fiAn Inventory Decision Support System to the Glass...
Transcript of fiAn Inventory Decision Support System to the Glass...
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An Inventory Decision support System to the Glass Manufacturing Industry - Nuno Órfão and Carlos Bispo
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�An Inventory Decision Support System �An Inventory Decision Support System to the Glass Manufacturing Industry�to the Glass Manufacturing Industry�
EWG-DSS 20011212thth International Meeting of the EURO Working Group on Decisions SuInternational Meeting of the EURO Working Group on Decisions Support Systemspport Systems
Cascais, Cascais, 18-20 May 2001
Nuno M. R. Órfão(Mechanical Engineering Department, ESTG-IPLEI)
Carlos F. G. Bispo(Institute for Systems and Robotics, IST-UTL)
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An Inventory Decision support System to the Glass Manufacturing Industry - Nuno Órfão and Carlos Bispo
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Summary
1. Problem framework2. What kind of decisions?3. Methodology4. The glass manufacturing process5. The IPA Approach6. The SimulGLASS package7. Numerical study example8. Future developments9. Conclusions
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1. Problem framework
• Generalized Portuguese glass industry production strategy is a producing-to-order one;
• Managers are often confronted with decisions on whether or not to hold inventories;
• Some lack of suitable inventory decision support systems;
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2. What kind of decisions?
• For a production process, and for a given production strategy (make-to-stock, make-to-order, ...) we decide on...
i) the base-stock levels that minimize the total cost
ii) the corresponding service level
• Compare the performance of alternative production strategies
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3. Methodology
i) Process data analysisii) Literature reviewiii) Model definitioniv) Software developmentv) The testing stagevi) Numerical studyvii) Analysis of results
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4. The Glass Manufacturing Process
AnnealingMolding Selection I Rough Cut Selection II
Hot-area:
Buffer
OperationU = Production Limit Z = Echelon Base-Stock
Stone CutMarking Acid Bath
Selection + Packing
Cold-area:
Up1 Zp1Up2 Zp2... Zp3
Up9 Zp9 Up8 Zp8 ...
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5. Model Definition
• Multiple machines in series with finite capacity
• Multi-products
• Lot Splitting
• Random Yield
• Random Demand
• Production decisions based on a weighted shortfall heuristic
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6. The Infinitesimal Perturbation Analysis (IPA) Approach
• Tool used on complex systems to compute Grad J (in order to the control parameters);
• Classical approach: M parameters ⇒⇒⇒⇒ M+1 Simulations
• IPA approach:M parameters ⇒⇒⇒⇒ 1 Simulation
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7.1 Software Package - Simulator
Start Simulation
Demand Generation
Inventory Update, E=∑∑∑∑ I
Shortfall Update, Y=Z-E
Production DecisionsP=min {Y,I,C/T,U}
No
Yes
n=n+1
End Simulation
n=N?
Cost UpdateJ=∑∑∑∑(h.I+)+p.I-
Simulation returns:– Total Cost (J)– Cost Gradient (Grad J)
( )[ ] ( ) ( ) ( )( )∑∑=
−+
=
+ −−+++=S
s
psppspsppn
ppn
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pn PmhbIhIhIJ
1
111
2
1 αααα
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7.2 Software Package - Optimizer
• Optimization algorithm:Fletcher & Reeves
(Conjugated Gradient Method)
750
1250
1750
2250
2750
3250
0 50 100 150 200 250
Optimizations
Cost
MTO
DD
MTS_DD_MTOMTS
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7.3 Software Package - Output
• Minimum cost for the simulated strategy;
• Optimal base-stock levels for all products at all stages;
• Optimal Production limits for all products at all stages;
• Average lead-time for all products;
• Corresponding service level;
• Internal Costs (In-house costs);
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7.3 Software Package � User interfaces
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7.4 Software Package - Tests
• Derivative accuracy
• Function cuts along gradient direction
• Control of state and decision variables(Inventory ≥ 0, Production ≥ 0, ...)
εJJ
ZJ nominalperturbed −
=∂∂
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8.1 Numerical Study Example - Data
• Process Parameters– 27 Products (Pareto’s Law)
• High demand level (80%)• Medium demand level (15%)• Low demand level (5%)
– 9 Machines (stages) and random yield
– 4 production shifts/day
– 4 Production strategies: MTS, MTO, DD and M/D/M
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8.2 Numerical Study Example - ResultsProduction
StrategyOptimal
CostConfid. Interval
In-house Cost
MTO 3079,6 ± 1,9 % 488,6DD 1529,4 ± 0,7 % 540,4
M/D/M 1075,8 ± 0,8 % 595,2
MTS 999,1 ± 1,4 % 631,5
[m.u.]
STRATEGY MTO DD M/D/M MTS Demand Level Lead-times [shifts]
High 7,21 2,71 1,24 2,73Medium 6,66 2,67 2,61 1,17
Low 4,67 2,74 4,87 0,73
All products 6,18 2,71 2,91 1,54
• Costs:
• Lead-times:
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9.1 Conclusions
• Development of an efficient tool to support production management teams;
• Evaluation of alternative production strategies;
• IPA provides rapid identification of good solutions;
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9.2 Future developments
• Use firm data base to update process parameters (processing times, yield rates, ...)
• Development of more complex model(with random capacity and processing times);
• Dealing with random yield
• Alternative optimization algorithms (Broyden-Fletcher-Goldfarb-Shanno, ...);
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A.1 Production Decisions
s stageany for ,
boundcapacity is if
bound production is if 0
boundinventory is if
boundshortfall is if
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A.2 Performance Measure - Cost
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A.3 IPA Validation
Validation procedure consists in show that:
– All variables are diferentiable;– Define their values;– Expected Value and Diferentiation are
permutable operatores;
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A.4 Optimality Condition
( ) 11
pp
ppp
nhb
bIDrP
+=≤
• Once the SERVICE LEVEL is defined, the estimation of the penalty costs becomes a simple task.
• The optimal base-stock levels, Z, for the performance measure COST and for any production strategy are such that the following relation holds:
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A.5 (s,S) Policy definitionG(y)
s S
k
y
Order Not to order
x = Inventory Level
Control Policy:- if x < s order up to S;
- If x ≥ s not to order;