##### A paper draft submitted to the 

##### "Practical Application of Agents and Multi-agent Systems" special session at ISAS'99

# An Agent-Based Approach For Supply Chain Management

Nenad Ivezic and Tom Potok

[{ivezicn\|potokte}@ornl.gov](mailto:{ivezicn|potokte}@ornl.gov)

Collaborative Technologies Research Center

Computer Science and Mathematics Division

Oak Ridge National Laboratory

Phone: (423) 574-5200

Fax (423) 241-6211

# Abstract

Agent-based technologies are identified as new software-development
approaches with a potential significant impact on supply chain
management solutions. Supply chains are defined as a collection of
business centers through which products pass at various stages of
completion from the provision of raw materials to final sales. Areas of
opportunity for agent-based approaches are explored in support of
advanced supply chain managent solutions. Manufacturing Agent-Based
Emulation Systems (MABES) is described as an open, agent-based framework
that supports planning and analysis of the transition from traditional
to lean manufacturing supply chain management.

# Introduction

Supply chains are defined as "a collection of business centers through
which products pass at various stages of completion from the provision
of raw materials to final sales" \[1\]. The market challenges of
improving product delivery and quality while decreasing production cost
and lead times are met through collaboration of many independent
contractors and suppliers \[2\].

The focus of this paper is on agent-based approaches that allow advanced
supply chain management solutions and, in particular, on Manufacturing
Agent-Based Emulation Systems (MABES), which is an instance of an
agent-based system for planning and analysis of lean manufacturing
supply chain management. In the rest of the paper, we introduce the
opportunities for agent-based approaches followed by a description of
the MABES system.

# 

# Opportunities for Agent-Based Approaches

We believe that opportunities for agent-based manufacturing systems
follow from two primary drivers. The first driver is the Internet that
has become a commercially viable medium for development, integration,
and management of distributed enterprise information systems. The second
driver is the need for change of existing human-computer and
computer-computer interaction paradigms.

The emergence of the Internet has, in turn, provided two fundamentally
new opportunities in software systems development. First, the
opportunity for distributed development and maintenance of software
systems carries possibly the biggest payoff for the agent-based software
development community. If software agent technologies can be developed
to enable packaging of new and legacy systems as autonomous agents
capable of efficient integration and interaction in new, unforeseen
situations, the software development would clearly be revolutionized.
Agent-based research has already developed or embraced new concepts that
are necessary to achieve such a capability, including behavior
encapsulation \[3\] and semantic unification which is a key for
successfull virtual enterprise integration and supply chain management
\[4\].

Second, the opportunity to develop new types of distributed software
systems, organizations, and artificial societies on the Internet with
emergent capabilities and potential to outperform existing software
systems is capturing imagination of developers and users of agent-based
systems. We already are observing advances in modeling, analysis, and
simulation of new multi-agent systems in manufacturing that deal with
issues such as collaboration, coordination, and negotiation \[2, 5\].
New fundamental concepts, such as agent commitments, emerge to
facilitate modeling and understanding of complex multi-agent systems
\[6\].

Additionally, the agent systems have an opportunity to provide a new,
unifying, and ubiquituous software metaphor that may change the existing
human-computer and computer-computer interaction paradigms from the
perspective of both the developer and user. For example, when modeling
components and roles for supply chain management, one may specify
responsibilities to each agent role including tasks, preconditions and
postconditions for task completions, and resource requirements for the
tasks. Also, distributed agent-based algorithms are naturally thought of
as entities to deal with exception handling, change orders, and other
supply chain disturbances. Certainly, agent paradigm lends itself well
to implementation of learning and adaptation of the system \[7, 8, 9\].
Further, agents' ability to respond quickly and independently from a
central command and to act autonomously and in asynchronous manner make
them valuable when rapid adaptation to a changing situation such as
customer order changes and inventory changes is needed \[10\]. Agents'
ability to cooperate with one another, exchange information and
negotiate contracts is especially valuable for reacting to anomalies
ranging from broken drill bits to required equipment maintenance, to
process planning on the fly \[11\].

The above three opportunities for the agent-based approaches -- to
enable distributed software development and maintenance; to enable new
types of distributed software systems, organizations, and artificial
societies; and to change existing human-computer and computer-computer
interaction paradigms -- collectively motivate a proposed agent
definition: An agent is a computer system, situated in some environment
(e.g., Internet), that is capable of flexible autonomous action in order
to meet its design objectives, where flexibility includes
responsiveness, pro-activeness, and social capability \[3\].

In the following, we present an agent-based systems that is already
being applied within its manufacturing domain and that subscribes to the
agent definition above.

# MABES: Agent-based Solution for Lean Manufacturing Supply Chain Management

Manufacturing communities have been rapidly advancing management
approaches over the past decade. Among these approaches, a significant
place is being held by the lean manufacturing practices \[12\]. An
important characteristic of the lean practices is the capability to
support alternative, cost-efficient management approaches capable of
supporting both core as well as extended enterprise operations. For
example, while the traditional approaches to manufacturing process
management rely on centrally scheduled production (e.g., Push systems),
the lean practices suggest customer-driven approaches (e.g., Pull
systems) that can be naturally implemented over extended enterprises to
increase process efficiency and quality while reducing waste and
inventories.

Transition from traditional to lean manufacturing approaches is a
complex enterprise-wide process that requires major capital investment
of a company. Understanding the impact of the change from traditional to
advanced 'lean' systems is very difficult to obtain. Typically,
multidisciplinary expertise in manufacturing production systems,
industrial engineering, computer simulation and others is required to
investigate the current state, project the future advanced manufacturing
\'lean\' state, and predict the performance of that lean setup.
Compounding the problem is the need to perform the transition to lean
manufacturing in the context of extended enterprises that include the
external supply chain, typically not under direct control of the
company.

Manufacturing Agent-Based Emulation System (MABES) is an open,
agent-based framework enabling design and analysis of discrete
manufacturing systems in support of the transition from traditional to
lean manufacturing approaches. MABES is founded on the distributed agent
paradigm with agents interacting according to some specified patterns of
behavior, established interaction protocols, and rules of coordination.
A number of agent types and interaction protocols have emerged from
analysis of traditional and advanced manufacturing processes, such as
Push, Pull, and Takt processes.

The agent types and interactions among these agent types are shown in
Figure 1 for a case of manufacturing process line implementing a Pull
approach. There are four types of agents adopted in the approach:
customer agent, stack agent, process center agent, and process agent.
The same agent types appear in other approaches such as Push and Takt.
These agents represent components of the manufacturing process, they do
not overlap in their respective competencies and responsibilities and
they together cover all components of a process line. As the customer
agent sends requests for products (i.e., pull signals) to the output
stack agent at specific times, the receiving agent propagates the signal
upstream to the other agents in the process line with the goal to "pull"
the resources required for completing the product. The agents implement
different patterns of behavior, depending on the agent's role and the
overall desired mode of behavior for the manufacturing agency (e.g.,
Push, Pull, or Takt mode). Figure 2 shows a user interface to the
multi-agent design and analysis tool with a capability to monitor
message passing among agents and statistics of the manufacturing process
with respect to a number of metrics (e.g., span time, work in progress).

![](media/image1.jpeg){width="6.0in" height="3.1770833333333335in"}

#### **Figure 1** -- Agent types and interactions among the agent types in a Pull process

![](media/image2.jpeg){width="5.995138888888889in" height="2.8625in"}

#### **Figure 2** -- A user interface to the multi-agent design and analysis tool

The agents observe the current, local context and make decisions as
reactions to the context. The context typically includes commitments to
deliver certain quantities of resources to the requesting downstream
agents or commitments to initiate and complete processes that would
result in requested products. Also, the context includes the knowledge
of expected completions of products that have been already initiated.
Nothing prevents the agents, however, to also rely on the past
experiences and to learn from previous episodes of manufacturing process
executions.

MABES is designed to allow work of collaborative teams that may include
manufacturing engineers, system analysts, shop-floor foremen and
managers of operations. MABES user interface is being developed as a
synchronous collaborative tool to implement various floor control
policies. A collaborative team is capable of synthesizing in real time
an agent-based model of manufacturing process, simulating that model,
and observing the results of the simulation. In this way, the
time-consuming information and knowledge exchange and negotiation during
collaborative decision making is being addressed.

We have developed a number of light-weight, flexible decision tools to
be embedded within the MABES framework to enable easy access,
communication, and, ultimately, collaboration among the members of the
team. This is especially crucial during the conceptual and preliminary
stages of a supply chain design where concept generation, analysis, and
evaluation take place. Resorting to heavy-weight design and simulation
tools in these early stages is not appropriate and leads to a less than
optimally explored space of alternative solutions \[7\].

As an open system that supports design, analysis, and initial
optimization of alternative discrete manufacturing systems, MABES also
provides for comparison of these alternative systems based on
user-provided metrics. The metrics that we have considered in the system
include span time, utilization, cost, work in progress, and others. The
distributed agent architecture used in MABES allowes easy extension of
the system to the extended enterprise situations where supply chain
management issues are more appropriately dealt within a distributed
framework.

# CONCLUSION 

In this paper, we have described how agent-based software systems can be
brought to bear to help accomplishing the transition from traditional to
lean supply chain management. We have accounted for the main drivers of
software agent approaches and the principal opportunities that have
presented themselves to the software agent developers. Finally, we have
given an account of the Manufacturing Agent Based Emulation System
(MABES) which is an innovative planning and analysis tool that addresses
the issue of transitioning from traditional to lean manufacturing supply
chain management.

# ACKNOWLEDGEMENTS 

The work of Nenad Ivezic and Tom Potok is supported by Oak Ridge
National Laboratory, managed by the Lockheed Martin Energy Research
Corporation for the U. S. Department of Energy, under contract number
DE-AC05-96OR22464.

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