Managing the Supply Chain

An AI Perspective

Mark S. Fox

Mihai Barbuceanu, Chris Beck, Andrew Davenport,

Mike Gruninger Enterprise Integration Laboratory

University of Toronto4 Taddle Creek Road, Toronto, Ontario M5S 3G8

tel: 1-416-978-6823 fax: 1-416-971-2479 internet: msf@ie.utoronto.ca

http://www.ie.utoronto.ca/EIL/

2

The Internet Effect

• The Internet has precipitated a major change in how we view retailing and the supply chain– Purchasing is becoming tightly integrated with

fulfillment

– Customers expect instantaneous response• Produce the product

• Tell me when it will be produced

• Tell me why it cannot be produced

3

Supply Chain Requirements

• The complexity of an enterprise, coupled with uncertainty in the performance of activities, plus the natural distribution of the organization, requires an information architecture where functions are distributed across a networked environment.

And are:

• Available - Informed - Flexible– Aware - Responsive - Smart

4

Problem

• Earlier ERP systems made the transition from static, batch oriented systems, to be more dynamic by incorporating messaging

• Never the less, these systems are still largely static– Most modules run on a batch basis or static sequence– Dynamic responses usually left to the human decision maker

• We need to re-think how we manage the dynamics of the supply chain– Information technology is making it possible to manage the supply

chain in ways not possible ten years ago.

5

Supply Chain Architecture

• A network of intelligent software modules that together dynamically manage the supply chain. Each module– is an expert at its task, thereby optimizing its goals

– coordinates its decisions with other modules, thereby optimizing supply chain wide goals

– quickly responds to changes in cooperation with other modules

6

Information Technology Enablers

• Four technologies are having a significant impact on the achievement of this vision:– The Internet/Web

– Intelligent Agents

– Constraint Directed Reasoning

– Enterprise Models/Ontologies

7

Intelligent Agents

• More and more of the tactical and operational decisions will have to be made by software systems that operate more autonomously than they do today.

• But, these systems will have to be endowed with operating characteristics a generation beyond what is available today.– We have to strike FIIR into our systems: Fast,

Informed, Intelligent Response.

• We call this software "intelligent agents”

8

Supply Chain Management Agents

Logistics Agent

Order Agent

Transport Agent

Factory Agent

Resource Agent

Scheduling Agent

Dispatching Agent

Information Agent

Information Agent

User Agent

User Agent

Enterprise Wide

Per Facility

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Agent Characteristics• Dynamic: Each agent performs its functions asynchronously in response to

events as they occur, modifying its behavior as required.

• Goal Directed: can dynamically construct plans in response to events and adapt

its plans to new situations.

• Intelligent: Each agent is an “expert” in its function.

• Least Commitment: The precision with which decisions are made should be

inversely proportional to the degree of uncertainty.

• Cooperative: Can cooperate with other agents in finding a solution.

• Interactive: May work with people to solve a problem - Intelligent Assistants. It

can respond to queries and explains its decisions.

• Entrusted: Aware of their rights and obligations and therefore trusted.

10

Collaboration

• Cultural Assumption: To enable agents to collaborate, we must make assumptions about how their decisions can be influenced, we call this the "cultural assumption”

Functional Agent

Customer

Management

Market

Operations

• Agents influence each others behavior by communicating:

Goals: Order Acquisition to Assembly Plant:

"Commit 100 yellow widgets on July 14 to

mfg order 49825."

Constraints: on how goals are to be achieved

"Maximum price for the 100 widgets is

$3/widget."

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Agent Architecture

Coordination

Communication

Knowledge Management

Information Distribution

Obligation

Management

Constraint-Based Reasoning

Conversation

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Coordination Services

• An organization is a set of agents playing roles constrained by mutual obligations, permissions, interdictions (OPI).

• Obligations triggered by communications in specified situations, create goals in the obliged party.– Incurs costs if not satisfied.– Contradictory obligations exist.

• An agent's behavior is determined by plans assigned to its role constrained by obligations, permissions, interdictions and the local situation.

13

Coordination Plans

• Agents may carry on multiple, multiple conversations with other agents. The framework includes:– conversation objects (both generic classes and instances),

– conversation rules,

– conversation continuation rules,

– error recovery rules, and

– multiple conversation management.

• Coordination plans include both communication with other agents, and invocation of local problem solving methods.

propose/

/reject

reject/

counter/

/counteraccept/accept

/accept

/satisfy

/fail

1

2 3

5

67

4

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Supply Chain Example

MB-Plant CBOX-Plant

SYS-Plant

DC-US DC-GER

Customers-US Customers-GER

CPU-Chip

Plastic-Board

Components Disks

Memory

Keyboards

Monitors

Power-supplies

Materials Production Dispatching

Planning

Mother-Board Computer-Box

Mother-BoardComputer-US Computer-GER

Distr ibution Center

Supplier / Customer Agent

Plant Agent

Workstation Agent

Bin (One agent manages all)

Memory

DC-US

Production

- 40 Agents- 100 week simulation- Thousands messageexchanged - Thousands conversationscreated<1h run time- Wealth of data collectedand displayed graphically

- Study of coordination protocolsthat handle unexpected events- Quantitative evaluations of these protocols

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Benefits

• A vision of how information systems will be structured in the future.– Architecture clearly identifies the differing roles of

function, information and user access

– Agents may dynamically respond to change, coordinating their responses with other agents

– Information is distributed to function agents automatically

– Information agents manage the evolution of information

– Users may tap into other agents, to browse, visualize and change information, limited by their authority

16

Agent Problem Solving Reqts

Every functional agent must be able to:

• reason about constraints and optimize a set of goals

• maximize enterprise flexibility by making "least commitment" decisions, i.e., maintaining alternatives as long as possible

• reveal its goals and constraints when necessary

• modify/relax its goals and constraints as part of the negotiation process

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Constraint-Directed Reasoning

• In the last 15 years, a new problem solving paradigm has emerged: Constraint-Directed Reasoning

• It is able to consider the myriad of constraints that exist in the organization and construct plans/schedules that satisfy constraints and optimize goals.

• It is able to revise these solutions in real-time as changes occur in the market and organization.

• It is able to consider tradeoffs among goals/constraints an relax constraints when necessary.

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Key Concept

• Identify the constraint that dominates - and deal with it!

Advanced Planning

Marketing

Controler

Tooling

Personnel

Materials

Production Status

Preferences

cutting fluid arrived

run machineat half speed

Joeis ill

toolIsn't ready

cut costmeet thedue date

use facility 1

Scheduling Agent

Maintenance

Prod. Eng.Cell 1

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Constraint Graph

• An integrated representation of all of the variables, e.g., activity start times, resource assignments, etc., and their constraints.

Task 1

Task 2= Precedence Constraint

= Resource Constraint

Due Date

Utility

No Weekends

Perturbation

ST ET

R1,R2

Solution: An assignment of values to every variable such that all constraints are satisfied.

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How it Works

•Remove alternatives that do not satisfy the constraints (Constraint Propagation)

•Determine what makes the problem difficult (Measure Textures)

•Identify the most critical constraint and make a decision (Opportunistic Commitment)

•Backtrack if dead end found (Retraction)

Successive Refinement Complete

SchedulePartial

Schedule

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Step 1: Constraint Propagation

• The domain of a variable may be reduced depending on its linkage to another variable via a constraint

End Time1

Start Time2

Activity 1 Activity 2Before

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Step 2: Select Decision Point

• Measure Problem Textures: constraint graph properties (e.g., Contention, Reliance)

• Identify Critical Constraint (Opportunism)

Task 1

Task 2

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Step 3: Commitment

Least commitment decision maintains as many alternatives as long as possible.• Assign/remove resource• Assign/remove start time• Sequence two or more activities • Retract prior commitment

Task 1

Task 2

ConstraintPosting

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Least Commitment Decisions

• Degree of commitment may vary with domain uncertainty

• Allows for flexible local response to changeActivity1

Latest Finish Time

Earliest Start Time

R1 R2 R3

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Benefits

• Able to consider the myriad of constraints that exist in real domains

• Able to relax constraints when no feasible solution exists

• Able to negotiate constraints with other agents• Iterative improvement• Anytime performance

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Information Challenge

• Successful management of the supply chain, whether human or agent-based, requires an operating model of the enterprise that is:– Understood and shared by all participants

– Able to answer the questions necessary to operate the enterprise, and

– As complete, correct and up-to-date as needed.

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Barrier

• The piecemeal development of information systems has led to systems, that are inter-connected, but cannot communicate because they do not share the same data models.

• ERP products have begun to address this problem, but only within a corporation.

Operations, employees, material

Activities, personnel, resources

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Barrier

• Much of what we want to know is not represented explicitly in a database, but can be derived from it.

• SQL helps but does not solve the problem, especially if answers have to be deduced from the data

• Cost of writing programs to derive answers to users' questions is very high.

??What % of the cost of my SKUs will reach their expiry date by friday?

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Is the Internet A Panacea?

• Some believe the Internet solves this problem.– Wrong: Web standards say nothing about content

standards

• Some believe that XML is the solution– Possibly, but most likely a Pandora’s Box unless

standards are quickly enforced!

• What should be standardized?

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Enterprise Model

• An Enterprise Model is a representation, both definition and description, of the structure, processes, resource and information of an identifiable business, government, or other organizational system.

• The goal of an enterprise model is to achieve model-driven enterprise design and operation.

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Enterprise Modeling Goals

• To provide an object library that is a shareable, reusable representation of supply chain information and knowledge.

• To define the objects in a precise manner so that it is consistently applied across domains and interpreted by users

• To support supply chain tasks by enabling the answering of questions that are not explicitly represented in the model

• To support model visualization that is both intuitive, simple and consistent

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Solution: Ontology

• An Ontology is a formal description of entities, their properties and relations among entities.

• An ontology is a set of key distinctions necessary to support reasoning.

• It is generic across domains.

msf 29 Oct 91

Term

inol

ogy

Semantics

Symbology

• Data Dictionary • Object Library

• Definitions • Constraints

• ICONS

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Spoilage Axiom

Successor axiom for the fluent spoiled:

( a, r, s) holds(spoiled(r), do(a,)) ((¬holds(spoiled(r), ) a=spoilage(r)) holds(spoiled(r), ))

Precondition axiom:

quantity(s,r,q) enables(s,a)

(Poss(a, ) ¬holds(spoiled(r), ))

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Example Ontologies

Base Ontology:

Activity, State, Time, Causality

Resource

Quality Cost

Agility Organisation

Product

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Example

• Given– Crates, pallets, and warehouses of resources

• We should be able to answer questions like– How many crates of apples do we have in Warehouse-

1? How many overall?

– How many pallets contain these crates?

– How many apples per crate? How many per pallet? How many per resource unit?

– Where do we have at least 10 boxes of bolts?

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Example

• Given– SKUs with code age and spoilage limits– Stock levels and min safety levels of SKUs

• We should be able to answer questions like– Will shiptment10 of oranges spoil if they are not

shipped before Friday?– Is any milk spoiled by Wednesday?– Is there any time at which the stock level for bolts at the

Scarborough factory reaches the minimum safety level?

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Benefits

• A shareable, reusable representation– Minimally, a language for communicating among

legacy agents

• A deductive database able to deduce anwers to common sense questions– Reduces the need for ad hoc report generators and

interfaces

• A standard for visualizing enterprise knowledge– A visual standard across enterprises

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Conclusion

• Most supply chain systems are based on technologies developed in the 60s and 70s

• Technological changes in the 80s and 90s enable us to create the next generation of supply chain management systems– Internet/Web

– Agency Theory

– Constraint-directed reasoning

– Enterprise Modeling/Ontologies