Some features and bug fixes in this release include:

TEAMS-Designer

• Dockable toolbars with new toolbar icons
• Usage Finder feature: Users can search where a particular function, technology label, setup, resource, or a switch mode has been used in the model. Clicking on the search result takes the user to the right place in the model.
• Function Groups and Function Mapper GUI revamps
• Bug fixes and more…

TEAMS-RDS

• PackNGo now supports tests with test designs. It can now compute the test outcomes based on user inputs.
• PackNGo Session logs can now be synchronized with the TEAMS-RDS server via HTTP/HTTPS, in addition to via email.
• ‘Surface Mode’ versus ‘Airplane Mode’:PackNGo users can troubleshoot while being in Airplane Mode (Offline) after receiving the job from TEAMS-RDS.  However, if the user cannot perform certain tests or setups during the troubleshooting session, they can still complete the job by having the mobile device be in Surface Mode (Online), and PackNGo will automatically connect to TEAMS-RDS and get a revised troubleshooting strategy.
• Ability to edit Case ID any time during the troubleshooting session.
• ‘Task Assignment’ page enhancements, sorting jobs, navigation links to pages, and performance improvement to load the pages faster with the job filters.
• Many more…

TEAMATE

• Model entitlement based on the user groups integrated to TEAMATEs model consolidation page.

Mean Time to Repair and Variability:  Variability is anything that causes the system to depart from regular, predictable behavior[1].  Typical sources of variability include machine breakdown, material/parts shortages, rework/revisits, technician skill levels, operator unavailability, transportation, changes in demand, and so on.

Variability increases the mean and variance of effective time to process a task.  Coefficient of variation is a normalized measure of variability.  Formally, if t_n is the mean of nominal processing time  and σ_n is the standard deviation of a task at a machine (sans sources of variability), both measured in hours, then the coefficient of variation c_n = σ_n/ t_n.  Evidently, the coefficient of variation is a dimensionless quantity; it is a measure of noise-to-signal ratio.

What are the mean, and coefficient of variation, of effective process time in the presence of machine failures and repairs?  Suppose the mean time to failure of a machine is MTTF and its mean time to repair is MTTR. Then, its availability A = MTTF/(MTTF+MTTR).   Then, the mean of effective process time, t_effective = t_n/A = t_n (MTTF+MTTR)/MTTF.  Let σ_r² be the variance of the time to repair so that its coefficient of variation, c_r = σ_r/ MTTR.  The squared coefficient of variation of effective process time, c_e² is given by

C_e² = σ_e²/ t_e² = C_n² + (1+ C_r²) A (1-A) MTTR/ t_n               (1)

This equation has interesting maintenance service implications.  Larger the MTTR, larger is the squared coefficient of variation.  In addition, larger the variability in MTTR as reflected in c_r², larger is the squared coefficient of variation.  The effective process time increases with increase in MTTR.  Thus, for a given availability, smaller the MTTR (i.e., shorter disruptions), smaller is the variability.

Rework/Revisits and Variability: Rework/revisits are significant sources of variability and lost capacity in manufacturing and service operations.  If u is the system utilization without rework and p is the fraction of time rework occurs, then the effective utilization of the system with rework,  u_r = u+p.  The effects of rework are amplified if it involves a multi-stage manufacturing/service line, and especially if the rework necessitates going back to the beginning of the line.  In addition, the coefficient of variation of effective processing time becomes C_e² = C_n² + p (1- C_n²).    The increased variability degrades performance by increasing congestion effects (e.g., increased work-in-process and response time), and these effects increase nonlinearly with system utilization, u_r (∝ 1/(1- u_r)).  Evidently, rework magnifies congestion effects by decreasing capacity and substantially increasing WIP and response time.

What causes the variability in MTTR? For most service organizations, the biggest unknown in the MTTR is the time it takes to identify exactly what has gone wrong. This time, called the troubleshooting time or Mean Time to Diagnose (MTTD), is often dependent on the ability of the service agent to think and infer the root cause. This usually leads to large variation in performance across service agents.

TEAMS and MTTR:  Humans are local optimizers, and are biased towards their more recent experiences.  Consequently, manual troubleshooting, especially on infrequent and atypical problems,  takes substantially longer time and is error prone.

The reasoner in TEAMS guides the service agent with optimized sequence of troubleshooting steps. The reasoner considers all the possible causes of failure, weighs them by probability  of occurrence,   and takes into account available skills, materials and knowledge, the observed failure symptoms, and user complaints, when optimizing the troubleshooting strategy. This enables all service agents to troubleshoot like an expert, delivering consistent performance and lowest possible MTTD.

Moreover, the reasoner guides the service agent through a systematic process of diagnosing the problem, correcting the problem, and then verifying the correctness of the fix, thereby ensuring that the problem is fixed right the first time.

Typically, the troubleshooting time is reduced by 75-80% over manual troubleshooting methods, and first time success rate of 90% or more is achieved.  Progressive service organizations can leverage Remote Diagnosis and Diagnose before dispatch capabilities of the TEAMS solution to further reduce the unproductive service calls, and increased first-time fix rates reduce rework and revisits.

As seen from Eq. (1), the reduction in MTTR has direct impact on reducing variability, and consequently, on cycle time and work-in-process.  As stated earlier, reduction in variability leads to highly capable processes, guaranteed lead times and high levels of consistent service; attributes essential to delivering performance and uptime.

What is Lean: Lean focuses on response time (cycle time) reduction, where cycle time is the value-added and non-value added time in manufacturing a product or in providing services. Lean identifies sources of waste to reduce the non-value added elements. The sources of waste can be classified into the following seven categories:

• Waiting: Any non-productive time waiting for parts, tools, supplies, personnel, failed systems to be brought on-line,…
• Transportation: wasted effort due to unproductive service calls, transport of materials, parts or finished goods to storage or between processes;
• Correction: Repair or rework or revisits;
• Inventory: Maintaining excess inventory of parts, raw materials or finished goods.
• Motion: Any wasted motion to pick up or stack parts or due to walking;
• Overproduction: Manufacturing more than needed before it is needed;
• Processing: Doing more work than is necessary.

Little’s theorem links work-in-process (WIP or queue length) with cycle time and throughput. Indeed, larger WIP implies larger cycle times and saturated throughputs (overworked personnel and systems). Lean improves throughput by creating and optimizing smooth operational flows by level loading, reducing setups, linking suppliers, and reducing time and waste.

What is Six Sigma: Six Sigma focuses on reducing variability, thereby improving product/process quality. Variability stems from failures, setups, long infrequent disruptions, synchronization requirements, and many others. Variability causes congestion (i.e., WIP/cycle time inflation), propagates through the system and inflates the seven sources of waste. Reduced variability leads to highly capable processes, guaranteed lead times and high levels of service.

TEAMS Toolset: A number of quality and inflated cycle time problems can be alleviated by verifying design attributes related to fault detectability and diagnosibilty, and system reliability, availability and life cycle cost. Design for Testability (DFT) facilitates such verification capability, and thereby reduces unexpected downtime, maintenance, warranty and logistic costs – leading to a higher degree of customer satisfaction. Diagnostic modeling and analysis capabilities of QSI’s TEAMS toolset enable the designers to perform DFT optimization for remedying deficiencies in the design phase, and service engineers to arrive at rapid operational fixes for deployed systems.

QSI’s TEAMS Toolset features a common model-based ‘systems engineering’ methodology, as well as off-line design and on-line implementation processes, to build highly reliable, dependable, and serviceable systems. The highly acclaimed integrated diagnostic modeling methodology embedded in the TEAMS toolset helps design engineers to

• Identify the potential failure modes in complex systems and characterize nominal and faulty behaviors under various modes of operation. This lack of knowledge often leads to long duration disruptions.
• Design tests to detect anomalies under varying usage, environmental and operating conditions; lack of detection and diagnosis capability is a major source of variability and throughput reduction.
• Perform model-based testability analysis and Failure Modes, Effects and Criticality Analysis (FMECA) a priori during design rather than be surprised by field failures,
• Design optimal sensor allocation strategies to maximize fault diagnosability and minimize maintenance and parts inventory costs,
• Provide on-line/off-line diagnosis and prognosis schemes that are robust and adaptive to different system configurations and operating conditions for proactive and predictive maintenance lading to high system availability,
• Sequence tests to minimize setups and mean time to isolate and repair; these are two major contributors of variability in a production or service system,’
• “Diagnose before dispatch” capabilities reduce the unproductive service calls,
• First-time fix rates reduce rework and revisits.

Use of the same models and algorithms throughout a system life-cycle ensure consistent specification and analysis of system requirements, rigorous evaluation of design for service trade-offs of system architectures, selection and implementation of the best design, easy verification of design implementation, and post-implementation assessment of how well the product meets the specified requirements. This “build a model once and use it many times” approach enhances communication and coordination among complex system stakeholders, and reduces development risk (cost and schedule) by improving productivity and quality.

The fact that funding vehicles being restructured and re-purposed towards maximizing the utilization of current capabilities has led the industry to put increased focus on extending the life of their current equipment. Several defense journals and newsweeklies cite the increased impetus on maintenance and sustainment of existing fleet. Aviation Week article (dt. Sept 25, 2012) cites the U.S. Air Force as “… pushing to more than double the life of its stalwart F-15 Eagles…” and “…delay fleet retirements…” while Defense News  (dt. Aug 31, 2012) mentions the same military arm is planning F-16′s modifications to “…extend the life and upgrade more than 300 jets in the coming years…”. Stripes (dt. Apr 26, 2012) talks about Congressional momentum to “…extend the service life of the Navy’s nuclear ballistic missile submarines…”. Defense News (dt. May 31, 2011) also says this about US Navy and service lives of ships ”…Revised U.S. Fleet Plan Extends Some Ships…”.

Defense News (dt. May 31, 2011) – “Revised U.S. Fleet Plan Extends Some Ships to 70 Years”

Aviation Week (dt. Sept 25, 2012) – “USAF Seeks Afterlife For F-15s”

Stars And Stripes (dt. Apr 26, 2012) – “Defense budget provision aims to extend service life of submarines”

Defense News (dt. Aug 31, 2012) – “Boeing Looks To Get into F-16 Sustainment Business Market”

This has intensified the spotlight on MRO operations. Over the recent years the MRO markets have scaled up and the MRO landscape continues to expand ever so rapidly.

PR Newswire (dt. Dec 11, 2012) – “Global Military Aircraft Maintenance, Repair and Overhaul Market to Reach $41.66 Billion in 2013″

The development of IT in the MRO sector has evolved into a vital part of fleet operations. Many operators have begun updating/upgrading their IT infrastructure software, into a more capable and powerful tool for managing maintenance costs. Current proposed solutions include “…software upgrade that is focused on the health management…” (Aviation Week, dt. Nov 5, 2012) and “…integrating (more storage-capable) software into existing hardware on newer airplanes…” (Defense News, dt. Dec 31, 2012).

Aviation Week (dt. Nov 5, 2012) - “Airlines Seeking Software Solutions For Avionics Upgrades”

Defense News (dt. Dec 31, 2012) – “Taiwan Plans to Upgrade About 60 Fighter Jets in 2013″

QSI has long been a provider of niche software solution which is a perfect fit for MRO IT infrastructure. QSI’s software interfaces are designed to work with existing legacy architecture, and at the same time can be integrated within enterprise systems. For the past two decades the TEAMS Tool-set has been an integral part of industry forecasting, maintenance-planning and scheduling processes thereby improving fleet reliability. QSI provides a suite of reliability-centered maintenance management products designed to eliminate mechanic research time, minimize excess inventory on hand, and increase service levels.

Learn more about QSI’s Integrated Diagnostics philisophy and the legacy of 20 years of providing cutting-edge fleet health management solutions:

http://www.teamqsi.com/solutions/

We believe that a partnership with QSI will go a long way in reducing supplier-side working capital costs and creating customizable service packages driven by innovative solutions. Let us be a principal factor driving your business strategies in this rapidly evolving world.

1. 32bit versions can comfortably scale to models of size 5000 failure modes and tests. Outside of NASA, almost none of our customers have models bigger than 5000 failure modes. Most of our customers create models that are a few hundred failure modes to a few thousand. Thus, 32bit versions provide sufficient scalability for almost all our customers.
2. Most customer sites have a mix of 32bit and 64bit OSs. Standardizing on 32bit version allows all users to run the same exact version.
3. Standardizing on the 32bit version  for most of our installed base allows us to simplify our quality control and distribution process.

See the following table for our performance test results for different models of different sizes.

Memory and runtimes for models of different sizes in 32bit TEAMS-Designer.

A NASA spinoff is a technology, originally developed to meet NASA mission needs, that has been transferred to the public and now provides benefits for the Nation and world as a commercial product or service. NASA spinoffs enhance many aspects of daily life, including health and medicine, transportation, public safety, consumer goods, energy and environment, information technology, and industrial productivity. These spinoffs are transferred to the public through various NASA partnerships including licensing, funding agreements, assistance from NASA experts, the use of NASA facilities, and other collaborations between the Agency, private industry, other government agencies, and academia.

QSI was selected for application of its toolset in Detection and Isolation of Problems on the International Space Station and Ares I-X programs. Click on the following for the listing and the full article from the NASA Spinoff website.

Without TEAMS, organizations rely on the intelligence of the individual Field Service Engineers (FSEs) to solve complex troubleshooting problems. However, not all the FSEs are equally smart, leading to inconsistent performance that no amount of training can overcome.

The consistent QoS delivered by the TEAMS solution is therefore a key motivation for adopting the TEAMS solution. But, how do you measure and demonstrate a consistent performance when there are so many variables that affect the field service process? More to the point, while variability is to be expected, how much variability is cause for concern?

Fortunately, quality measurement is a mature science, and we need to look no further than ”Control Charts for attributes“, originally conceptualized in 1924 by Walter Shewart and extended by Edward Demming.

The underlying theory is very simple.

Supposing you want a consistent success rate of S%, where success implies the Guided Troubleshooting Solution enabled the FSE to identify the root-cause of the failure and apply the appropriate corrective action. How would you periodically verify that you are still achieving S% success rate?

If periodically you sampled N cases, the standard deviation, s, associated with the estimate of S from N measurements is inversely proportional to the sqrt(N). This makes intuitive sense; the more data you have the more precise your estimates of S will be.

Assuming the cases you sampled are independent of each other, the expected number of successes from N trials will be between (S – 3s) and (S + 3s) with 99% confidence. This is basic property of normal distribution, and we will assume that it is applicable to S.

So, what does this all mean? Let’s plugin some values to make sense.

Let’s assume your goal is to have a success rate S = 80%. So, when you have a large enough data set, say thousands of troubleshooting test cases, you will get approximately 80% successful troubleshooting outcomes.

But how many successes should you expect if you just sampled 30, 100, 300 or 1000 test cases? The following table gives you the answer:

You could monitor the accuracy of your Guided Troubleshooting Solution by periodically sampling N test cases and counting how many of those are successful. How to interpret these results?

1. 80% success rate does not mean that every 5th case will be unsolved. When you look at small enough data set, expect to see large enough variation, and that is no reason for alarm. Even if you see 5 successes in 10 trials, that still does not prove you are not going to have 80% success rate. Gather more data, maybe you just had a bad day!
2. Whenever you get successes outside these bounds, do take a closer look. There is still a small chance that there is nothing wrong. But may be there is something else going on. Has your target system changed, and models need to be updated? Is this a new operating condition? Are the FSEs getting confused by the instructions? Whatever it is, it is worth a second look.
3. Do track the moving average. If your average is falling and you are consistently getting worst case results, chances are you are falling behind on your Success rates. On the other hand, if you are consistently beating the average, may be you are overachieving!
4. These tests are known to be too insensitive to small variations in the underlying process, and are therefore suited to measuring the performance of a Global Field Service Organization. However, you can add a rule of thumb or two  to improve its sensitivity. For example
   • If you get two or three back to back data sets that are more than two-thirds of the way to the best or worst case, or four or five back to back data sets that are more than one-thirds away from the expected value, you may want to take a closer look at the underlying data.
   • If you get 8 or more data sets that are all above or below the expected value, it may be time to reevaluate S.

Hope this helps you in measuring success in your Field Service Organization. Let me know how it works out.

Some features and bug fixes in this release include:

TEAMS-Designer

• Users can now generate usage report (or where used report) for Technology Labels, Hierarchy Labels, Test Labels, Resources, Setups, Functions, and User Roles.
• Fixed bugs related to usage of function groups
• Fixed memory problems with Testability Analysis with 8 or more cores.

TEAMS-RDS

• Fixed a bug where the reasoning engine gives preference to multi-outcome tests over two outcome tests
• Users with Supervisor role can now delete any non-active job
• Users with Supervisor role can reassign field jobs to another user
• The maximum model media size for PackNGo payloads can now be configured via a parameter

TEAMATE

• Model entitlement now works seamlessly with TEAMATE.  TEAMATE users can only consolidate models and troubleshoot systems that belong to their group(s) that gets assigned on the server.

If the customer is in DoD/Aerospace world, or has an established R&D and Health Management program, the short answer is yes!

However, customers asking this question often are field service organizations that maintain expensive assets and are looking for an alternative to the traditional break-fix service model. The term Prognosis has become quite popular over the years, thanks to millions of dollars invested by the US Department of Defense in programs such as the Joint Strike Fighter. The promise of Prognostics is simple — wouldn’t it be nice if you could predict how much life each component has left, so that you could replace them just before they failed? But is Prognosis the right tool for you?

First, how good is your Diagnosis? If you are struggling with faults that have already happened, chances are you won’t do any better with faults that have not happened yet! Think of Prognosis as something that involves Diagnosing impending failures and predicting when they will develop into full blown faults. So, Diagnosis is the foundation for Prognosis, and the need for predictive capability makes Prognosis a powerful but expensive technique that should be used wisely only where it is necessary.

This brings us the second question: do you really need Prognosis? For example, your car has two headlights. While it is no fun driving in the rain with one headlight (have you noticed how headlights always seem to fail on rainy days?), having two headlights means that you can still get back home when one has failed. So, redundancy and fault-management are effective ways of reducing unscheduled downtime. For your critical components, evaluate what is the most cost-effective method to avoid disruption in service, and choose wisely.

And now, the all important third question: why do you want Prognosis?

Some people will answer I want to use less maintenance. For example, you may be used to changing the oil in your car every 3 months (schedule-based maintenance) or 3000 miles (usage-based maintenance), but by monitoring the condition of the oil and the filter, you could change the oil only when needed (condition-based maintenance or CBM). This is a valid application of condition monitoring, although strictly speaking, this is not Prognosis. Also, keep in mind that you may not be able to extend maintenance interval for safety critical components without exposing yourself to more liability.

However, most of our customers answer they need prognosis to reduce unscheduled downtime by doing preemptive repairs. Here too, Prognosis is not the only answer.

Let’s take an example — supposing you want to avoid being stranded on the highway due to tire failures. You could add sophisticated techniques that monitor the tread of the tires and how it is wearing out, how the underlying structure of the tire is holding up, the stress on the tire, etc, and predict when failure is imminent. You could develop such Prognosis at significant R&D expense, or, you could simply replace the tires when they look worn (CBM) (e.g., cracks on sidewall and/or tread-depth of 1/32nd inch or less). The second method may cause you to use at most one extra set of tires over the life of the car since you will throwaway tires with still some useful life left on it, but newer tires also improves your safety and performances, which has its own reward. Best of all, you can use the second technique on your current installed base without having to develop new technology.

Let’s also not forget, Prognosis or CBM does not completely prevent unscheduled downtime. You could still hit a pothole and get a flat tire – no matter how new your tire is!

To sum up, there is more than one way to reduce unscheduled downtime: Prognosis, Condition Based Maintenance, redundancy and fault tolerance and fault management. QSI can help you implement all of the techniques discussed here in a balanced health management solution. Health Management is not just Diagnosis or Prognosis, but an effectively engineered delivery of uptime at a reasonable cost.

Let us help you find the right balance of techniques to achieve your objectives.