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Production Planning and Control Simulation
创建日期：2024-07-26 16:26:00
标签： MANAGEMENT
Production Planning and Control Simulation
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Production Planning and Control Simulation 
PRODUCTION MANAGEMENT GAME
The Manufacturing Situation
A company manufactures 4 products, Pilts, Relts, Stuns, and Super Stuns which are assemblies of
different combinations of 5 basic components A, B, C D, and E The product structure (Bill of Materials)
of each product is as follows with the number in brackets being the number of each component required
per product:

These products are manufactured by processes involving 6 machines (M1-6) and an assembly process:

Components A and B are fabricated on Line 1 which involves three sequential processes on M1, M2 and
M3. Components C and D are fabricated on Line 2 which involves process on M4 and M5. Component E
is fabricated on line 3 involving a single process M6. Each machine has an average production rate of
parts per day as indicated, although considerable process variability exists. Changeover times are
involved on individual machines when they change from one component to another. However, the
assembly line is flexible and can change between different products without any loss of production time.
The weekly demand for the products over the past 6 weeks has been as follows (wk-1 represents last
week, wk-2 the week before last etc):
Wk -6 Wk -5 Wk -4 Wk -3 Wk -2 Wk -1
Pilt 5 5 4 4 3 3
Relt 3 4 4 3 3 4
Stun 1 1 1 1 3 2
S.Stun 1 0 1 2 1 1

The Super Stun is a product that is progressively expected to replace the Relt. Thus sales of the former are
expected to rise and sales of the latter to drop.
Playing the Game
 The object of the exercise is to simulate 30 working days (six 5-day weeks) of production in such a
way as to meet as much of the expected demand as possible.
o The daily variability of each process is simulated by dice throw as follows:
o M1, M3, M4: random dice throw between 1and 6.
Production Planning and Control Simulation by S. Kara 2
o M2: random dice throw between 1 and 5 (i.e. if a 6 is thrown, it is rejected and the dice is
thrown again until a number below 6 is obtained.
o M5: random dice throw between 1 and 3 (i.e. the dice is thrown until a number between 1 and
3 is obtained.
o M6: random dice throw between 1 and 4
o There is no process variability on the assembly line.
 Changeover times on the relevant machines between components A and B, and between C and D are
simulated by a loss of production as follows:
o M1, M3, M4: loss of one day production whenever a component changeover takes place.
o M2: two units production less than the dice throw whenever a component changeover takes
place.
o M5: one unit of production less than the dice throw whenever a component changeover takes
place.
 It is possible to invest money in reduction of machine changeover time and reduction in machine
variability according to the following rules which have been invented by the Accounting Department
to reflect the cost of making improvement:
o For an additional operating cost of $100 per week for every product produced the changeover
penalty of one machine may be reduced by one unit of production. Thus the more machines
are subject to this type of improvement, the greater will be the operating cost.
o For an additional operating cost of $100 per week for every product produced the range of
process variability of any machine may be reduced by two units of production e.g. for an
additional cost of $100 per week, you can alter the dice throw of (for example) machine M3
from 1-6 to 2-5.
 Components A, B, C, D and E may be represented by different types of paper clip, which are moved
through the individual processes according to the dice throws indicated above.
 Paper clips may be "assembled" into products by hooking together the appropriate clips required for
each product.
 Each machine, and the assembly line, will have an input buffer stock of each of each type of
component, and the number of components in each buffer will vary from day to day according to the
variability of the individual machines, and decisions about which component to produce on any
particular day, or series of days.
 For each day’s production, production should occur (i.e. dice should be thrown) in the following
sequence: M1: M4: M2: M5: M3: M6
 The output of each machine's production is available for processing by the next machine on the
same day.
 Components are passed each day according to dice thrown from each machine to the input buffer of
the next machine. A decision must be made as to which component to produce on a particular day
BEFORE the dice is thrown. If the dice throw is greater than the quantity in the input buffer stock of
the chosen component, only the quantity in the buffer can be passed to the next machine.
 On component changeover, loss of one day’s production means the machine will lose a dice throw on
the day that the production would have taken place. Loss of a certain number of units of production
means that after the dice throw, the units of production lost by the changeover should be subtracted
from the number thrown by the dice.
 It will not be necessary to run each machine at its full capacity if this is not required. Thus a 4 may be
thrown but only two units passed on if it is thought that over-production may be occurring. In fact,
any quantity of units may be passed on provided this is (a) not greater than the dice throw, and (b) not
greater than the quantity in the input buffer.
 Customer orders are delivered weekly (at the end of each week). The customers have a "blanket"
ordering system i.e. customers have long term agreements in which they commit themselves to
cumulative orders of a certain aggregate quantity of products over a six month period, but reserve the
right to vary the individual quantities ordered on a week by week basis according to current market
conditions (products delivered to customers are sub-assemblies whose arrival time needs to be
coordinated with their own production schedule for delivering products of which Pilts, Relts etc. are a
Production Planning and Control Simulation by S. Kara 3
component part.). Thus, some degree of assembly flexibility is required to cater for these variations,
and stocks of unsold products may arise.
 Note the one-week lead time for ordering raw materials. The amount to be available the following
week should be specified on the suppliers’ score sheet. Thus, if a decision is made at the start of week
1 to order 10 units of A, this should be entered in the appropriate column for week 1. However, the
materials cannot be physically transferred to be input buffer until the start of the following week. Also
note that the first input of raw materials can only be made at the start of week 2 based on orders
placed at the start of week 1.
 At the start of each week’s production, the actual demand on each product required to be delivered at
the end of the week will be given in the classroom during the execution of the game.
 Initially, ONE of each product type is in stock. In addition, a total of not more than 15 units of work
in progress for A, B, C and D may be distributed amongst the input buffers of each machine. These
may be distributed over buffers in any quantity or combination provided the TOTAL number of all
types of components in all input buffers does not exceed 15.
 Delivery of raw components from suppliers is made in weekly batches (which arrive in stock at the
start of each week). Requirements to the suppliers for each type of component must be specified one
week in advance. Thus, at the start of each week, it will be required to specify to the supplier the
quantity of each raw component required at the start of the following week. After one week (five days
production has elapsed) the corresponding quantity may be moved into the input buffers of the first
processes (M1 and M4). These quantities may NOT be changed during the week.
 ALSO, if a sale in a particular week is lost because inadequate products were available, this sale is
lost irretrievably (i.e. NO BACKORDERS ARE ALLOWED. IF THE CUSTOMER IS NOT
SATISFIED IN THE WEEK THE PRODUCT IS REQUIRED, THEY WILL GO ELSEWHERE)

Objective of the game
 The objective is to maximise profit over a 6 week (30 day) simulated period of production. However,
at the end of the 10 week period, for every unit of unsold product you have in stock either greater than
or less than of your starting stock position, $2000 will be deducted from the final profit, and for every
component of Work-in-Progress in either greater or less than the initial Work in Progress of 15 units
at the start of the game, $1000 will be deducted. Your profit will therefore be reduced if you finish the
game with excessive amounts of inventory or if your stocks run so low that the future trading position
of the company is threatened. Your average inventory costs must also be assessed each week for a
final deduction from your total profit. The average inventory carrying cost for each week is estimated
as an approximation, by multiplying the total number of components in stock at the end of each week
by $100, and the total number of finished products in stock at the end of each week by $200. You may
find that changeover times are such that it will be necessary to produce individual components for
several consecutive days so as not to lose too much production time in changeovers.
 The exercise will be to plan the weekly assembly schedule, and the daily production of each machine
and the raw material quantities to be ordered weekly from suppliers to support the assembly schedule,
and then to simulate this activity using dice and paper clips, using one of the Production Planning and
Control Techniques (e.g. MRP, JIT).
 When you adopt the JIT approach, you will have the option of investing in new equipment and
processes that will reduce changeover times as follows:
o M1, M3: changeovers may be reduced from one day of production to three units of
production less than the dice throw on that day, at an additional cost of $50 per week
o M2, M4: one unit of production less than the dice throw for $50 per week.
o M5 set-up time can be eliminated at a cost of $100 per week.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 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