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

Multi-agent model for a complex

supply chain: Case of a Paper

Tissue Manufacturer

byPartha 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

Contents

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

CustomerOrders

Supplier RDC Agentor Factory Agent

CSL: customer service level, FGI: Finished Goods Inventory

Figure 1: The General Agent structure used in the model

CustomerAgent

Variables & ParametersSales, Forecast, Production Capacity, SKU list, Lead Time, Packing constraints

Decision Making StageDetermine RDC preferences for dispatching materials, Determine SKU preference for production - both based on a combination of forward cover and inventory turnover (to avoid over-forecasting errors), Inventory targets based on CSL

Functioning Stage

Internal KPIsAverage Inventory, CSL, Sales backlogs

Network KPIsNetwork Average Inventory per SKU, CSL, Production set-up costs

Order Queu

Delivery Queue

Order Management

Delivery Management

Inventory Planning

Production Planning & Control

FGI

Production

Goods Inward

Agent Based Model Building Blocks

• 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

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

Case Study – A Paper Tissue Manufacturing Company

S2

E3

E3

E3

E3E5 E3/E5

E5

E5

E5

E5

Fig. 9. Supply Chain structure under study[ E3/E5/S2 are the pallet sizes ordered by the countries]

Koblenz Factory Central Warehouse at

Koblenz

load + ship

Repal to S2

FLINT UK order bank

load + ship

NIEDERBIPP CH order bank

load+shipLOGIS

CZ order bank

RU order bank

load+ship RUSSIA

order bankDE/NL/BE/CH/Nordic

+ DDXM France

VSEload+ship FR order bank

load+ship Marene IT order bank

load+ship ArceniegaES/PT order

bank

load+ship EDE order bank CH

Factory Agent

Customer Agents

Distribution Centre Agent

Distribution Centre AgentsDelay Objects

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

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

• 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

warehouse- 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.

Table 8a: Validation Results - Inventory Figures

Product Code RDC

Actual Model DifferenceWypall7122 UK 741 751 1.35%

Wypall7198 Koblenz 19784 19879 0.48%

Wypall7122 Niederbipp 195 175 10.26%

Kimcel7025 France 309 312 0.97%

Wypall7190 Italy 4032 3487 13.52%

RDC Average Inventory

Table 8b: Validation Results - Production Figures

Product Code

Actual Model Difference

Wypall7122 298 290 2.68%

Wypall7126 94 94 0.00%

Wypall7190 533 473 11.26%

Wypall7196 44 48 9.09%

Wypall7198 366 322 12.02%

Wypall7341 343 308 10.20%

Wypall7342 117 131 11.97%

Average Production Amounts

Performance Measures

• Customer Service Level (CSL)

• Production Change-Over• Average Inventory at each regional distribution

centre• Total Network Inventory

CSL =

H

H

T

ttn

T

ttn

D

AS

1,

1,

and ASn,t = min (In,t-1,Dn,t)

Where, ASn,t = actual sales in simulation n at time instance t Dn,t = demand in simulation n at time instance t In,t = ending stock level in simulation n at time t n = simulation number TH = simulation time horizon

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

• 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

• 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

Nomenclature

Product Specifications

Processes

Storage points

Tasks/Actions

Stages in process

interdependencies

particular actions

Central Planning

Actual Customer

Order

Order acceptance

Order Bank

Sales Forecast

Monthly RDC stock plan

Yearly production

budget

Rough planning

basesheet production

Rough planning

converting

Current Stock

CONVERTINGBASESHEET

PRODUCTION

Fine planning basesheet production

Fine planning

converting

Inventory control

Rough Planning Transport

Fine Planning Transport

Distribution

Distribution

Rough Planning Transport

Fine Planning Transport

Mill Basesheet

Stock

Koblenz Basesheet

Stock

Changing Premises of Industrial Organisation

Source: www.dti.gov.uk

Modelled System vs Actual System Performance

Actual Sales and Modelled Stock Levels for product X12 at UK RDC

0

500

1000

1500

2000

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358

time in days

sto

ck in

nu

mb

er

of

cases

Modelled Stock Actual demand

Modelled System vs Actual System Performance

Stock at Koblenz

Balance in Factory

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 expresssynergetic behaviour of its components in that environment:

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

Beginning

Later...

System 11 type

System 22 types

System 34 types

System 48 types

System 56 types

StructuralChangeoccurs...

Instabilities

Time

Production Planning & Control

tp

th > tpyes

no

th=tp

yes

no

th

Flowchart 2: Production, Planning & Control

time for producing the top-ranked product

Decision making stage of the agent

Target finished goods inventory and ranking for

production priority

[Finished goods inventory/ total forecast] for each product ranked > 1

change-over time for a certain time-period (CO)

CO>CO*

continue production

produce products according to stipulated

maximum and minimum time periods

produce for the calculated time

period

Yes Yes

No

No

No

Yes

Yes

No

Yes

No

Flowchart 3: Ranking mechanism based on local information

forward cover of all products in next stockpoint arranged in ascending order

time, each product is last produced

If top ranked product is produced within the past 7 days?

Top ranked product's forward cover < 0

If top ranked product is produced within the past 4 days?

Do not consider the product for production for

4 days

Do not consider the product for production for

7 days

If top ranked product target finished goods inventory in next stock-point >0

Start producing the top-ranked product

If top ranked product cumulative sales until a particular time-period >0

Start producing next ranked product for which the above are non-zero and

positive and the cumulative sales/total inventory at next available stock-point is

the highest

target finished goods inventory of all products in next stockpoint

cumulative sales of all products in the network

total stock of all products in the next available stock-point