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Computers and Chemical Engineering 34 (2010) 793–801

Contents lists available at ScienceDirect

Computers and Chemical Engineering

journa l homepage: www.e lsev ier .com/ locate /compchemeng

erformance analysis of a multi-plant specialty chemical manufacturingnterprise using an agent-based model

ehzad Behdania,∗, Zofia Lukszoa, Arief Adhityab, Rajagopalan Srinivasanb,c,∗

Faculty of Technology, Policy and Management, Delft University of Technology, P.O. Box 5015, 2600 GA Delft, The NetherlandsInstitute of Chemical and Engineering Sciences, A*STAR (Agency for Science, Technology and Research), 1 Pesek Road, Jurong Island, Singapore 627833, SingaporeDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore

r t i c l e i n f o

rticle history:eceived 31 August 2009eceived in revised form 5 January 2010ccepted 21 January 2010vailable online 2 February 2010

a b s t r a c t

Modern day manufacturing enterprises consist of networks of worldwide production sites, each of whichhas its own supply chain. There are complex interactions between the decisions at various levels of suchenterprises that lead to intricate dynamics. To make holistic decisions, it is necessary to measure andanalyze performance of the enterprise and its constituents under various conditions. Such performanceanalysis calls for appropriate modeling and simulation tools. Agent-based modeling has been demon-

eywords:gent-based modelingupply chainerformance analysisimulation

strated as a promising approach for modeling such complex networks of distributed actors. In this paper,we demonstrate how an agent-based model can be developed to explicitly capture the interactions amongthe various constituents including the plants, functional departments, and external entities. As an illus-trative case, an agent-based model of a lube additive manufacturing supply chain is introduced andthe performance of the system studied under a significant range of behaviors, business policies, and

bnormal situation managementecision support

environmental events.

. Introduction

Modern day enterprises operate in a global scale with produc-ion capabilities spread out around the world. Further, each ofhese production plants operates their own global supply chainnd sources and supplies globally. As an example, consider a focalnterprise and its customers and suppliers. The enterprise itselfay have some production plants in different locations. Each pro-

uction plant may have several internal functional departmentshat are responsible for various internal activities (such as plan-ing, scheduling, inventory management). Coordination is required

n a multi-plant enterprise at various levels—each plant shouldoordinate all its internal activities at the plant-level; at the sup-ly chain-level the interactions between a plant and its suppliersnd customers are managed; the relations between the differentlants are optimized at a higher level, the enterprise-level.

Such networks of entities at different levels can be seen associo-technical system in which the physical network and the

ocial network of actors involved in its operation collectively formn interconnected complex system where the actors determine

∗ Corresponding author at: Department of Chemical and Biomolecular Engineer-ng, National University of Singapore, 4 Engineering Drive 4, Singapore 117576,ingapore. Tel.: +65 65168041; fax: +65 677 91936.

E-mail addresses: B.Behdani@tudelft.nl (B. Behdani), chergs@nus.edu.sgR. Srinivasan).

098-1354/$ – see front matter © 2010 Elsevier Ltd. All rights reserved.oi:10.1016/j.compchemeng.2010.01.020

© 2010 Elsevier Ltd. All rights reserved.

the development and operation of the physical network, and thephysical network affects the behavior of the actors. Usually, eachactor in this complex network has its own (local) goals that aresometimes in conflict with others. However, from a supply chainperspective, achieving the optimum overall system performancedoes not necessarily mean the optimum local goals for all localactors. In addition, in many cases the relation between the localgoals and the overall performance is not clear and therefore appro-priate modeling and simulation tools supporting understanding ofa supply network components’ behavior and their effects on systemperformance are called for.

Such models can support decision makers by studying the sup-ply chain dynamics and also evaluating the effectiveness of newpolicies before implementation. However, developing appropriatemodels to study the performance of global enterprises offers itsown set of challenges. First and foremost is the complex natureof these systems. The complex dynamics of the entire system aregoverned not only by the technical manufacturing capability butalso by the social nature of the interactions among the actors. Thismotivates consideration of both the technical and social aspects ofthe system in one comprehensive model. Further, not only is therecomplex and nonlinear behavior in the heterogeneous components

of the physical sub-system but also the decision making in thesocial sub-system is not centralized and distributed across variousself-interested actors (e.g., suppliers, manufacturers, customers).The interactions between the social and physical sub-systems andtheir inherent capabilities and constraints add to the complexity.

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94 B. Behdani et al. / Computers and C

or effective decision support, a model should be able to reflect allhese complexities of the system.

Analytical models of chemical supply chains have receiveduch attention in literature during last two decades. Most of

hem are based on mathematical programming and operationsesearch approaches. Shah (2005) describes a comprehensive liter-ture on different mathematical models for chemical supply chains.impe and Kallrath (2000) describe a general mixed-integer linearrogramming model covering the relevant features for manag-

ng a multi-plant production network with emphasis on chemicalndustries. Their model considers plants located in different coun-ries and different customers and combines the important aspectselated to production, distribution as well as marketing. This wasurther developed by considering simultaneous strategic and oper-tional planning and applying to a real world problem (Kallrath,002). Bok, Grossmann, and Park (2000) present a mathematicalodel for similar problem but with the continuous productionode. The proposed model is a multi-period optimization model

hat considers inventory profiles, changeover costs, intermittentupplies, process operating modes and product sales for a networkf production facilities located at different sites.

Moon, Kim, and Hur (2002) propose an integrated process plan-ing and scheduling (IPPS) model for multi-plant supply chains.he model is formulated as an integer programming model andts objective is to determine the schedules for minimizing tardi-ess through analysis of the alternative machine selection and theperation sequences in the multi-plant supply chain. A mixed-nteger programming model which is mainly focused on tacticalransportation and production decisions is developed and evalu-ted under varying degrees of information availability by Haehlingon Lanzenauer and Pilz-Glombik (2002). It is subsequently appliedo the specific supply chain configuration of the Beer Distribu-ion Game and solved for several problems settings. The resultsemonstrate enormous potential for performance improvementspecially through coordinating information and material flows.

Neiro and Pinto (2004) propose a general framework for model-ng petroleum supply chains consisting of a set of crude oil suppliersnd refineries that can be interconnected by intermediate and finalroduct streams and a set of distribution centers. The main decisionariables include stream flow rates, operational variables, inven-ory, and facilities assignment. The resulting model is a large-scale

ixed-integer nonlinear programming model that can be used forhe planning of a complex petroleum supply chain under differentcenarios. Schulz, Diaz, and Bandoni (2005) describe two multi-eriod mixed-integer nonlinear programming models for shorterm planning of petrochemical complexes. It includes production,roduct delivery, inventory management and decisions such as

ndividual production levels for each product, as well as operatingonditions for each plant. The model solution provides coordinationf responses to demands, production planning, production dis-ribution and inventory level management. Similarly, Grossmann2005), You and Grossmann (2008) and Dondo, Mendez, and Cerda2008) discuss mathematical formulations for operation manage-

ent in multi-plant industrial networks. They propose the termnterprise Wide Optimization for solving the combined produc-ion/distribution scheduling problem in multi-plant environments.

ost of these analytical models do not explicitly take into accounthe social aspects of the system; hence the dynamics of the modeledystem are dominated by physical laws rather than the interactionsetween decision makers.

An alternative approach that overcomes this is offered by agent-

ased models which adopt an actor-centric perspective instead ofhe activity-centric one (van Dam, Adhitya, Srinivasan, & Lukszo,008). This paper considers a global chemical manufacturing enter-rise and its performance analysis using an agent-based model. Thisodel can capture different levels of decision making in a supply

al Engineering 34 (2010) 793–801

chain, thus allowing experiments with different realistic opera-tional policies and analysis of the dynamic behavior of the supplychains. The paper is structured as follows. Section 2 describes theagent-based modeling approach and its applicability for modelingsupply chains. The process for developing an agent-based model fora global enterprise is presented in Section 3, followed by the modelof the specialty chemical enterprise in Section 4. Finally, Section 5gives some concluding remarks and areas for future research.

2. Agent-based modeling

Agent-based modeling is a promising approach to model com-plex systems comprising of interacting autonomous agents (Macal& North, 2005). In this approach, a system is described by definingthe actors (agents) and the possible interactions between them.The system behavior then emerges from the behavior of the modelcomponents and their interactions. Instead of taking a top-downview in modeling, the model is hence constructed from a bottom-upperspective. Because of this, agent-based modeling is often con-sidered a natural approach for systems involving distributed anddecentralized decision making.

Generally speaking, agents in an agent-based model have thefollowing main characteristics. They have a certain level of auton-omy, which means that they can take decisions without a centralcontroller or commander. To achieve this, agents are driven by a setof rules that determines their behavior. Agents are capable of actingin their environment, which means that they are able to perceivechanges in the environment in which they are immersed and thenrespond to those changes with their own actions, whenever neces-sary. Agents are proactive, which means that they have their owngoals—they do not just act in response to changes in their environ-ment. Finally, agents have social-ability to communicate with eachother (Wooldridge & Jennings, 1995).

A global enterprise is a modular, decentralized, complex andadaptive system. It has many heterogeneous components. Its over-all behavior emerges from the interaction of these components.These components are customers, the focal enterprise and sup-pliers. The enterprise itself may have several plants in differentlocations, each with some level of autonomy to control its ownactions and states. Decision making within each plant is furtherdistributed across various functional departments, such as pro-curement department and operations department, each having aspecific role and performing certain tasks. Each department has itsown policies and some level of autonomy to perform its activities.The departments interact with each other and also with customersand suppliers through material and information flows. Accordingly,the overall dynamic behavior of the enterprise emerges from theindividual behavior of different actors and their interactions.

It is evident from the above that the constituents of the enter-prise have the same basic characteristics as an agent in agent-basedmodels—autonomy, social-ability, reactivity and pro-activeness.Further, decision making in an enterprise is not centralized; ratherit is distributed between different actors. The collective decisionsmade by these autonomous actors at various levels of hierar-chy result in the overall system behavior. Therefore, agent-basedmodeling seems to be an appropriate approach for modeling andanalyzing the performance of enterprises and their supply chains.

There has been recent interest in agent-based models forenterprises and supply chains. Garcia-Flores and Wang (2002)present a multi-agent system for chemical supply chain simulationand management support. Julka, Srinivasan, and Karimi (2002a)

propose an agent-based framework as a decision support systemin which entities in a supply chain, i.e., various enterprises andtheir internal departments are modeled as agents and the flows ofmaterial and information are modeled as objects. The applicationof this framework to support the decision making process in a

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B. Behdani et al. / Computers and C

efinery is also discussed (Julka, Srinivasan, & Karimi, 2002b).rinivasan, Bansal, and Karimi (2006) develop a platform forodeling and simulating chemical supply chains using a Grafcet-

ased agent description. They discussed the application of this toimulation for decision making in a chemical supply chain andvaluating the effect of different supply chain business processesnd configurations. Mele, Guillén, Espuna, and Puigjaner (2007)ddress the supply chain design and retrofit problem by couplingdynamic multi-agent model for assessing the performance of a

upply chain configuration with genetic algorithms to optimise theperation variables. More recently, van Dam et al. (2008) describen agent-based model for an oil refinery supply chain and comparet with the equation-based model reported by Pitty, Li, Adhitya,rinivasan, and Karimi (2008).

In the next section, the key steps in developing an agent-basedodel for a global enterprise are presented.

. Agent-based model of a global enterprise

The following are the key steps in developing an agent-basedodel of a global manufacturing enterprise:

. Formulation: Identify the actors, their activities and interactionsTo start developing an agent-based model of a multi-plant

enterprise, the basic building blocks of supply chain, i.e., theactors involved and the physical systems they own, operateand maintain, as well as their possible interactions should beidentified. For this purpose, the supply chain is considered as asocio-technical system in which the physical network (of facili-ties) and the social network (of actors) involved in its operationcollectively form an interconnected network. The multi-actorsocial network determines the development and operation ofthe physical network, while the physical network affects thebehavior of the actors, see Fig. 1. In agent-based models, actors(a manufacturing site, a functional department, a logistics mode)can be modeled as agents.

Since socio-technical systems can be viewed as a network, tomodel a supply chain of a multi-plant enterprise an ontologydescribed in van Dam (2009) and van Dam and Lukszo (2010)has been used. This ontology allows the formal description ofthe various entities in the enterprise, their properties, relation-ships, and constraints in a form that is both machine-readableand machine-understandable. Specifically, the formalized con-cepts in this ontology to represent nodes (social nodes calledagents; physical nodes called technology) and different typesof edges (to represent interactions between them), properties,configurations, labels, etc. (see Fig. 2) are adopted for modelingenterprises. The ontology has an open character and thereforeusing it for new projects results in extensions of its “building

blocks” which can be reused in future modeling assignments.The behavioral “building blocks” developed for the current sys-tem described in this paper, i.e., activity of agent, policy (e.g.,scheduling or procurement policies), order, due date, and timeduration are integrated into the generic ontology as a part of

Fig. 1. A global enterprise as a socio-technical system.

al Engineering 34 (2010) 793–801 795

a knowledge base. This knowledge base is also a medium forsharing the information and communication between agents.Sharing these concepts through ontology is crucial in developingan agent-based model, because it can guarantee having a similarinterpretation of the main concepts when heterogeneous agentscommunicate.

2. Implementation: Encode the rules and activities of the agents in themodel

The second step in model development is implementing thementioned ontology concepts as source codes in Repast simula-tion platform (North, Collier, & Vos, 2006) and Java programmingenvironment. Using these source codes, in a flexible manner, wecan construct an agent-based model representing the configura-tion of the socio-technical system under study.

3. Verification and validation of modelClearly, the developed model should be verified and validated

with available data or through comparison with other modelingapproaches. The final model can be used to formulate experi-ments and study the performance of the system under differentscenarios.

The next section illustrates such a development of an agent-based model and its performance analysis using a multi-sitespecialty chemical manufacturing case study.

4. Case study: A global lube additive manufacturingenterprise

In this section, a global lube additive manufacturing enterprise isintroduced and its agent-based model presented. The performanceof the enterprise under a variety of conditions is studied throughexperiments.

The lube additive manufacturing enterprise studied here com-prises three main actors: Customers, suppliers, and the enterpriseitself (Wong, 2007; Wong, Adhitya, & Srinivasan, 2008; Zhang,Adhitya, & Srinivasan, 2008). The enterprise has multiple plantsat different locations. Each of these plants has its own functionaldepartments, each with a specific role (Fig. 3).

All production plants can produce various types of productsfrom different raw materials. The goal is to fulfill a set of customerorders by assigning them to different plants and coordinating thebehavior of different departments in each plant.

The actors in this supply chain can be viewed in three levels asshown in Fig. 3:

• Global level: There are three actors at the global level—customer,the enterprise, and supplier.

• Enterprise-level: The manufacturing enterprise consists of theglobal sales department and a number of plants.

• Plant-level: Each plant has six different functionaldepartments—scheduling department, operations depart-ment, storage department, packaging department, procurementdepartment and logistics department.

In the following, the behavior of each of these actors isdescribed:

(1) Customer: The customer creates orders and sends them to theenterprise. Two types of customers are considered:• Important customers whose orders have special priority, espe-

cially during disruptions;• Regular customers.

Customers are located in different geographical locations.Depending on their own situation and requirements they cancreate orders by taking into account seasonal consumption pat-

796 B. Behdani et al. / Computers and Chemical Engineering 34 (2010) 793–801

of on

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Fig. 2. A small fragment

terns or historical data. Each order is described by:• Product type and grade• Order quantity• Due date range (Earliest Due Date and Latest Due Date)• Packaging type• Transportation type (customer pick-up or delivery arranged

by plant)2) Manufacturing enterprise: The manufacturing enterprise

itself consists of two main sub-systems: A global sales depart-ment and the production plants.

Global sales department: After receiving a customer order, theGlobal sales department (GSD) assigns it to one of the availableplants. For this, it passes the order details to the schedulingdepartments of each plant. The schedulers reply with the ear-liest date when the plant can produce the order and deliver tothe customer. Based on the replies, GSD assigns the customerorder to one of the plants according to its “order assignmentpolicy”.

Plant: Plants have different geographical locations and dif-ferent production characteristics. All of them operate asmake-to-order (MTO). Each plant can be further decomposedinto a number of departments which are responsible for specificactivities:

tology (van Dam, 2009).

Scheduling department: This department performs two mainfunctions:

Firstly, after receiving a new order from GSD, it reports therequired information (e.g., the first possible time for fulfillingthe new order) to GSD. For this purpose, the scheduler attemptsto insert the new order into its production schedule. The firstpossible time for fulfilling the new order is sent to GSD to decideon assigning the order.

Secondly, the scheduling department activates the next orderto be processed by the operations department. For this purposeit communicates with storage department to ensure the inven-tory of raw materials before sending the order to operationsdepartment.

The scheduling department can use many different schedul-ing policies such as Earliest Due Date (EDD), Processing EarliestDue Date (PEDD), First Come First Serve (FCFS) and ShortestProcessing Time (SPT).

Operations department: The operations department processes

each order and oversees the conversion of raw materials toproducts. Firstly, it sends a request for release of raw mate-rials to the storage department. Each batch of reactants are fedto the reactors and blenders (not described in detail here) andprocessed following pre-specified recipes to produce the prod-

B. Behdani et al. / Computers and Chemical Engineering 34 (2010) 793–801 797

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sages for managing the material flow through the physical network.Table 2 gives an overview of the main assumptions made during theconceptual realization of the model.

To study the influence of the behavior of each actor on theperformance of the system as a whole some performance indica-

Table 1Relationships between agents and technologies in the model.

Social node (agent) Relationship Physical node (technology)

Customer Owner/operator Customer facilitiesGlobal sales department – –

PlantOwner Production facilitiesOwner Storage facilitiesOwner Packaging facilities

Scheduling department – –Operations department Operator Production facilities

Fig. 3. Lube additive manufacturing enterprise (solid lin

ucts matching the specifications in the order. Following thisproduction step, the products are sent for packaging.

Packaging department: The packaging department packs thefinished orders.

Storage department: The storage department provides rawmaterials for operations and manages their inventory.

Procurement department: The procurement department com-municates with suppliers and places orders for raw materialsbased on a raw material procurement policy.

Logistics department: The logistics department arranges theshipping of raw material and distribution of finished orders ifnecessary.

3) Supplier: Suppliers provide various raw materials according tothe plant requests. In general, each plant has many suppliers fordifferent raw materials. Each supplier has different geographi-cal locations that can affect the delivery time.

This global multi-plant specialty chemical manufacturing enter-rise has been modeled as described next.

.1. Agent-based model of lube additive manufacturing enterprise

For analyzing the behavior of the lube additive manufacturingnterprise, an agent-based model is developed. Table 1 presentshe social and physical nodes, i.e., agents and technologies, respec-ively, in the agent-based model. It is worth mentioning that the

w material flow and dashed lines are information flow).

manufacturing enterprise is not considered as a separate agent butas a virtual one consisting of the plants agents and GSD.

The social network of agents and physical network of technolo-gies as well as their interactions provide a realistic representationof the global enterprise. The decision making process, includingsocial interactions in the social network, results in control mes-

Storage department Operator Storage facilitiesPackaging department Operator Packaging facilitiesProcurement department – –Logistics department – –Supplier Owner/operator Supplier facilities

798 B. Behdani et al. / Computers and Chemical Engineering 34 (2010) 793–801

Table 2Main assumption made for model conceptualization.

Aspect of enterprise Assumption

1. Capabilities The enterprise manufactures 3 products from 8 raw materials. It has 3 manufacturing sites.

2. Plant location Any location of customers, suppliers and plants can be represented by coordinates between (0, 0) to (10, 10) on10 × 10 grid map.The location of the three plants is (2, 4), (9, 4) and (8, 7)

3. Plant operation Only one order can be processed at a particular time. Multiple orders can be packaged and delivered at the sametime.

4. Products and feedstocks There are three lubricant product types (A, B and C) with five different grades for each product which is producedfrom 3 base oils and 5 other additives based on a particular recipe. Plant 1 is closer to main suppliers; the averagetime for Plant 1 to receive the raw material is 4 days and for other plants is 5 days.The maximum capacity for base oils is 2500 units and for other raw materials are 500 units

5. Customers and orders There are 50 customers around the world; 6 of them are important customers. Customers generate orders; theorder amount is limited by packaging capacity. Generally, Plants 1 and 3 are closer to the market and importantcustomers.The order amount is limited to packages of 500 and 1000 units. “Product A” is more favorite product in market and70% of its orders have 1000 units’ amount. This ratio for “Product B” is 50% and for “Product C” is 30%.

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ment. Thus, the number of processed orders is the dominant factorin the former case while the reorder point becomes dominant inthe latter. In summary, average inventory levels are not monotonicfunctions of reorder point, which also motivates the proposed sim-

On average, 50% of orders aorder pick-up after finishing

6. Time horizon One year (360 days).

ors at the enterprise and plant-level can be defined. In general,he performance of a supply chain can be analyzed in terms ofustomer service level (e.g., tardiness, number of late orders), finan-ial aspects (e.g., profit, overall operational cost) or a combinationf both. The performance indicators considered in this paper areNumber of late orders”, “Total tardiness (in days)”, “Average leadime for orders” and “Average inventory level”. It should be stressedhat the flexibility of an agent-based model guarantees an easyxtension of the performance indicator set in case additional anal-sis is necessary.

.2. Experiments

.2.1. Base caseThe analysis of the nominal behavior of the multi-plant enter-

rise in the base case is presented here. In Table 2, the mainssumptions for modeling are mentioned. Table 3 also shows theolicies for different departments, i.e., order assignment, schedul-

ng and raw material procurement policies. The order assignmentolicy is Earliest Completion Date (ECD) which means that the orderent by customer will be assigned to a plant with earliest comple-ion date for that order. Fig. 4 shows the cumulative amount ofrders for all products received by GSD as a function of time overhe simulation time horizon. The scheduling policy is Processingarliest Due Date (PEDD) and it indicates that the order with earlierrocessing due date should start earlier:

EDD = Earliest Due Date − Packaging Time − Processing Time

− Expected Delivery Time

inally, the raw material procurement is done on the basis ofeorder point policy that is set to 25% in the base case.

The inventory level for the two raw materials, base oil and andditive, for Plant 1 is shown in Fig. 5. The saw tooth profile corre-

able 3olicies for different departments in the base case.

Department Policy

Global sales department Order assignment policy: Earliest CompletionDate (ECD)

Scheduling department Scheduling policy: Processing Earliest DueDate (PEDD)

Procurement department Raw material procurement policy: ReorderPoint Policy (25%)

t by plant to customers and for other orders the customer itself is responsible for

sponds to the inventory level trending down due to consumptionduring production stages and a step increase when a new ship-ment of raw materials is delivered by the supplier. Similar profileswere observed for all the other raw materials and also for other twoplants.

As presented in Table 4, there are 17 late orders with 58 tardydays in total during the time horizon (1 year). By changing the poli-cies for different departments, the normal operation of supply chainunder different scenarios can be analyzed. As an example, the effectof changing the reorder point (procurement policy) on system per-formance is shown in Table 4. As the reorder value increases, theoverall performance of the system, in terms of total tardiness andnumber of late orders, improves. The effect on average inventorylevels is more complex as it is an increasing function of reorderpoint and a decreasing function of number of orders processed.For instance, in Table 4, increasing the reorder point from 20% to22.5% decreases the average inventory level for Plant 2 by 78 unitssince the additional inventory enables the plant to accept additionalorders (133 compared to 127 at 20%) which then leads to more rawmaterial consumption. On the other hand, increasing the reorderpoint from 25% to 27.5% leads to an increase in the inventory levelsince all the available jobs can be handled by the enterprise evenat 25% and there is no further benefit from the additional procure-

Fig. 4. Cumulative quantity of product orders received by GSD.

B. Behdani et al. / Computers and Chemical Engineering 34 (2010) 793–801 799

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lation based approach to determine good reorder point settings.able 4 can be used for this purpose. As increasing the reorder pointrom 25% to 27.5% has minor effect on the number of late orders butesults in higher average inventory level, 25% can be considered ashe upper limit for the reorder point. Additionally, a detailed finan-ial analysis incorporating inventory holding cost, lateness penalty,tc., can be conducted before a final recommendation. The currentodel does not incorporate financial factors and this is part of our

uture research.

.2.2. Experiment 1: Abnormal situationBesides studying the normal operation, the model can be used

o study the effects of disruptions on supply chain performance.

Table 5 shows the simulation results for a scenario with a shut-

own in Plant 1 at the 240th day of the time horizon. Consequently,ll orders should be fulfilled by Plants 2 and 3. All assumptions andolicies for the different departments are as mentioned for the basease.

able 4ffect of reorder point on the enterprise performance.

Reorder value

20%

Number of orders assigned to Plant 1 147Number of orders assigned to Plant 2 127Number of orders assigned to Plant 3 145

Number of orders assigned to all plants 419

Number of late orders by Plant 1 41Number of late orders by Plant 2 33Number of late orders by Plant 3 29

Number of late orders by all plants 103

Total tardiness for Plant 1 (days) 117Total tardiness for Plant 2 (days) 110Total tardiness for Plant 3 (days) 128

Total tardiness for all plants (days) 355

Average order lead time for Plant 1 (days) 18.6Average order lead time for Plant 2 (days) 16.8Average order lead time for Plant 3 (days) 16.8

Average order lead time for all plants (days) 17.4

Average inventory level for Plant 1 (units) 5315Average inventory level for Plant 2 (units) 5420Average inventory level for Plant 3 (units) 5222

Average inventory level for all plants (units) 5319

aw materials for Plant 1.

Because of this plant disruption, the number of assigned ordersdecreases from 440 to 401 and there are 60 late orders (instead of17) with total tardiness of 279 days.

To handle this disruption, many policies can be defined andtheir effects can be studied with the model. For example, two fol-lowing policies are defined and modeled to manage the effects ofdisruption:

• Policy 1—changing the procurement policy: In this policy, after dis-ruption in Plant 1, Plants 2 and 3 change their reorder point to anew value (30%).

• Policy 2—order rejection after disruption: To reduce the effects ofdisruption, GSD rejects 20% of orders, if they are not from impor-

tant customers.

The results for these policies are also shown in Table 5. Accordingto this table, increasing the reorder point reduces the number of lateorders and total tardiness. Policy 2 also improves the overall system

22.5% 25% 27.5%

149 158 158133 134 133148 148 149

430 440 440

10 6 111 5 619 6 9

40 17 16

43 16 147 13 2885 29 19

175 58 48

14.8 13.6 12.913.8 12.7 12.115.2 13.0 12.2

14.6 13.1 12.4

5341 5794 57935342 5380 55705395 5444 5620

5359 5539 5661

800 B. Behdani et al. / Computers and Chemical Engineering 34 (2010) 793–801

Table 5Effect of disruption management on the enterprise performance.

Plant disruption Plant disruptionmanagement Policy 1

Plant disruptionmanagement Policy 2

Transportationdisruption

Transportation disruptionmanagement

Number of orders assigned to Plant 1 111 111 111 155 157Number of orders assigned to Plant 2 135 140 140 133 134Number of orders assigned to Plant 3 155 152 145 148 146

Number of orders assigned to all plants 401 403 396 436 437

Number of late orders by Plant 1 0 0 0 7 4Number of late orders by Plant 2 27 17 12 10 7Number of late orders by Plant 3 33 28 22 4 9

Number of late orders by all plants 60 45 34 21 20

Total tardiness for Plant 1 (days) 0 0 0 34 23

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from 419 to 433, while the number of late orders and total tardinessboth decrease. This shows that the negotiation process provides anopportunity for performance improvement by allowing the enter-prise to take in more orders and have more flexibility in their due

Table 6Effect of delivery date negotiation on the enterprise performance.

Withoutnegotiation

Withnegotiation

Number of orders assigned to Plant 1 147 155Number of orders assigned to Plant 2 127 137Number of orders assigned to Plant 3 145 141

Number of orders assigned to all plants 419 433

Number of late orders by Plant 1 41 19Number of late orders by Plant 2 33 36Number of late orders by Plant 3 29 35

Number of late orders by all plants 103 90

Total tardiness for Plant 2 (days) 109 64Total tardiness for Plant 3 (days) 170 154

Total tardiness for all plants (days) 279 218

erformance significantly but this policy may decrease the totalrofit because of less order acceptance. Totally, a detailed financialnalysis can be very useful to make a final decision.

Similar to plant disruption, other possible abnormal situationsan be modeled and the performance of supply chain can be ana-yzed. One other common disruption in a supply chain is raw

aterial delivery disruption. As an example, a raw material orderor one additive that was planned to arrive at Plant 1 on 301st days delayed for 5 days. The effect of this disruption is also simulated

ith similar assumptions and policies as mentioned for base case.Because of raw material stock out, this disruption not only

ffects the performance of Plant 1 but also the performance of sup-ly chain as a whole. Number of assigned orders decreases from 440o 436, there are 21 late orders with total tardiness of 103 days.

To handle this disruption, some policies can be defined andheir effects can be studied with the model. For example, GSD mayhange the order assigning policy during the disruption period.onsidering the shortage of this additive in Plant 1, GSD does notssign orders that need this raw material to Plant 1 in disruptioneriod (day 301st to 306th). Implementing this policy reduces theumber of late orders and total tardiness. Of course, this improve-ent in the performance of supply chain does not necessarilyean the performance improvement for all plants. According to

able 5, this new policy would increase the number of late orders forlant 3.

.2.3. Experiment 2: Negotiation between customer and GSD onrder due date

One of the main features of an agent-based model is the possibil-ty of modeling the social interactions between agents. In previousases, this interaction was a one-way interaction from one agento another. For example, the customer sends the order informa-ion to GSD and based on the capacity availability and committedrders for each plant, the order is assigned to one of them. If nonef the plants can fulfill the order in the deadline specified by cus-omer, GSD sends the message for customer that the enterprise isot able to fulfill the order. But in reality, there are many situations

n which a negotiation among agents (that can be done in more thanne step) may determine the final value for some common desiredariables. In fact, two agents will decide about some decision vari-bles through convincing each other in a negotiation process (Xue,u, Wang, & Shen, 2007).

For example, in this case if all plants can only complete therder after Latest Due Date, GSD starts negotiation with customery proposing a new deadline (in fact, two agents are going to decidebout order due date with each other). The customer may accepthis proposal, send another proposal or reject it. This process will

155 51 3039 18 29

194 103 82

continue to reach an agreement between two agents. To have a spe-cific experiment, a negotiation process between GSD and customerin the following three steps is modeled:

• Step 1: If the first possible time is after the order due date, GSDsends a proposal for extending the order deadline to a new onethat is “the first possible time plus two days” for flexibility in itsproduction. To create an incentive for customer, GSD will commititself to cover 20% of order transportation cost to customer.

• Step 2: The customer sends a new proposal that it is willing toaccept the extension of order due date only to first possible dateand GSD pays 10% of transportation cost.

• Step 3: GSD revises its previous proposal by suggesting extensionof due date to “first possible time plus one day” and covering 15%of transportation cost.

Clearly, Step 1 is on the basis of preference of GSD, Step 2 ismore focused on customer preference but Step 3 can provide appro-priate level of satisfaction for both agents. In this experiment, weassume that the probability of a successful negotiation process is70%. Accordingly, Table 6 shows the system performance consid-ering this negotiation process between customer and GSD. Withnegotiation, the number of orders accepted by all plants increases

Total tardiness for Plant 1 (days) 117 78Total tardiness for Plant 2 (days) 110 105Total tardiness for Plant 3 (days) 128 161

Total tardiness for all plants (days) 355 344

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You, F., & Grossmann, I. E. (2008). Design of responsive supply chains under demanduncertainty. Computers and Chemical Engineering, 32, 3090.

Zhang, H., Adhitya, A., & Srinivasan, R. (2008). Agent-based simulation of a spe-

B. Behdani et al. / Computers and C

ates that would result in less tardiness and higher customer sat-sfaction. However, as the enterprise covers part of transportationost, a detailed financial analysis would be essential before arriv-ng at a final decision. This financial component of the model wouldncorporate an economic utility function (such as profit) for eachctor that serves as the basis for accepting or rejecting proposalsuring the negotiation phase. Incorporation of this financial com-onent is beyond the scope of the current work and is one of theain directions of our future work.Moreover, we can define other negotiation procedure between

ther agents (e.g., plants and suppliers or two plants) in the systemnd study its effect on system performance improvement.

. Concluding remarks

The main objective of this paper is to present how an agent-ased model can be developed for analyzing the performance ofglobal enterprise. In addition, the application of an agent-basedodel to support decision making in a chemical supply chain dur-

ng normal and abnormal situations is illustrated with a numericalase study for a lube additive manufacturing enterprise. The resultsf the experiments highlight the applicability of agent-based mod-ls to identify key drivers for improving the performance of thisnterprise and quantify their impacts on operational performance.

Decision making in a global enterprise is distributed among aet of autonomous and heterogeneous actors and overall systemehavior emerges from interaction of these actors. Accordingly, to

mprove the overall performance in such systems the main focushould not be on a single actor or a special department, but onmproving the performance of all involved departments and finallyn the system as a whole. Consequently, the design of appropri-te incentives to steer individual agents’ decision making towardsverall goals and to enforce adequate communication and collabo-ation schemes is a challenging question.

In the future, the proposed model will be further extendedy endowing the agents with more complex decision makingapabilities. We will also explore means to engender multi-actorollaboration across the enterprise, for instance between plants.lso, comparing this model with other modeling approaches wille another important direction for future research.

cknowledgements

This work was supported by the Next Generation Infrastructuresoundation (http://www.nginfra.nl/), the Delft Research Centre forext Generation Infrastructures, and the Institute of Chemical andngineering Sciences, A*STAR (Agency for Science, Technology andesearch), Singapore.

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