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So, every event during the pre-mission, mission, and postmission phases of operation becomes a critical staging point for after action and follow-up reporting This requires three actions: 1 Establishing record-keeping systems 2 Ingrain professional discipline in PERSONNEL to record a mission or maintenance event 3 Thoroughly document the sequence of actions leading up to a mission or maintenance event Depending on the consequences of the event, failure boards may be convened that investigate the WHO, WHAT, WHEN, WHERE, WHY, and WHY NOT of emergency and catastrophic events People, in general, and SEs, in particular, often lack proper training in reporting malfunctions or events Most people, by nature, do not like to document things So-called event reporting tools are not User friendly and typically perform poorly For these reasons organizations should train personnel and assess their performance from day 1 of employment concerning: 1 2 3 4 5.



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function for each ow. However, solving linear programming formulations against multiple objectives is known to be computationally dif cult. Thus, there is a need for a single objective function that captures the overall network performance without sacri cing individual connection performance. Equation (7.7) states a simple objective function to minimize the commitment period of all the nodes, and Eq. (7.8) is an objective function to minimize the hot-spot node that has the highest commitment period. Minimize Minimize

WHAT data records are to be maintained and in what media WHY the data are required WHEN data are to be collected WHO the Users are HOW the User(s) apply the data to improving system performance.

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(7.7) (7.8)





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The preceding discussions are intended to illustrate HOW TO THINK about system performance monitoring. Earlier we described a condition referred to as analysis paralysis. These areas are prime example. As a reality check, SEs need to ask themselves the question: If we simply asked Users to identify and prioritize 3 to 5 system areas for improvement, would we glean as much knowledge and intelligence as analyzing warehouses full of data The answer depends. If you have to have objective evidence in hand to rationalize a decision, the answer is yes. Alternatively, establishing database reporting systems that can be queried provide an alternative. IF you choose to take the shortcut and only identify the 3 to 5 areas for improvement, you may overlook what seems to be a miniscule topic that may become tomorrow s headlines. Choose the method WISELY!

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You may ask: WHY do SEs need to analyze system performance data If the system works properly, WHAT do you expect to gain from the exercise WHAT is the return on investment (ROI) These are good questions. Actually there are at least several primary reasons WHY you need to analyze system performance data for some systems: Reason 1: Reason 2: Reason 3: Reason 4: Reason 5: Reason 6: Reason 7: To benchmark nominal system performance. To track and identify system performance trends. To improve operator training, skills, and pro ciency. To correlate mission events with system performance. To support gap analysis. To validate models and simulations. To evaluate human performance.

It is acceptable if one of the guidelines is not followed any one as long as it is not the first one If two of the guidelines are not followed there had better be very good reasons, and extra precautions need to be taken to compensate for the increased risk What are these precautions If more than two of the guidelines are not followed, then the design is fatally flawed the system cannot be built on time or on budget if it can even be built at all..

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Establish statistical performance benchmarks via baselines, where applicable, for WHAT constitutes actual nominal system performance. For example, your car s gas mileage has a statistical mean of 30 miles per gallon as measured over its rst 20,000 miles. Remember, the original System Performance Speci cation (SPS) was effectively a Design-To limits set of requirements based on human estimates of required performance. Veri cation simply proved that the physical deliverable system or product performed within acceptable boundary limits and conditions. Every HUMAN-MADE system has its own unique idiosyncrasies that require awareness and understanding whether it is performing nominally or drifting in/out of speci cation. Consider the following example:

If a hypothetical performance requirement is 100 10, you NEED TO KNOW that System X s nominal performance is 90 and System Y s nominal performance is 100. Sometimes this is important; sometimes not. The borderline 90 system could stay at that level throughout its life while the perfect 100 system could drift out of tolerance and require continual maintenance.

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