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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

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

slide2

Contents

  • 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 & 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 networks1
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 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
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
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 blocks1
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 blocks2
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
Case Study – A Paper Tissue Manufacturing Company

Distribution Centre Agents

Customer Agents

Delay Objects

Factory Agent

Distribution Centre Agent

the complex supply network details
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
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
slide14
Data
  • 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
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
the functioning and decision making stages
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

model validation
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
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
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
conclusion
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

contribution
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.
scope for further research
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
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)
a complex system includes the system you see and the hidden processes that change it

Structural

Change

occurs...

Beginning

System 1

1 type

Instabilities

System 2

2 types

System 3

4 types

System 4

8 types

Time

System 5

6 types

Later...

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: