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MIT 2 810 - Measuring Performance; Metrics for Time, Rate, Cost, Quality, Flexibility, and the Environment

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Tim Gutowski, July 8, 2002IntroductionCompetitive Attributes1Measuring Performance;Metrics for Time, Rate, Cost, Quality, Flexibility, and the EnvironmentTim Gutowski, July 8, 2002IntroductionManufacturing processes and systems operate in a competitiveenvironment. We generally evaluate their competitive performance in six keyareas: time, rate, cost, quality, flexibility, and the environment.Thefirstfourofthese attributes can be measured directly, the fifth, “flexibility” can be definedbut there is no universal agreement on how to measure it. The sixth,“environment” depends upon the particular issue under consideration. In thispaper, we will discuss common units of measure for these attributes. Note thatthese measures can be made for different sized units of the manufacturingenterprise. In general the relationship between a process level attribute and asystem level attribute is not straightforward. For example, even for cycle time,the cycle time for a system is not just the linear sum of the cycle times for theprocesses within the system. This is because processing can be done in parallel,and because in most systems, parts spend most of their time doing things otherthan being processed. Examples of these "other things" are; waiting (as rawmaterials, work in progress and/or finished goods), transport, and inspection.The most important point to keep in mind is that optimizing a processattributewill not, in general, optimize the system attribute.Comparisons between manufacturing alternatives usually require makingtrade-offs. That is, often we will find that while one attribute will improve,another is degraded. For example, a common trade-off when increasingproduction rate by the use of hard tooling, is balancing the positive effect of theincreased production rate with the negative effects of increasing the initial cost,and lead time for tooling, and decreasing the flexibility of the process.Technically, these kinds of problems are known as multivariable decisionproblem. They cannot be optimized without placing some valuation on eachattribute. One way around this is to place constrains on most attributes (e.g.make rate greater than or equal to some target etc.) and then optimize onevariable, usually cost.Competitive Attributes1) TIMEAmong the various attributes, time, “t”, is one of the easiest to define andmeasure. Figure 1 shows three important lead times; 1) Customer Lead Time, 2)2Manufacturing Lead Time, and 3) Factory Lead Time. In the time between anorder being made and being released, all necessary raw materials andprefabricated parts need to be procured. Upon release a part is queued into themanufacturing system, and starts production when all of the appropriateresources (machines, materials and operators) are available. During the “Factorylead time” the part moves from station to station as specified in the process plan.The “value added” steps in this phase including all of the processing steps(e.g.casting, machining, assembly). Usually however, the Factory Lead Time isdominated by “non-value added” steps such as waiting in queues, transportationbetween stations and inspection. A final delay is due to transportation and inmany cases storage.Factory Lead TimeManufacturing Lead TimeCustomer Lead TimeFigure 1 Lead Times from various perspectivesUnfortunately there is no universal agreement on the different measuresof time, and you may hear alternative terms or definitions. Therefore alwaystake care to define terms before any discussion of manufacturing time. The termsused here are in agreement with those in the APICS Dictionary [ref 1].At the machine level we can also define a “machine process time” as thetime to make one part. If parts are made in batches such as when machining on“tombstones” or when injection molding using multiple cavity dies, we mustdistinguish between the time the parts spend in the machine, say tresidence,andthe average time per part. For a batch of “n” parts that would be tresidence/n. Tomake the first part, the total processing time at a specific machine is made up ofthe set-up time and the process time. Set up time is the time required to preparethe equipment to produce a good new part. This usually involves changing tools,checking out software and making final adjustments.Receipt ofcustomer’sorderRelease oforder toshop floorFirstmanufacturingprocess startsManufacturingcompleteReceipt bycustomer/or receipt tostock3We can also use these same ideas to identify the time a unit of productionspends in a manufacturing cell. A manufacturing cell is a collection ofmanufacturing processes arranged next to each other for the purpose of makinga part. (In some cases a cell may be only one process, but usually it is more thanone.) Hence a “cell” is usually smaller than a factory and larger than a process.2) RATERate “ λ“, is the rate of material flow through the system. The “flow in” israw or unfinished goods, while the “flow out” is the finished or semifinishedproduct. Rate is usually measured as “units per time”. The average time in thesystem can be related to the average steady state rate and the average number ofunits in the system by a very useful expression derived by John Little of MIT.[See Ref 2].λλλλinλλλλoutSystemFig. 2 Representation of material flow througha manufacturing system.Note that in steady state, the rate of material coming into thesystem is equal to the rate of material going out, therefore:λλλλin= λλλλout= λλλλ Under these conditions, we may state Little’s law as,L=λ λ λ λ W eq. 14where L = units in system ( or inventory )and W = time in systemThe above law applies to a single process, a cell, or a factory. Theimportant point is to define what is the system and be clear about what is inside,outside, and passing through the boundaries. A simple example would be amachine operating at rate µ, with material supplied at rate λ < µ.Inadeterministic system this machine would operate in a “starved” mode at a rate“λ”. However, with statistical fluctuations in the processing times, a queuewould build up in front of the process. The system would still operate at rate“λ”, and the queue plus the part in the machine would be the inventory “L”. Thisexample is similar to the M/M/1 queue, for those of you familiar with queuingtheory.We can us eq 1 to define terms when discussing time and rate forprocesses and systems. For example “cycle time” for a


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MIT 2 810 - Measuring Performance; Metrics for Time, Rate, Cost, Quality, Flexibility, and the Environment

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