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Renee Coogan, President

Dear Readers,

As I mentioned in the introduction of last

quarter’s issue, ALE is celebrating our

35th Anniversary this year. To celebrate

our success as a family-owned business,

and to honor our founder, Charlie Coogan,

we held a Founder’s Day Picnic on May

22nd in honor of Charlie’s 87th birthday.

We are truly thankful for the

opportunities and mentorship he has

provided over the last 35 years, not only

to the ALE staff, but to many folks in the

Logistics Engineering field.

Inside this issue of The Logista, our Quality Manager, David Aurand, breaks down

Quality issues being largely related to Safety issues in the first article, “Applying Safety to

Quality: Human Error Traps”. The second article, “Considerations in Mechanical

Reliability”, discusses the fundamental approaches to modeling reliability for mechanical

components – a core capability of ALE’s Specialty Engineering Group.

Also included in this issue is information regarding our publicly-available Failure Mode,

Effects, and Criticality Analysis (FMECA) Workshop on July 23-25, 2019 at the WestGate

Academy near Crane, IN. Here at ALE, we do what we teach and teach what we do. We

offer workshops as one of the best first-steps a logistics analyst or engineer can take to

building skills and reducing program life cycle cost. Registration is open for the FMECA

Workshop, but seating is limited so early registration is encouraged.

I hope you enjoy your summer and please keep ALE in mind for your life cycle and

logistics engineering needs!

Kindest Regards,

The Logista ALE’s Quarterly Newsletter | Volume 9, Issue 2 | 6.2019

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Applying Safety to Quality: Human Error Traps By David Aurand Considerations in Mechanical Reliability By Stephen Brunner FMECA Workshop Registration Information

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Inside this Issue:

ALE staff enjoys the Founder’s Day Picnic

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Applying Safety to Quality: Human Error Traps By David Aurand, ALE Quality Manager

Call me a slow learner, but after working in a safety-related field for more than 20 years, it finally dawned on me

one day that safety and quality are flip sides of the same coin. They are both largely subject to human errors.

This, then, begs the question: Are there things we can apply from the field of Safety to improve Quality?

HUMAN ERROR

It is a safety truism that roughly 80% of accidents are caused by human error. However, in looking a bit deeper at

human error, the US Department of Energy found that about 70% of human errors were actually attributable to

organizational weaknesses.

Yes, humans make mistakes! But blaming someone for being human isn’t very helpful - especially if the error was

foreseeable. In The Field Guide to Human Error Investigations, author Sidney Dekker says “human error is a

symptom of trouble deeper in the system.”

ERROR TRAPS

Such trouble might be in the form of a “Human Error Trap” – i.e.,

a condition or circumstance that provides fertile ground for

mistakes to happen. Top human error traps include:

∎ Time Pressure

∎ Distractive Environment

∎ High Workload

∎ First Time Task

∎ First Working Day After > 4 Days Off

∎ One-Half Hour After Wake-Up or Meal

∎ Vague or Incorrect Guidance

∎ Overconfidence

∎ Imprecise Communication

∎ Work Stress

The list contains some intriguing possibilities for their application to quality and strategies for mitigation. Should

one eat first and then go for a walk in the second half of their lunch hour, rather than vice versa? Should one make

the extra effort to get work done before leaving on vacation so that critical work does not have to be done the first

few days back?

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GROUPINGS

Human errors can be grouped into 4 categories as

listed below:

∎ Those that arise from the specific task ∎ Those that arise from a particular human’s

suitability for the given task ∎ Those that arise from the work environment ∎ Those that arise simply from human nature

Error trap examples for each group are shown in the

quadrants of the matrix at right.

The groupings give insight into the kinds of things

that can be done to exert influence in each area. The

more related the traps are to the specific person and

task, the more influence can be exerted to affect the

outcome. Conversely, the more the trap is related to human nature and tasks in general, the more difficult it would be

to exert effective control.

PERFORMANCE MODES

When searching for ways to prioritize and reduce human error, one further dimension to consider is in what

“performance mode” the specific person doing the task will be working in.

Performance Modes include:

∎ Skill-based: The task is a habit and can be performed with low or no conscious thought, it is performed 50

to 100 times within a 6 month period, and involves less than 7 complex to 15 simple steps to perform from

memory. An example would be driving your car. Skill-based errors tend to be just slips and lapses, where the

action made is not what was intended. The error rate is on the order of 1-in-1000. This mode is strongly

influenced by the error traps of distractions, simultaneous tasks, and fatigue.

∎ Rule-based: The task is familiar and is addressed by application of a rule that you know. Rule-based errors

tend to be those where actions match intentions but they do not achieve their intended outcome because a

rule was applied incorrectly or a plan was inadequate. The error rate when working in rule-based mode is on

the order of 1-in-100. This mode is strongly influenced by the error traps of confusing displays, confusing

procedures, and mindset.

∎ Knowledge-based: The task involves a novel situation, and actions must be planned using conscious

analytic processes and stored knowledge. This is the “you don’t know what you don’t know” mode; actions

are intended but do not achieve the intended outcome because of missing bits of knowledge. The error rate

when working in knowledge based is on the order of 1-in-2 to 1-in-10. This mode is strongly influenced by

the error traps of assumptions, first time performance of the task, time pressure, and lack of

knowledge/experience.

EXAMPLE

It is a bright, dry, clear morning, and you are taking your usual route to work. Traffic is smooth today – but you don’t

really notice because you are listening to someone on the radio talking about a topic of great interest to you (say,

investments and retirement planning). The person is relaying some fascinating facts that you want to make sure to

remember, all while you are contemplating possible actions to take as a result. And as you glide into the parking lot

where you work, you sort of wake up and wonder how you got here. And the strange absence of other cars in the lot

immediately snaps you back to reality: it’s Saturday, and you were headed to Lowe’s. Oops. Human again!

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The error trap here was Distraction. Despite being in Skill-based performance mode, your own thoughts about non-

task-related matters put you in “autopilot”, and the visual cues early along the route you were driving said you were

going to work, which is why you missed the turn you should have taken to Lowe’s. You probably wouldn’t have made

this mistake with other cues present, such as wearing the jeans with the hole in the knee and driving the old dirty van

instead of the commuting car.

TAKE AWAY

Human error traps are leading indicators of trouble, whether in the safety or quality arena. Be on the lookout! But also

design your processes and systems with the most likely traps already in mind, making the system conform to the

people, not the other way around. Make it easy to do the right thing. Make it hard to do the wrong thing. And also make

it so that doing the wrong thing doesn’t lead to catastrophe.

Considerations in Mechanical Reliability By Stephen Brunner

MECHANICAL RELIABILITY: WHAT IS IT?

In Logistics Engineering and Management, logistics

engineering pioneer Benjamin Blanchard defined

Reliability as the probability that a system or product will

perform its intended function in a satisfactory manner for

a given period of time when used under specified

operating conditions. Similarly, reliability engineering

emphasizes satisfactory performance over a product’s

lifecycle. To be an effective reliability engineer requires

an understanding of failure mechanisms from experience

and applied knowledge, as well as possessing a basic level

of competency across engineering disciplines. "Reliability

is, after all, engineering in its most practical form,” stated

by James R. Schlesinger, Former U.S. Secretary of Defense.

In the past, items were made more reliable by over

designing them. This process mitigated the need to

understand dominant failure modes and material

characteristics. Today, mechanical reliability is viewed

much differently. The present-day designer is not

afforded the luxury of over-engineering due to

demanding customer requirements, the criticality of

weight (particularly in aircraft), and product cost.

The current reliability engineering environment is made

complex by the existence of a myriad of analysis and

prediction approaches. These different approaches stem

from the availability of advanced materials data and

statistical modeling techniques using various software

tools and computational advantages. Different

approaches also apply multiple factors that can better

model considerations such as manufacturing quality,

material variations, system complexity, and design

tolerances. To manage today’s reliability engineering

efforts, we propose an approach that employs the

Systems Engineering philosophy and quickly narrows

focus to only those analysis activities that provide the

insights desired by our program.

TRADITIONAL ANALYSIS METHODOLOGY

Typical program requirements utilize MIL-STD

documentation as guidance for developing fielded

reliability estimates or predictions. The reliability

analysis process, presented in Figure 1, includes the

estimated life or test program scope that considers

reliability over a distribution of failures and typically

involves the following steps:

1. Understand the design requirements

2. Characterization of the system and its hardware

configuration

3. Define the system operating conditions

4. Define the system utilization rate/mission profile

5. Establish reliability prediction method(s)

6. Perform reliability prediction

7. Summarize results and highlight conclusions

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Step 5, “Establish reliability prediction method(s)”,

presents a particular challenge for mechanical

component reliability analysis. For electronic items,

there exist analytical models that characterize the

basic reliability of each component type, and include

adjustments for operating conditions (pi factors). The

most notable is MIL-HDBK-217, Reliability Prediction

of Electronic Equipment. There are also multiple

software tools that implement the MIL-HDBK-217

methodology.

Unfortunately, there is no “standard” methodology

that can be applied to mechanical component

reliability predictions. Unlike electronic components,

most mechanical components and assemblies are

wear-dependent over their life cycles, as shown in

Figure 2. This limits the accuracy of some of the

common reliability prediction models such as the

exponential (constant failure rate) reliability method

used in MIL-HDBK-217.

MECHANICAL RELIABILITY PREDICTION

It is difficult to model the reliability for the initial

“wear-in” period, and for the subsequent “slow wear”

period that represents most of the service life. Since

the wear-in period is brief, oftentimes models ignore

it. Other models treat this complex wear-in and slow

wear behavior as constant over a given period of time.

An average value is then used in simple reliability

models. This prediction approach utilizes generic

failure data from a wide range of applications and

environments. Average mechanical failure rates are

selected from government-published reliability data,

such as from NPRD-16, EPRD-14, and NSWC-11.

One challenge in using average values in predicting

mechanical reliability is that mechanical components

are often operated at a variety of different stress

levels over their life cycle, which is different than the

constant stresses experienced by electronic

components. Think of the varying stresses on the

mechanical components in your car’s engine.

Many OEMs are generating significant databases of

failure modes, operational stresses, and field

reliability data for use in predicting future reliability

of traditional product-line items. The major benefit of

using OEM data to predict reliability is that many of

the variables or applied factors are removed from the

analysis computation. Specifically, reliability

estimates would presumably be based on field data

from parts that have actually had the same (or Figure 2. Mechanical Reliability across the

Lifecycle

Figure 1. Mechanical Reliability Analysis Process

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similar) production processes, operating

environments, supplier component parts, etc., to the

items under analysis.

However, there are a few issues to consider with this

approach. Namely, historical data and related insights

lag the development of new components. Also, the

inexact science of factoring the subtle differences

between the configuration and operation of the fielded

item and the new development item, and their impact

on system reliability, needs to be addressed.

We recommend employing the following methods

when performing mechanical reliability predictions:

∎ Weibull Distribution. Empirical reliability

relationships can be established using limited field

(or test) data from representative materials,

environment, loading, and configuration. The

Weibull distribution is recognized as a practical

and useful statistical model of predicted life of a

population of items based on field/test data, and

best models a non-constant failure rate over time.

The analysis begins with a review of available data

to determine the type of Weibull function to use. If

failures exist, the preferred 2-or 3-parameter

Weibull function is applied. If no failures have

occurred, and there’s no indication of impending

failure, then it’s impossible to empirically define

the shape factor, Beta (β), which describes the

change in failure rate over time. Therefore, data

points must be treated as “suspended” data, and a

one-parameter Weibull function can be employed

with an assumed shape factor (β). A component

failure rate can be predicted using the following

Weibull function equation:

Where:

f(t) = Failure rate of component at time t C = Constant, assumed Weibull shape factor (β) η = Characteristic life estimated from test data t = Service life of the end item e = Mathematical constant (~ 2.71828)

∎ Stress-Strength Interference Method. The

Stress-Strength Interference Method attempts to

obtain a point estimate of reliability within the

service life of a mechanical component that

accounts for the variability of loading and material

strength properties. Application of this method

requires knowledge of a component’s material

characteristics, as well as the stress distribution

experienced during operation. The quality of the

reliability estimate depends on the accuracy of the

material characterization (developed through lab

testing) and mechanical loading characterization

(developed through finite element modeling). This

method treats both stress and material strength as

random variables subject to probabilistic scatter.

The area of intersection between the distributions

of stress and strength is the “interference” and

represents the probability of mechanical

component failure, as shown in Figure 3.

∎ Physics of Failure. “Physics of Failure” (POF) is

particularly applicable when describing physical,

chemical, mechanical, or electrical contributions to

degradation of structural material and, eventually,

failure. As applied to mechanical reliability, POF

offers algorithms (for studied circumstances) that

relate degradation functions to reliability

parameters. Much the same as Stress-Strength

Interference, this approach is predicated on the

material and loading characteristics being well

understood. Often, this reliability assessment

approach has not been fully developed to represent

a particular material and loading situation.

The Reliability Engineer must apply an analysis method

that best represents the expected failure mechanism

for component type and application. It is also

important to keep in mind that even if the reliability

estimate does not accurately reflect operation, the

predicted reliability still serves as a useful measure of

the scope of the support system requirements and

highlights the reliability drivers to be addressed during

design and test to achieve reliability growth.

Figure 3. Stress-Strength Interference

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Failure Mode, Effects, and Criticality Analysis

FMECA WORKSHOP

Presented by:

REGISTER NOW! Space is Limited.

July 23-25, 2019 | Crane, IN

Attendees will become familiar with current FMECA techniques and develop

hands-on experience in applying these techniques using sample products, and

documenting the results in accordance with industry standards. Attendees will

discuss how to communicate and use results of other analyses to improve their

team’s products or processes.

For further information about this workshop and to register, please contact us at:

E | rcoogan@ale.com

P | (614) 436-1609

F | (614) 436-1295

WHERE:

WestGate Academy

13598 E. WestGate Dr.

Odon, IN 47562

WHEN:

Tues, July 23 -

Thurs, July 25

8:00 AM-4:00 PM

COST:

$1,200 / person www.ale.com

− Eliminate product or process downtime

− Eliminate undesired failure modes through design improvements

− Reduce the effects or probability of failure mode occurrence

− Improve safety and availability of the product

− Provide critical inputs to product support planning

ALE’s Beginner

to Intermediate

Level 3-Day

Course is

perfect for

Logisticians

and System

Engineers