AGENT BASED SIMULATION APPROACH TO ASSESS SUPPLY CHAIN COMPLEXITY AND ITS IMPACT ON PERFORMANCE

Transcription

1 AGENT BASED SIMULATION APPROACH TO ASSESS SUPPLY CHAIN COMPLEXITY AND ITS IMPACT ON PERFORMANCE DR.-ING. MAYOLO LOPEZ, MBA Hamburg University of Technology Institute of Business Logistics and General Management Schwarzenbergstraße 95, Hamburg Tel.: Berlin - October 23 rd, 2013

2 Agenda INTRODUCTION LOGISTICS RELEVANCE & INNOVATION CONTENT SCIENTIFIC CONTRIBUTION & PRACTICAL RELEVANCE CONCLUSIONS

3 Introduction Companies aim to increase & sustain profitability Operational efficiency Market competitiveness Building competences Reducing uncertainty impact Manage by looking through the rearview mirrors Implement the appropriate analytics Transform data into useful information Traditional Supply Chain Management Improves individual business functions Assumes collective behaviour of uniform companies Supply chains are complex systems Structure and behaviour do not follow linear cause-effect relationships Dr.-Ing. Mayolo Lopez - 3

4 INTRODUCTION Research Objective and Structure Holistic assessment of complexity and its impact on performance across the supply chain RESEARCH QUESTIONS 1. What characterizes complex systems? 2. What is the impact of complexity on the supply chain performance? 3. How to assess and control the complexity impacting on the supply chain performance? 4. How to evaluate the complexity development and identify its causes on the supply chain? Literature Review Event Study-Abnormal Returns Systems Modelling Agent-based Simulation Dr.-Ing. Mayolo Lopez - 4

5 INTRODUCTION Theoretical Foundations Complexity Science FOUNDATIONS SYSTEMS THINKING Structural design + CYBERNETICS Emergent behaviour Complexity DEFINITION A systems attribute, which describes the events and outputs across different detail levels as abstracted by the observer. [Holistic definition based on complementary perspectives] + APPROACHES Algorithmic Describe structural configuration [Shannon, 1948] Complex Systems ANALYSIS Aggregated Deterministic Explain emergent behaviour [Manson & O Sullivan 2006, Schuh et al. 2008] Control complexity effects [Scuricini 1988, Battini et al. 2007] Dr.-Ing. Mayolo Lopez - 5

6 Agenda INTRODUCTION LOGISTICS RELEVANCE & INNOVATION CONTENT SCIENTIFIC CONTRIBUTION & PRACTICAL RELEVANCE CONCLUSIONS

7 Logistics Relevance The empirical findings * about the supply chain complexity sources and mitigation actions highlighted: Knowledge Gaps Requirements Complexity phenomena has not been assimilated by practitioners Partial analysis of supply chains Static assessment of complexity s impact on supply chain performance Improve understanding about complexity development Holistic approach as complex systems Dynamic analysis of supply chain performance * [based on a Secondary Data Event Study of Abnormal Returns ] Dr.-Ing. Mayolo Lopez - 7

8 Innovation Content Approach for modelling supply chains as complex systems Framework for complexity assessment System Definition + Algorithm Execution Agent based platform for the generic modelling of supply chain designs Computational tool for modelling, simulating, experimenting and assessing supply chain performance Supply Chain Building Blocks Spatiotemporal characterization and emulation of supply chain designs Geo-positioned agents linked by multi-modal transportation routes Dr.-Ing. Mayolo Lopez - 8

9 INNOVATION CONTENT Agent Based Model SUPPLY CHAIN BUILDING BLOCKS Agent Autonomous entities Goal oriented Operate asynchronously Policy driven Social and interactive [Fox et al. 2000, Nissen2001, Fromm 2004, Gilbert 2007] Agent Based Modelling System elements are represented as entities with discrete and heterogeneous behaviour Supply Chain Participants The agent s behaviour is characterized by the business functions included in their internal structure Supply Chain Networks [Macal & North 2007, Marks 2007] Dr.-Ing. Mayolo Lopez - 9

10 INNOVATION CONTENT Agents Structure and Alignment with Theory Supply Chain s Complex System Model Supply Chain Environment Agents Functions Plan Source Make Deliver Inventory Management SCOR processes relationships and SCM business functions Production Management Demand Transporter Generic Functions Source Make Procurement Logistics Material Handling Network Deliver Demand Management Sales Accounting [based on Mentzer et al. 2001, SCOR 2010] Dr.-Ing. Mayolo Lopez - 10

11 Agenda INTRODUCTION LOGISTICS RELEVANCE & INNOVATION CONTENT SCIENTIFIC CONTRIBUTION & PRACTICAL RELEVANCE CONCLUSIONS

12 THEORETICAL IMPLICATIONS Scientific Contribution Enhances algorithmic complexity frameworks (entropy based) in describing complexity Supports aggregated complexity frameworks in causality analyses Evaluates mitigation and control actions proposed by deterministic complexity frameworks Delivers a flexible platform that can integrate modules to analyse phenomena characterizing the supply chain system and its performance Foundations of future works aiming to integrate the strategic supply chain planning and analytic driven business models Dr.-Ing. Mayolo Lopez - 12

13 FRAMEWORK DEVELOPMENT Approach for Complexity Assessment Supply Chain Complex System Model & Algorithmic Complexity Assessment Material demand KPI SOURCE (SUPPLIERS) Material delivery (CUSTOMERS) Products delivery Delivery Performance (Focal company) KPI MAKE KPI Transporters Products demand KPI Transporters KPI DELIVER System attributes Variety and numerousness of performance states Performance states contribution to the element s goal Uncertainty of elements Network Relationships Serial Parallel Material flow Information flow Control cycle Dr.-Ing. Mayolo Lopez - 13

14 FRAMEWORK EXECUTION Holistic Assessment of Complexity Block I Block II Block III Block IV Block V Supply Chain Complexity Development Supplier A Supplier B Producer A Producer C Distributor A Customer A Supply chain design defines the structural relationships The elements performance states describe the emergent behaviour Effects Supply chain participant Impact ASSESSMENT & CONTROL Source Make Deliver Delivery Performance ACA (difference) Reference line (Zero) Material flow Information Flow Control cycle Causes Dr.-Ing. Mayolo Lopez - 14 Sources Observation (i)

15 MANAGERIAL IMPLICATIONS Practical Relevance Supports the strategic planning and decision-making process by enabling the evaluation of: Supply chain designs Business strategies Decision policies/rules Scenario analysis at company and network levels Sensitive characterized of complexity parameters Evaluates the dynamic control of inventory systems Assesses supply chain performance and its logistics indicators Enhances Business Intelligence (BI) dashboards by enabling the scenario and sensitivity analyses based on data (Big Data) collected from: Inside the organization Interactions within the supply chain Environment Simulation outputs

16 PRACTICAL RELEVANCE Supports the Strategic Decision-Making process Structural Features Multi-modal transportation Multi-product supply chains Number of agents and relationships Variety of agent types and relationships Local National Behaviour Features Business functions characterization Business policies and strategies Demand behaviour Glitches Transportation Lead-times Integrates Geographical Information Regional Evaluates supply chain designs Global Enables Scenario Analysis Inventory Forecast Method Single Sourcing Make to Stock Supplier Selection Producer Capacity CO 2 factors Make to Order Multiple Sourcing Costs & Prices Bill of Materials Retrospective Demand Dr.-Ing. Mayolo Lopez - 16

17 PRACTICAL RELEVANCE Holistic Performance Assessment Process Level Functional level Dr.-Ing. Mayolo Lopez - 17

18 PRACTICAL RELEVANCE Holistic Performance Assessment Overview at Network Level Overview at Participant Level Dr.-Ing. Mayolo Lopez - 18

19 PRACTICAL RELEVANCE Scenario and Sensitivity Analyses Scenario A = Inventory Control System Scenario B = Make to Order = Demand Performance overview Dr.-Ing. Mayolo Lopez - 19

20 Agenda INTRODUCTION LOGISTICS RELEVANCE & INNOVATION CONTENT SCIENTIFIC CONTRIBUTION & PRACTICAL RELEVANCE CONCLUSIONS

21 Conclusions This is neither the start nor the end, but a contribution towards... Further research Complementary phenomena analysis Optimization based on simulation models Agent s proactive behaviour Include additional SCOR processes Business Intelligence for improving the strategic decision-making process Realistic modelling and simulation of supply chain phenomena Dynamic supply chain analysis and optimization Interactive business game for managerial training in decision-making and policy design Dr.-Ing. Mayolo Lopez - 21

22 VIELEN DANK FÜR IHRE AUFMERKSAMKEIT! DR.-ING. MAYOLO LOPEZ, MBA Hamburg University of Technology Institute of Business Logistics and General Management Schwarzenbergstraße 95, Hamburg Tel.: Berlin - October 23 rd, 2013