EFFICIENCY - Yasar
1
Buse VURAL Gizem İNCE Elifnas KAHYA Korgün ÖZMEN A D V I S O R S Bülent AKYIL Gülce GÜLER HANER Melis VARDAR Orçun DİKDURAN Zeynep Ceren YÜZGEÇ FORD OPEL R E N A U L T VOLKSWAGEN LAND ROVER AUDI DAIMLER VOLVO Symptoms Production plannıng is performed manually by one person, it reduces the speed of communication LACK OF COMMUNICATION MATERIAL INVENTORY LEVEL MISTAKES Material inventory level seen in MRP and the existing physical stocks are different Productivity is around 30% - 40% LOW PRODUCTIVITY Opening: 1997 Izmir - Aegean Free Zone Total Area: 39,000 m Reference Asemi, A., Safari, A., and Zavareh, A. A. (2011). “The role of management information system (MIS) and Decision support system (DSS) for manager’s decision making process.” International Journal of Business and Management, Vol.6, No.7, pp. 164-173. Gebauer, H., Tennstedt, F., Elsässer, S., and Betke, R. (2010). “The Aftermarket in the Automotive Industry–How to Optimize Aftermarket Performance in Established and Emerging Markets.” Power, D.J., (2002) “Decision support systems: concepts and resources for managers”, Westport, Conn., Quorum Books. Rolstadaas, A., Hvolby, H. H., & Falster, P. (2008). “Review of after- sales service concepts.”, Lean business systems and beyond, pp. 383-391. Springer, Boston, MA. APPLICABILITY Potential for success is high and can be applied to other fabrication processes Applicable in automotive, electronics and similar industries Applicable to companies that are active against growth and change By using mathematical modelling method, the optimal number of workers depending on the optimal workload to be included in the system Creating a decision supportsystem by considering the output of the mathematical model, which provides more systematic and controllable process planning MAXIMUM EFFICIENCY Post-sale production has a more complex production schedule and stages than serial production. (Rolstadaas et al., 2008) The storage of data in the data warehouse indicated that the data warehouse could be linked to the database, and the user interface was observed to link the application with the user. (Asemi and Safari, 2011) Components : Decision support system model Decision support system network structure Database User interface Information technology should be supported by process follow-up software in ensuring productivity and continuity in aftersales production process. (Gebauer et al., 2010) The decision support system consists of four components. (Power, 2002) Maximum efficiency in production processes Delivering products to the buyers in the lead time CRITICAL SUCCESS FACTORS T I M E PROBLEM DEFINITION ! PRODUCTION PROCESSES Organized By One Person Manually In The Productı on Plannı ng Department • Approval for production from each department, the process is slowing down. • Each customer's production process is kept on separate excel pages M A N U A L S Y S T E M • Manually performed system causes both lack of information and misunderstanding Cause errors such as lack of materials Causes confusion in reaching the desired information. P R O D U C T I O N The workforce Cannot be used, Production Interrupt GOALS ACKNOWLEDGEMENTS This project was supported by the Scientific and Technical Research Council of Turkey (TUBITAK) under 2209B-Bachelor Final Thesis Focused on Industry Program with Project number 1139B411800095. The person planning the parameters of Ready time and due date day and hour. In our model, however, one weekly working hours 90 time slots. The weekly total product range is countA formula and saw how many kinds of product requests he had received that week as well as our data for IBM OPL CPLEX we bring it dynamically. The first parameter in the excell file be scheduled for the week and then ask for request from the file that week and the demanded products dpn numbers are drawn into the operator file. Listed products dpn Standard times are based on numbers using standard Excel VBA standard time in the operator file from the time file column. Then, some formulations used. Monday Tuesday Hour Time Period Hour Time Period 08.00- 08.30 1 08.00- 08.30 16 08.30- 09.00 2 08.30- 09.00 17 09.00- 09.30 3 09.00- 09.30 18 09.30- 10.00 4 09.30- 10.00 19 10.30- 11.00 5 10.30- 11.00 20 11.00- 11.30 6 11.00- 11.30 21 11.30- 12.00 7 11.30- 12.00 22 13.00- 13.30 8 13.00- 13.30 23 13.30- 14.00 9 13.30- 14.00 24 14.00- 14.30 10 14.00- 14.30 25 14.30- 15.00 11 14.30- 15.00 26 15.30- 16.00 12 15.30- 16.00 27 16.00- 16.30 13 16.00- 16.30 28 16.30- 17.00 14 16.30- 17.00 29 17.00- 17.30 15 17.00- 17.30 30 Wednesday Thursday Hour Time Period Hour Time Period 08.00-08.30 31 08.00- 08.30 46 08.30-09.00 32 08.30- 09.00 47 09.00-09.30 33 09.00- 09.30 48 09.30-10.00 34 09.30- 10.00 49 10.30-11.00 35 10.30- 11.00 50 11.00-11.30 36 11.00- 11.30 51 11.30-12.00 37 11.30- 12.00 52 13.00-13.30 38 13.00- 13.30 53 13.30-14.00 39 13.30- 14.00 54 14.00-14.30 40 14.00- 14.30 55 14.30-15.00 41 14.30- 15.00 56 15.30-16.00 42 15.30- 16.00 57 16.00-16.30 43 16.00- 16.30 58 16.30-17.00 44 16.30- 17.00 59 17.00-17.30 45 17.00- 17.30 60 Friday Saturday Hour Time Period Hour Time Period 08.00- 08.30 61 08.00- 08.30 76 08.30- 09.00 62 08.30- 09.00 77 09.00- 09.30 63 09.00- 09.30 78 09.30- 10.00 64 09.30- 10.00 79 10.30- 11.00 65 10.30- 11.00 80 11.00- 11.30 66 11.00- 11.30 81 11.30- 12.00 67 11.30- 12.00 82 13.00- 13.30 68 13.00- 13.30 83 13.30- 14.00 69 13.30- 14.00 84 14.00- 14.30 70 14.00- 14.30 85 14.30- 15.00 71 14.30- 15.00 86 15.30- 16.00 72 15.30- 16.00 87 16.00- 16.30 73 16.00- 16.30 88 16.30- 17.00 74 16.30- 17.00 89 17.00- 17.30 75 17.00- 17.30 90 Monday 1 08.00-08.30 1 Tuesday 2 08.30-09.00 2 Wednesday 3 09.00-09.30 3 Tuesday 4 09.30-10.00 4 Friday 5 10.30-11.00 5 Saturday 6 11.00-11.30 6 11.30-12.00 7 13.00-13.30 8 13.30-14.00 9 14.00-14.30 10 14.30-15.00 11 15.30-16.00 12 16.00-16.30 13 16.30-17.00 14 17.00-17.30 15 [(Day-1)]*15+Hour Example: Thursday 13.00-13.30 [(4-1)*15]+8 53. Time Period (3) (4) (5) (6) (7) (8) (0) (1) (2) There are two set definitions in the model, which can be be listed as follows: : Product set : Time set Model parameters are defined as follows: : Standard time of the product i : Due date of product i : The time when the product i is allowed to start being produced : Request for product i for one week The decision variables of the model are determined as follows: : For the product type i, number of workers working in time t N : Number of employees per week Additional decision variables of the model are determined as follows: : Time period when the product starts to be produced : Declares that the product is produced 70% 30% 55% 45% EFFICIENCY
Transcript of EFFICIENCY - Yasar
Buse VURAL
Gizem İNCE
Elifnas KAHYA
Korgün ÖZMEN
ADVISORSBülent AKYIL
Gülce GÜLER HANER
Melis VARDAR
Orçun DİKDURAN
Zeynep Ceren YÜZGEÇFORDOPELRENAULTVOLKSWAGEN
LAND ROVER AUDIDAIMLERVOLVO
Symptoms
Production plannıng is performedmanually by one person, it reducesthe speed of communication
LACK OF COMMUNICATION
MATERIAL INVENTORY LEVEL MISTAKES
Material inventory level seen inMRP and the existing physical stocksare different
Productivity is around 30% - 40%
LOW PRODUCTIVITY
Opening: 1997Izmir - Aegean Free ZoneTotal Area: 39,000 m
Reference Asemi, A., Safari, A., and Zavareh, A. A. (2011). “The role of management information system (MIS)and Decision support system (DSS) for manager’s decision making process.” International Journal of
Business and Management, Vol.6, No.7, pp. 164-173. Gebauer, H., Tennstedt, F., Elsässer, S., and Betke, R. (2010). “The Aftermarket in the Automotive Industry–How to Optimize
Aftermarket Performance in Established and Emerging Markets.” Power, D.J., (2002) “Decision support systems: concepts and resources for
managers”, Westport, Conn., Quorum Books. Rolstadaas, A., Hvolby, H. H., & Falster, P. (2008). “Review of after- sales service concepts.”, Lean
business systems and beyond, pp. 383-391. Springer, Boston, MA.
APPLICABILITY
Potential for success is high and can be applied to other fabrication processes Applicable in automotive, electronics and similar industriesApplicable to companies that are active against growth and change
By using mathematicalmodelling method, theoptimal number ofworkers depending onthe optimal workload tobe included in the system
Creating a decisionsupportsystem byconsidering the output ofthe mathematical model,which provides moresystematic andcontrollable processplanning
MAXIMUMEFFICIENCY
Post-sale production has amore complex productionschedule and stages than
serial production.(Rolstadaas et al., 2008)
The storage of data in the datawarehouse indicated that the
data warehouse could be linkedto the database, and the userinterface was observed to linkthe application with the user.
(Asemi and Safari, 2011)
Components :
Decision support system modelDecision support systemnetwork structureDatabaseUser interface
Infor
mation
tech
nolog
y
shou
ld be
supp
orted
by
proc
ess f
ollow
-up
softw
are in
ensu
ring
prod
uctiv
ity an
d
conti
nuity
in af
tersa
les
prod
uctio
n pro
cess
.
(Geb
auer
et al.
, 201
0)
The decision support
system consists of four
components.
(Power, 2002)
Maximumefficiency
in productionprocesses
Deliveringproducts
to the buyersin the lead
time
CRITICAL SUCCESS FACTORS
TIME
PROBLEM DEFINITION
!
PRODUCTION PROCESSES
Organized By One Person Manually In The
Productıon Plannıng Department
• Approval for production from each department, the process is slowing down.
• Each customer's production process is kept on separate excel pages
MANUAL SYSTEM
• Manually performed system causes both lack of information and misunderstanding
Cause errors such as lack of materials
Causes confusion in reaching the desired
information.
PRODUCTION
The workforce Cannot be used, Production Interrupt
GOALS
ACKNOWLEDGEMENTSThis project was supported by the Scientific and TechnicalResearch Council of Turkey (TUBITAK) under2209B-Bachelor Final Thesis Focused on Industry Programwith Project number 1139B411800095.
The person planning the parameters of Ready time anddue date day and hour.
In our model, however, one weekly working hours 90 time slots. The weekly total product range is countA formula and saw how
many kinds of product requests he had received that weekas well as our data for IBM OPL CPLEX
we bring it dynamically.
The first parameter in the excell filebe scheduled for the week and then ask for
request from the file that week and the demanded products dpnnumbers are drawn into the operator file. Listed products dpn
Standard times are based on numbers using standard Excel VBAstandard time in the operator file from the time file
column. Then, some formulationsused.
Monday
Tuesday Hour Time Period
Hour Time Period
08.00-08.30 1
08.00-08.30 16
08.30-09.00 2
08.30-09.00 17
09.00-09.30 3
09.00-09.30 18
09.30-10.00 4
09.30-10.00 19
10.30-11.00 5
10.30-11.00 20
11.00-11.30 6
11.00-11.30 21
11.30-12.00 7
11.30-12.00 22
13.00-13.30 8
13.00-13.30 23
13.30-14.00 9
13.30-14.00 24
14.00-14.30 10
14.00-14.30 25
14.30-15.00 11
14.30-15.00 26
15.30-16.00 12
15.30-16.00 27
16.00-16.30 13
16.00-16.30 28
16.30-17.00 14
16.30-17.00 29
17.00-17.30 15
17.00-17.30 30
Wednesday
Thursday Hour Time Period
Hour Time Period
08.00-08.30 31
08.00-08.30 46
08.30-09.00 32
08.30-09.00 47
09.00-09.30 33
09.00-09.30 48
09.30-10.00 34
09.30-10.00 49
10.30-11.00 35
10.30-11.00 50
11.00-11.30 36
11.00-11.30 51
11.30-12.00 37
11.30-12.00 52
13.00-13.30 38
13.00-13.30 53
13.30-14.00 39
13.30-14.00 54
14.00-14.30 40
14.00-14.30 55
14.30-15.00 41
14.30-15.00 56
15.30-16.00 42
15.30-16.00 57
16.00-16.30 43
16.00-16.30 58
16.30-17.00 44
16.30-17.00 59
17.00-17.30 45
17.00-17.30 60
Friday
Saturday Hour Time Period
Hour Time Period
08.00-08.30 61
08.00-08.30 76
08.30-09.00 62
08.30-09.00 77
09.00-09.30 63
09.00-09.30 78
09.30-10.00 64
09.30-10.00 79
10.30-11.00 65
10.30-11.00 80
11.00-11.30 66
11.00-11.30 81
11.30-12.00 67
11.30-12.00 82
13.00-13.30 68
13.00-13.30 83
13.30-14.00 69
13.30-14.00 84
14.00-14.30 70
14.00-14.30 85
14.30-15.00 71
14.30-15.00 86
15.30-16.00 72
15.30-16.00 87
16.00-16.30 73
16.00-16.30 88
16.30-17.00 74
16.30-17.00 89
17.00-17.30 75
17.00-17.30 90
Monday 1
08.00-08.30 1 Tuesday 2
08.30-09.00 2
Wednesday 3
09.00-09.30 3 Tuesday 4
09.30-10.00 4
Friday 5
10.30-11.00 5 Saturday 6
11.00-11.30 6
11.30-12.00 7
13.00-13.30
8
13.30-14.00 9
14.00-14.30 10
14.30-15.00 11
15.30-16.00 12
16.00-16.30 13
16.30-17.00 14
17.00-17.30 15
[(Day-1)]*15+Hour Example: Thursday 13.00-13.30 [(4-1)*15]+8 53. Time Period
(3)
(4)
(5)
(6)
(7)
(8)
(0)
(1)
(2)
There are two set definitions in the model, which can be be listed as
follows:
: Product set
: Time set
Model parameters are defined as follows:
: Standard time of the product i
: Due date of product i
: The time when the product i is allowed to start being produced
: Request for product i for one week
The decision variables of the model are determined as follows:
: For the product type i, number of workers working in time t
N : Number of employees per week
Additional decision variables of the model are determined as follows:
: Time period when the product starts to be produced
: Declares that the product is produced
70%
30%
55%
45% EFFICIENCY
