Multi agent model for a complex supply chain case of a paper tissue manufacturer
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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. Contents. Complex Systems & Supply Networks Need for new supply chain modelling framework

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Multi-agent model for a complex supply chain: Case of a Paper Tissue Manufacturer

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Multi-agent model for a complex supply chain: Case of a Paper Tissue Manufacturer


Partha Datta

Martin Christopher &

Peter Allen

Cranfield University School of Management


  • Complex Systems & Supply Networks

  • Need for new supply chain modelling framework

  • Agent Based Modelling Framework

  • Case Study

  • Application of the Framework – Results

  • Conclusion

  • Contribution

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

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

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

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

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

Agent Based Model Building Blocks

Agent Based Model Building Blocks

Production Factory agent

  • 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

Agent Based Model Building Blocks

Distribution centre agents

  • 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

Case Study – A Paper Tissue Manufacturing Company

Distribution Centre Agents

Customer Agents

Delay Objects

Factory Agent

Distribution Centre Agent

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

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


  • Forecast and Sales data collected during period from 1st January to 31st 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

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

Applying the framework

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


    - absorptive power of product codes

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.

Performance Measures

  • Customer Service Level (CSL)

  • Production Change-Over

  • Average Inventory at each regional distribution centre

  • Total Network Inventory

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


  • 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


  • 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.

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

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)

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

Actual Stock Levels

Actual Stock Levels at Koblenz and Ede for product X9

The information and material flow - Actual

Changing Premises of Industrial Organisation


Modelled System vs Actual System Performance

Modelled System vs Actual System Performance

Stock at Koblenz

Balance in Factory





System 1

1 type


System 2

2 types

System 3

4 types

System 4

8 types


System 5

6 types


A “Complex System” creates and destroys transitory traditional Systems…..

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:

Production Planning & Control

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