Transcript Make to order or Make to Stock Model and Application” S

“Make to order - Make to
Stock Production Systems
By: ÖNCÜ HAZIR
OUTLINE
INTRODUCTION
LITERATURE REVIEW
PROPOSED MODEL
INSIGHTS AND CONCLUSION
Make to Stock(MTS) Systems
Generally products are produced in batches.
Finished goods inventories for most of the
items are held.
The advantage is that customer delivery times
are minimized as expense of inventory holding
costs.
Typically, companies producing standard items
with accurate demand forecasts prefer make to
stock production systems.
Make to Order(MTO) Systems
Generally prefered when exact needs
of customers are difficult to anticipate.
There exists large number of product
configurations.
Typically no finished goods inventory
are held, customer orders are
backlogged and due dates for each item
are negotiated with customers.
HYBRID SYSTEMS
The models mostly focus on determining
which items should be made to order, or to
stock; establishing a good inventory policy for
make to stock items and evaluating the
performance of the system.
System performance is usually measured with
waiting time distributions, as well as average
setup, holding and backlogging costs.
The models in the literature can be roughly
classified according to the assumptions
related to setups and number of servers.
Literature Review
Williams(1984)
Williams(1984) assumed lower demand items
are MTO and higher demand items as MTS.
However this is not a strong assumption and
most of the current models do not have such
an assumption.
He assumes a (Q, r) policy for MTS items.
Priority is given to order or batch with the
largest waiting time.
Literature Review
Federgruen and Katalan (1999)
They mainly focus whether to interrupt
production of make to stock items when an
order is faced.
Under absolute priority rule, priority is given
to MTO items. Preemption may or may not be
allowed or not.
Under postponable priority rules make to
order items are inserted into the production
schedule of the MTS items, but only when the
facilities would switch between MTS items.
Literature Review
Rajagopolan (2002)
Rajagopolan (2002) focuses to decide
whether an item is MTS or MTO and what
type of inventory policy to use for the items
made to stock.
He models the system as single server M/G/1
queue, on first come first served base. When
demand occurs for a MTO item, the demand
is satisfied in that period.
(Q, r) inventory model is used for MTS items.
The congestion effect, negative effect of an
item to other items is modeled.
Literature Review
Carr and Duenyas (2000)
Carr and Duenyas (2000) focuses to
model how a firm should accept or
reject an additional order and which
type of product to produce next .
Unit profits of MTO items are assumed
to be higher; however large shortage
penalties exist for MTS items.
Literature Review
Carr and Duenyas (2000)
They model the system as a Markov decision
process, where the states are number of MTS
items in the stock (n1) and number of units
of MTO items in process (n2).
Then this analysis works to establish a
dynamic decision mechanism to accept or
reject the order by looking at the current
state of the system.
Literature Review
Carr and Duenyas (2000)
Proposed System
A system of many MTO items and a single
MTS item is considered.Up to time t,
processes are the same for MTO and MTS
items.
At this time point t intermediate inventory of
generic work in process is held.Finished
goods inventory will be held for MTS items.
Performance measures are expected number
of backlogged units for MTS item and
response time to all customer orders at a
given time for MTO items.
Objective is minimizing the inventory holding
costs.
Proposed System
t
Intermediate Stock
l1
MTO
T-t
l2
MTS
Assumptions
It is possible to delay production
differentiation up to point t. Manufacturing
lead time(T) is fixed.
Demand for MTO and MTS items follows
Poisson distribution with means l1, l2
respectively
MTO items have absolute priority and
preemption is also allowed.
No fixed ordering cost exists.
MODEL
min h 1S1  h 2 S 2
subject to
 ( S 2 | l 2 )  b
E (Y )  y
 ( S 2 | l 2 ) 
x 
 (x  S
x  S1 1
2
) p( x | l 2 )
  T  t   ( S1 )t
t 1
E (Y )  T  t   P( D(t  r )  S1 )
r 1
or
t 1

E (Y )  T  t    p( x | l1 (t  r ))
r  0 x  S1
NOTATION
S1: Base stock level at intermediate stockpile
S2: Base stock level for MTS items
Y: Customer response time
P(x/ lT): probability of having a demand of x
in the period lT.
(S1, S2): Expected number of backorders for
MTS items.
: Effective lead-time
(S1) Fill rate at the intermediate inventory
stockpile
Insights about the Model
Finding the fill rate at the intermediate
inventory stockpile (S1) is crucial.
Effective lead-time for the MTS item is a
function of (S1) , which is a function of
the number of customer orders for the
MTO items.
So fill rate at MTS items is a function of
stock level at the intermediate level
stockpile, as well as number of
customer orders for MTO items.
Insights about the Proposed System
The proposed system is a combination of a
pull and push system.
By the applied system customer respond
times for MTO will decrease, since a generic
inventory exists to be processed. The
customer lead-time will be shortened.
Inventory holding costs will be less for MTS
items. Since unit holding cost of generic
inventory stockpile will be less than finished
goods inventory.