Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
.
Manufacturing Information System & .
Review of Operation Management Oran Kittithreerapronchai 1
1
Department of Industrial Engineering !hulalong"orn #niversity $ang"o" %'' ()*I+*,D
last updated: ,ovem-er %'
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Outline ".
2
'
.
.
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Keys !oncepts of Manufacturing System Review of /orecasting in 0roduction Environment *ggregated 0lanning and Sale & Operation planning Material 0roduction Scheduling Inventory !ontrol $ill of Material
(
)
.
.
Material Re1uirement 0lanning Integrating !apacity 0lanning into M0S
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
0reviously lecture...
!hange is everywhere everywhere and and ever increasing increasing so does competition E ective response ! $etter communication ! Right I( Issue surrounding I( in manufacturing Information technology is strategic advantage advantage not strategic necessity (his lecture... Reali2e important of Manufacturing System Review of "ey concepts of Operation Management esp information flow in manufacturing system Illustrate how IS 3i.e. MS E4cel5 helps with *ggregate planning Inventory control MR0 !R0
Manuf Sys
Forecasting
Agg. & SOP
MPS
Manufacturing System who care...
!riticism 6lo-ali2ation
+arge capital !ooperate 7 Evil /uture is in service
Manufacturing !lassifications $usiness !lassification Inventory 0osition 0roduct & 0rocess Matri4
MRP
Capacity
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$usiness !lassification
*+at: economic classification of industry and -usiness ,eneral: Statistic & loo" for information Pro MIS: understand -usiness find implementation method
Sources ,orth *merica Industry !lassification System8www.census.gov
O ce of Industrial Economic 3OIE58www.oie.go.th !ontract Directory8 www.on-id.org
Capacity
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Office of Industrial Economic
source: www.oie.go.th
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
Inventory 0osition E(O M(O *(O M(S
.. Engineering
0rocurement
/a-rication
*ssem-ly
Delivery
source: *dopted from Smith, S. %9:9.
-ngineer to Order -/O0: wor" with the customer to design and then ma"e the product Ma1e to Order M/O0: ma"e the customer;s product from raw mat; Asse3le to Order A/O0: com-ine a num-er of preassem-led modules to meet
customer;s specifications Ma1e to Stoc1 M/S0: serve customers from finished goods inventory
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
0roduction 0rocess
source: !hase &
Pro4ect: product remains in a fi4ed location *or1 center: similar e1uipment or functions are grouped together Manufacturing Cell: dedicated area where similar products are produced Asse3le line: processes are arranged according to the progressive steps Continuous process: assem-ly line only the flow is continuous
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
System Structure
6ierarc+ical: decision in upper=level ! constraint in lower=level Feed3ac1: systematic correction Man7Mac+ine interface: 9/ e4ceptions ! including man 8Single8 data3ase & Integration: maintaining data integrity and accuracy
/ransparency: understanding logic and algorithm -ehind system Specific response tie: designing for improvement
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Related issues with operation management
source: !enter for Supply !hain Research 0enn State #niversity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
/rom /orecasting to Shipping...
6uessing customer;s demands () /orecasting Smoothing resources () *ggregated planning & Demand management Sourcing R>M needed to produce () 0urchasing & Material re1uirement Estimating producing time () 0roduction planing Managing stoc"s () ?arehousing & Inventory control Defining appropriated service level () !hannel & Distri-ution management Ensuring commitment and delivery products() Shipping
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
(ime )ori2on
9ecision +orion
S+ort7ter 0urchasing Scheduling Maintenance Inventory
Interediate /orecasting Manpower ..
Distri-ution
;ong7ter
/acility Sale channels Service
O-@ective
Issues source: Nahmias, S. %.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
Evaluation of Manufacturing IS
source: www.arhum.com
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
!losed=+ooped 0roduction 0lanning +. /orecasting
*gg. 0lanning
RR0
+ong=(erm 0lan 0O R!!0
Demand Mgt. S. /orecasting
Intermediate 0lan !R0 $OM MR0 Inventory
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?hat is /orecastingA
0redicting future value with data or information e.g. weather forecasting
/orecasting affects all activities in organi2ation *or1force and Capacity: hiring training Operation: purchasing scheduling ?I0 negotiation planning Accounting: cash flow e1uipment investment Mar1eting: pricing promotion place
. )ow much products>services do we need and at which time 3which location5A
.
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?hat should we aware -efore forecastA
9escription: story relationship with other data /ie 6orion: hour day wee" year
*ctual 7 /orecast B error Pattern of 9ata: seasonal trend cycle Forecasting Model: assumption data re1uired parameters static
CS dynamic Accuracy: measuring how to improve
Capacity
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E4ample #S *ir 0assengers %99=%9F%
s r e g n e s s a P r i A
Y e ar1 9 4 9
0 8
0 6
Jan
Feb
Mar Ap r
May Ju n
J u l
Au g
Se p
Oc t No v
De c
J an
Feb
Mar Apr May
J un
J u l
Mont h
Au g Sep
Oc t
No v Dec
J an
Feb Mar
Ap r
May
J un
Ju l
Au g
Se p
Oc t Nov
De c
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/acts a-out /orecasting
/orecasting is typically incorrect /orecasting is suita-le for a group of products /orecasting is inaccurate as time hori2on increases source:Chopra, S. & Meindl %. pp. %99
?hy do we still need /orecastingA Incorrect future is -etter than "nowing nothing
Incorrect result is managea-le
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Smoothing Simple /orecasting Methods Assuption: recent past
future
/ie 6orion: short period 9ata Pattern: nearly constant
0 0 2 3
0 0 8 2
0 0 4 2
s e l a S e f i n K
0 0 0 2
0 0 6 1 0 0 2 1
0 0 8 0 0 6 0 0 4 0 0 2 0
MRP
Capacity
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Moving *verage M*315
using average value of q pervious periods as forecast
.
q
= ,um-ers of interested period
MRP
Capacity
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E4ample of Moving *verage Month
Manuf Sys
Forecasting
Agg. & SOP
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MRP
Capacity
?hich is a -etter M* model for Knife SalesA
s e l a S e f i n K
H
H
0
J an
Feb
Mar
Apr
Ma y
J un
J ul
Aug
Sep
Oc t
No v
Dec
Mont h
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*ccuracy of /orecasting Idea:averageJ of *ctualt
/orecastt
-=aple: Mean Error 3ME5 Mean *-solute Deviation 3M*D5 Mean S1uare
Error 3MSE5 Mean *-solute 0ercentage Error3M*0E5 (rac"ing signal 3(S5
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Measurements of Error
If actual "nife demand in Dec is Is M*3'5 -etter than M*3F5A
j j
Capacity
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E4ponential Smoothing Model using a previous value and previous error as forecast
.
= E4ponential factor ; 2 [0; 1] Idea: /orecast 7
*ctual B (1
) Old /orecast
MRP
Capacity
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?hy do we call E4ponential SmoothingJA
F t
=
At 1 + (1
)Ft
"
= At 1 + (1) [ At 2 + (1)F t 2] = At 1 + (1) At 2 + (1)2Ft
2
?hat does it meanA E ects of actual value and error e4ponentially decay controls the decay rate F 1 is initial forecast value if = 0 no e ect of actual value if
= 1 no e ect of forecast value
Capacity
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)ow to choose F 1 and
MPS
A
,ood >e!s e ect of F 0 will decay typically F 1 = A1 e!s select Lright; is di cult ! try out and error
Month
MRP
Capacity
Manuf Sys
Forecasting
?hich
Agg. & SOP
MPS
MRP
is a -etterA
s e l a S e f i n K
H
H
H
H
0. 9
H
α =
Capacity
J an
Feb
Mar
Apr
Ma y
J un
J ul
Aug
Sep
Oc t
No v
Dec
Mont h
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Model Selection ?hich one is -etterA
ME
M*D MSE M*0E
?hat is the -est parameter of these two modelsA ?hat do they suggestA /luctuation comes from randomnessA
Capacity
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6uideline for Selection Method Regression
Moving average or E4ponential smoothing ?eighted moving average E4ponential smoothing with trend source:Jacob. etal %%.
. ?hat if data is not stationaryA
.
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Item Master /ile
Ite nu3er: Sale I9. #0! $arcode 3% digits in #S and %' digits in other5 ;ot#3atc+ nu3er: e.g. pharmaceutical ceramic tiles Color#sie code: shade color 3#/ case5 c Supplier code: lin" to paya-le -an" a>c S?@ I9. shortage and retrieving code 9ra!ing code: lin" to engineering data-ase Faily group: category e.g. ,roup tec+nology code: dimension holes type material strength
Capacity
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$OM & Routing /ile $OM /ile descri-ing 1tys to manufacturing item Coponent ite nu3er: Parent ite nu3er: uantity & @nit: Scrap allo!ance:
Routing file descri-ing wor" to manufacturing item Routing nu3er:
for each operation Operation nu3er: Mac+ine nu3er & /ool nu3er & *C nu3er: 9ra!ing nu3er: S+rin1age: Std la3or & Std. setup tie
MRP
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?or" !enter & (ool /ile ?or" center descri-ing groups of machine>tool *or1 center nu3er: ;ocation: -
cient & @tiliation:
6ours & S+ift: Rate
(ool /ile descri-ing machines>tool and their status ! maintainace /ool nu3er: Status: Storage location: Current assignent: /ool life & @se since last repair: >e=t repair date
Capacity
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*ggregated 0lanning
*+at: planning for family of product Idea: smoothing demands & "ey resources 3wor"ers machine5 Occurred: -eginning of fiscal year 3unit month of sale5 Input: long=term forecasting B "ey resources policy 3overtime
su-contract -ac"log machine capacity5 Output: production plan capacity inventory
Capacity
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S"y!ell *ggregated planning S"y!ell a European cell phone manufacturer and its customers service providers come up with monthly forecast 3in thousand cell phone5 as follow Month
/e-. Mar. *pr. (he manufacturing process is govern -y num-ers of assem-ly wor"er totally %F wor"ers. Each wor"er can assem-le each cell phone every % minute. (he plant operate days a month : hours a day and paid N per hour and %.F time wage for any overtime 3ma4imum hour per wor"er=month5. (otal raw materials cost N and handling cost is N' per unit=month. *t the mean time the company has no=lay o policy. S"y!ell has inventory of F units in
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
S"y!ell uestion
*nswer with following 1uestions. % *ssuming no -ac"log no su-contractor and no new hire>leave what is optimal production schedulingA Is any value to increase overtime from hours per wor"er=month to hour per wor"er=month ' )ow the solution in 3a5 and 3-5 change if the num-ers of assem-ly wor"ers is % and %' wor"ers respectively ?hat S"y!ell can do to smooth its production 3 open!end question5A source: Chopra, S. & Meindl %. pp. '
Manuf Sys
Forecasting
Agg. & SOP
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Capacity
S"y!ell /ormulation
O34ective Function: minimi2e total costs of production inventory
and wages 9ecision Baria3les: units produced units storage overtime prod t 7 units produced in month t in# " reg t 7 units storage at the ending of month t y t 7 man=hours of regular=time in month t o#er y t 7 man=hours overtime in month
"
t Constraints: cell phone -alancing each month regular=time and overtime wor"loads of assem-ly wor"er each mouth wor"loads re1uired to produce cell phones in each month
Manuf Sys
Forecasting
Agg. & SOP
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S"y!ell /ormulation
∑ prod t
min $ = 20 "
∑
∑ in# t
+3
" t
+(20)(8)(20) % t
t
t
s.t.
limit overtime
= t + " tin# = y t o#er + y t reg 20% t
limit regular=timeK
160% t
non neg.
0
-alancing production
non neg.
0
∑ +30
o#er t
y t
Manuf Sys
Forecasting
Agg. & SOP
(rial & Error % ,o Inventory
Demand R( prod. O( prod. Outsource Inventory
Demand R( prod. O( prod. Outsource Inventory
MPS
MRP
Capacity
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(rial & Error !onstant ?or"force
Demand R( prod. O( prod. Outsource Inventory
Demand R( prod. O( prod. Outsource Inventory
MRP
Capacity
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Optimal
Demand R( prod. O( prod. Outsource Inventory
Demand R( prod. O( prod. Outsource Inventory
Agg. & SOP
MPS
MRP
Capacity
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S"y!ell *nalysis Pre7solve analysis Capacity Regtie:
1250 Capacity overtie: 1250
160 20
60/10 7 % units> month 60/10 7 %F units> month
% unit>month
Average 9eands:
Inventory BS Overtie BS Outsource: minimi2e outsource
Inventory $20 +
$20
+ $3 t 7 N '.'' B 't 6
Outsource $40 Cost Coparison Material
R( +a-or O( +a-or Inventory Outsouce (otal
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0ro-lems with aggregated planning
Method Price ta1er: price can influence demand or negotiation 3what if5 ! S&O0
Inventory of coponent: lac" of components ! M0S & MR0 Capacity issues: simplified production constraints ! R!!0 & !R0
*pplication Aggregated product: no information on e4act product ! communication
9elivery plan: fre1uency product mi4ed lot si2e ! communication
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Sales and Operations 0lanning 3S&O05 or demand demand to smooth production given *+at: manage supply or predicta-le varia-ility -y involving related involving related parties Idea: seasonal consumer demand -ut fi4ed plant capacity *+o: mar"eting production logistic finance )R Man7Po!er Strategy: chasing 3hire CS fire5 constant wor"force
level 3-uild inventory5 Managing supply: capacity 3fle4i-le wor"force & machine su-contracting5 investing 3common component5 Managing deand: shift or manipulate demand 3using price5 or promotion may result to low demand smooth demand ! low margin> high inv. cost items pea" demand steal share e4pansion ! high margin items
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
S"y!ell S&O0 uestion
*nswer with following 1uestions. F )ow solution in 3a5 change if -ac"log is allowedA (he estimated cost of -ac"log is NG per unit=month P )ow solution in 3a5 change if temporary wor"er is allowedA *ssuming that each new hire costs NF and has FQ of productivity in his first month G If S"y!ell has a promotion that can increase FQ of one month demand -ut decreases Q of demands ne4t two months. ?hich month should S"y!ell apply the promotionA source: Chopra, S. & Meindl %. pp. '
Manuf Sys
Forecasting
Agg. & SOP
MPS
Master 0roduction Schedule 3M0S5 *+at: planning for finish good 3end=item 0lanning5 Idea: rough plan for produce or stoc" time capacity Occurred: -eginning of each 1uarter 3 unit wee" of sale5
Output: time production 1uantity
MRP
Capacity
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Capacity
+in"age -etween S&O0 and M0S
source:Jacob. et al %%.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
?hich item should we do its M0SA
Custoer oriented: focus on /6 & su-. assem-ly ! smoothing production
& communicating with customer Resources: consist of small & managea-le 3 ! times5 Structure: have $OM Capacity: address "ey capacity of factory 3?D5
Role and Responsi-ility of M0SA Planning: create>maintain M0S Production: verify M0S chec" capacity Purc+asing: ensure & trac" inventory receiving *are+ouse# Store: ensure 1uantity & 1uality of inventory
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Key components in M0S
Production constraints: lead time site 9eands: same time scale Sale forecast: 3M(S5 e.g. forecast & prod. forecast Custoer order: 3M(O5 e.g. actual demand Safety stoc1: Seasonal 3uild: SongKran =mas 3*D5
Inventory: on=hand CS on=order received allA Planning
MRP
Capacity
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Capacity
?hy does inventory 7 evilA
source:Jacob. etal %%.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
Real ?orld Random Demand and +ead (ime
source: !hase and
Review Methods Periodic revie!: chec" inventory using chronological criteria e.g.
every fi4ed period >on7Periodic revie!: chec" inventory non=chronological criteria
e.g. 1uantity order from customer visual inspection
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
0opular lot Si2ing Rules E4cluding !osts 3*D5 ;ot7for7;ot ;F;0: order 7 demand in each period Period Order uantity PO0: order 7 total demand in fi4ed periods
+/+ =? 0O3t7A5 Fi=ed Order uantity FO0: order 7 fi4ed 1uantity follow condition
e.g min ma4 multiple One /ie Only O/O0: order one unit and one time
Including !osts handling cost & ordering cost -conoic Order uantity -O0: /O handling B ordering *agner7*+itin: Optimal of handling B ordering Part Period
ordering Silver7Meal#least period cost: total demand in period min3 handling + ordering 5
period
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Important constraints in lot si2ing rule
Min7Ma=: similar to multiple or batch Spoilage: order 1ty. =
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Capacity
E4ample !omparing lot si2ing rule
!onsider this net re1uirement wee" item *
If the handling cost is N% per wee" and ordering cost is N% per order compare the released order and total cost of the following lot si2ing rule lot= for=lot EO 0O S=M ?=? +#! +0!
Manuf Sys
Forecasting
Agg. & SOP
E4ample +ot si2ing rules
EO ' =
√
0O t = 0art 0eriod $alancing
MPS
MRP
Capacity
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E4ample +ot si2ing rules +east #nit !ost wee" % '
+east 0eriod !ost> Silver=Meal wee" % '
MPS
MRP
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
E4ample !omparing lot si2ing rule
+ot=for=lot %F
EO 0O ?=? 00$ +#! +0!
MRP
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
!haracter item affecting inventory
Ite value: high value ! low cycle stoc" Ipact of s+ortage: ! safety stoc" FreDuency of reDuests: ;ead tie: custom made ! long lead time ! high safety stoc" Ot+er: e.g. alternative shipping su-stitution product price change variation
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$ill of Material 3$OM5 *+at: 1ty of components needed for /6
total demand 7 independents demand B dependent demands
*arning: low=level code ! time to run in MR0
$OM (erminology ;o!7;evel Code: lowest level that a component is assem-led Suaried
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E4ample of $ill of Material
source:Jacob. etal %%.
E4tended $OM
Summari2ed $OM +evel
+evel
%
%
. . .' .' .
. .' . . .' . .'
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
Regular $OM
source:Smith, source: Smith, S. (. %9:9.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
Modular $OM> Multilevel $OM
source:Smith, source: Smith, S. (. %9:9.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
(ime /ence
source:Smith, source: Smith, S. (. %9:9.
9eand /ie Fence 9/F0: changing order is very e4pensive Production /ie Fence P/F0: changing order is annoying /ie 6orion: forecasting time period
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Forecasting
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MPS
MRP
S"y!ell (ime /ence 3D(/ & 0(/5
pad'F +( %
4'F%
4'FP
+( %
+(
Capacity
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Forecasting
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MPS
MRP
Capacity
M0S (erminology Re1uirement Related ,ross reDuireents:total re1uirements 3MR0 term5 >et reDuireents: re1uirements after calculating scheduled receipt
3MR0 term5 Inventory Related Planned order release: releasing period of an order 3-ac"
scheduling of planned order due5 Planned order due: received period of an order Sc+eduled receipts: actual delivery period of an order On76and inventory# Pro4ected availa3le: ?I0 that should have if
nothing wrong Availa3le to proise A/P0: availa-le 1ty. for other additional customers
Availa3le to located: 1ty. less than orders source:Jacob. etal %%.
Manuf Sys
Forecasting
Agg. & SOP
S"y!ell /irmed order & $OM
4'F%
3M(S5 4'FP
3M(S>M(O5
MPS
MRP
Capacity
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MPS
MRP
Capacity
S"y!ell 4'F% wee" %=9
source:Smith, S. (. %9:9.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
S"y!ell 4'FP wee" %=9
source:Smith, S. (. %9:9.
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
!oncepts of MR0 . 0rinciple of dependent demand
.
(otal 1uantities re1uired 7 independent B dependent 1uantities re1uired
. !larification ,ross reDuireent: total 1ty. after netting process >et reDuireent: total 1ty. after considering inventory and
scheduled receipts planned receipts: pro@ected 1ty receipted planned inventory: pro@ected on=hand inventory according to planned receipts
Capacity
Manuf Sys
Forecasting
Agg. & SOP
Differences -tw M0S & MR0
*+at
Scope Process Input Output @pdate
MPS
MRP
Capacity
ProMIS v2.: revie!
$#
%$ Manuf Sys
Forecasting
Agg. & SOP
S"y!ell Inventory Information
item no. 4'F% 4'FP pad'F cpu'F% cpu'FP ca'F% ca'FP
MPS
MRP
Capacity
ProMIS v2.: revie!
$"#
%$ Manuf Sys
Forecasting
S"y!ell MR0 cpu(): 315 7 3%5
ca(): min 7 ma4 7 %
Agg. & SOP
MPS
MRP
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
+imitation MR0
9eterinistic data: assuming that every parameters is
deterministic including ;firm order; Capacity planning: assuming that "ey constraint is material re1uirement or infinite or infinite capacity Syste nerviness: rolling hori2on and lot=si2ing rule ! di erent MR0
;ead tie depends on Duantities:
de lays 9ata integration: purchasing forgets to order supplier delays
Capacity
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
RR0 vs R!!0 vs !R0
0roduction activity control
RRP: long=term 3'=F years>month5 purchasing machines e4pansion RCCP: mid=time 3one year>wee"5 hiring permanent sta
ignore components components using std man=hour
components CRP: short time 3one year>wee"5 over=time consider components using e4act lot si2e ProMIS v2.: revie!
$'#
%$ Manuf Sys
Forecasting
Agg. & SOP
S"y!ell Standard time & lot si2e
pad'F
ca'F% ca'FP 4'F% 4'FP
MPS
MRP
Capacity
Manuf Sys
Forecasting
Agg. & SOP
$ill of !apacity
. $ill of !apacity *verage hour used to produce each product
.
?or" center % '
MPS
MRP
Capacity
ProMIS v2.: revie!
$)#
%$ Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
S"y!ell M0S & R!!0
4'F%
4'FP
If M0S using lot=for=lot policy then R!!0 3resource of each wor" center5 -ecomes
%
'
%
'
Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
+imitation of R!!0 and E4tension *ssumption of R!!0 not consider 1ueuing in each wor" center ! lead time in $O! not address e ciency and switch=over time ! lead time in $O! transition time from one ?! to other ?! ! lead time in $O! e4cluding time that need each component ! !R0 (ime 0hased $ill of !apacity 3(0$O!5 *+at: create rough & realistic schedule of each ?! to modify the standard time Idea: 1ueue & transition time impacts lead time not standard time Elapsed time =
-lapsed tie: minimal wor"ing time for first lot
std hour>unit lot si2e util. e
Capacity
O set tie: minimal completed time for first lot
transitto + 1ueue time + elapsed time + transit)rom ProMIS v2.: revie!
$5#
%$ Manuf Sys
Forecasting
Agg. & SOP
MPS
MRP
Capacity
ui2 I
% ,ame companies and e4plain their operations that ena-les its information technology 3I(5>information system 3IS5 to gain following strategic advantages !ost reduction !reating new products>services Enhance products>services
?hat are factors contri-ute to a low success rate of information technology in manufacturing environmentA ' E4plain similarities and di erences -etween Material Re1uirement 0lanning 3MR05 and Enterprise Resource 0lanning 3ER05