Automated Negotiation in Supply Chain Management Using Multi-Agent System

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Automated Negotiation in Supply Chain Management
Using Multi-Agent System

• Masabumi Furuhata
• University of Western Sydney
• Computing and Information Technology
• 22.08.2005

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Outline

• Research Objectives
• Research Overview
• Motivation
• Prospected Advantages of the Research Model
• Industrial Benefits
• Research Model
• Conclusion
• Future Works
• Relevant Research

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Research Objectives

• Internal and external automated negotiation model
  and algorithm establishment in supply chain
  management area using multi-agent system
  techniques.
• We suppose that organizational issues and
  understanding market equilibriums in automated
  negotiation market are most important things to
  realize the model.

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Negotiations in Supply Chains
Competitor x
Customer A
Supplier 1
CORPORATION
Customer B
Supplier 2
Competitor y
Customer C
Supplier 3
Legend
Company
Agent cluster
Agent

• External negotiation inter-corporate negotiation
• Internal negotiation inner-corporate
  negotiation, among agent clusters and between
  agents in the same agent cluster

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Motivation
Strategy
Target
Action Plan
Planning and Transaction
IT coverage

• Information technology coverage of current best
  practices in supply chain management is limited.
  It is no doubt that there are requirements to
  extend the area of IT coverage.
• Some of planning and transaction is automated.
• For strategy development, target setting and
  action plan development, IT used as support tools
  or decision support systems.
• We spend too much time on solving problems for
  the exceptional situations.
• Generally speaking, staffs spend their 80 of
  their time for correspondence to the 20 of
  irregular situation, and they spend the rest of
  the time for 80 of the normal situation.
• Are we ready to connect to a real-time market?

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Prospected Advantages of the Research Model

• Reduction of the manual negotiation among
  planners and back-office staffs.
• Agility in response to market environmental
  change.
• Deficit planned inventory and automatic
  adjustment, dynamic pricing, etc.
• More solid planning with putting off the planning
  confirmation deadline.
• Agents run with assuming that the prior planning
  results contain probability. Moreover, unlike the
  centralized model, the distributed model runs
  with limited information. Therefore, a successive
  agent dose not always require results from a
  prior agent.
• Quick entrance to new location and product area.
• Reduction of planners and executers learning time.

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Industrial Benefits

• Using our platform, we can analyze the market
  behaviors of e-trading market
• Dynamic pricing
• Choosing competitors strategies
• Changing market conditions, such as interest
  rate, demand, number of competitors, BOM,
  production lead-time, distribution lead-time,
  storage cost, and etc.

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Research Model

• Agent Definition Level
• Basic Behavior of Agent
• Agent, Organization, KPI, and KGI
• External Negotiation Process
• Internal Negotiation Architecture

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Agent Definition Level
Department A
Department B
Actual Organization
Organizational Unit
Agent cluster
IT model
Agent

• In the research, we define agents as small
  particles.
• For example, the level of sales agents is
  equivalent to the multiplied dimension of
  (product) x (customer) x (distribution channel).
• Compared to centralized agents, we have more
  transactions among agents, but there are many
  advantages.
• Distributed agents are able to map to many
  different type of actual organizations easily.
• Unlike the centralized system, we do not need the
  global supply chain parameter settings by super
  planners. This type of the people do not exist in
  the most companies.

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Basic Behavior of Agent
Get common knowledge from blackboard
Start of event
Get KGI (Key Goal Indicator)
Execute plan or transaction
End of event
Get KPI (Key Performance Indicator)

• Functions of agents are event driven.
• When agents are kicked by an event, each agent
  gets datum, common knowledge, from the blackboard
  to comprehend the situation. Here, all datum that
  are able to share among other agents are saved on
  the blackboard.
• To determine the preference among decisional
  options, each agent gets KGI (key goal
  indicators, ex. sales, resource utilization,
  etc.) from their belonging organization and KPI
  (key performance indicators, ex. order fill rate,
  inventory turn over, etc. ).
• Agents make decisions according to common
  knowledge, KGI and KPI.

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Agent, Organization, KPI, and KGI
KPI (Key Performance Indicators)
Department A
Department B
Key Goal Indicator
Organizational Unit
Agent cluster
Agent

• Each agent belongs to one organizational unit.
• Each organizational unit has some key goal
  indicators.
• Each agent gets some KGIs from its belonging
  organizational unit.
• Some KPIs cover different departments, therefore
  they are effective to different agents clusters.
• Agents autonomous behaviors are based on KGIs,
  and coordinating behaviors are based on KPIs.
• If autonomous decision makings are not feasible,
  then agents make reasonable decision with
  coordination rules.

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External Negotiation Process
Customer
Supplier
Demand forecast
Request for proposal
Sales offer
Purchase order
Purchase order (update)
Available-to-promise
Available-to-promise (update)
Advanced ship notification
Advanced ship notification (update)
External negotiation
Delivery
Payment
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Internal Negotiation Architecture
Sales Department
KPI
Logistics Department
Sales agent cluster
Transportation Department
Logistics agent cluster
Transportation agent cluster
Production agent cluster
Purchase agent cluster
Production Department
Purchase Department
Legend
Agent cluster
Agent
Organizational unit
KGI
KPI
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Agent Definition Level, Roles and Functions
Agent Sales Logistics Purchase Production Transportation
Definition Level - Customer x distribution channel x Product - Storage Location x Product - Supplier x Product - Production Resource (or Line) - Transportation Lane
Principle Roles - Maximize customer satisfaction - Manage of customer demand - Maximize sales opportunity - Minimize procurement cost - Minimize purchase cost - Maximize purchase request - Minimize production lead-time - Maximize production resource utilization - Minimize transportation lead-time - Maximize transportation utilization
Main Functions - Demand forecast generation - Sales offer generation including dynamic pricing -Sales order prioritization for ATP processing - Inventory level determination - Inventory deployment - Procurement quantity determination - Make or buy determination - Purchase forecast generation - RFP generation - Purchase Order generation and update Production planning - Production scheduling - Dispatching - Transportation planning - Vehicle scheduling - Dispatching
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Functional Example Sales Offer Generation -
Sales agent
RFP receiving start
Get common knowledge from blackboard
RFP receiving end
Generate offer
Offer sending start
ltRFPgt ID Customer Product Quantity Due
Date Price Penalty
ltRFPgt ltOffergt ltSalesgt
ltInventorygt ltMarket Datagt ltForecastgt
ltOffergt ID Customer Product Quantity Due
Date Price Penalty
ltOffergt ID Customer Product Quantity Due
Date Price Penalty
Get KGI (Key Goal Indicator)
ltKGIgt
Get KPI (Key Performance Indicator)
ltKPIgt
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Functional Example Purchase Delinquency
Recovery -
Logistics agent
Purchase parts delinquency info receiving start
Get common knowledge from blackboard
Purchase parts delinquency info receiving end
Check parts inventory allocation options
ltInventory Allocation Optiongt Option Date Stock
Point Part Quantity
ltPOgt ID Supplier Ship-to Product Quantity
(original) Quantity (new) Due Date (original) Due
Date (new) Price Penalty
Get KGI
Get KPI
Production agent
Option Plans Negotiation
Get common knowledge from blackboard
Check production plan options
ltProduction Plan Optiongt Option Date Stock
Point Product Quantity
Get KGI
Get KPI
Sales agent
Get common knowledge from blackboard
Determine sales order preferences
ltSales Order Optiongt ID Customer Product Quantity
Due Date
Get KGI
Get KPI
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Conclusion
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Future Works

• Algorithm development to comprehend KGI and KPI.
• Mathematical representation.
• Generalization.
• Agent coordination mechanism development.
• Especially for the case that some agents have to
  concede their benefit to satisfy the constraints.
• Internal negotiation model development.
• General model.
• Industry specific model.
• Assembly industry.
• Chemical industry.
• Automotive industry.
• External negotiation model development.

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Future Works

• Simulation analysis on external market conditions
  and reasonable market behavior.
• Competitiveness number of competitors, and
  market share.
• Lead-time pressure delivery lead-time,
  production lead-time, customer expected
  lead-time.
• Interest rates bank interest rates.
• Demand fluctuation average demand, variance, and
  probability distribution function.

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Relevant Researches

• MASCOT (Multi Agent Supply Chain COordination
  Tool)
• N. Sadeh, MASCOT An Agent Architecture for
  Multi-Level Mixed Initiative Supply Chain
  Coordination, Internal Report, Intelligent
  Coordination and Logistics Laboratory, Carnegie
  Mellon University, 1996
• ANTS (Agent Network for Task Scheduling)
• J. Sauter, H. Parunak, and J. Goic, ANTS in the
  Supply Chain, the Workshop on Agents for
  Electronic Commerce at Agents '99, Seattle, WA,
  May 1-5, 1999
• ISCM (Integrated Supply Chain Management)
• M. Barbuceanu and M. S. Fox, "Coordinating
  Multiple Agents in the Supply Chain", Proceedings
  of Fifth Workshop on Enabling Technologies
  Infrastructure for Collaborative Enterprises,
  Stanford, CA, IEEE Computer Society Press, pp
  134-142. 1996

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MASCOT

• MASCOT (Multi Agent Supply Chain COordination
  Tool)
• Blackboard architecture Knowledge Sources (KS)
  and Blackboard
• Functionalities
• Coordination
• Integration with heterogeneous plans and
  scheduling module
• Mixed-initiative decision support
• Alternative problem instances and solutions
• Selective problem definition
• Controller of the module visualization

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MASCOT
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MASCOT
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ANTS

• ANTS (Agent Network for Task Scheduling)
• Unit Process Broker (UPB)
• Part Broker (PB)
• Resource agent
• Supplier agent
• Customer agent
• Market architecture

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ANTS
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ISCM

• ISCM (Integrated Supply Chain Management)
• Function agents
• Order fulfillment
• Logistic resource management
• Transportation resource management
• Production resource management
• Dispatching
• Scheduling
• Information agents
• Central communication
• Knowledge management
• Conflict solving
• Coordination support

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ISCM