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