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Managing the Supply Chain - An AI Perspective


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Managing the Supply Chain - An AI Perspective

  1. 1. Managing the Supply Chain An AI Perspective <ul><li>Mark S. Fox </li></ul><ul><li>Mihai Barbuceanu, Chris Beck, Andrew Davenport, </li></ul><ul><li>Mike Gruninger </li></ul><ul><li>Enterprise Integration Laboratory </li></ul><ul><li>University of Toronto </li></ul><ul><li>4 Taddle Creek Road, Toronto, Ontario M5S 3G8 </li></ul><ul><li>tel: 1-416-978-6823 fax: 1-416-971-2479 internet: </li></ul><ul><li> </li></ul>
  2. 2. The Internet Effect <ul><li>The Internet has precipitated a major change in how we view retailing and the supply chain </li></ul><ul><ul><li>Purchasing is becoming tightly integrated with fulfillment </li></ul></ul><ul><ul><li>Customers expect instantaneous response </li></ul></ul><ul><ul><ul><li>Produce the product </li></ul></ul></ul><ul><ul><ul><li>Tell me when it will be produced </li></ul></ul></ul><ul><ul><ul><li>Tell me why it cannot be produced </li></ul></ul></ul>
  3. 3. Supply Chain Requirements <ul><li>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: </li></ul><ul><li>Available - Informed - Flexible </li></ul><ul><ul><li>Aware - Responsive - Smart </li></ul></ul>
  4. 4. Problem <ul><li>Earlier ERP systems made the transition from static, batch oriented systems, to be more dynamic by incorporating messaging </li></ul><ul><li>Never the less, these systems are still largely static </li></ul><ul><ul><li>Most modules run on a batch basis or static sequence </li></ul></ul><ul><ul><li>Dynamic responses usually left to the human decision maker </li></ul></ul><ul><li>We need to re-think how we manage the dynamics of the supply chain </li></ul><ul><ul><li>Information technology is making it possible to manage the supply chain in ways not possible ten years ago. </li></ul></ul>
  5. 5. Supply Chain Architecture <ul><li>A network of intelligent software modules that together dynamically manage the supply chain. Each module </li></ul><ul><ul><li>is an expert at its task, thereby optimizing its goals </li></ul></ul><ul><ul><li>coordinates its decisions with other modules, thereby optimizing supply chain wide goals </li></ul></ul><ul><ul><li>quickly responds to changes in cooperation with other modules </li></ul></ul>
  6. 6. Information Technology Enablers <ul><li>Four technologies are having a significant impact on the achievement of this vision: </li></ul><ul><ul><li>The Internet/Web </li></ul></ul><ul><ul><li>Intelligent Agents </li></ul></ul><ul><ul><li>Constraint Directed Reasoning </li></ul></ul><ul><ul><li>Enterprise Models/Ontologies </li></ul></ul>
  7. 7. Intelligent Agents <ul><li>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. </li></ul><ul><li>But, these systems will have to be endowed with operating characteristics a generation beyond what is available today. </li></ul><ul><ul><li>We have to strike FIIR into our systems: Fast, Informed, Intelligent Response. </li></ul></ul><ul><li>We call this software &quot;intelligent agents” </li></ul>
  8. 8. Supply Chain Management Agents Enterprise Wide Per Facility
  9. 9. Agent Characteristics <ul><li>Dynamic: Each agent performs its functions asynchronously in response to events as they occur, modifying its behavior as required. </li></ul><ul><li>Goal Directed : can dynamically construct plans in response to events and adapt its plans to new situations. </li></ul><ul><li>Intelligent: Each agent is an “expert” in its function. </li></ul><ul><li>Least Commitment : The precision with which decisions are made should be inversely proportional to the degree of uncertainty. </li></ul><ul><li>Cooperative: Can cooperate with other agents in finding a solution. </li></ul><ul><li>Interactive: May work with people to solve a problem - Intelligent Assistants. It can respond to queries and explains its decisions. </li></ul><ul><li>Entrusted: Aware of their rights and obligations and therefore trusted. </li></ul>
  10. 10. Collaboration <ul><li>Cultural Assumption: To enable agents to collaborate, we must make assumptions about how their decisions can be influenced, we call this the &quot;cultural assumption” </li></ul>Functional Agent Customer Management Market Operations <ul><li>• Agents influence each others behavior by communicating: </li></ul><ul><ul><li>Goals : Order Acquisition to Assembly Plant: </li></ul></ul><ul><ul><ul><li>&quot;Commit 100 yellow widgets on July 14 to mfg order 49825.&quot; </li></ul></ul></ul><ul><ul><li>Constraints: on how goals are to be achieved </li></ul></ul><ul><ul><ul><li>&quot;Maximum price for the 100 widgets is $3/widget.&quot; </li></ul></ul></ul>
  11. 11. Agent Architecture Coordination Communication Knowledge Management Information Distribution Obligation Management Constraint-Based Reasoning Conversation
  12. 12. Coordination Services <ul><li>An organization is a set of agents playing roles constrained by mutual obligations, permissions, interdictions (OPI). </li></ul><ul><li>Obligations triggered by communications in specified situations, create goals in the obliged party. </li></ul><ul><ul><li>Incurs costs if not satisfied. </li></ul></ul><ul><ul><li>Contradictory obligations exist. </li></ul></ul><ul><li>An agent's behavior is determined by plans assigned to its role constrained by obligations, permissions, interdictions and the local situation. </li></ul>
  13. 13. Coordination Plans <ul><li>Agents may carry on multiple, multiple conversations with other agents. The framework includes: </li></ul><ul><ul><li>conversation objects (both generic classes and instances), </li></ul></ul><ul><ul><li>conversation rules, </li></ul></ul><ul><ul><li>conversation continuation rules, </li></ul></ul><ul><ul><li>error recovery rules, and </li></ul></ul><ul><ul><li>multiple conversation management. </li></ul></ul><ul><li>Coordination plans include both communication with other agents, and invocation of local problem solving methods. </li></ul>
  14. 14. Supply Chain Example
  15. 15. Benefits <ul><li>A vision of how information systems will be structured in the future. </li></ul><ul><ul><li>Architecture clearly identifies the differing roles of function, information and user access </li></ul></ul><ul><ul><li>Agents may dynamically respond to change, coordinating their responses with other agents </li></ul></ul><ul><ul><li>Information is distributed to function agents automatically </li></ul></ul><ul><ul><li>Information agents manage the evolution of information </li></ul></ul><ul><ul><li>Users may tap into other agents, to browse, visualize and change information, limited by their authority </li></ul></ul>
  16. 16. Agent Problem Solving Reqts <ul><li>Every functional agent must be able to: </li></ul><ul><ul><li>reason about constraints and optimize a set of goals </li></ul></ul><ul><ul><li>maximize enterprise flexibility by making &quot; least commitment &quot; decisions, i.e., maintaining alternatives as long as possible </li></ul></ul><ul><ul><li>reveal its goals and constraints when necessary </li></ul></ul><ul><ul><li>modify/relax its goals and constraints as part of the negotiation process </li></ul></ul>
  17. 17. Constraint-Directed Reasoning <ul><li>In the last 15 years, a new problem solving paradigm has emerged: Constraint-Directed Reasoning </li></ul><ul><li>It is able to consider the myriad of constraints that exist in the organization and construct plans/schedules that satisfy constraints and optimize goals. </li></ul><ul><li>It is able to revise these solutions in real-time as changes occur in the market and organization. </li></ul><ul><li>It is able to consider tradeoffs among goals/constraints an relax constraints when necessary. </li></ul>
  18. 18. Key Concept <ul><li>Identify the constraint that dominates - and deal with it! </li></ul>
  19. 19. Constraint Graph <ul><li>An integrated representation of all of the variables, e.g., activity start times, resource assignments, etc., and their constraints. </li></ul>Task 1 Task 2 ST ET R1,R2 Solution: An assignment of values to every variable such that all constraints are satisfied. = Precedence Constraint = Resource Constraint Due Date Utility No Weekends Perturbation
  20. 20. How it Works <ul><li>Remove alternatives that do not satisfy the constraints (Constraint Propagation) </li></ul><ul><li>Determine what makes the problem difficult (Measure Textures) </li></ul><ul><li>Identify the most critical constraint and make a decision (Opportunistic Commitment) </li></ul><ul><li>Backtrack if dead end found (Retraction) </li></ul>Successive Refinement Complete Schedule Partial Schedule
  21. 21. Step 1: Constraint Propagation <ul><li>The domain of a variable may be reduced depending on its linkage to another variable via a constraint </li></ul>End Time 1 Start Time 2 Activity 1 Activity 2 Before
  22. 22. Step 2: Select Decision Point <ul><li>Measure Problem Textures : constraint graph properties (e.g., Contention, Reliance) </li></ul><ul><li>Identify Critical Constraint (Opportunism) </li></ul>Task 1 Task 2
  23. 23. Step 3: Commitment <ul><li>Least commitment decision maintains as many alternatives as long as possible. </li></ul><ul><ul><li>Assign/remove resource </li></ul></ul><ul><ul><li>Assign/remove start time </li></ul></ul><ul><ul><li>Sequence two or more activities </li></ul></ul><ul><ul><li>Retract prior commitment </li></ul></ul>Task 1 Task 2 Constraint Posting
  24. 24. Least Commitment Decisions <ul><li>Degree of commitment may vary with domain uncertainty </li></ul><ul><li>Allows for flexible local response to change </li></ul>Activity 1 Latest Finish Time Earliest Start Time R 1 R 2 R 3
  25. 25. Benefits <ul><li>Able to consider the myriad of constraints that exist in real domains </li></ul><ul><li>Able to relax constraints when no feasible solution exists </li></ul><ul><li>Able to negotiate constraints with other agents </li></ul><ul><li>Iterative improvement </li></ul><ul><li>Anytime performance </li></ul>
  26. 26. Information Challenge <ul><li>Successful management of the supply chain, whether human or agent-based, requires an operating model of the enterprise that is: </li></ul><ul><ul><li>Understood and shared by all participants </li></ul></ul><ul><ul><li>Able to answer the questions necessary to operate the enterprise, and </li></ul></ul><ul><ul><li>As complete, correct and up-to-date as needed. </li></ul></ul>
  27. 27. Barrier <ul><li>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. </li></ul><ul><li>ERP products have begun to address this problem, but only within a corporation. </li></ul>
  28. 28. Barrier <ul><li>Much of what we want to know is not represented explicitly in a database, but can be derived from it. </li></ul><ul><li>SQL helps but does not solve the problem, especially if answers have to be deduced from the data </li></ul><ul><li>Cost of writing programs to derive answers to users' questions is very high. </li></ul>
  29. 29. Is the Internet A Panacea? <ul><li>Some believe the Internet solves this problem. </li></ul><ul><ul><li>Wrong : Web standards say nothing about content standards </li></ul></ul><ul><li>Some believe that XML is the solution </li></ul><ul><ul><li>Possibly, but most likely a Pandora’s Box unless standards are quickly enforced! </li></ul></ul><ul><li>What should be standardized? </li></ul>
  30. 30. Enterprise Model <ul><li>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. </li></ul><ul><li>The goal of an enterprise model is to achieve model-driven enterprise design and operation. </li></ul>
  31. 31. Enterprise Modeling Goals <ul><li>To provide an object library that is a shareable, reusable representation of supply chain information and knowledge. </li></ul><ul><li>To define the objects in a precise manner so that it is consistently applied across domains and interpreted by users </li></ul><ul><li>To support supply chain tasks by enabling the answering of questions that are not explicitly represented in the model </li></ul><ul><li>To support model visualization that is both intuitive, simple and consistent </li></ul>
  32. 32. Solution: Ontology <ul><li>An Ontology is a formal description of entities, their properties and relations among entities. </li></ul><ul><li>An ontology is a set of key distinctions necessary to support reasoning. </li></ul><ul><li>It is generic across domains. </li></ul>
  33. 33. Spoilage Axiom <ul><li>Successor axiom for the fluent spoiled : </li></ul><ul><li>(  a, r, s) holds(spoiled(r), do(a,  ))  </li></ul><ul><li>((¬holds(spoiled(r),  )  a=spoilage(r))  </li></ul><ul><li>holds(spoiled(r),  )) </li></ul><ul><li>Precondition axiom: </li></ul><ul><li>quantity(s,r,q)  enables(s,a)  </li></ul><ul><li>(Poss(a,  )  ¬holds(spoiled(r),  )) </li></ul>
  34. 34. Example Ontologies
  35. 35. Example <ul><li>Given </li></ul><ul><ul><li>Crates, pallets, and warehouses of resources </li></ul></ul><ul><li>We should be able to answer questions like </li></ul><ul><ul><li>How many crates of apples do we have in Warehouse-1? How many overall? </li></ul></ul><ul><ul><li>How many pallets contain these crates? </li></ul></ul><ul><ul><li>How many apples per crate? How many per pallet? How many per resource unit? </li></ul></ul><ul><ul><li>Where do we have at least 10 boxes of bolts? </li></ul></ul>
  36. 36. Example <ul><li>Given </li></ul><ul><ul><li>SKUs with code age and spoilage limits </li></ul></ul><ul><ul><li>Stock levels and min safety levels of SKUs </li></ul></ul><ul><li>We should be able to answer questions like </li></ul><ul><ul><li>Will shiptment10 of oranges spoil if they are not shipped before Friday? </li></ul></ul><ul><ul><li>Is any milk spoiled by Wednesday? </li></ul></ul><ul><ul><li>Is there any time at which the stock level for bolts at the Scarborough factory reaches the minimum safety level? </li></ul></ul>
  37. 37. Benefits <ul><li>A shareable, reusable representation </li></ul><ul><ul><li>Minimally, a language for communicating among legacy agents </li></ul></ul><ul><li>A deductive database able to deduce anwers to common sense questions </li></ul><ul><ul><li>Reduces the need for ad hoc report generators and interfaces </li></ul></ul><ul><li>A standard for visualizing enterprise knowledge </li></ul><ul><ul><li>A visual standard across enterprises </li></ul></ul>
  38. 38. Conclusion <ul><li>Most supply chain systems are based on technologies developed in the 60s and 70s </li></ul><ul><li>Technological changes in the 80s and 90s enable us to create the next generation of supply chain management systems </li></ul><ul><ul><li>Internet/Web </li></ul></ul><ul><ul><li>Agency Theory </li></ul></ul><ul><ul><li>Constraint-directed reasoning </li></ul></ul><ul><ul><li>Enterprise Modeling/Ontologies </li></ul></ul>