1. Multi-agent model for a complex supply chain: Case of a Paper Tissue Manufacturer by Partha Datta Martin Christopher & Peter Allen Cranfield University School of Management

2. Complex Systems & Supply Networks Need for new supply chain modelling framework Agent Based Modelling Framework Case Study Application of the Framework – Results Conclusion Contribution Contents

3. Complex systems & Supply Networks Complex Systems Consist of different interacting elements, The elements may be very different and change with time The elements have some degree of internal autonomy Supply Networks A supply chain is a network of organizations Firms in seemingly unrelated industries can compete for common resources Firms keep on moving in and out of network Firms have own decision making ability

4. Complex systems & Supply Networks Complex Systems Elements are coupled in a non-linear fashion Behavioural patterns created through myriads of interactions Supply Networks A small fluctuation at the downstream can cause large oscillations upstream (BULL-WHIP) Collective behaviours emerge beyond the control of any single firm

5. Existing supply chain modelling techniques Existing network planning tools are deterministic Optimization models are offline and brittle Strongly focus on physical transactions Investigate various supply chain activities in an isolated way Historically modelling has been top-down Abstraction and assumptions limit representing reality - None of these approaches is rich enough to capture the dynamical behaviour of the entire supply network

6. Need for a new modelling framework Is bottom-up, starts by identifying the most basic building blocks – the agents Should be able to model the independent control structures of each agent Should be able to model the mutual attuning of activities based on interdependence Should reveal and aim to integrate the material structure, the information structure, the decision structure and the strategic structure

7. Agent Based Modelling [ABM] Provides a method for integrating the entire supply chain as a network system of independent echelons (Gjerdrum et al, 2001) Can represent many actors, their intentions, internal decision rules and their interactions (Holland, 1995 and 1998; Axelrod, 1997; Prietula, 2001) –Agents have some autonomy –Agents are interdependent –Agents follow simple rules

8. Agent Based Model Building Blocks

9. Decision Making Stage – –1.Target finished goods inventory determination –2.Ranking of products for determining priority for production Functioning Stage – –1. Production, Planning & Control : based on the forecast demand during approximate production time window, fixed production rate for each product, –2. Palletisation & Delivery : delivery to central warehouse in specified pallet types Production Factory agent

10. Agent Based Model Building Blocks Decision Making Stage – –1.Safety and Target Stock Determination, –2.Replenishment Policy Adoption, Functioning Stage – –1. Order Management : aggregates all demands, forecasts –2. Goods Dispatch Management : availability based partial fulfilment of orders –3. Finished Goods Inventory Management : replenishment of inventory based on target inventory and reorder point levels based on safety stock levels estimated at decision making level Distribution centre agents

11. Case Study – A Paper Tissue Manufacturing Company Factory Agent Customer Agents Distribution Centre Agent Distribution Centre Agents Delay Objects

12. The Complex Supply Network - Details Varying lead times for different countries Different pallet size requirements Different product portfolio requirements Some products are demanded by single country Different products have different demand patterns All products share the same machine resource for production Different products have different times of set-up

13. Bottlenecks – “Marketing driven” production – not “market driven” Mismatch between real demand and forecast - Higher repalletisation costs - Lack of balance in production - Correct products not in stock at right place No common KPIs

14. Data Forecast and Sales data collected during period from 1 st January to 31 st December 2004 Forecast data is monthly and Sales is approximated by the daily delivery amounts Data on daily inter-company deliveries and delivery to customers are collected Theoretical and Empirical distributions are fitted to the sales data to generate replications for simulation

15. Additional Data Production Rates Production Categories for change-over Change-over times Swiss Sales Data Maximum and Minimum Production Cycle Times for some products Pallet Size Constraints Product, Market, Supplier, Pallet-size combination Delivery Lead Times

16. Applying the framework

17. The functioning and decision making stages Rationing and priority based on increasing order size order backlogs have the highest priority Ordering is based on forecast, forecast error, stock position and forecast bias Order quantity is decided based on each RDC agent’s - knowledge of central warehouse stock - perception of stock wear out and demand variability Use of global information for allocating time for production Priority for production is decided based on - forward cover of product codes in RDCs and central warehouse - absorptive power of product codes

18. Model Validation The difference between Modelled (83838) and Actual (84124) Total Average Network Inventory across 8 codes for the stipulated time period (for which actual data was obtained) found to be within 0.34% of Actual.

19. Performance Measures Customer Service Level (CSL) Production Change-Over Average Inventory at each regional distribution centre Total Network Inventory

20. Model Performance Vs Actual System Performance (Over-all/Global performance) The model shows improved inventory and CSL performance in a balanced manner across the supply chain The total number of changeovers is 80 as compared to 132 in actual case The model idle time = 22 days, actual system idle time = 47 days Repalletisation Modelled value = 197379 as compared to actual value of 202606, a reduction of 2.6% The model also produced better balance in allocating total production time across codes with respect to actual demand

21. Conclusion Firm's operations must be driven by current customer requests Methodology to understand the key issues essential for improving operational resilience in a complex production distribution system - knowing earlier - managing-by-wire - designing a supply network as a complex system - production and dispatching capabilities from the customer request back

22. Contribution Studies and provides methods for improving the management of uncertainty and thereby improving resilience in complex multi-product, multi-country real-life production distribution system Provides a generic agent-based computational framework for effective management of complex production distribution systems.

23. Scope for further research Use of market data to include effects of competition in different country markets Extension to include raw material supply chain Inclusion of cost data to understand various trade- offs

24. Why Supply Chain Management is so difficult? Nonlinearities – 1. Reliance on forecasts at each stage for basing decisions 2. Different demand patterns of different products over time 3. Different constraints (lot-sizing, transport capacity etc.) 4. Different supply chain structures Results into upstream demand amplification (Bull-whip)

25. Actual demand, actual average stock and actual total time of production at Koblenz

26. Actual Stock Levels

27. Actual Stock Levels at Koblenz and Ede for product X9

28. The information and material flow - Actual

29. Changing Premises of Industrial Organisation Source: www.dti.gov.ukwww.dti.gov.uk

30. Modelled System vs Actual System Performance

32. Stock at Koblenz

33. Balance in Factory

34. A Complex System includes the “system you see” and the hidden processes that change it This is not just asking how a system runs, but WHY it exists. It must express synergetic behaviour of its components in that environment: A “Complex System” creates and destroys transitory traditional Systems….. Beginning Later... System 1 1 type System 2 2 types System 3 4 types System 4 8 types System 5 6 types Structural Change occurs... Instabilities Time

35. Production Planning & Control