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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
Outline
• Research Objectives• Research Overview• Motivation• Prospected Advantages of the Research Model• Industrial Benefits• Research Model• Conclusion• Future Works• Relevant Research
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.
Negotiations in Supply Chains
• External negotiation: inter-corporate negotiation
• Internal negotiation: inner-corporate negotiation, among agent clusters and between agents in the same agent cluster
CORPORATION
Competitor x
Competitor y
Customer A
Customer B
Customer C
Supplier 1
Supplier 2
Supplier 3
Legend Company Agent cluster Agent
Motivation
• 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?
Strategy Target Action PlanPlanning
andTransaction
IT coverage
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.
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.
Research Model
• Agent Definition Level• Basic Behavior of Agent• Agent, Organization, KPI, and KGI• External Negotiation Process• Internal Negotiation Architecture
Agent Definition Level
• 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.
Department A Department B
Agent cluster
Agent
Actual Organization
IT model
Organizational Unit
Basic Behavior of Agent
• Functions of agents are event driven.• When agents are kicked by an event, each agent gets datum, commo
n 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.
Start of event
Get commonknowledge from
blackboard
Get KGI (Key GoalIndicator)
Execute planor transaction
End of event
Get KPI (KeyPerformance
Indicator)
Agent, Organization, KPI, and KGI
• 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 dif
ferent agents clusters.• Agents’ autonomous behaviors are based on KGIs, and coordinating beh
aviors are based on KPIs. • If autonomous decision makings are not feasible, then agents make reas
onable decision with coordination rules.
Department A Department BKPI(Key Performance Indicators)
Agent cluster
Agent
Key Goal Indicator
Organizational Unit
External Negotiation Process
Customer Supplier
Demand forecast
Request for proposal
Purchase order
Available-to-promise
Advanced ship notification
Available-to-promise (update)
Advanced ship notification (update)
Delivery
Payment
Sales offer
Purchase order (update)
External negotiation
Internal Negotiation Architecture
Sales agent cluster
Logistics agent cluster
Purchase agent clusterProduction agent cluster
Transportation agent cluster
Legend Agent cluster Agent Organizational unit
Sales Department
LogisticsDepartment
PurchaseDepartment
ProductionDepartment
TransportationDepartment
KPI
KGI KPI
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
Functional Example – Sales Offer Generation -
<RFP>IDCustomerProductQuantityDue DatePricePenalty
RFP receivingstart
Get commonknowledge from
blackboard
RFP receivingend
Get KGI (Key GoalIndicator)
Get KPI (KeyPerformance
Indicator)
<RFP><Offer><Sales>
<Inventory><Market Data><Forecast>
<KGI>
<KPI>
Generate offer
<Offer>IDCustomerProductQuantityDue DatePricePenalty
Offer sendingstart
<Offer>IDCustomerProductQuantityDue DatePricePenalty
Sales agent
Functional Example – Purchase Delinquency Recovery -
<PO>IDSupplierShip-toProductQuantity (original)Quantity (new)Due Date (original)Due Date (new)PricePenalty
Purchase partsdelinquency info receiving start
Get commonknowledge from
blackboard
Purchase partsdelinquency info
receiving end
Get KGI
Get KPI
Check partsinventory
allocation options<Inventory Allocation Option>OptionDateStock PointPartQuantity
Get commonknowledge from
blackboard
Check productionplan options
<Production Plan Option>OptionDateStock PointProductQuantity
Get KGI
Get KPI
Production agent
Get commonknowledge from
blackboard
Determine salesorder preferences
<Sales Order Option>IDCustomerProductQuantityDue Date
Get KGI
Get KPI
Sales agent
OptionPlans
Negotiation
Logistics agent
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.
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.
Relevant Researches
• MASCOT (Multi Agent Supply Chain COordination Tool): – N. Sadeh, “MASCOT: An Agent Architecture for Multi-Level Mixed Initiat
ive 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 Wo
rkshop 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 Sup
ply Chain", Proceedings of Fifth Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, Stanford, CA, IEEE Computer Society Press, pp 134-142. 1996
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:
ANTS
• ANTS (Agent Network for Task Scheduling) – Unit Process Broker (UPB):– Part Broker (PB): – Resource agent: – Supplier agent: – Customer agent: – Market architecture:
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