A Model to Enhance the Effectiveness of Machining Centers with Automatic Multi-Pallet Changers: a Case Study

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A M ^{ODEL TO} E NHANCE THE E FFECTIVENESS OF M ACHINING C ENTERS WITH A UTOMATIC

M ÛLTI -P ÂLLET C ^HANGERS : Â C ÂSE S ^TUDY

EN MODELL FÖR ATT FÖRBÄTTRA EFFEKTIVITETEN AV MASKINCENTRA MED AUTOMATISKA MULTI-PALLETVÄXLARE: EN FALLSTUDIE

VÄXJÖ,MAY 2006 THESIS NO:TD018/2006 CARSTEN DAUB

CAMILLA DUH

DEPARTMENT OF TEROTECHNOLOGY

SCHOOL OF TECHNOLOGY AND DESIGN,TD

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ORGANISATION/ORGANIZATION FÖRFATTARE/AUTHORS

VÄXJÖ UNIVERSITET Carsten Daub

Institutionen för teknik och design, TD Camilla Duh VÄXJÖ UNIVERSITY

School of Technology and Design, TD

DOKUMENTTYP/TYPE OF DOCUMENT HANDLEDARE/TUTOR EXAMINATOR/EXAMINER

Examensarbete/Diplomawork Imad Alsyouf Basim Al-Najjar

TITEL OCH UNDERTITEL/TITLE AND SUBTITLE

En modell för att förbättra effektiviteten av maskincentra med automatiska multi-palletväxlare: en fallstudie / A Model to Enhance the Effectiveness of Machining Centers with Automatic Multi-Pallet Changers: a Case Study

SAMMANFATTNING

Syftet med denna uppsats är att utveckla en modell för att förbättra effektiviteten av maskincentra med automatiska palletväxlare (APCs) för multi-palleter. När en kritisk litteratursökning genomfördes hittades inga relevanta teorier inom det aktuella området. Multi-pallet APC tillåter att många jobb kan förberedas samtidigt och gör planeringen av jobben mer flexibel. Modellen, tillsammans med den utvecklade heuristiska planeringsalgoritmen med målet att minimera den totala viktade förseningen, kan användas för att planera in n jobb med m palleter på ett verkstadsgolv. Rätt underhålls policy försäkrar en hög tillgänglighet vilket tillsammans med programmet garanterar en hög utnyttjandenivå av maskinerna. Som följd kommer effektiviteten att höjas. En fallstudie utfördes på Växjöfabriken i Sverige för att utvärdera modellen, på företaget efterbehandlas gjutgods. Resultatet från denna fallstudie blev ett effektivare utnyttjande av maskinerna, med minskade förseningskostnader, ökad kundtillfredställelse och goodwill för företaget. Denna uppsats bidrar med en modell och en flexibel, anpassningsbar och utvecklingsbar heuristisk planeringsalgoritm, vilken kan användas i alla industriföretag som använder maskincentra med multi-pallet APCs.

NYCKELORD: Effektivitet, Maskincentra, Multi-Pallet APC, Automatisk Palletväxlare, Planera, Verkstadsgolv, Totala Viktade Förseningen

ABSTRACT

The purpose of this thesis is to develop a model to enhance the effectiveness of machining centers with multi-pallet automatic pallet changers (APCs). From critical literature review no existing theories within this field were found. The multi-pallet APC allows multi-setups and a more flexible sequencing of jobs. The model together with the developed heuristic scheduling algorithm with the objective to minimize the total weighted tardiness can be used to plan in n jobs on m pallets in a shop-floor. The right maintenance policy ensures a high availability, which together with the program guarantees a high level of utilization of the machinery. Consequently the effectiveness will be enhanced. A case study approach was used to test the model at Växjöfabriken in Sweden, which treats cast material. The results of this case study are a more effective utilization of the machines with decreased tardiness costs, increased customers’ satisfaction and goodwill of the company. The contribution of this thesis is a model with a flexible, adjustable and expandable heuristic scheduling algorithm, which can be applied in all manufacturing companies using machining centers with multi-pallet APCs.

KEY WORDS: Effectiveness, Machining center, Multi-Pallet APC, Automatic Pallet Changer, Scheduling, Shop-floor, Total Weighted Tardiness

UTGIVNINGSÅR/YEAR OF ISSUE SPRÅK/LANGUAGE ANTAL SIDOR/NUMBER OF PAGES

2006 Engelska/English 116

I /WWW

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MASTER THESIS ACKNOW LEDGEMENTS

A

C K N O W L E D G E M E N T S

We would sincerely like to thank all people that directly or indirectly have contributed to the work of this thesis.

First of all we would like to thank our tutor, Imad Alsyouf, who has supervised us in an excellent way, given good and concrete criticism to the process of the work and encouraged us in tough situations. This has enhanced the quality of this thesis and motivated us to work harder.

We would like to thank Växjöfabriken for giving us the opportunity to work on an actual task in the production and being part of a real and present project. Most of all we would like to thank the respondents Michael Blom, Torbjörn Ahlm, Meho Kaljanac, Maria Stillman and Nils Ericsson who have supported us with all necessary information and data. Without their participation and support the compilation of this thesis would not have been possible.

Our special thanks go to Anders Ingwald who has initiated the contact with the production manager Michael Blom at Växjöfabriken.

VÄ X J Ö, MA Y 2 0 0 6

_________________________

Carsten Daub

_________________________

Camilla Duh

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MASTER THESIS EXPLANATION OF KEY WORDS

EX P L A N A T I O N O F KE Y WO R D S

AC T I V E J O B: A job is called active at a specific time if the setup for this job can be started and no other job could be done before without delaying its setup.

AL G O R I T H M: An algorithm is a clearly specified set of simple instructions to be followed to solve a problem, (Weiss, 1993).

EF F E C T I V E N E S S: It is the accomplishment of the ‘right’ thing on time, and within the quality requirements specified, (Sink and Tuttle, 1989).

EF F I C I E N C Y: It is a measure of how economically the firm’s resources are utilized when providing a given level of requirements, (Sink and Tuttle, 1989).

FI X T U R E S: Are customized constructional systems, which are necessary to fix the specific item or items.

GR A H A M A N O M A L I E S: They show unexpected behaviors of constructive procedures.

In few cases a simplification of the job properties or the constraints can lead to a worse result of the objective function, (Dorndorf, 2002).

HE U R I S T I C: It is an enumeration rule(s) that reduce or limit the search for solutions in algorithms. Heuristics do not guarantee optimal or even feasible solutions, (Dorndorf, 2002).

JO B: It is an object to work on, consists of a batch of items that have to be manufactured, which is characterizes by its properties.

NO N-D E T E R M I N I S T I C P O L Y N O M I A L-T I M E D I F F I C U L T ( N P -

D I F F I C U L T) : It is a problem where the running time of an algorithm is not any more in a polynomial relation to the input parameters, which means that it is not solvable in polynomial time, (Ergun and Orlin, 2006).

OR D E R: It is a request from the customer, which may consist of many different jobs.

OV E R A L L EQ U I P M E N T EF F E C T I V E N E S S ( O E E ) : It is the overall performance of a single piece of equipment or even an entire factory, which will always be governed by the cumulative impact of the three OEE factors: availability, performance rate and quality rate.

PA L L E T: It is a fixed platform for setting up and processing items on a machining center.

PR E V E N T I V E MA I N T E N A N C E: It is any task designed to prevent failures or mitigate their effects, (Sherwin, 2000).

PR O D U C T I V I T Y: The ratio of output quantity produced (in a specific time period) to the sum of one or all input factors required to produce the output quantity such as manpower, materials and energy, (Alsyouf, 2006).

SE N S I T I V I T Y FA C T O R: Is an additional tool which checks how the generated schedule reacts to less interruption of the already set up jobs with respect to the sum of the weighted tardiness.

TO T A L WE I G H T E D T A R D I N E S S: It is the maximum of finish date, minus due date and zero, multiplied with the weighting as a sum for all considered orders within a certain time period, (Chou et al., 2005 and Dorndorf, 2002).

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MASTER THESIS LIST OF ABBRE VIATIONS

LI S T O F AB B R E V I A T I O N S

A P C : AU T O M A T I C PA L L E T CH A N G E R

C B M : CO N D I T I O N- BA S E D MA I N T E N A N C E

C N C M A C H I N E: CO M P U T E R NU M E R I C A L L Y CO N T R O L L E D M A C H I N E

C R : CR I T I C A L RA T I O

E D D : EA R L I E S T DU E DA T E

E L I N : EL E C T R O N I C LI B R A R Y IN F O R M A T I O N NA V I G A T O R

F B M : FA I L U R E- BA S E D MA I N T E N A N C E

F C F S : FI R S T CO M E, FI R S T SE R V E D

F M S : FL E X I B L E MA N U F A C T U R I N G SY S T E M

H : HO R I Z O N T A L

I S O : IN T E R N A T I O N A L OR G A N I Z A T I O N F O R ST A N D A R D I Z A T I O N

J I T : JU S T-I N- TI M E

M A : M AC H I N I N G C E N T E R

M P S : MA S T E R PR O D U C T I O N SC H E D U L E

N C : NU M E R I C A L CO N T R O L

N P -D I F F I C U L T: NO N-D E T E R M I N I S T I C PO L Y N O M I A L-T I M E D I F F I C U L T

O E E : OV E R A L L EQ U I P M E N T EF F E C T I V E N E S S

O T P : OP P O R T U N I T Y- TR I G G E R E D M A I N T E N A N C E PO L I C Y

P M : PR E V E N T I V E MA I N T E N A N C E

S O T : SH O R T E S T OP E R A T I O N TI M E

S T R : SL A C K TI M E RE M A I N I N G

W I P : WO R K IN PR O C E S S

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MASTER THESIS LIST OF CONTENTS

LI S T O F CO N T E N T S

ABSTRACT... i

ACKNOWLEDGEMENTS... ii

EXPLANATION OF KEY WORDS... iii

LIST OF ABBREVIATIONS... iv

LIST OF CONTENTS... v

LIST OF FIGURES... viii

LIST OF TABLES... viii

LIST OF PICTURES... ix

LIST OF FORMULAS... ix

LIST OF APPENDIX... ix

LIST OF FIGURES IN APPENDIX... ix

LIST OF TABLES IN APPENDIX... x

1 . IN T R O D U C T I O N ... 1

1.1BACKGROUND...1

1.2PROBLEM DISCUSSION...2

1.3PROBLEM PRESENTATION...3

1.4PROBLEM FORMULATION...3

1.5PURPOSE...3

1.6RELEVANCE...3

1.7LIMITATIONS AND DELIMITATIONS...3

1.8TIMEFRAME...4

2 . ME T H O D O L O G Y ... 5

2.2SCIENTIFIC PARADIGM...5

2.2.1POSITIVISTIC...5

2.2.2HERMENEUTIC...6

2.3RESEARCH METHOD...6

2.3.1QUANTITATIVE...6

2.3.2QUALITATIVE...6

2.4RESEARCH APPROACH...7

2.4.1INDUCTION...7

2.4.2DEDUCTION...7

2.4.3HYPOTHETICAL-DEDUCTIVE...8

2.4.4ABDUCTION...8

2.5SCIENTIFIC CREDIBILITY...8

2.5.1VALIDITY...8

2.5.2RELIABILITY...9

2.5.3GENERALIZATION OF RESULTS...9

2.6DATA COLLECTION...9

2.6.1QUANTITATIVE INVESTIGATIONS...9

2.6.2QUALITATIVE INVESTIGATIONS...10

2.7PRE-UNDERSTANDING...10

2.8METHOD SELECTION...10

3 . TH E O R Y ... 11

3.1MASTER PRODUCTION SCHEDULE...11

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3.2SHOP-FLOOR SCHEDULING...12

3.2.1FORWARD VS BACKWARD SCHEDULING...12

3.2.2PRIORITY RULES...12

3.2.3OBJECTIVES...13

3.2.4THE CHALLENGE OF SHOP-FLOOR SCHEDULING...13

3.3OPERATIONS RESEARCH...14

3.4GANTT CHART...15

3.5MAINTENANCE FOR MACHINING CENTERS...15

3.6MACHINING CENTERS...18

3.7MEASURING PROCESS PERFORMANCE...19

4 . MO D E L DE V E L O P M E N T ... 22

4.1LITERATURE REVIEW...22

4.2THE OBJECTIVE FUNCTION OF THE MODEL...23

4.3THE CONSTRAINTS OF THE MODEL...23

4.4DESIGNING THE MODEL...24

4.5THE MODEL TO ENHANCE THE EFFECTIVENESS OF MACHINING CENTERS...25

4.6DESCRIPTION OF THE COMPUTER PROGRAM...25

4.6.1INPUTS TO THE PROGRAM...26

4.6.2THE FLOWCHART TO THE PROGRAM FOR GENERATING A SCHEDULE...27

4.6.3OUTPUT FROM THE PROGRAM...28

5 . EM P I R I C A L FI N D I N G S ... 30

5.1GENERAL COMPANY PRESENTATION...30

5.2MACHINERY...30

5.3THE PRODUCTS...32

5.4PRODUCTION SCHEDULE INFORMATION...32

5.5PRODUCTION PLANNING AND SCHEDULING AT VÄXJÖFABRIKEN...33

5.6MAINTENANCE ISSUES...33

5.7QUALITY ISSUES...34

5.8PRODUCTION GOALS OF VÄXJÖFABRIKEN...34

6 . AN A L Y S I S ... 35

6.1COMPUTING SCHEDULING EXAMPLES...35

6.1.1TYPICAL EXAMPLE NUMBER ONE...35

6.1.2TYPICAL EXAMPLE NUMBER TWO...38

6.2THE EMPIRICAL DATA...41

6.3TESTING THE MODEL WITH EMPIRICAL DATA...42

6.3.1RESCHEDULING MACHINE CENTER H400,H500 AND MA60 ...42

6.3.2IMPLEMENTING THE NEW MACHINING CENTER MA600 IN THE SCHEDULE...42

6.3.3RESCHEDULING WITH RESPECT TO THE OVERALL WEIGHTED TARDINESS...43

6.3.4ESTIMATIONS OF NUMBER OF PALLETS FOR PERMANENT OCCUPATION IN MA600 ....45

6.4OEE CALCULATIONS FOR MA600 ...47

7 . RE S U L T S ... 48

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8 . CO N C L U S I O N S ... 50

8.1ANSWER TO THE PROBLEM FORMULATION...50

8.2RECOMMENDATIONS TO THE CASE COMPANY...50

8.3EVALUATION OF THE MODEL...51

8.4CRITICISM TO THIS THESIS...52

8.5SUGGESTIONS FOR FURTHER RESEARCHES...52

LI S T O F RE F E R E N C E S ... 54

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MASTER THESIS OTHER LIST S

LI S T O F FI G U R E S

FIGURE 2.1–THE CONNECTION,(LUNDAHL AND SKÄRVAD,1999) ... 5

FIGURE 3.1–THEORETICAL FRAMEWORK... 11

FIGURE 3.2–GANTT CHART,(CHASE, ET AL.,2006)... 15

FIGURE 3.3–TOTAL MAINTENANCE COST,(GAITHER AND FRAZIER,1999) ... 17

FIGURE 3.4–A MACHINING CENTER,(WANG AND LUH,1996) ... 18

FIGURE 3.5–PROCESS PERFORMANCE METRICS CONSTRUCTURED FROM CHASE, ET AL,2006 . 19 FIGURE 4.1–MULTI-PALLET APC,(OKUMA,2004) ... 23

FIGURE 4.2–THE MODEL... 25

FIGURE 4.3–FIRST INPUT SHEET TO THE PROGRAM - INPUT JOB DATA... 26

FIGURE 4.6–THE FLOWCHART TO THE PROGRAM... 27

FIGURE 4.4–FIRST OUTPUT SHEET FROM THE PROGRAM - JOB SCHEDULE... 28

FIGURE 4.5–SECOND OUTPUT SHEET FROM THE PROGRAM - JOB DELAYS... 28

FIGURE 6.1–THE SCHEDULE AS A GANTT CHART AT TIME 0... 36

FIGURE 6.2–THE SETUP MATRIX AS A GANTT CHART AT TIME 0 ... 36

LI S T O F TA B L E S TABLE 1.1–TIMEFRAME... 4

TABLE 4.1–SUMMATION OF THE ARTICEL SEARCH ... 22

TABLE 5.1–THE CUSTOMER WEIGHTING... 32

TABLE 5.2–THE MAINTENANCE ACTIVITIES... 33

TABLE 5.3–AMOUNT OF DEFECT ITEMS PRODUCED IN 2005... 34

TABLE 6.1–DATA FOR A TYPICAL EXAMPLE... 35

TABLE 6.2–THE SCHEDULE AT TIME 0 ... 35

TABLE 6.3–THE SETUP MATRIX AT TIME 0 ... 36

TABLE 6.4– THE WEIGHTED TARDINESS,3 PAIRS OF PALLETS... 37

TABLE 6.5–THE WEIGHTED TARDINESS FOR 2 PAIRS OF PALLETS... 38

TABLE 6.6–THE WEIGHTED TARDINESS FOR 1 PAIR OF PALLETS... 38

TABLE 6.7–INPUT DATA TO THE GRAHAM ANOMALIES EXAMPLE... 38

TABLE 6.8–THE SCHEDULE FOR 5 PAIRS OF PALLETS... 39

TABLE 6.9–THE SETUP MATRIX WITH 5 PAIRS OF PALLETS... 39

TABLE 6.10–THE SUM OF THE WEIGHTED TARDINESS FOR 5 PAIRS OF PALLETS... 39

TABLE 6.17–THE SCHEDULE WHEN USING THE SENSITIVITY FACTOR... 41

TABLE 6.18–THE SETUP MATRIX WHEN USING THE SENSITIVITY FACTOR... 41

TABLE 6.19–THE SUM OF THE WEIGHTED TARDINESS WHEN USING THE SENSITIVITY FACTOR. 41 TABLE 6.20–A SUMMARY OF THE INFORMATION... 41

TABLE 6.21–RESCHEDULING OF ORDERS FOR YEAR 2005... 42

TABLE 6.22–SIMULATING REPLACEMENT POSSIBILITIES... 43

TABLE 6.23–THE OVERALL TOTAL WEIGHTED TARDINESS FOR CASE 1 ... 44

TABLE 6.27–ASPIRANTS FOR PERMENANT SETUP... 45

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TABLE 6.28–THE THREE SELECTED PRODUCTS... 46

TABLE 6.29–ALL THREE PRODUCTS ARE GIVEN PERMANENT SETUP... 46

TABLE 6.30–TWO PRODUCTS ARE GIVEN PERMANENT SETUP... 46

TABLE 6.31–ONE PRODUCT IS GIVEN A PERMANENT SETUP... 47

TABLE 8.1–THE ADVANTAGES AND DISADVANTAGES OF THE PROGRAM... 52

LI S T O F PI C T U R E S PICTURE 5.1–A PUMP... 30

PICTURE 5.2–AN ITEM FIXED AT THE FIXTURE WITH SCREWS... 32

LI S T O F FO R M U L A S FORMULA [1]–OVERALL EQUIPMENT EFFICIENCY... 20

FORMULA [2]–AVAILABILITY... 20

FORMULA [3]–PERFORMANCE EFFICIENCY... 20

FORMULA [4]–OPERATING SPEED RATE... 21

FORMULA [5]–NET OPERATING RATE... 21

FORMULA [6]–QUALITY RATE... 21

FORMULA [7]–THE OBJECTIVE FUNCTION... 23

FORMULA [8]–SORTING CRITERION... 24

FORMULA [9]–SORTING CRITERION WITH SENSITIVITY FACTOR... 24

FORMULA [10]–DELAY FOR EACH ORDER... 28

FORMULA [11]–WEIGHTED TARDINESS FOR EACH ORDER... 28

FORMULA [12]–MINIMUM TOTAL WEIGHTED TARDINESS... 29

LI S T O F AP P E N D I X

APPENDIX I–OKUMA BROCHURE ABOUT MA-H SERIES

APPENDIX II–THE MATLAB CODE

APPENDIX III–ORGANIZATION CHART

APPENDIX IV–STANDARD PRODUCTS ON THE MA60 MACHINE

APPENDIX V–STANDARD PRODUCTS ON THE H500 MACHINE

APPENDIX VI–STANDARD PRODUCTS ON THE H400MACHINE

APPENDIX VII–THE CHRONOLOGICAL PRODUCTION DIARY FOR 2005 APPENDIX VIII–THE COMPANY'S PRODUCTION SCHEDULE

APPENDIX IX–TIME UNIT 2 UNTIL 10 FOR THE TYPICAL EXAMPLE

APPENDIX X–REVISED ORDER INFORMATION

LI S T O F FI G U R E S I N AP P E N D I X

FIGURE A.1–THE SCHEDULE AS A GANTT CHART AT TIME 1...APPENDIX IX FIGURE A.2–THE SETUP MATRIX AS A GANTT CHART AT TIME 1 ...APPENDIX IX FIGURE A.3–THE SCHEDULE AS A GANTT CHART AT TIME 2...APPENDIX IX FIGURE A.4–THE SETUP MATRIX AS A GANTT CHART AT TIME 2 ...APPENDIX IX FIGURE A.5–THE SCHEDULE AS A GANTT CHART AT TIME 3...APPENDIX IX FIGURE A.6–THE SETUP MATRIX AS A GANTT CHART AT TIME 3 ...APPENDIX IX FIGURE A.7–THE SCHEDULE AS A GANTT CHART AT TIME 4...APPENDIX IX FIGURE A.8–THE SETUP MATRIX AS A GANTT CHART AT TIME 4 ...APPENDIX IX FIGURE A.9–THE SCHEDULE AS A GANTT CHART AT TIME 5...APPENDIX IX

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FIGURE A.10–THE SETUP MATRIX AS A GANTT CHART AT TIME 5 ...APPENDIX IX FIGURE A.11–THE SCHEDULE AS A GANTT CHART AT TIME 6...APPENDIX IX FIGURE A.12–THE SETUP MATRIX AS A GANTT CHART AT TIME 6 ...APPENDIX IX FIGURE A.13–THE SCHEDULE AS A GANTT CHART AT TIME 7...APPENDIX IX FIGURE A.14–THE SETUP MATRIX AS A GANTT CHART AT TIME 7 ...APPENDIX IX FIGURE A.15–THE SCHEDULE AS A GANTT CHART AT TIME 8...APPENDIX IX FIGURE A.16–THE SETUP MATRIX AS A GANTT CHART AT TIME 8 ...APPENDIX IX FIGURE A.17–THE SCHEDULE AS A GANTT CHART AT TIME 9...APPENDIX IX FIGURE A.18–THE SETUP MATRIX AS A GANTT CHART AT TIME 9 ...APPENDIX IX FIGURE A.19–THE SCHEDULE AS A GANTT CHART AT TIME 10...APPENDIX IX FIGURE A.20–THE SETUP MATRIX AS A GANTT CHART AT TIME 10 ...APPENDIX IX

LI S T O F TA B L E S I N AP P E N D I X

TABLE A.1–THE SCHEDULE AT TIME 1...APPENDIX IX TABLE A.2–THE SETUP MATRIX AT TIME 1 ...APPENDIX IX TABLE A.3–THE SCHEDULE AT TIME 2...APPENDIX IX TABLE A.4–THE SETUP MATRIX AT TIME 2 ...APPENDIX IX TABLE A.5–THE SCHEDULE AT TIME 3...APPENDIX IX TABLE A.6–THE SETUP MATRIX AT TIME 3 ...APPENDIX IX TABLE A.7–THE SCHEDULE AT TIME 4...APPENDIX IX TABLE A.8–THE SETUP MATRIX AT TIME 4 ...APPENDIX IX TABLE A.9–THE SCHEDULE AT TIME 5...APPENDIX IX TABLE A.10–THE SETUP MATRIX AT TIME 5 ...APPENDIX IX TABLE A.11–THE SCHEDULE AT TIME 6...APPENDIX IX TABLE A.12–THE SETUP MATRIX AT TIME 6 ...APPENDIX IX TABLE A.13–THE SCHEDULE AT TIME 7...APPENDIX IX TABLE A.14–THE SETUP MATRIX AT TIME 7 ...APPENDIX IX TABLE A.15–THE SCHEDULE AT TIME 8...APPENDIX IX TABLE A.16–THE SETUP MATRIX AT TIME 8 ...APPENDIX IX TABLE A.17–THE SCHEDULE AT TIME 9...APPENDIX IX TABLE A.18–THE SETUP MATRIX AT TIME 9 ...APPENDIX IX TABLE A.19–THE SCHEDULE AT TIME 10...APPENDIX IX TABLE A.20–THE SETUP MATRIX AT TIME 10 ...APPENDIX IX

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MASTER THESIS INTRODUCTION

1 . IN T R O D U C T I O N

T

his chapter is intended to give the reader a good understanding of the purpose of this thesis. The background will be described, the problem situation will be discussed and the problem formulation will be stated, which leads to the purpose and the relevance of this thesis. The limitations and delimitations will be made and the timeframe of the thesis will also be presented.

___________________________________________________________________________

1.1 BACKGROUND

Since the mid-1980s the strategic benefits of production planning and scheduling have become obvious. Success in terms of higher effectiveness and efficiency in manufacturing has been achieved by investing and developing production planning and scheduling methods since that time. The last two decades have shown that these methods are critical in long-term survival and competitive advantage. Furthermore every organization has to be aware of that implementing sophisticated production methods will not solve all problems. Choosing the correct approach combined with adjustments to the changing variables in an operationally and strategically time window is the real challenge analysts are confronted with, (Chase, et al., 2006, Gaither and Frazier, 1999 and Silver, et al., 1998).

New developments in technology combined with changing customer patterns lead to shorter life cycles of manufactured products. The emphasis of manufacturing strategy has shifted to small batch production. In an increasingly competitive environment, meeting the due date is one of the most important issues that firms need to consider, (Chou, et al., 2005). Short lead- time deliveries and low cost products are demanded in order to survive in a highly competitive environment, (Chase, et al., 2006 and Lambert, et al., 1998).

Since the manufacturing process becomes more automatic, it gets more capital intensive rather than labor intensive. Due to this development the manufacturing industry use more and more machining centers. The numerical control, NC unit allows a variety of settings and operations in continuous cycles, (Wang and Luh, 1996). Because of the machine centers' complexity and flexibility these types of machinery are expensive considering the initial costs. On the other side low per-unit costs, high product flexibility and quality, award this kind of machinery. Machining centers are becoming more and more cost effective because they fulfill the needs for small batch production with respect to high productivity, (Gaither and Frazier, 1999 and Silver, et al., 1998).

High customer satisfaction, a smooth production flow with minimum setup times for tools, parts and new production batches is important. Therefore a machining center is equipped with a tool magazine and an automatic pallet changer, APC, which carry out simultaneously both machining and loading- and unloading activities. Newest developments with additional equipped multi-pallet APC also allow doing the setting up activities without interrupting the processing activities of the machines. Consequently, effective scheduling of this complex and high costly machinery with all its variables like scheduling objectives, shop structure and product structure can result in significant improvement of systems performance, (Hitomai, et al., 1987 and Jawahar, et al., 1998).

Nowadays the availability of the machines is of great importance in order to utilize the costly resources. As a result of increased automation, maintenance has to be considered in order to

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assure the availability of machining centers, (Savsar, 2006). Effective maintenance is important and critical to many operations and among other things it improves the equipments availability. Bad maintenance can lead to more malfunctions and bad utilization of the equipment, which can result in delayed production schedules, (Swanson, 2001). This issue is important due to the fact that costs of maintenance can be 15-40% of the total production costs, (cited by Savsar, 2006).

1.2 PROBLEM DISCUSSION

Due to increasing labor costs in Western Europe and avoiding a relocation to low labor cost countries many companies are forced to optimize their production. The companies have to deal with uncertain future and to estimate the demand situation in short and long term. New technologies force the companies to invest continuously in new machinery and production equipment. The more complex and expensive the machines are the more often it demands non-stop production in order to utilize the versatile processing capabilities, (Chase, et al., 2006, Lambert, et al., 1998 and van Weele, 2005).

In an increasingly competitive environment, meeting the due date is one of the most important issues that firms need to consider, (Chou, et al., 2005). There is a need for better coordination and scheduling of production and logistic activities on the shop-floor. Production scheduling is a dynamic approach. Anytime deviations from the actual plan like changes in customer orders and forecasts, manufacturing problems, like machine breakdowns, quality difficulties with a particular batch, lower average yield, new released orders etc., engineering changes and so on demands a reschedule of the whole production plan. The objective is to match the trade-off between scheduling production orders to be completed when promised to customers and to avoid over- or under-loading of the production facility. This leads to efficiently utilized production capacity and lower production costs, (Gaither and Frazier, 1999, Silver, et al., 1998 and Wiendahl et al., 2004).

When it comes to which criteria should be considered to sequence jobs at a machine center several priority rules exist. But the more complex the system of jobs and machines becomes in a company, the higher the need is for more sophisticated mathematical methods, like operations research modeling, in order to find good and feasible solutions, (Chase, et al., 2006, Dorndorf, 2002, Gaither and Frazier, 1999, Silver, et al., 1998 and Taha, 2003).

Availability is directly correlated to the production output rate, which has to be as high as possible for capital-intensive machines. A machining center will typically operate at 70-80%

utilization while a traditional machine may operate at as low as 20% utilization, (cited by Savsar, 2006). Maintenance on one-side decreases the likelihood for machine breakdowns but on the other side, must be considered as non-operating time in the schedule thus it decreases the available time of the machines, (Campbell and Jardine, 2001 and Chase, 2006).

Machining centers normally have a tool magazine and pallets arranged like twin table planners, which have the feature to carry out simultaneously machining inside the machine and setting activities outside the machine, (Wang and Luh, 1996). The specific objective is to reduce the idle times of machine and labor, (Jawahar, et al., 1998 and Sakawa, 1996).

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1.3 PROBLEM PRESENTATION

The more complex and expensive the machines are the more often it demands non-stop production in order to effectively utilize the versatile processing capabilities. Scheduling becomes a critical and dynamic approach in order to utilize the capacity on a high level.

Therefore the availability of the machinery is one important constraint. The higher the capital investments are the more important it is to have the availability as high as possible in order to cover the fixed costs. For a machining center the specific objective is to minimize the idle times of the machines and meeting the due dates of the customers. Preventive maintenance, PM activities interrupt the production, machine breakdown as a consequence of bad maintenance leads to unplanned and unexpected stoppages.

1.4 PROBLEM FORMULATION

Based on the previous sections the problem formulation of this thesis is:

¾ How to enhance the effectiveness of machining centers with multi-pallet APCs?

1.5 PURPOSE

Consequently the purpose with this thesis is to develop a model that will enhance the effectiveness of a machining center with multi-pallet APCs. This model will handle the task between maximizing the utilization of the machinery and minimizing the customer delays of the orders to be scheduled on the shop-floor level. The solution will enhance the effectiveness in matters of meeting the due dates of the customers and having lower costs of production, which is a result of maximizing the availability and the utilization of the capital-intensive machinery. By including the maintenance in the model the available machining time will be maximized.

1.6 RELEVANCE

In today’s business an effectively utilization of the machinery and a smart scheduling of orders and maintenance are a prerequisite for staying competitive. Thus all efforts regarding to decrease idle time and minimize delivery delays should be made, (Gaither and Frazier, 1999).

It is important that companies, which are using the machining centers with or without the multi-pallet APC, know how to effectively utilize the equipment and meet the customers due dates, which prerequisites a high level of availability. The utilization is directly correlated to the production output rate. Increased production output rate may have a direct influence on productivity, higher cash flow and thus return on investment, which results in more profit for the company, (Chase, et al., 2006 and Heizer and Render, 2004).

1.7 LIMITATIONS AND DELIMITATIONS

Considering the limited time of 20 weeks this thesis has been delimited to include one case company. The analysis will be based on empirical data obtained from a case company. This thesis will not take into consideration bottlenecks in the material supply when scheduling the production. Sequence constraints in scheduling are assumed not to exist.

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1.8 TIMEFRAME

Since this thesis is written within a limited time a timeframe has to be developed. In table 1.1 the timeframe for this thesis is presented. It shows when the different chapters will be conducted. The dates for the submission deadlines are also displayed.

T^{A B L E}1.1 –TI M E F R A M E

M T W T F M T W T F M T W T F T F M T W T F M T W T F M T M T W T F M T W T F

Project start Selecting company Introduction Methodology Theory

Empirical findings Analysis

Results Conclusions Recommendations

Submit draft to chapter 1 Submit chapter 1-3

Week 13 Week 5 Week 6 Week 7 Week 9 Week 10 Week 11 Week 12

Activity ^M^{Week 8}^{T W} ^{W T} ^F

February March

M T W T F M T W T F M T W T F M F M T W T F M T W T F M T M T F

Project start Selecting company Introduction Methodology Theory

Empirical findings Analysis

Results Conclusions Recommendations

T

April

T

Submit chapter 1-6

Week 14 Week 15

W T

Week 16 Week 17

Submit final version

Week 21

Activity Week 18 Week 19

T

May

W

Week 20

W F

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MASTER THESIS METHODOLOGY

2 . ME T H O D O L O G Y

I

n this chapter different scientific paradigm, research methods and approaches, scientific credibility, data collection and pre-understanding will briefly be explained. The chosen methodology will be presented in the end of this chapter with a motivation for why implementing the selected one.

___________________________________________________________________________

2.1 FROM PARADIGM TO RESEARCH APPROACH

Scientific paradigm is a subject that treats how scientific knowledge is conducted and how reliable the new knowledge is and also its position in the society, (Wallén, 1996). The aim of the scientific investigations is to contribute with theoretical knowledge and it should be made according to scientific procedures. The chosen paradigm, thus positivistic or hermeneutic, is the base for the decision of which research method that is going to be used, quantitative or qualitative. Through these research methods the scientific paradigms are connected to practical research approaches, which are concrete instructions for how the investigation should be done, see figure 2.1, (Lundahl and Skärvad, 1999).

FI G U R E 2.1 –TH E C O N N E C T I O N, (LU N D A H L A N D SK Ä R V A D,1999)

2.2 SCIENTIFIC PARADIGM

There exist many different paradigms today but essentially there are only two different, the positivistic and the hermeneutic, which will be presented in this chapter. All paradigms have similarities and differences, some more than others. It is important to know which paradigm that where used when a result within any science is presented so one can criticize it and understand the ability to apply the result in other situations and how the result was appointed, (Patel and Davidson, 2003).

2.2.1 POSITIVISTIC

The philosopher Auguste Comte who lived between 1798 and 1857 is mostly stated as the originator of positivism. Comte called the positivistic paradigm a positive philosophy and he associated the concept positivism with preciseness, safety and reality. This concept was a contrast to the metaphysical thinking of the time, which includes religious, speculative, idealistic and in general romantic ideas that could not be verified or falsify by observations and empirical experience, (Lundahl and Skärvad, 1999). In time new variants of positivism were developed even though today’s positivism has a lot in common with Comet’s. One of the most important variants, which were developed in the beginning of the 20th-century, is the logical positivism. The biggest problem for the logical positivism was to find a criterion that was able to distinguish between scientific researches and other operations. This resulted in the famous principle of verification, (Johansson and Liedman, 1993). Roughly, the principle

Scientific paradigm

Positivistic paradigm Hermeneutic paradigm

Quantitative research method

Qualitative research method

Research approach Research approach

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implies that, a statement is true if it is in accordance with how it works in reality.

Consequently approaches arose for how to investigate statements, (Wallén, 1996). For example, induction, deduction, hypothetical-deduction and abduction, which all will be explained later in this chapter. It can also be said that the positivistic paradigm is underlying the quantitative research method, which also will be explained deeper, later in this chapter, (Lundahl and Skärvad, 1999).

2.2.2 HERMENEUTIC

The hermeneutic paradigm is harder to capture than the positivistic paradigm and the main purpose of the hermeneutic is to interpret and understand, (Lundahl and Skärvad, 1999).

Hermeneutics can briefly be translated to interpret science and during the 17^th- and 18^th- century the method was used to interpret bible texts and later also other non-religious texts, (Patel and Davidson, 2003). One can interpret many different things from poems, to understanding the humans’ life situation, interpret traditions or actions, etc., (Wallén, 1996).

According to the hermeneutic paradigm there are more sources to knowledge than our five senses and our logical intelligence, and that understands, which is not counted in the positivistic paradigm. A person can look inside him or her self, called introspection, and by that understands other humans’ feelings and experience, called recognizing and compassion.

Interpretations of other humans belongs to the human science, for example history, science of literature etc. but it also belongs to the social field like sociology, anthropology, jurisprudence etc. Science within areas that makes interpretations like this are called hermeneutical, (Thurén, 1991). It can also be stated that the hermeneutic paradigm is underlying the qualitative research method, which will be explained deeper, later in this chapter, (Lundahl and Skärvad, 1999).

2.3 RESEARCH METHOD

A research method is a tool, a way of solving problems and a way to reach new knowledge.

There are two different methods, a quantitative and a qualitative, and the most important difference between them is how they use figures and statistical data, (Holme and Solvang, 1997).

2.3.1 QUANTITATIVE

The aim of the quantitative research method is to measure. The measurements can be used to either describe or explain a phenomenon. If it should describe a phenomenon one must measure and quantitatively describe the phenomenon and if the aim is to explain, the quantitative measurements will be concentrating on measuring the correlation between different characteristics. In practice the main aim of the quantitative investigations, that aim to explain a phenomenon, is mainly about testing hypotheses, (Lundahl and Skärvad, 1999). The quantitative research method is better formalized and structured than the qualitative method.

The method characterizes itself by control and it also selects the possible answers. In short the quantitative research method is using hard data, (Holme and Solvang, 1997).

2.3.2 QUALITATIVE

With qualitative research it is meant to do investigations that aim to create results and conclusions based on qualitative analyses and mainly qualitative data. The qualitative investigations are aimed for individuals, group of individuals and their surrounding. The aim

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is to describe, analyze and understand the behavior of an individual or a group. When making a pure qualitative research the researcher is not interested in how the world is, but how it is perceived. The results that are gained from these kinds of researches helps us to understand why humans or an individual act as they do in different situations and the result helps us to forecast the behaviors, (Lundahl and Skärvad, 1999). In short the qualitative research method are using soft data, (Holme and Solvang, 1997).

2.4 RESEARCH APPROACH

The desire of every scientific work is to continuously improve the understanding of the phenomena they work with and to do so they are in need of theories and developing theories.

The theory itself can appear in different forms and some theories are vaguer and less unspecified than others. The form of the theory is crucial in terms of what approach should be used. Not all theories can be empirical verified. Even though it is hard to capture the social conditions in a clear and easy theory there is a great need for making this in a systematic theoretical approach, (Holme and Solvang, 1997). It exists two different approaches, induction and deduction, and if these are combined the hypothetical-deduction approach accurse, which is the positivisms favorite, (Thurén, 1991). The abduction approach can be seen a bit as an opposite to the hypothetical-deduction approach, (Wallén, 1996).

2.4.1 INDUCTION

Induction means that one try to come to common and general conclusions by using empirical data, (Thurén, 1991). When making data collection it is important that it is made totally unprejudiced, (Wallén, 1996). One important issue, to be aware of, is that one can never be totally sure that an inductive conclusion is true because it is based on empirical data, which normally never represents the whole population. Even if the induction would be built on enormous data it could turn out to be non-true. The only thing one concludes, after making an induction, is a higher or lower probability for something, (Thurén, 1991). One example of how to make an induction is (ibid):

Premise: All people we know through history have died.

Conclusion: Thus all people are mortal, including myself.

Inductive conclusion means that, one starts with a premise that states something is valid for a few phenomena of some kind and then to conclude that it includes all phenomena of this kind, (Johansson and Liedman, 1993). The induction approach is an explorer approach, (Holme and Solvang, 1997).

2.4.2 DEDUCTION

Deduction means that one makes logical conclusions, which are assume to be valid if it is logical connected. An important thing to notice is that deductive conclusions validity has nothing to do with if the premises are true or not so therefore one should be observant, to not make to logical arguments without investigating the validity of the premises, (Thurén, 1991).

One example of how to make a deduction is (ibid):

Premise: All people are mortal.

Premise: I am a human.

Conclusion: Thus I am mortal.

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Deductive conclusion means that; if the premises are true then the conclusion also must be true, (Johansson and Liedman, 1993). The deduction approach is an evidence approach, (Holme and Solvang, 1997).

2.4.3 HYPOTHETICAL-DEDUCTIVE

The theory is more important and more independent in the hypothetical-deductive approach than in the induction approach, (Wallén, 1996). This approach is the most common way of developing theories and it means that it derives new hypothesis from a system of connected premises, (Holme and Solvang, 1997). Thus one makes a deductive conclusion and then the new hypothesis can then be tested by empirical investigations if they are real (“true”) or not.

By this approach one uses both empirical data and logic, (Thurén, 1991). The theory itself will never be complete, there will always derive new premises that can be tested. Then by empirical investigation the reliability of the theory can be strengthen or weaken, (Holme and Solvang, 1997). To put it short, in the hypothetical-deductive approach one investigates connections by varying different thinkable cause-factors and then see what the affects are, (Wallén, 1996).

2.4.4 ABDUCTION

In the abduction approach a partly inversed situation exists compared to the hypothetical- deductive approach and it has similarities to the induction approach. It means that one stands in front of an effect and searches for the cause-factors behind without being able to manipulate these. It is a way to draw conclusions to what the reasons are behind an observation. An investigation of a disease is a good example of an abduction approach. One starts with probably connections, make conclusions by excluding different factors, and so on.

To be able to apply this approach one need a lot of experience both concerning the question area and other experience. The conclusions from this approach is not strict logical valid thus practical tests must be done, (Wallén, 1996).

2.5 SCIENTIFIC CREDIBILITY

This part is about validity, reliability and about generalization of result. The investigation could be made in the wrong way, the wrong things may be measured or the right things will be measured in the wrong way. So without considering the validity and the reliability thought the whole investigation the result cannot be taken serious or be considered believable (Thurén, 1991).

2.5.1VALIDITY

Validity means that one has investigated the subject that was aimed to investigate and nothing else, (Thurén, 1991). Validity has to do with how well a measurement captures the actual phenomena. The biggest problem is to avoid the sources of errors that will undermine the validity and it is also about how to judge the validity in different situations. An obvious source of error is the environment and these factors have to be evaluated in order to find out if there are any factors that are affecting the validity. If there are any, these can perhaps be eliminated or decreased by making the wanted observations in a laboratory instead, (Hartman, 2003). The validity in a measurement can be defined as the absence of systematic failure in the measurement, and it is divided into two different types, (Lundahl and Skärvad, 1999):

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• Internal validity, it aims to if the measuring instrument, for example the questionnaire, measures what it was aimed to measure.

• External validity, it aims to the accordance between the chosen indicators, for example the questions on the questionnaire, and the relationship one tried to measure.

2.5.2 RELIABILITY

Reliability concerns the question if a result from an investigation will be the same if the investigation would be made again or if it is influenced by random or coincidence conditions, (Bryman, 2002). An investigation with high reliability distinguishes itself by the fact that the measurements are not influenced by the person who is making the measure or under what circumstance it is made, (Lundahl and Skärvad, 1999). If a public opinion poll is made it is important that a representative amount of people are interviewed or else the random factor can influence the result and thereby the reliability of the result, (Thurén, 1991).

2.5.3 GENERALIZATION OF RESULTS

The main idea with generalization is to get knowledge about generally applicable connections, to be able to explain, make forecasting, test premises and it is also a foundation for apply ability of the result, (Wallén, 1996).

When a survey is made one must question if the result is valid for a larger population than was included in the survey, thus can the results be generalized? Every time an investigation is made where not the whole population are include it is important to know how to chose the representative for the population. The selection should be done randomly so the representative will be a miniature of the population, so-called random sample. If this is done in a correct way the representative will represent the population and this means that one can generalize from the random sample to be valid for the whole population, (Patel and Davidson, 2003).

2.6 DATA COLLECTION

There are many ways of collecting data to be able to answer the questions at issue. One can use existing information, tests and experiments, attitudes scales, observations, interviews and questionnaires. None of the ways can be said to be better than the other. Which method or way one chooses to use, depends on which of them that seems to give the best answer to the question of issue in reasonable proportion to the time and resources available, (Patel and Davidson, 2003).

2.6.1 QUANTITATIVE INVESTIGATIONS

When making quantitative investigations it is important to stick to the plan that was establish for how the collection of data were going to be made. If not one keep to the plan the prerequisites for the measurements are destroyed. The collection itself do not contain any theoretical difficulties except the importance of eliminating random disturbances, thus to assure the reliability, of the result of measurements. But the practical difficulties can be of significance, (Lundahl and Skärvad, 1999).

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2.6.2 QUALITATIVE INVESTIGATIONS

The aim of making qualitative investigations is to get a deeper knowledge than in the quantitative investigations. The qualitative investigations mostly use texts as source of information. Even though it is a movie or another verbal source, for example an interview, it is normal to write it down so the researcher gets literature to work with. It is important that the transcription is made with the awareness that it is influencing the analysis of the data. The one who transcript the verbal information can be tempted to make the language clearer than the original by putting in dots or comma etc. Anyhow it does not matter which source that is used, the importance is that not only sources that supports the investigator thoughts are chosen, or else it will give a false impression of the situation, (Patel and Davidson, 2003).

2.7 PRE-UNDERSTANDING

A well-developed pre-understanding is an important condition in order to capture the subject of the study. Pre-understanding acts as an input and refers to people’s knowledge as well as insights into a specific problem and social environment before they start with their research activity. Thus the pre-understanding does not only consist of expertise gained by theory but also the own experiences made in this specific research area. A researcher’s pre- understanding can be divided into first- and second-hand knowledge. The first-hand knowledge is based on the individual’s own personal experience from both private and working life while the second-hand knowledge is obtained via intermediaries like lectures, textbooks, scientific articles and so on. The sources of the intermediaries are gained from the experiences of others, (Gummesson, 2000).

2.8 METHOD SELECTION

This thesis will be based on feasible empirical data, which is hard data. The processing of the data will be done with mathematical techniques. Hard data is underlying the quantitative research method and consequently this thesis is built on a positivistic paradigm. Clear computations and results support objectivity and exclude hermeneutic interpretations of the output. In accordance with the positivistic alignment this thesis will use the hypothetical- deductive approach to achieve the conclusions of the problem formulation addressed in this thesis.

The data will be based on existing information collected from a database. The question that occurs when dealing with a database is if the data is valid and reliable. This means that the authors do not have any influence if the right data was measured and put in the database and if the data has been obtained with reliable measurement tools. The authors assume a high validity and reliability of the data and combined with using the data in the right way it will lead to high validity and reliable results.

The results and the conclusions to the problem formulation are aimed to be applicable to all machining centers with multi-pallet APCs, even though only empirical data from one case company will be used and analyzed.

The authors’ pre-understanding of this thesis is based on personal experience within the practical field of metal treating machinery. The authors have been studying logistics, production management, operations research as well as mechanical engineering, which all contribute to a good pre-understanding and a wide base of knowledge for solving the problem addressed in this thesis.

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MASTER THESIS THEORY

3 . TH E O R Y

___________________________________________________________________________

I

n this chapter all necessary theories for conducting this thesis are presented. This is done in order to make the reader more acquainted with the subject and also to increase the understanding in the analysis chapter.

___________________________________________________________________________

In figure 3.1 the theoretical framework is presented with the relationships between the different theories that will be included in this chapter. First the master production schedule, section 3.1, will be presented, which provides the shop-floor scheduler with the necessary information, section 3.2. As an approach when scheduling the jobs operations research can be used, section 3.3, which converts a real world decision-making problem into a mathematical model and thereby an optimal solution can be reached. An effective way to schedule and visualize the jobs on machines is by using Gantt charts, section 3.4. When scheduling the jobs it is important to also include the maintenance on the machines, section 3.5, which could be machining

centers, section 3.6. In the end it is important to measure the performance of the processes to be able to continuously improve the processes and to find deviations and discover other malfunctions before a breakdown occur, section 3.7.

3.1 MASTER PRODUCTION SCHEDULE

The master production schedule, MPS is a plan for future production and has a planning horizon from several weeks to a few months, which is the short-range planning in a company.

The medium-range planning is the aggregate planning and the aggregate capacity plan is an input to the MPS. The aim of the MPS is to schedule the quantity of each end item that should be produced in each week. Finished products and if it is only parts of products that are produced, are counted as end items, (Gaither and Frazier, 1999).

Making the MPS is a challenging job and many different areas in the company have a great interest in the MPS and put a lot of pressure on the master scheduler, (Chase, et al., 2006):

• The sales department want to meet the customers due date.

• The finance department wants to minimize the inventory.

• The management wants to maximize the productivity and customer service, and also minimize the resources needed.

• The manufacturing department wants to have level schedules and minimize setup time.

FI G U R E 3.1– TH E O R E T I C A L F R A M E W O R K 3.1 MASTER PRODUCTION SCHEDULE

3.2 SHOP-FLOOR SCHEDULING

3.3 OPERATIONS RESEARCH

3.4 GANTT CHART

3.5 MAINTENANCE FOR MACHINING CENTERS

3.6 MACHINING CENTERS

3.7 MEASURING PROCESS PERFORMANCE

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MASTER THESIS THEORY

To be able to make the MPS, the master scheduler gets information weekly about market forecasts, customer orders, inventory levels, facility loading and capacity information, (Gaither and Frazier, 1999).

There are two objectives with the MPS. The first is to make sure that the end items are completed promptly and when promised to customer. The second is to avoid over- and underloading of the facility so the production capacity is efficiently utilized and results in low production costs. Underloading means that there has not been scheduled enough finished end items to fill up the total production capacity of the facility. Overloading is the opposite, which means that the facility have insufficient capacity to fulfill the schedule, (Gaither and Frazier, 1999).

The flexibility in the MPS depends on many different factors such as how long the production lead time is, how committed the parts and components are to the end item, the relationship between the customer and seller, the amount of non filled capacity that exists but it also depends on how much the management want to make changes, (Chase, et al., 2006).

3.2SHOP-FLOOR SCHEDULING

Once the master production schedule provides the operations management with information about which order has to be executed in which time span, the next issue is to schedule the jobs in the shop-floor with consideration of finite loading. This means that resource constraints like a finite amount of running time of the various machines exist. Items are produced in batches, which are either the customer order size or the economic production quantity. Each job is addressed to one or more machines depending on the job prerequisites and if a special routing through different kinds of machines is required. The challenge is to decide in what sequence the waiting orders are handled in order to meet the companies’ objectives, (Gaither and Frazier, 1999).

3.2.1FORWARD VS BACKWARD SCHEDULING

A characteristic of scheduling systems is whether it uses forward or backward scheduling.

Forward scheduling plans in jobs as soon as the machinery is idle and the jobs are available in the facility in order to utilize the capacity. The system can tell the earliest finish date of a order, which is not related to the due date. This ensures a feasible schedule, places loads within the available capacity, which is especially important in terms of critical resources like bottleneck machines, for instance. In practice, machine idleness needs to be avoided on the shop-floor especially for a bottleneck resource. Entities will keep running as long as there is work in process, WIP in queue. But consequently it also may lead to high inventories and high WIP. Backward scheduling starts from the due date in the future and schedule the activities in reverse sequence to estimate the date a job has to be started in order to be finished in time. This is in accordance to the Just-in-Time- philosophy. Just-in-Time, JIT is supposed to fulfill the actual demand at the right time, in the right quality and amount at the right place and it eliminates all non-value adding activities, (Chase, et al, 2004, Chou, et al., 2005 and Christopher, 1998).

3.2.2PRIORITY RULES

Determining the sequence of the jobs is strongly related to the customers, because it determines the due dates of the orders and thus estimates if the promised delivery times can be fulfilled. Besides meeting the due dates and minimizing the lead times the company also

A Model to Enhance the Effectiveness of Machining Centers with Automatic Multi-Pallet Changers: a Case Study