Towards an Agent-Based Mass Customization

Towards an Agent-Based Mass Customization Environment: Architecture and Coordination Vijayan Sugumaran School of Business Administration Oakland University Rochester, MI 48309 [email protected]

Stefan Kirn, Andreas J. Dietrich Department of Information Systems (510 O) Universität Hohenheim Stuttgart, 70593, Germany

{kirn, adietr}@uni-hohenheim.de Abstract

Mass Customization (MC) necessitates a value chain with several spatially distributed actors that require extensive coordination. Agent technology is beginning to be utilized in supply chain management because of its ability to facilitate collaboration and coordination. Thus, agents are suitable for constructing MC environments and interfacing the various participants working together in order to archive the common goal of producing customized goods. This research-in-progress paper presents a multiagent system architecture for MC, which takes into account the coordination problems that exist in MC. These agents have limited capability for global planning and scheduling because of their asymmetric and incomplete knowledge base. Therefore, we suggest a three-tier model of coordination and apply partial global plans for addressing the coordination problem. Keywords: mass customization, coordination, multi-agent system architecture, partial global plan

1. Introduction In today’s global economy, organizations face a very competitive environment and employ a number of strategies to gain competitive advantage. One such strategy is Mass Customization (MC), which combines mass production and made-to-order production. The phenomenon of mass-customization is observable in a variety of physical and digital goods and services sold over the Web. The ubiquitous Web serves as a disintermediator by replacing the links in a traditional supply chain with a direct channel to the consumer. Consequently, producers get better signals regarding consumer preferences and demand levels, which in turn leads to better inventory management and production planning [12, 19]. Mass customization combines the advantages of the following two concepts: efficiency in manufacturing and customer satisfaction [14, 13]. However, for a successful implementation of mass customization, a number of difficult problems have to be solved. In addition to the need for effective logistics and information management, a big challenge is to manage the coordination between the various actors (nodes) within this supply chain [15, 4]. Particularly, coordination of adjacent economic activities and competition pose considerable challenges. If a supply chain is not fully vertically integrated, much complication arises from the need to coordinate nodes that are competing with each other. Thus, systematic collection of relevant information from the actors within the value chain, propagating it in a timely manner, and using it to orchestrate a production plan, are difficult tasks. However, intelligent agents have the potential to provide solutions to some of these problems [1, 11, 17, 20]. Thus, the objective of this paper is to develop: a) an architecture for a multi-agent system (MAS) for mass customization, and b) an approach for facilitating coordination between agents in the MC application. Our proposed architecture consists of customer-centric and resource-centric agents. These agents not only collaborate with each other in carrying out the mass customization tasks, but also provide a seamless interface to existing legacy systems. Our agent coordination approach is flexible and supported by partial global plans [5]. The partial global planning technique for coordinating distributed problem solving supports each node (agent) with a reasoning capability for developing its partial global view by exchanging node models among the involved nodes. The overall goal is to form a consistent partial global plan so that a node

can determine when to work and whom to work with; i.e., the partial global plan guides the nodes to achieve the global goal.

2. Mass Customization in Shoe Industry Intense competition is causing footwear manufacturers to consider other methods to grow market share and they are looking to mass customization [6, 10]. With the emergence of Internet, new imaging and manufacturing technology, customization in the shoe industry is becoming a reality [18]. This technology is user-friendly, able to scan a consumer's foot and seamlessly link the imaging data to footwear products and transmit it over the Internet to the manufacturer. Businesses that learn to embrace mass customization are able to create greater variety in their products and services. In the footwear industry, customers can provide personal information and design a custom shoe to meet their size, shape and taste. For example, the Adidas web site (www.miadidas.com) provides a step-by-step instruction for consumers to build their own shoe (Figure 1). The first step is to measure the feet to create a shoe completely adapted to the individual’s length, width and fit preferences. The second step is to generate an in-depth foot-scan that will reveal everything about the distribution of pressure in motion. Based on the sport, personal performance and unique physical attributes, the outsole and technology features that best suit a topperformance model is selected. The customer can then choose the color and trim, and personalize the footwear with a signature on the tongue or heel. Figure 1. Customization with Adidas Footwear Typically, within a month the customized shoes will be delivered. The typical actors and flows in a MC value chain for the footwear industry are shown in Figure 2. The major interactions between the nodes in this value chain (numbered flows in Figure 2) are briefly described below. (1) The starting point is the interaction between customer and a configuration system. This could production system be a hardware/software system, which supports retailer forwarder 5 the process of formalizing customer’s desire into a product specification. (2) Finished orders are ls transmitted to the vendor of the shoes. An d optional retailer may act as an intermediary d 4 f (shop-in-shop for example). The vendor may or d 1 2 3 d may not be the producer of the shoes. (3) The customer configurator vendor producer d vendor sends order and product specification to f the producer’s production system. (4) Fabrication is finally coordinated by the producer and consists of planning, scheduling, supplying and various production processes. For Figure 2. Aggregated view of MC actors this specific shoe industry example, assume that there are two fabrication facilities f1 and f2 that get used. The last stage is assembling of the fabrications and the shoe itself. Thus, the last fabricator ls, supplies the fabrications (some of them may be partially assembled) that are customer specific or some 1 2

1

3 4 5

2

variants. Each fabrication facility has its own suppliers, for example, d1 … d5 are suppliers of fabricator f1 (suppliers of leather, rubber, etc.). (5) A forwarder carries the finished product to the vendor, retailer or directly to the customer.

3. MAS Architecture for Mass Customization As discussed in the shoe industry example (Figure 2), several important activities take place within the mass customization value chain. In particular, we focus on three major activities: a) eliciting and managing customer information and preferences, b) aggregating and transferring customer information for production planning and scheduling, and c) utilizing customer-centric information in product design and new product development. For mass customization to be successful, product and service developers have to perceive and capture latent market niches and subsequently develop technical capabilities to meet the needs of target customers. This starts with capturing customer information through an intelligent interface that provides a meaningful experience as well as elicit customer preferences and other pertinent specifications in a nonobtrusive manner [3]. While in the short term, this information could be utilized in adjusting production schedules and ordering raw materials, in the long term, it could be analyzed to detect trends in customer behavior and buying patterns. Often, mass customization efforts fail because customer information is not propagated to backend operations efficiently. If done correctly, this customer-centric information could be used to adjust production schedules to accommodate product customization within existing lines and also have better capabilities for demand forecasting. Mass customization depends on three elements: features, cost and schedule. In order to balance these three, we need an infrastructure that can support the necessary processes and systems across multiple divisions. Though most of the manufacturing organizations have mature systems and processes within each division, there is great difficulty in getting these systems to share information. In addition, most of the processes are quite complex and time consuming for individuals. In order to mitigate the inter-operability problems between existing systems as well as minimizing the cognitive burden employees involved in production planning and control, intelligent agents are beginning to be used [2, 7]. A multi-agent system is appropriate for managing the important activities within mass customization. Such a multi-agent system would not only take care of the customer interaction aspect, but also provide an efficient means to inter-connect disparate systems that exist within the organization [16, 2]. A MAS essentially provides the link between individual systems and ultimately manage the customer demand chain. In light of the above discussion, we envision a MAS specifically targeted towards capturing customer information and disseminating it to the production environment in a timely manner. The architecture of such a MAS is shown in Figure 3. The proposed MAS architecture comprises of two categories of agents: a) customer-centric and b) resource-centric. The customer-centric agents are responsible for dealing with customer information management, whereas, the resource-centric agents are responsible for utilizing the customer information in a meaningful way to create an efficient mass customization environment. The functionalities of each of the agents are briefly described below. Customer-centric Agents: The Customer Interface Agent (CIA) supports customer interaction and is responsible for eliciting relevant information regarding customer choices, preferences and specifications. It also generates user profiles and facilitates customization and parameterization of tasks. It provides an intelligent interface for the customer to input personal data relevant for product customization. The Aggregation Agent (AA) gathers customer specifications and facilitates the identification of appropriate product designs and specifications. It essentially maps customer information to available product lines and communicates the specifications to the resource agents. Customer preferences and profiles are stored in a database for further analysis to detect trends. Resource-centric Agents: The Process Modeling Agent (PMA) allows the production manager to specify the necessary processes that have to be executed in order to produce the requested customized goods. It captures the essential controls that have to be imposed on the shop floor resource agents to meet the targets.

Customer

Customer

Production Manager

Production Manager

Internet/Intranet

Internet/Intranet

Customer Interface Agent (CIA)

Process Modeling Agent (PMA)

Planning & Control Agent (PlCA)

Aggregation Agent (AA)

Simulation Agent (SA)

Inventory & Logistics Agent (ILA)

Product Customization Agent (PCA) Resource-Centric Agents

User-Centric Agents Planning Sub-System

Customer Preferences and Profiles

Inventory Sub-System

Simulation Results

Logistics Sub-System

Product Design Sub-System

Existing Systems Supply Chain Information Flow

Figure 3. MAS Architecture for Mass Customization The Simulation Agent (SA) allows to reason about consequences of physical actions before actually executing them. It can simulate the dynamics of the shop floor and help study the expected overall system behavior. The Planning and Control Agent (PlCA) assists in integrating product customization into the overall production plan and manages the constraints associated with product assembly, service and raw materials. It also interfaces with the existing planning sub-system. The Inventory & Logistics Agent (ILA) helps address concerns related to materials requirements and transportation logistics. It monitors inventory levels by interfacing with existing inventory control systems. The Product Customization Agent (PCA) helps analyze modification requests and ensures integrity by enforcing product constraints. It also supports development of new products based on customization trends. The afore-mentioned agents within the MAS architecture provide a seamless interface to the existing systems. These agents also coordinate various activities within and across these systems, which is a non-trivial task. For example, the job shop floor activities and the overall planning activities have to be synchronized. This coordination needs to take place at different levels. A formal description of the coordination protocol is provided in the next section.

4. Coordination Protocol As indicated earlier, in distributed or federative systems there is no complete global knowledge. Agents’ knowledge may be time-dependent, inconsistent, and sometimes contradictory [8]. Solutions for coordination problems must be based on decentralized negotiations. Hence, in our MAS architecture, different layers of coordination problems have to be distinguished. We present a three-tier model for coordination, which enhances the static architecture into a dynamic environment that uses negotiation processes. The first tier provides planning and scheduling of each separate resource (e.g. machine) belonging to a particular enterprise. Each resource is represented by an agent, which has to make its own plans using the available knowledge base. In the second layer the whole enterprise is considered: all the resources of a specific enterprise have to integrate and expand their plans in order to realize the underlying goal, for example, successfully manufacture MC shoes on time. The third layer focuses on the global supply chain of the MC scenario: here, spatially distributed enterprises along with their resources have to work together. The coordination between them leads to plans for the whole value chain.

Coordination problems relate to order processing from configuration, order preparation, production scheduling, delivery, etc. This approach is based on hierarchical interactions among agents [21, 22]. 4.1 Using Partial Global Plans for Solving Coordination Problems Assume a task T that needs to be decomposed. Decomposition results in a local decomposition plan (TDPT). For the subtasks in TDPT, potential agents that can execute them are identified and this information is stored in the task allocation table (TATT). If all subtasks have been assigned to at least one competent agent, the TATT is processed to generate the local task allocation plan TAPT, which shows the assignment of specific agents to specific tasks. TDP

negotiation

TAP

aggregation (TDP, TAP) NTP generalization (NTP) PGP Figure 4. The Concept of Partial Global Plans

If unassigned subtasks remain, the agent must change its TDPT using the knowledge received from the preceding negotiation process. Then, the TDPT and the TAPT are aggregated to form the node’s task plan (NTPT), which specifies the agent’s overall activities in completing the task T. Generalizing the NTPT, the agent constructs its partial global plan PGPT for task T, which serves as a communication unit (Figure 4). If the manager of T agrees with the PGPT submitted by agent KBSj, that agent is requested to process NTPT. The decomposition DT of T is a function:

DT: D(T) → TDT; with TDT := (Ti, i, RELik)n

(1)

where, n indicates the number of subtasks. i describes how to integrate the results of Ti into the overall result. RELik defines the relationships between Ti, and Tk. Bargaining subtasks requires additional knowledge. This is captured in its description (DESCR): DESCRT: DESCR(T) → (, , )T (2) Type indicates the cooperation style (e.g., distributed or multi-agent problem solving, team-work etc.) and the problem solving type for the task (e.g., diagnosis, configuration). Semantics includes the task definition, its input data or goals. Requirements include the problem solving strategies needed, the knowledge representation formalisms, the definition of the task's search space and the resources that are needed to solve T. To construct the local task decomposition plan TDPT, DESCR applies to all Ti included in TDT. So, the TDPT can now be defined as: TDPT := (Ti, (, , )Ti, RELik, )n (3) The competence evaluation function COMPT of an agent is given by: COMPT: COMP(KBSj, DESCRT, Cmin, Emin) → (C, E)j,T

(4)

where, m indicates the number of agents of the overall system and j ∈ {1, ..., m}. KBSj (Knowledge Based System) identifies agent j. C stands for the expected amount of competence to solve T. E defines the evidence of C and (C, E)j,T stands for the result of the competence evaluation of KBSj. In short, C is represented by a vector. Among other things, the values of the components of this vector describe the ability of an agent to deal with the classes, instances and problem solving strategies, the knowledge representation formalism, the resources and task description language required by T. Obviously, these components correspond to the characteristics of a task being included in the function DESCRT. For details see [9]. Now we introduce the process of negotiating task allocations NEGOTIATE_ALLOCT. NEGOTIATE_ALLOCT : NEGOTIATE_ALLOC (DESCRT) → TAT(T) TAT(T) := (Ti, {(KBSj, Cij, Eij) | (Cij ≥ Ci,min) AND (Eij ≥ Ei,m)}n success(NEGOTIATE_ALLOCT) :⇔∀ Ti ∃ KBSj: (Cij ≥ Ci,min) AND (Eij ≥ Ei,min))

(5) (6)

Negotiating introduces (temporal) global knowledge. If [∃ Ti ∈ TDPT: there is no KBSj with Cij ≥ Ci,min] the actual TDPT cannot be satisfied. In that case, the evaluation of the above condition triggers a modification, changing the actual TDPT. After having generated a complete and consistent TATT, task allocation TAT takes place to construct the local task allocation plan TAPT TAT: TA(TATT) → TAPT; with TAPT := (Ti, KBSj, Cij, Eij)n

(7)

Cij := max {C | j ∈ {1, ..., m} AND Cij ≥ Cmin} indicates the competence of KBSj | Ti. The aggregation AGGR of the TDPT and the TAPT forms the node’s task plan NTPT. AGGRTDP, TAP : AGGR(TDPT, TAPT) → NTP(T); NTP(T) := (Ti, i, RELik, KBSj, Cij, Eij)n

(8) (9)

Generalizing the NTPT leads to the partial global plan PGPT: GENERALIZENTP: GENERALIZE (NTPT) → PGP(T) PGP(T) := ((local_agent_id, T, C, E), (Ti, KBSj)n)

(10) (11)

Local_agent_id identifies the local agent. T identifies the current task. C indicates the expected competence of an agent to solve T and E indicates the evidence that holds for C. 4.2 Mass customization example In this section we apply the partial global planning approach to a selected coordination problem within MC in the footwear industry. Consider the overall objective in this domain: timely delivery of customized shoes to the customer. This could be treated as a high level task (T) for the shoe manufacturer. From the point of view of the producer (see Figure 1), the local task decomposition plan TDPT consists of several subtasks: (1) validation of product configuration, (2) order preparation, (3) manufacturing bottom part, (4) purchase desired leather, (5) manufacturing upper part, (6) Assembling shoes and (7) transport delivery. Using the expected competence of agents to solve T, the following task allocation table TATT will be created (Figure 5). This table shows the sequence of tasks that have to be executed in order to meet the overall objective and the allocation of potential agents that can execute these tasks. For example, task number 3 can be potentially executed by agents ILA1 and PCA3 or ILA1 and PICA1. The actual names of agents that correspond to the abbreviations used in the task allocation table are shown in Figure 3. If it is not possible to assign at least one agent to every subtask, TDPT must be changed. As shown in Figure 5, there is at least one competent agent 1 CIA1 allocated for each subtask, including disposition of transport (subtask 7). Thus, TDPT can be satisfied. PCA2 2 After generating a complete and consistent TATT, the local TAPT will be created by task allocation. ILA1 ∧ PCA3 ILA1 ∧ PlCA1 4 3 For each agent, TAPT is described by the corresponding subtask, the amount of competence 5 PlCA2 ∧ ILA2 ∧ PCA3 C, and the evidence E of C (e.g. cooperation of ILA1 and PlCA1 for manufacturing shoe bottom). 6 PlCA2 ∧ ILA2 ∧ PCA3 After that, TDPT and TAPT will be aggregated into the NTPT. After generalizing NTPT, the agent 7 PlCA1 ∧ ILA3 constructs the partial global plan PGPT. If the suggested PGPT is agreeable, the node’s task plan Figure 5. Task allocation plan TAPT for task T is processed. Although there is incomplete knowledge about the whole process of realizing task T, it is possible for each agent to create a specific plan for the subtask.

TDPT

TATT (see Section 3 for abbreviations)

5. Conclusion Agent-based mass customization is fast becoming a reality in the B2C e-commerce domain. In this paper two main aspects have been illustrated: firstly, a MAS architecture has been described that models several actors in a MC value chain. Secondly, partial global plans have been suggested for solving coordination problems within MC value chain. Because of autonomy of these actors, the PGP approach for decentralized negotiation has proved to be advantageous. Future work will focus on enhancing the architecture for simulation of orders and negotiation processes. In addition, evaluations of creating partial global plans are needed to assess the effectiveness of this approach.

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