Procedia - Social and Behavioral Sciences 81 ( 2013 ) 613 – 617

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.Selection and peer review under the responsibility of Prof. Dr. Andreea Iluzia Iacob.doi: 10.1016/j.sbspro.2013.06.485

1st World Congress of Administrative & Political Sciences (ADPOL-2012)

Decision Support Systemof H Production Schedulling Based on Good Traditional Medicine Manufacturing Practices

(GTMMP) Standard

Rakhma Oktavinaa*,Retno Maharesia a Gunadarma University, Jl. Margonda Raya No. 100 Depok, 16424, Indonesia

Abstract

The purpose of this research is to develop a decision support system in the herbs production scheduling appropriates to Good Traditional Medicine Manufacturing Practices (GTMMP).Design of algorithm modelfor scheduling decision support system that complies with GTMMP standard was done using a network analysis technique, which combines the techniques Evaluation and Review Technique Program (PERT) and Critical Path Method (CPM). The structure of decision support systems consists of database management syatem and modelbase management system. The implementation of decision support systems is the consideration for companies that intend to certify GTMMP.

2013 Published by Elsevier Ltd.

Selection and peer review under the responsibility of Prof. Dr. Andreea Iluzia Iacob.

Keywords:Decision Support System, Production Schedulling, Herb, GTMMP;

1. Introduction

Harmonization of ASEAN as a trade agreement between ASEAN countries and China requires herbal medicine manufacturers that are wished to export their products to other countriesmust have a safety certificate as Good Traditional Medicine Manufacturing Practices (GTMMP). This is in line with business policies that enable efficient management of the established standard operating procedures for daily activities (Peltier, 2004). In fact, it is not easy for an industry to adopt the GTMMP standard. Among many herbal industries in indonesia, there are only 32 companies having certified GTMMP (Ministry of Health, 2011).

Implementation of GTMMP is not easy when an industry to adopt that standard. The standard reference choice in GTMMP implementation is based on the determination of an efficient production schedule. Furthermore, as a mean of supporting decision-making, companies can take advantage of computer technology, especially using project planning application on the production process that implements GTMMP. Because of a production system with standard GTMMP has not been available yet, using the term pilot project as an analytical tool can be an option. It is an alternative way to assess the production system feasibilityby usinga project management tool. Hopefully, the herbal industry can decide if the GTMMP pilot project that can be used as a standard operating procedures. The

* Corresponding author: Rakhma Oktavina. Tel.: +62-818-931-588 E-mail address:oktavina@staff.gunadarma.ac.id

Available online at www.sciencedirect.com

© 2013 The Authors. Published by Elsevier Ltd.Selection and peer review under the responsibility of Prof. Dr. Andreea Iluzia Iacob.

614 Rakhma Oktavina and Retno Maharesi / Procedia - Social and Behavioral Sciences 81 ( 2013 ) 613 – 617

purpose of this research is to produce a decision support system in the herbs production scheduling appropriates to Good Traditional Medicine Manufacturing Process (GTMMP).

2. Research Method

The method of this study consists of three stages. The first stageis the construction ofa databasemanagement system. The second stageis the construction ofmodelbasemanagement systemschedulingproduction processes using PERT and CPM technique. The third stageisto integratethe databaseand the modelbaseintoa productionschedulingdecision supportsystemaccording tothe GTMMP standard. 3. Result and Discussion 3.1 Requirement Analysis

In order to buildthe decision support systems, the database and the modelbase management systems are needed.

The database management system is used to process the data. The data are entered and stored in a database management system which can be retrieved when they are needed.Based on related informationof theproductionofvarious herbs industrialguidedby NA-DFC, the network can be madeas a pilotproduction project. The seven processescontaining inthe production modelof traditional herbal medicineareillustratedin the Figure 1.

Figure 1. Block diagram ofthe production processof herbal medicineBased onGTMMP 3.2ProblemFormulation

The efficient proceduresinthe decision-making processrelating to theGTMMPadoption are not availableyet.

3.3DatabaseManagement System

GTMMPguidelinesin accordance withNA-DFCcover 12aspects:quality management, personnel, buildings

andfacilities, equipment, sanitation andhygiene, qualified and trained production, quality control, inspectionandquality audititself, systems of recall and investigation of compaints (http://moko31.wordpress.com, 2010). Among the12 aspectsGTMMP, qualified and trained productionbecomesa priority.

Networkactivityin herb schedulingprocesswas basedon astageproduction ofmedicinalherbsproduction process consists of: (1) activity ofraw materials handling, (2) production process, and (3) activity of finished goods to GTMMP standard. 3.4 Modelbase Management System

Quantitative methodssuch asoperationsresearchhave beenrecognized andproved useful inoptimizingcompany

resourcestoachieve their goals, such asFordMotors(Chelst, Sidelko, Przebienda, Lockledge, &Mihailidis, 2001),

Processing the final products before the distribution

The manufacturing of herbs products

The final product packaging

The determination of methods of manufacturing medicinal products

The process of production preparation

The extraction of raw materials

The registration process and the process of obtaining a permit for small traditional medicine industries

615 Rakhma Oktavina and Retno Maharesi / Procedia - Social and Behavioral Sciences 81 ( 2013 ) 613 – 617

MerrillLynch(Atschuler, Batavia, Benneth, Labe, Liao, Nigam,&Oh, 2002), AT&T(Ambs, Cwilich, Deng, Houck, Lynch, &Yan, 2000), andSamsung (Leachman, Kang, &Lin, 2002). U.S. militaryrecruiting office(Knowles, Parlier, Hoscheit, andAyer, 2002) andWarnerRobinsAirLogisticsCenter(Srinivasan, 2007)won theworld'smostprestigiousawardin 2006for thebest achievement ofoperational research. This studyusesoperational research.Analytical methods PERT and CPM networks are used in a framework for generating schedules as needed (Taha, 2010). The output of the method is used as an input of PERT CPM method that will be used again with the output of the CPM method for scheduling activities. 3.5IdentificationSystem

Tocarry outthe production processing accordancewith GTMMP, a framework based on the determination of GTMMP implementation is needed. An engineeringmodelof herb productionschedulingwas designedin a computer based Decision SupportSystem (DSS). The configurationmodel is presentedin the following figure.

Data Base Management System

- - Time of processing data

Model Base Management System

Network model using PERT and CPM techniques

Data Model

Central processing system

Dialog management system

User

For performing PERT analysis, one needs to fill the data of every actvity in the form provided by PERT analysis

tool box, as presented in Figure 4.

The next step is to get PERT analysis to provide the users for the optimistics, pesimistics and expected Gantts.

The result of PERT analysis must be saved first to be used in CPM analysis afterwards. To get a graph of the fast

schedule after together along with the critical activities, one can choose the desired graph presentation of Gantt chart. The networkdiagramformedicinalproduction schedulebasedGTMMPthen is displayed below.

Production scheduling is a process to allocate appropriate resources for the required manufacturing tasks (Baker,

1974; Xue, Sun, &Norrie, 2001). Based on thescheduling, the time of the herb production processbased onthecriticalpathis 13306.33minutes. The expectedtimeduration should be used tooptimizethe production under resources constraints (Davis, 1973; Elsayed, 1986; Elmaghraby, 1993; Chan, 1997). Otherwise, PERT-CPM integratedschedulingprovides an opportunityto acceleratethe completion ofthe production processthrough the calculation ofthe probability, so it canbe acontrolforthe production process (Ballard & Howell, 1998; Chua, 1999). In reference to thenormaltable, there isa 50%chancefor thecompany to completethe productionin 13306.3minutes. The 50% of chance means the scheduleof the production processcan be acceleratedorslowed down. There is opportunityto accelerate thecompany'sproduction processes into12 960minutes or less with the chanceof20.33%.

Byutilizinga computing technology,it is expected to acceleratethe more accuratecomputation process(Andersen, 1995; Chan, 1997; Xue, Sun, &Norrie, 2001) and quickerone (Bonfatti & Manari, 1995; Mezgar, Kovacs,

Figure2.Decision Support SystemConfigur ProductionSchedulingBased on GTMMP

616 Rakhma Oktavina and Retno Maharesi / Procedia - Social and Behavioral Sciences 81 ( 2013 ) 613 – 617

&Paganelli 2000),besidesby giving some leewaytochoose thevalueof criticalactivitiesas the basis forscheduling,especially forsmalland mediumindustries. 4. Conclusion

Decision support systems consists of database management syatem and model base management system.

Implementation of decision support systems is the consideration for companies that intend to certify GTMMP. By utilizing a computing technology it is expected to accelerate the more accurate computation process and

quicker, besides allowing leeway to choose the value of critical activities as the basis for scheduling, especially for small and medium industries.

Acknowledgements

This research is funded by the DirectoryofResearch and Community Services, Directorate of Higher Education,

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