Multi-agent model for a complex supply chain: Case of a Paper Tissue Manufacturer by Partha Datta...
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Transcript of Multi-agent model for a complex supply chain: Case of a Paper Tissue Manufacturer by Partha Datta...
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