SCM 405 Exam 1 Flashcards
Corporate strategy
- select the market in which to compete in
- acquire and allocate resources to the business unit
Levels of strategy
corporate strategy
business unit strategy
functional (manufacturing) strategy
Business unit strategy
- identify the boundaries of the markets served
- select the desired competitive advantage
Functional (Manufacturing) Strategy
- determine how best to support the competitive advantage
- integrate and communicate with other functions
Supporting the competitive advantage (measure of manufacturing success)
- cost
- conformance quality (reliability)
- time to market (speed)
- flexibility
- delivery dependability
work station (station)
a collection of one or more identical machines
parts (jobs)
a component, sub-assembly, or an assembly that moves through the work stations
production line (line)
sequence of workstations needed to make a part
Performance measures
Throughput (TH)
Cycle Time (CT)
Work in progress (WIP)
Throughput (TH)
average quantity of good (non-defective) parts produced per unit time
Cycle Time (TH)
average time it takes for a part to travel between the start and endpoint of a production line (has no unit of time)
Work in Progress (WIP)
average inventory between the start and endpoint of production line
Line Parameters
represent the optimal performance of a production line. this is the best the production line can perform
Types of line parameters
Bottle Neck Rate
Raw processing time
Critical WIP
Bottle neck rate
- Average processing rate (part/time) of the slowest work station
- On a line with multiple parts, Rb, will depend on the product mix
- TH is always less than or equal to bottle neck
Raw process time
- sum of the average process times of each workstation in the production line
- this doesn’t include wait time
- Cycle time d greater than or equal to raw process time
Critical WIP
- WIP level in which a line with no variability would achieve both maximum TH and minimum CT
- TH = bottle neck rate
- Cycle time = raw process time
Little’s law
- Fundamental relationship between WIP, TH, and CT over the long-term
- WIP = TH * CT (rate * time)
- those performance measures are long term average
What is variability?
any departure from uniformity
Classes: common cause and assignable cause
What is variability?
any departure from uniformity
Classes: common cause and assignable cause
Common cause (random)
artifact of incomplete knowledge
variability that we don’t understand what is causing it
management implication: robustness is key
Assignable cause (controllable)
variability that we know what is causing it
Sources of variability
machine failure set up material shotages yield loss rework operator unavailiabilty
process time in relation to variability
natural process time usually have LV (low variabilty)
For effective process time; LV, HV,MV are all possible
Variability classes in relation to Performance cases
LV - between best case and minimum expected performance case (Lean region)
MV - minimum expected case
HV - between minimum expected performance case and worst case (Fat region)
Range for LV (c^2)
0 -0.50
MC (c^2)
0.50 - 1.50
High Variability (C^2)
1.50 - greater
variabilty and set up time
high variability usually happens when machines stop for set up and other things
natural process time
-average process time when a machine is working properly
effective process time
average process time when machine down time is also considered (eg setup, break up, etc)
squared coefficient of variation of natural process time Co^2
lets us know the magnitude of variation relative to the mean. the standard deviation alone doesn’t tell us how natural process time is related to the mean (wether it is big or not)
sources of natural process time
- operator pace
- material fluctuations
- product quality
what does variability depend on
Variability depends on repair times in addition to availability
Extra
-down time doesnt change the average processing time, but it adds variability
-as average repair time gets longer, it adds more variability
impact of machine break down on variability
- failures increase the mean and SCV of effective process time
- SCV increases when either Tr(time to repair) or Cr^2 (squared coefficient of repair time) increases
- for constant availability, A, long infrequent outages increase SCV
why does long infrequent outages increase SCV more than short frequent ones?
This is because Tr and Cr^2 for long and infrequent outages is larger than the Tr and Cr^2 for short and frequent outages. The longer one has more variability because the SCV is larger.
Conclusion about the hare and the tortoise
Capacity and arrival variability are the same. CT and WIP are inflated greatly due to process variability
Why is process variability bad?
- it inflates station WIP
- it inflates station CT
- it causes lower utilization of capacity
What else does process variabilty cause?
- uneven arrivals to other stations down stream
- inflated WIP and CT at other stations
- it causes lower utilization of capacity throughout the line
Illustrating flow variability
- Low variability arrivals = smooth
- High variability arrivals = bursty
HVA will need more capacity to handle it, but utilization is lower
Propagation of Variability (high utilization) (close to 100%)
- flow variability out of a high utilization station is determined primarily by the process variability at that station
- variability at arrival doesn’t matter that much
- the fact that the middle line determines outbound variability doesn’t mean it is the bottleneck
propagation of variability (low utilization) (close to 0%)
- flow variability out of a low utilization station is determined by flow variability into that station (arrival variability)
- because machines are never being used, machine variability doesn’t play a large role
- departure variability matches up with arrival variability
what is departure variability
this is a weighted average or mixture of arrival variability and process variability
-performance goes them where there is queueing
importance of queueing
- manufacturing plants are queueing networks
- queueing ( or waiting0 comprises majority of a station’s CT
queueing parameters (can be described in 5 parameters)
Ra (average arrival rate) Ca^2 (squared coefficient of inter-arrival times) m (number of machines) te (average effective process time) Ce^2 (SCV of effective process time)
Queueing performance measures
- CTq - expected waiting time a job spends at a station
- WIPq- expected WIP level in queue
- CT - expected total time a job spends at the station (queuing time + processing time)
- WIP - expected WIP level at the station (jobs waiting + jobs in process)
Throughput and rate for a stable queueing station
TH for a stable queueing station is just the arrival rate Ra
queue time at a work station
- flow variability, process time variability or both can combine
- variability causes waiting me - high utilization makes it much longer
- CTq (and the other measures) increases Ca^2, Ce^2
What happens to TH when more WIP is added to the system after maximum TH has been reached
more WIP does not increase throughput, it increases cycle time @ optimimun TH
CT vs. WIP (best)
at best CT any increase in WIP will increase CT dramatically
most common bottle neck?
The most common bottle neck is labor or workers
Best case?
This is when you have minimum CT, and highest TH (which is = to bottle neck)
Worst case?
Maximum CT, and minimum TH
-this time, all jobs will be processed at a station before any are allowed ro be moved to the next station (every job waits for every other job at every station)
What will case TH to decrease?
TH will decrease is the bottle neck station is starved
Tools to evaluate how a production line is performing
- collect data and compute average for current WIP and TH
2. Calculate TH (MEP)(w)
MEP and performance
- Systems that perform better than MEP are lean [TH > TH(mep)(w)]
-systems with perfromance worse than MPE are “fat”
TH < TH (mep)(w)
to improve a badly performing line
- reduce variability (this will have the greatest impact)
2. absorb variability better
absorb variability
- an unbalanced line absorbs variability better than a balanced line
- assuming the same total capacity, multiple slower machines absorb variability better than one fast machine
(this is important when designing a production line)(this could be expensive to configure a new line to)
