Journal of Systems and Software Order Fulfillment Monitoring in Agile ...

Volume 1 No. 1, APRIL 2011

ARPN Journal of Systems and Software ©2010-11 AJSS Journal. All rights reserved http://www.scientific-journals.org

Order Fulfillment Monitoring in Agile Supply Chain by Software Agents Yee Ming Chen Department of Industrial Engineering and Management Yuan Ze University, Taoyuan Taiwan, Republic of China

ABSTRACT Nowadays, in turbulent and violate global markets, agility has been considered as a fundamental characteristic of a supply chain needed for survival. Consequently Agile Supply Chain is considered as a dominant competitive advantage. However, so far a little effort has been made for order fulfillment monitoring an agile supply chain in recent years. Therefore, in this study a fuzzy approach has been developed based on software agents’ technique. Software agents that gather event-related information are one promising approach to monitor a large number of different order fulfillment autonomously. Fuzzy logic provides mechanisms for heuristic human-like evaluated of these data. An agent-based concept is introduced that includes autonomous software for monitoring orders on the one hand and fuzzy logic mechanisms for analyzing order data on the other hand. A prototype implementation illustrates this concept. Results of evaluation experiments using this prototype system are presented. Keywords: agility, fuzzy inference, supply chain

1. INTRODUCTION Supply chain management (SCM) is a business function which basically aims at organizing and synchronizing monitoring the use of the various resources distributed across the supply chain in order to optimize its response to customer orders and shareholders expectations for profitability[1,2]. Now days, many companies are facing constantly increasing competition stimulated by technological innovations, changing market environments and changing customer demands. Agility makes processes and organization individuals keep pace with the advanced technology and meets customer requirements in a relatively short period of time based on high quality products and services. The company and organization which is able to practice agility, might have a dominant presence worldwide. The capabilities that an agile organization should have to be able to make appropriate response to change taking place in its business environment, are basically divided into four major categories. These are: (1)Responsiveness: Which is the ability to identify changes and respond fast to supply chain. (2) Competency: Which is the extensive set of abilities that provide productivity, efficiency, and effectiveness of activities towards the aims and goals of the company. (3) Flexibility: Which is the ability to process different products and achieve different objectives with the same facilities. (4) Quickness: Which is the ability to carry out tasks and operations in the shortest possible time. Realizing agility is a business process and customer

requirement, social, economic and political drivers result in increasing effects on the order fulfillment of on agile supply chain.

Figure 1 The Model of Agile Supply Chain

As you see in figure 1, the order fulfillment is assessed based on the three elements of deliver time, quantity delivered and quality and if organizations try to improve these elements, they would achieve organizational agility. Enhanced agility require that partners ceaselessly integrate within a network of organizations. In order to achieve this coordination/integration of all the links in the supply chain 19

Volume 1 No. 1, APRIL 2011

ARPN Journal of Systems and Software ©2010-11 AJSS Journal. All rights reserved http://www.scientific-journals.org

Our research in the OFMA module applied fuzzy methodology to monitor order fulfillment, the objective being the determination of an optimal sequence for dynamic event arrivals such that potentially conflicting priorities are satisfied. Current OFMA implementations primarily focus on inter-organizational processes. All functions of the proposed OFMA solution are aggregated in a generic process for event management (see fig. 2). Inter-organizational communication Logistics Service agent’s POMMAS

Layer

Inter-organizational communication

information is critical. It is thus an essential function that is to often carry out in a distributed manner from both the perceptive of planning partners’ responsibilities, and the proactive of supply chain event management to face uncertainties of the order fulfillment process. Software agents perfectly suit these demands for agility [3,4]. To address these problems, we propose a SCMAS (Supply Chain Multi-agent Systems) framework of software agents for supply chain planning and scheduling[5]. In our framework, there are three modules including Bilateral Negotiation agents(BNA), Order Fulfillment Monitoring agents (OFMA), and Production Planning and Scheduling agents(PPSA). The SCMAS could support the managing supply chains resulting in many different tasks such as negotiating, planning, scheduling and order fulfillment monitoring of production, transportation and transaction processes. When an order comes, the SCMAS may emerge and evaluate the suppliers’ offers through BNA module. Software agents located at the different supply chain potential partners carry on negotiation processes. After negotiation, the initial predictive production plan and scheduling between supply chain partners being generated through the PPSA module. The module of OFMA may proactive monitoring and fulfillment according to the external situation even during supply chain partners execution. In this paper, we introduce SCMAS (Supply Chain Multi-agent Systems), an agent-based decision support environment for agile supply chain for order fulfillment monitoring. The module of OFMA is the proactive order fulfillment monitoring agents. During supply chain partners’ execution, the agents can be subject to several types of external situations that may disturb the predictive planning and scheduling by PPSA. Thereby, this module may proactive monitoring and dynamic order fulfillment according to the external situations. For example, resource requirements or availability may vary, ready times and due dates may change; new orders may have to be inserted. This module can perform not only quantitative attributes but also qualitative attributes employed fuzzy logic. The fuzzy approach in this research offers an enabling technology to alleviate the complexity of proactively monitor work-in-process scheduling for external situations. The rest of this paper is organized as follows: Section 2 examines framework of OFMA. Section 3 explores order fulfillment monitoring work-in-process demonstrated by the experimental results. Section 4 ends this paper with some conclusions.

Decision agent

Layer

Layer

Decision agent

Monitor agent Layer

Wrapper agent

Wrapper agent

ERP system Layer

Monitor agent

Production controlling database

Computer producer agent’s POMMAS

Wrapper agent

Wrapper agent

ERP system

Production controlling database

Layer

Hardware producer agent’s POMMAS

Figure 2 OFMA module

The data source layer includes the external data repository, which is incorporated into the legacy ERP system. The first activity is the Monitor agent which is initialized by different triggers: queries from buyers, alerts from suppliers and internally available order profiles. Critical order profiles are used to identify orders with a high probability of encountering disruptive events and thus focus monitoring efforts on high-risk orders. A critical order profile is a collection of criteria describing a certain order type. Typical order profile criteria are destinations of transportation orders, contracted carriers, produced products, ordered quantities and combinations of these criteria. This activity is cyclically initiated as long as a monitored order is not finished. Data is gathered both from internal data sources (e.g. an ERP system) and from external supply chain partners. Decision agent is responsible for the outcome of monitor agent processing performed in the previous layer and generating sequence priority of dynamic scheduling about how the order should be transacted. This decision agent is implemented as a fuzzy rule-based agent in here (see fig. 3).

Decision Agent Rule Base

2. OFMA ARCHITECTURE

Order critical Insert profile

To realize agent based order fulfillment monitoring within an agile supply chain. The Order Fulfillment Monitoring agents(OFMA) in each enterprise assures that initialization of monitoring efforts as well as management of external status requests and alerts is handled consistently within an enterprise. The coordination agent also provides an overview of all monitored orders of an enterprise and serves as a management cockpit for event management activities.

Inference Engine fire rule

Fact Base Insert

Perception

Order/Profiles etc

Fuzzy engine with knowledgebase Execution

Monitor agent Start monitor agent

Figure 3 Order fulfillment monitoring using decision agent in

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Volume 1 No. 1, APRIL 2011

ARPN Journal of Systems and Software ©2010-11 AJSS Journal. All rights reserved http://www.scientific-journals.org

OFMA

3. EXAMPLE OF ORDER FULFILLMENT MONITORING USING OFMA PROCESS The decision for the order fulfillment monitoring process is best described as the “dynamic order”[6,7]. Its elements on-time delivery, quantity of delivered order and quality of delivered order, as identified in the figure 4. Table 2. Figure 6 Input variable “order quantity”

TABLE 2 Quantity measurements Figure 4 Graphic layout of fuzzy rule-based function in decision agent

Order delivery time is measured and evaluated based on a range from the number of days earlier or later than the promised/expected delivery date. The categories in the measure are: ‘very early’, ‘early’, ‘optimum’, ‘late’, and ‘very late’, see Tab. 1. Fig. 5 shows the fuzzy sets delivery time that represents the standard in degree of membership.

TABLE 1 Delivery time measurement Linguistic Term

Range (Days)

Very early Early Optimum Late Very late

-5 -5~-2 -2~2 2~5 5

Linguistic Term

Range (% of delivered Quantity)

Very low Low Optimum High Very High

-10% -5%~-10% -5%~5% 5%~10% 10%

Quality value is measured based on the defect rate of the delivered order quality. There are three categories of the delivered order quality, namely, ‘poor’, ‘good’, and ‘excellent’ as shown in Tab. 3 and Fig 7.

TABLE 3 Quality measurements Linguistic Term

Range (% of Defects)

Poor

-5%

Good

-1%~-5%

Excellent

-1%

Figure 5 Input variable “delivery time”

Order quantity is measured and evaluated based on the range of the percentage of the quantity ordered. The categories in the measure are: ‘Very low’, ‘low’, ‘optimum’, ‘high ’and ‘very high’, as shown in Fig. 6 and 21

Volume 1 No. 1, APRIL 2011

ARPN Journal of Systems and Software ©2010-11 AJSS Journal. All rights reserved http://www.scientific-journals.org

Figure 7 Input variable “Quality”

Order fulfillment monitoring output evaluated based on a range from “Reject”,” Poor”, “Acceptable”,” Satisfied”, and ”Optimum”. As shown in Fig. 8 and Table 4.

TABLE 4 Fuzzy rules for decision agent in OFMA

Figure 8 “order fulfillment monitoring” output ranges

TABLE 3 Ranges for “order fulfillment monitoring” output Linguistic Value

Scores

Reject

0~0.29

Poor

0.3~0.49

Acceptable

0.5~0.69

Satisfied

0.7~0.89

Optimum

0.9~1

For three inputs and one output of a possible rule sets is given (see Table 4). The rule set reflects a typical agile supply chain strategy of order fulfillment monitoring which depends on on-time delivery, quantity of delivered order and quality of delivered order. By comparing the fuzzy rule base to the perceptions associated with the input values, a number of evaluations is generated for each perception[8,9]. These evaluations are aggregated, and a single value for the OFMA is calculated.

Fig. 9 depicts the order fulfillment monitoring behavior in relation to variations in delivery time, quantity, and quality. After the decision agent has inferred all available data and returns the information to PPSA module. Rescheduling reacted to the critical event[10]. Thereby this optimized predictive production plan and scheduling generated by PPSA after supply chain partners emerge. The OFMA module monitor on every stage of the agile supply chain to proactively detect external situations that endanger the order fulfillment monitoring process. In case of an event (e.g. a disruption in a production line) the SCMAS is engaged in a communication with the related partner enterprises and inform them of the event.

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Volume 1 No. 1, APRIL 2011

ARPN Journal of Systems and Software ©2010-11 AJSS Journal. All rights reserved http://www.scientific-journals.org

performance that the methodology could potentially deliver.

ACKNOWLEDGMENT This research work was sponsored by the National Science Council, R.O.C., under project number NSC 99-2622-E-155 -005 -CC3.

REFERENCES [1] E. Turban, D. King, D. Viehland, D., and J. Lee, J. Electronic Commerce, A Managerial Perspective. Upper Saddle River, HJ: Prentice Hall, 2006. [2] D.F. Ross. Introduction to E-Supply Chain Management: Engaging technology to build market winning business partnerships, Boca Raton, Fla: St. Lucie Press, 2003. [3] M.P. Papazoglu,, “ Agent-Oriented Technology in Support of E-Business”, Communications of the ACM, 44(4),pp.71-79, 2001.

Figure 9 Shows the fuzzy rule viewer of the decision agent in OFMA

Both, analysis of gathered SCMAS data as well as decisions on order fulfillment monitoring rely on fuzzy rule-based function in decision agent (see Fig. 4). In Fig. 9 results of tests with the OFMA agent of the assessments are depicted. In these tests several test data sets are analyzed by the SCMAS agents’ coordinate strategies are reflected by different Fuzzy Logic rule sets. It is assumed that a human actor can provide consistent heuristic assessments, if confronted with various input data sets. Decisions on order fulfillment are made depending on the bilateral negotiation (BNA) and the planning/scheduling status (PPSA) of an order.

4. CONCLUSION The Fuzzy approach of the order fulfillment monitoring presented in this paper contributes to the qualitative understanding of agile supply chain management and software agents implementation. Software agents imitate human assessments of complex situations during order fulfillment processes. Different strategies a human actor pursues in its interpretations are adequately reflected by the agent based concept through fuzzy rule sets. Results coming from experimental and practical studies in real business cases show that software agents are able to take over time consuming monitoring tasks in an intelligent way. Further research should address the strategic outcomes of improved collaborative

[4] Y.M. Chen,and S.C. Wang, “ Framework of agentbased intelligence system with two-stage decision making process for distributed dynamic scheduling” , Applied Soft Computing, No. 7,pp. 229-245,2007. [5] J. M. Swaminathan and S. R. Tayur, “ Models for Supply Chains in E-Business” . Management Science ,49(10), pp.1387-1406, 2009. [6] G. E. Vieira,, J. W. Herrmann, and E. Lin. ,”Rescheduling manufacturing systems: A framework of strategies, policies, and methods” . International Journal of Scheduling, March 14,pp.1899-1911, 2002. [7] J. M. Swaminathan and S. R. Tayur,” Models for Supply Chains in E-Business”. Management Science ,49(10), pp. 1387-1406, 2008 [8] I. Rahwan,, R. Kowalczyk, and H. H Pham, ” Intelligent agents for automated one-to-many ecommerce negotiation”. Proceedings of the twentyfifth Australasian conference on Computer science ,pp. 197-204, 2009. [9] M. D. Biagi, “ Transaction automation on the internet: open electronic markets, private electronic markets and supply network solutions,” International Journal of Electronic Business , Vol 2, Issue 2,pp. 674-685, 2004. [10] J. Vassileva, S. Breban and M.C. Horsch, “ Agent reasoning mechanism for long-term coalitions based on decision making and trust”. Computational Intelligence 18(4), pp.583-595, 2009

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