“Cipher’n”
By Philip T. Frohne, CPL
Does the thought of combining ‘Mean Time Between’ anything frighten you? Confused about how to perform basic logistics calculations? Loggie math skills get’n a little rusty? Fear not, because you are not alone. I, too, am mathematically challenged. Many school teachers told my parents that I should stick to the violin (I play French horn). My book on logistics math began as a desk drawer full of illegible sticky notes. So let’s calm those fears, take control of those formulas, and make them “sit up and beg for their supper.”
As always, we begin with definitions: “Logistics math” provides numerical bench marks and methodologies by which we can evaluate and compare alternative support solutions; assist in developing award fee criteria; plan, schedule, and manage support activities; monitor the health of the support system; prevent parts shortages; contribute to mission success … even allow us to earn a paycheck.
Calculations are performed in order to turn raw data into useful data. We should begin by qualifying and/or quantifying the data. Qualitative data categorizes objects based on specific attributes such as age, gender, economic status, etc. Quantitative data indicates how much (amount) of an item exists (e.g., hours, rounds, miles, and cycles). We often measure supply/demand chain management issues, supportability concerns, inventory management, R&M and the other “-ilities”, etc. to better manage our jobs. The Quantitative supportability Measurements can be processed as ‘metrics’ for most of our Logistics needs. <Shameless SOLE Press subliminal advertising>
When comparing values it is vitally important to ensure that the units match, or are at least compatible with the resulting units. Comparing apples to oranges only works if the end ‘result’ is expressed under the sub-category of fruit, food, things grown on trees, etc. You cannot compare operating hours to flight hours unless a conversion factor is introduced that makes the units either the same, or non-dimensional. We will learn how a technique called Dimensional Analysis will help convert units from what we have, to what we want.
Make logistics information meaningful. How much horsepower does it take to power a cuckoo clock? Not much! (I did the math). However, by using scientific notation to turn the number into an integer, it can be made easier to manipulate and understand. We do the same thing when we compare the number of failures to a million hours. While the horsepower requirement for one clock may be insignificant, the associated support of hundreds or thousands of End Items (e.g., cuckoo clocks) might not be. The logistics requirements can be expressed in terms of manpower or units of fuel; with then can be expressed in PHS&T elements; and other logistics Life Cycle Cost (LCC) analyses. [“For want of a nail, the shoe was lost.”]
Make logistics information understandable. I tend to convert numbers into units that I understand. For instance, I like to convert units of power into the equivalent number of 60 watt light bulbs that can be lit. I have pedaled generators in science museums that powered several 60 watt light bulbs in succession, so I have a mental image of how much physical energy is wasted when the kids leave the lights on. One horsepower equals 12+ 60w bulbs. We do the same thing with logistics metrics by basing many values from zero (0.0) to one (1.0), comparing them to 100 (the “percent”), or scaling them to one million (1,000,000) hours, cycles, or other events.
Logisticians must format the raw data such that it will perform an adequate job of providing the desired information. Analog and digital formats may be applicable in different situations. Logistics data can be presented in an unlimited number of ways. We must choose the method that will provide the most correct data without being misleading or misrepresented. “Lies, damned lies, and statistics” – British Prime Minister Benjamin Disraeli (1804–1881)
How many decimal places are enough? Accuracy costs money. When dealing with forecasts and averages, it doesn’t always make sense to be accurate down to the southbound end of a northbound gnat. How precise one calculates a value will depend on what the data will be used for. With mean times (averages), we usually don’t need to express anything to the right of the decimal point. But with computers we get the fractions calculated for free. We just don’t always need to see’m or use’m.
The basic building block of most logistics metrics is the comparison of one numerical value to another. We do this by dividing one number by the other to obtain a single value, providing useful and understandable comparisons. We sometimes multiply fractional values by 100.0 to convert the answer into a percentage. Most of the logistics formulas I have found are simply one number divided by another. “Logistics math” does NOT have to be complicated! The hardest part is deciding which value should be the numerator (dividend on top) or denominator (devisor on bottom). It all depends on what value you are comparing to another.