160615 Progress Update WP3 (Nils Knofius)

1. SINTAS
WORK PACKAGE 3
NILS KNOFIUS
Supervision:
Dr. Matthieu C. van der Heijden
Prof. dr. W. Henk M. Zijm

2. Motivation:
 Most important benefit of AM for operations (Wohlers Report, 2014)
 No quantitative insights available on total costs
Objective:
 Quantify the total costs of consolidation
 Study under which circumstances consolidation is beneficial
13/12/16n.knofius@utwente.nl 2
PRINT ASSEMBLY STRUCTURE IN ONE PIECE PART
CONSOLIDATION WITH ADDITIVE MANUFACTURING
Print multiple
components in one
piece

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SIMPLE EXPERIMENTS
EXPERIMENT 1
Part 1 Part 2 vs. Part 1+2
Segmented design Consolidated design
Parameters Values
Part holding cost (ℎ 𝑛) low, high
Part replenishment lead time (𝑙 𝑛) low, high
Part demand rate (𝑚 𝑛) low, high
Scaling factor holding costs (α) 0.5, 1, 1.5
Scaling factor replenishment lead time (β) 0.5, 1, 1.5
Scaling factor demand rate (γ) 0.5, 1, 1.5
Characteristics Experiment 1:
 No hierarchy involved
 Consolidation to monolithic design
 Holding cost consolidated design ℎ = 𝛼 σ 𝑛 ℎ 𝑛
 Replenishment lead time consolidated design 𝑙 = 𝛽 σ 𝑛
𝑚 𝑛
σ 𝑚 𝑝
𝑙 𝑛
 Demand rate consolidated design 𝑚 = γ σ 𝑛 𝑚 𝑛
If scaling factor
is <1 then
improvement

4. 13/12/16n.knofius@utwente.nl 4
SIMPLE EXPERIMENTS
EXPERIMENT 1
Part 1 Part 2 vs. Part 1+2
Segmented design Consolidated design
Parameters Values
Part holding cost (ℎ 𝑛) low, high
Part replenishment lead time (𝑙 𝑛) low, high
Part demand rate (𝑚 𝑛) low, high
Scaling factor holding costs (α) 0.5, 1, 1.5
Scaling factor replenishment lead time (β) 0.5, 1, 1.5
Scaling factor demand rate (γ) 0.5, 1, 1.5
Results Experiment 1:
 91% segmentation design superior if no improvements (𝛼, 𝛽, 𝛾 = 1)
 In remaining 9% of cases, total costs reduction marginal (at most 0.2%)
 Even if all parameters improve (𝛼, 𝛽, 𝛾 = 0.5) in 2% of cases segmentation
design superior
 In each case, all parameters unbalanced:
 E.g.: ℎ1 = 𝑙𝑜𝑤 & ℎ2 = ℎ𝑖𝑔ℎ; l1 = 𝑙𝑜𝑤 & l2 = ℎ𝑖𝑔ℎ; m1 = 𝑙𝑜𝑤 & m2 = ℎ𝑖𝑔ℎ

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SIMPLE EXPERIMENTS
EXPERIMENT 2
Part 1 Part 2 vs. Part 1+2
Segmented design Consolidated design
Parameters Values
Part holding cost (ℎ 𝑛) low, high
Part replenishment lead time (𝑙 𝑛) low, high
Part demand rate (𝑚 𝑛) low, high
Scaling factor holding costs (α) 0.5, 1, 1.5
Scaling factor replenishment lead time (β) 0.5, 1, 1.5
Scaling factor demand rate (γ) 0.5, 1, 1.5
Characteristics Experiment 2:
 Assembly structure
 Consolidation to monolithic design
 Consolidation design parameters as Experiment 1
 Assembly parameters obtained by setting (𝛼, 𝛽, 𝛾 = 1)
 Failure assembly occurs due to Part 1 and Part 2 relative to demand rate
 Failure solved by replacing failed part

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SIMPLE EXPERIMENTS
EXPERIMENT 2
Part 1 Part 2 vs. Part 1+2
Segmented design Consolidated design
Parameters Values
Part holding cost (ℎ 𝑛) low, high
Part replenishment lead time (𝑙 𝑛) low, high
Part demand rate (𝑚 𝑛) low, high
Scaling factor holding costs (α) 0.5, 1, 1.5
Scaling factor replenishment lead time (β) 0.5, 1, 1.5
Scaling factor demand rate (γ) 0.5, 1, 1.5
Results Experiment 2:
 Consolidation design always superior if parameters remain (𝛼, 𝛽, 𝛾 = 1)
 Even if all parameters worsen (𝛼, 𝛽, 𝛾 = 1.5) in 6% of cases consolidation
is still superior
 In each case assembly stock of segmented design equals total stock
of consolidation design
 Additional stock for Part 1 and Part 2 leads to higher costs for
segmented design  required to keep replenishment lead time short

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SIMPLE EXPERIMENTS
EXPERIMENT 3
Part 1 Part 2 vs.
Segmented design
Characteristics Experiment 3:
 Assembly structure
 Partial integration on one hierarchy level
 Consolidation design parameters as Experiment 1
 Assembly parameters as in Experiment 2
Part 3 Part 1 Part 2+3
Consolidated design

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SIMPLE EXPERIMENTS
EXPERIMENT 3
Part 1 Part 2 vs.
Segmented design
Results Experiment 3:
 Consolidation is more often desirable than in Experiment 1 (no hierarchy,
monolithic design) but still only in 39% of cases
 Partial consolidation offers more options for the stocking locations than
monolithic design
 If Part 2 and Part 3 have identical parameters, consolidation design is in
7% more cases superior
Part 3 Part 1 Part 2+3
Consolidated design

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SIMPLE EXPERIMENTS
CONCLUSIONS
1. On the same hierarchy level consolidation is more desirable between
parts with comparable parameters
2. If consolidation eliminates hierarchy it is desirable even if parameters
do not improve

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OUTLOOK
 Identify case study (master project Thales)
 New experiments
 More detailed costs breakdown
 More parameter ranges
 More complex designs
 Pooling effect
vs.
pooling
Part A Part B Part B Part BPart A+B

11. THANK YOU!
Nils Knofius
n.knofius@utwente.nl