Transcript Customer Service in Pull Production Systems

Customer Service in Pull
Production Systems
Mark L. SPEARMAN
Presented By: Ahu SOYLU
Outline
The Comparison of Pull & Push Systems
Overview of Pull Systems
Customer Service in Pull Systems
Customer Service in CONWIP
Comparison of Pull Systems with
CONWIP
Conclusion
The Success of Pull Systems
The success of Japanese manufacturing
systems has attracted attention
The system is a set of techniques known
as Just-In-Time (JIT)
An integral feature of JIT is the use of pull
shop floor control systems
There are still a number of issues that
require study; such as customer service.
Push vs. Pull
In a push system customer service is
measured with well known methods like
the fraction of jobs on-time and the
average tardiness.
A job is on-time if the time to complete a
job is less than or equal to its lead time.
Tardiness is the positive difference
between the completion time and the due
date of a job (Tj=max{Cj-dj,0}).
Push vs. Pull
In a pull system, each process is both a
supermarket for downstream processes and a
customer to preceding processes.
A supermarket is a place where a customer
can get
1.
2.
3.
What’s needed
At the time needed
In the amount needed
Service measures are the probability of
stockout, the expected time to fill demand, the
expected backlog of orders.
Push vs. Pull
MRP and Master
Production Schedule
are used to control
production
Input/Output control
provides capacity
check
Controls throughput
and measures WIP
“The day is not done
until every job has
been completed”
The due date of a
new job is established
by considering the
current production
quota and the current
backlog of jobs
Controls WIP and
measures throughput
Push vs. Pull
A system employs push if it schedules the
release of work a priori.
A pull system authorizes the release of
work based on current plant conditions
A hybrid system involves aspects of both.
Two-card Kanban System
Benefits of Pull System
All workers can immediately see what work
needs to be done
Excessive WIP is not pushed to the system
whenever the system capacity is overestimated
It is easier to control WIP than output
When the system works well, there is no need to
schedule production
Adapting the production environment to
improvements is easier
Kanban is not for everybody
The conditions necessary for Kanban to
work well are:
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“Smooth” production involving a stable
product mix
Short setups
Proper machine layout
Standardization of jobs
Improvement activities
Autonomation (autonomous defect control)
Modeling a Pull System
A simplified -one card- version of the
Kanban system is examined
Assumptions
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The cards move instantly so that when a
demand arrives to an idle system, production
will start at each station immediately
Last station has always parts
The standard containers are small and
multiples of the container size
Modeling a Pull System
The service measure will be the expected
time required to fill the demand.
The probability of stockout is not sufficient
in terms of being a service measure
because in a pull system, the longer the
stockout occurs, the more likely to result in
disruption of downstream processes.
N single server stations in series
producing a single product.
Modeling a Pull System
Si~Fi: The service times for station i
{Sij}: set of service times
Dj: Times that demands in the form of kanbans
from an external source are received
Demands are not iid
T(i,j): The time the jth container is sent from
station I
mi: number of kanbans attached to the standard
containers residing in the stockpoint of station i
Modeling a Pull System
T(0, j) = max{D j,T(1, j - m0 ),T(0, j -1)} + S0j
j =1,2,...
T(i, j) = max{T(i -1, j) - Si-1,j,T(i +1, j - mi ),T(i, j -1)} + Sij,
j =1,2,...; i =1,2,...,N
These equations represent the relations
between the completion times, service
times and the demands in the kanban
system.
Measures of Customer Service
The time to satisfy the jth demand
τ j =[T(1, j -m0 )-Dj ]+
The expected time to satisfy demand after
the nth demand
1 n
Un =  E[ j ]
n j=1
Average time to satisfy demand
U  lim U n
n 
Some Results
T(i,j) is nonincreasing in mi
T(i,j) is increasing convex in Sik, k=1,2,…,j
τj is increasing convex in Sij
Stochastic Ordering in Kanban
Systems
(k)
Consider two kanban systems K(S ,m)
k=1,2. Then Si(1)≤st Si(2) implies U(1)≤ U(2)
Consider two systems having processing times
Sij(k)=θi(k) + ξj, k=1,2; i=1,…,N; j=1,2,…, where θi(k)
is a constant and ξj are iid random variables with
zero mean. Then θ(1) ≤ θ(2) implies U(1)≤ U(2)
For systems with processing times whose
means represent a location parameter, faster
processing times imply a better service.
Stochastic Ordering in Kanban
Systems
These results deal with variability reduction.
(j)
Consider two kanban systems K(S ,m)
j=1,2. If Si(1)≥icx Si(2) , i=1,2,…,N implies
T(1) (0,j)≥icx T(2)(0,j), j=1,2,… and U(1)≥icx U(2)
If Sij(1)~F and Sij(2)~G where F and G have the
same mean and where F crosses G at most
once and from below, then U(1)≤ U(2)
If Sij(k) ,k=1,2 ~N((k),(k)) and (1) < (2)
then U(1)≤ U(2)
The Effect of Increasing Inventory
Levels
(j)
Consider two kanban systems, K(S ,m)
j=1,2. Then mi(1)≥ mi(2); i=1,…,N implies
U(1)≤ U(2)
Tradeoff of inventory vs. service
Although extra inventory improves service,
it also reduces flexibility
Also, inventory hides major sources of
variability and allows us to live with
problems that could be eliminated.
The Effect of Increasing Inventory
Levels
In a make-to-order push system with fixed
lead time and constant throughput rate
increasing WIP levels will degrade
customer service.
Little’s Law:
Average WIP
Average Flow Time =
Throughput
CONWIP
Controlling WIP is more robust than controlling
throughput
The fact that WIP is bounded is more important
than the practice of “pulling” everywhere
CONWIP maintains a constant amount of WIP
on each production line and does not pull at
every station.
Line production quantities are measured in
terms of standard parts.
CONWIP can be used when significant setup
times exist
CONWIP
While the pull in kanban occurs between stations
and is to replenish the particular part that has
just been used, the pull in CONWIP is over the
entire line and for the parts having the same
routing.
CONWIP is similar to a closed queueing
network. The flow time and throughput rate tend
to be less variable than an equivalent open
network with the same output.
Push-Pull-CONWIP
Modeling CONWIP
T(0, j)  max{D j , T(1, j- m 0 ), T(0, j-1)}  S0 j
j  1, 2,...
T(i, j)  max{T(i  1, j- mi ), T(i, j  1)}  Sij,
j  1, 2,...; i  1, 2,..., N
These equations represent the relations
between the completion times, service
times and the demands in the kanban
system.
Results
T(i,j)- Sij≥T(i-1,j) – Si-j, for i=1,2,…,N;
j=1,2,…
Consider a kanban system K(S,m) and a CONWIP
system C(S,m) such that the service times are on the
same probability space. Then T(i, j)  T(i, j) , i = 0,1,2,...,N
j =1,2,...
Both systems having the same service time distributions
at each station implies U  U
Discussion
CONWIP behaves like a closed queueing
network, kanban looks like a closed queueing
network with blocking.
With the same card counts, CONWIP tends to
have higher WIP
CONWIP can dominate kanban in terms of both
service and average WIP
A simulation study is performed and it is seen
that CONWIP exhibited customer service that
was statistically superior.
Discussion
Both CONWIP and kanban limit WIP
growth.
Implementation of CONWIP is simpler
than kanban.
CONWIP lines can split into many
segments, then kanban is a subset of
CONWIP.
So, kanban cannot be superior to
CONWIP.
Conclusion
Customer service in both kanban and
CONWIP production systems is improved
by:
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Faster machines
Extra WIP
Less variable processing times
CONWIP systems have better customer
service than do pure kanban systems.