Technische Universität München © Prof. Dr. H. Krcmar An Ontology-based Platform to Collaboratively Manage Supply Chains Tobias Engel, Manoj Bhat, Vasudhara Venkatesh, Suparna Goswami, Helmut Krcmar

Technische Universität München 2 Problems in Supply Chain Management ► Supply Chain (SC) Collaboration is on a low level. Some reasons: ► Missing solutions for process integration and information sharing ► Different levels of knowledge (how to use tools, about the supply chain itself, etc.) Cognizant (2014); Ooi et al. (2011) ► Studies reflect the need for improved collaborative and integrated supply chain planning, calculation and execution systems. Cognizant (2014); Lori Simonson, IBM (2010) ► Template based approaches for reusing knowledge and past experiences in SC projects lack an extensible conceptual model. Franzese et al. (2006); Ruiteret et al. (2000) ► MS Excel is often used to perform calculations, while Excel suffers from draw-backs

Technische Universität München 3 Agenda ► Introduction ► Definition ► Research Method ► iSupply Platform & Ontology ► Implications ► Limitations & Future Research

Technische Universität München 4 Introduction ► There is a growing interest in applying knowledge based approaches in the field of Supply Chain Management. Marra et al.(2012); Samuel et al.(2011) ► SC Strategic Goals: Agility, adaptability, and alignment can be achieved through knowledge flows and knowledge sharing process. Whitten et al.(2012) ► Knowledge management (KM) is a systematic approach to capture, structure, disseminate, manage, and reason about the knowledge. Douligeries and Tilipakis (2006); ► Supply chain (domain) ontologies play a major role in KM. ► However, existing SC ontology approaches: low granularity level Douligeries and Tilipakis (2006); Huang and Lin (2010) ► SC ontology problems: dynamics, complexity, and heterogeneity. Fayez et al. (2005)

Technische Universität München 5 Definition ► Ontology: explicit formal specification of concepts in a domain and relations among them. ► Individuals: instances/objects ► Classes: sets, collections, concepts, types ► Attributes: aspects, properties, features, parameters (possible for both, classes and instances) ► Relations: relationship between classes ► Restrictions: descriptions of what must be true ► Rules: statements in the form of an if-then sentence

Technische Universität München 6 Research Method ► Technology-oriented design approach to create constructs, models, etc.  Relevance: Requirements from the Application Domain  Design Process: Ensure functionality, completeness, consistency, etc.  Rigor Cycle: Ground the building and evaluation process of the artifact

Technische Universität München 7 iSupply Ontology

Technische Universität München 8 iSupply Platform The ontology forms the core of the knowledge base, the simulation engine allows “what-if” analysis, the calculation model provides business logic and the diagram editor supports the perception of changes and requirements for the supply chain experts and practitioners.

Technische Universität München 9 Components of iSupply platform (1) ► Diagram Editor

Technische Universität München 10 Components of iSupply platform (2) ► Calculation and Simulation models

Technische Universität München 11 Implications ► Architectural design of an ontology-based, knowledge-assisted SCM platform ► Practitioners: reuse SC knowledge to handle dynamic, heterogenic and complex supply chain environments. ► Reusing domain knowledge to reduce the time and effort involved in SC projects. ► The architecture is extensible and configurable ► flexibility to enhance the system by adding/replacing components.

Technische Universität München 12 Limitations & Future Research ► Limitations ► No focus on the annotation of heterogeneous information from repositories and mapping of heterogeneous ontologies. ► Scalability of platform: not yet tested in real life industry projects. ► Future Research ► Test platform in real projects ► Evaluate and further develop the platform ► Link platform to ERP systems and use it as Interorganisational Information System

Technische Universität München © Prof. Dr. H. Krcmar Thank you

Technische Universität München 14 Backup

Technische Universität München 15 Research Gap Supply Chain Management Supply Chain Planning Logistics Mgmt Procurement Supply Chain Strategy Assets Mgmt Information Management Manufactures Suppliers Customers Stores Warehouses Collaboration Geographically Dispersed Data Sources Interoperability The focus on the knowledge engineering aspects including knowledge structuring, reasoning and reuse is missing in the existing SCM software packages. We propose an ontology-based, knowledge-assisted platform to collaboratively create, adapt and steer supply chain networks.

Technische Universität München 16 Usage scenario Start User: Log in User: Create New Project User: Add Context parameters System: Retrieve similar projects User: Import Supply chain scenarios User: Update Supply chain model User: Run simulation User: Analyze results User: Tag Project as complete End Satisfactory result ? User: Update Knowledge Base Update KB? End No Yes No Jump-start Project Enable Knowledge Reuse

Technische Universität München 17 iSupply Platform Integration Bus Knowledge Base Ontology Supply Chain Context Logistics Database Diagram Editor Collaboration Engine Tool Adapters Calculation ModelSimulation EngineERP Wiki Poll Discussion Forum