headshot_prof michael kennedy 10-14-18

Michael H. Kennedy PhD, MHA, FACHE Assoc Prof & Chair Dept of Healthcare Policy, Economics and Management University of Texas Health Science Center at Tyler

I was first exposed to ProModel simulation products as an alternative to GPSS-H when earning my doctorate in Decision Sciences and Engineering Systems at Rensselaer Polytechnic University from 1988 – 1992.  I taught one simulation course as a graduate elective for the U.S. Army-Baylor University Graduate Program in Healthcare Administration soon after graduation.  Since then, I have employed MedModel as a component of several health administration courses, with simulation comprising approximately one-third of the course content.  Simulation modeling is a wonderful tool to reinforce concepts from statistics and probability such as the effects of random variation, practical application of probability distributions, and employment of goodness-of-fit testing.

After I arrived in January 2017 at the University of Texas Health Science Center at Tyler to begin teaching in our Master of Public Health Program, I decided to revise my approach to teaching quality.  Previous iterations of my quality course had generally proceeded as follows:

image_kennedy blog chart 1

Content was taught face-to-face and students used their laptops to create process analysis tools and control charts with me in class.

My decision to include Lean in the quality course provided the opportunity to learn and teach Process Simulator, another ProModel product.  Process Simulator operates as an add-in to Visio.  I couldn’t think of a better way to teach flow than to have students build process flow diagrams in Visio and then to model the flow of entities through the system using Process Simulator.  The revised course proceeded as follows:

image_kennedy blog chart 2

The first process model was built after an introduction to M/M/1 queuing formulas in an exercise to establish the correspondence between queuing and simulation.  I modified a problem provided by Ragsdale (2004) to model Acme Pharmacy which has one pharmacist with the capacity to fill prescriptions from 12 customers per hour.  The pharmacy averages 10 customers per hour seeking to fill prescriptions.  Students used an M/M/1 Excel template to compute and characterize Acme Pharmacy operations and to record queuing formula and simulation results.  The Process Simulator model looked like this:

image_kennedy blog chart 3

The comparison of the first two columns of Table 1 between queuing results and simulation results confused me until I remembered that the M/M/1 queuing formulas represented a steady state solution.  An 8-hour run with 20 replications shown in the third column produced results more closely aligned, but not as close as expected.

image_kennedy blog chart 4

Table 1.  Comparing Queuing and Process Simulator Results                                                        Note – Expected times have been converted from hours to minutes.

An extended 2080-hour run and an 8-hour run after an 8-hour warm-up were also subsequently modeled to determine if those efforts to move the system towards steady state produced results more closely aligned with queuing formula results.  The results in the last two columns showed that to be true.

During the remainder of the course, Lean principles were reinforced with a variety of Process Simulator models.

Reference

Ragsdale, C. T. (2004). Spreadsheet modeling and decision analysis: A practical introduction to business analytics (4th ed.). Mason, OH: Thomson South-Western.

About Dr. Michael H. Kennedy

Dr. Michael H. Kennedy, FACHE, arrived at the University of Texas Health Science Center at Tyler on January 23, 2017 to begin service as an Associate Professor in the Department of Healthcare Policy, Economics and Management in the School of Community and Rural Health. He has 42 years’ experience in teaching and health services administration that have been divided between academic positions and operational assignments in the military health system as a human resources manager, equal opportunity advisor, ambulatory care administrator, and other positions of leadership culminating as the Chief Operating Officer of a small military hospital.

In past academic assignments, Dr. Kennedy has served as Director of the Health Services Management Program at East Carolina University, Director of the Doctor of Health Administration Program at Central Michigan University, and Associate Professor in the Health Services Administration Program at Slippery Rock University. Dr. Kennedy was also Deputy Director of the U.S. Army-Baylor University Graduate Program in Healthcare Administration where he was twice selected by the students as Instructor of the Year.

Dr. Kennedy is a Fellow in the American College of Healthcare Executives.  He was appointed as Chair of the Department of Healthcare Policy, Economics and Management on September 1, 2018.

Ingalls Develops Automated Unit Lay-Down ‘Advisor’ with Capacity Planning Tool

Image_Ingalls from theSigal MagazineHuntington Ingalls Industries – Ingalls Shipbuilding (Ingalls) identified substantial savings potential in the lay-down placement and assignment process that had been previously utilized for managing asset location throughout the construction process.

Building four different hull forms in the tight shipyard footprint is a challenge. Ingalls Shipbuilding work instructions define the processes and responsibilities for the proper allocation and optimization of real estate (lay-down spaces) for structural units and assemblies under construction, while providing forward visibility for scheduled or potential overloads to capacity.

However, the old capacity planning processes were tedious and overly time-consuming. Resulting real estate allocations were seldom optimal and often required substantial rework to resolve space allocation conflicts, as the construction schedules for each hull form jockey for the same production resources.

The Ingalls team developed an automated process that optimizes unit layout and scheduling, and increases the construction of many units under a covered structure, significantly improving production rates—a plus in the hot southern climate.

“The new tool has taken a process that historically took 10 weeks to complete and can now finish the scheduling activity in less than an hour. Following project completion and full system implementation, we expect to reduce ‘real estate’ allocation processing time by 30% and place 20 more units ‘under cover’ annually, with an estimated cost savings of over $990K per year.”

 (Article Courtesy of “theSignal” and DefenseNews.com)

Click here to read the rest of the Ingalls story