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

Conclusion

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:

MASCOT

MASCOT

ANTS

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

ANTS

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

ISCM