170614 MSc Project Thales (Michel Reimert)

17-7-2017 Commercial In Confidence

Agenda
• Research context
 Thales
 SINTAS-project
• Research problem
 Research goal
 Research question
• Solution approach
 Life cycle cost analysis
 Model formulation
• Results
 Model evaluation
 Case studies
• Conclusions & recommendations
17-7-2017 Commercial In Confidence1

Research context: Thales
Thales Nederland B.V.
 Defence
 Securtity
 Transporation systems
Thales Hengelo
 Development and production of radar systems, command and control systems, and sensors & weapon system
integrators for combat management systems.
 After sales service of systems

Research context: SINTAS-project
Sustainability Impact of New Technologies on After-sales service Supply chains:
 Impact of additive manufacturing (from now on: 3D printing) on failure behaviour and maintainability
 Redesign of spare parts
 Planning of spare parts supply chains
Collaboration between:
 University of Twente
 Eindhoven University of technology
 Partners from industry
 Partners from defence

Research problem
The potential of 3D printing

Research problem
3D printing affects a lot of areas:
 Failure behaviour
 Manufacturing capabilities
 Design possibilities
 Maintainability of parts
 Costs

Research problem
3D printing affects a lot of areas:
 Failure behaviour
 Manufacturing capabilities
 Design possibilities
 Maintainability of parts
 Costs
Life cycle cost which are affected by using 3D printing instead of conventional manufacturing,
during the entire life span of a one-off part.

Research problem: research goal
“Construct a model to assess the impact of the use of 3D printing instead of the use of Conventional
Manufacturing at some point during the life cycle, on the Life Cycle Cost of one-off parts within
radar systems at Thales Hengelo”
 3D printing: a process of joining materials to make objects from 3D model data, usually layer upon layer.
 Conventional manufacturing: manufacturing of parts, by means of known processes such as: drilling, milling, casting, etc.
 Life Cycle Cost: the sum of all costs affected by using 3D printing instead of conventional manufacturing, during the
entire life span of a one-off part. Regarding the life span of these parts we distinguish four different phases: design &
development, production, use, and disposal.
 One-off parts: mechanical parts that fail because of external incidents such as extreme weather conditions, damage
during inspection or during maintenance instances, or battle damage. These parts don’t have a predetermined Mean
Time To Failure.

Research problem: research question
“How can Thales Hengelo use 3D printing, in order to decrease Life Cycle Cost of one-off parts?”

Agenda
 Thales
 SINTAS-project
 Research goal
 Life cycle cost model
• Results
 Case studies

Solution approach: life cycle cost analysis
For the life cycle cost analysis we used the ten steps strategy proposed by Greene & Shaw:
1. Determine the purpose of the life cycle cost analysis
2. Define and scope the system/support system
3. Select the appropriate estimating methodology/life cycle cost models
4. Gather data and make the appropriate inputs to the methodology/model
5. Perform sanity checks of inputs and outputs
6. Perform sensitivity analysis and risk assessment
7. Formulate the results of the life cycle cost analysis
8. Document the life cycle cost analysis
9. Present the life cycle cost analysis
10. Update the life cycle cost analysis/baseline

Solution approach: life cycle cost model
The model formulated, determines the optimal moment to start the use of 3D printing as
manufacturing method, in order to minimize life cycle cost of one-off parts
• production of new parts (initial production phase) vs. production of parts as replacement of a
failed ones (after-sales phase)
 New parts are assumed to be produced at once
 Demand of replacement parts follow a Poisson process with rate λ per time period
• The initial production phase, is considered as a period (t=0)
• We divide the after-sales phase into a number of periods
 we choose to use periods of a year, so (t=1,…,T+1)

Decisions to be made at the start of every period:
• Which manufacturing method should be used in case production is required?
 Conventional manufacturing
 3D printing
• How to deal with special tooling?
 Do nothing concerning special tooling (if available, it is stored otherwise nothing happens)
 Discard tooling
• Should 3D printing be prepared in advance?
 Do nothing concerning the 3D printing method
 Prepare the 3D printing method in advance
• How much parts should be produced to put in inventory?

Stochastic Dynamic Programming (SDP)
• State period t
 Decision made
 Demand
• State of the end of period t
• State period t+1
 Decision made
 Demand
• State of the end of period t+1
• State period t+2

Stochastic Dynamic Programming (SDP)
• Backward recursion
1. Calculate the costs for all possible decision in period T+1
 Select for every state the minimum costs
2. Calculate the costs for all possible decision in period T
 Add up the costs in period T+1 of every state multiplied by the probability you will end up in that state, based on the decision made
 Select for every state the minimum costs
3. Do this until period t=0

Solution approach: explanation of the model
Model input parameters:
• Based on:
 Analysis of the current life cycle
cost determination at Thales
 Literature study

Agenda
 Thales
 SINTAS-project
 Research goal
• Results
 Case studies

Results: model evaluation
Some model input parameters are hard to estimate. Therefore, we performed a sensitivity analysis
with respect to the following parameters:
Regarding the sensitivity analysis, we take into account the following output
 The expected cost saving of the optimal strategy using 3D printing, compared to the optimal strategy using
conventional manufacturing: E[cost savings]
Sensitive parameters:
 Variable production costs of 3D printing per piece in period 0 (cAM(0))
 Demand rate of a single part per period (λ)
 Life cycle length of the analysis (T)
 Acquisition costs of special tooling (ST)

Results: case studies
During this research we have analysed two different cases:
The protection cover of the STIR

Results:
 Expected costs in case of the model: €350.000
 Expected costs in case of conventional manufacturing in combination with
Inventory and the optimal strategy of discarding special tooling: €379.000
 Expected costs in case of conventional manufacturing without the
possibility of inventory: €417.000
 Use of 3D printing saves costs of €29.000
 Use of 3D printing in combination with inventory saves costs of €67.000
 So, inventory saves costs of €38.000

• 7,65% cost savings as a result of the use of 3D printing
• 16,07% cost savings as a result of the use 3D printing in combination with inventory

The sunshade of the STIR

Results:
 Expected costs in case of conventional manufacturing in combination with
Inventory and the optimal strategy of discarding special tooling: €77.000
 Expected costs in case of conventional manufacturing without the
possibility of Inventory and the optimal strategy of discarding special
tooling: €77.000
 Use of 3D printing saves costs of €5.500
 No inventory applicable (no downtime costs)

• 7,14% cost savings as a result of the use of 3D printing
• No downtime costs, so no cost savings as a result of the use of inventory

Conclusions of the case studies:
 The use of 3D printing decreases the expected costs with more than 7% regarding both cases
 The use of inventory decreases the costs in case of the protection cover with more than 8% (the protection cover is
subjected to downtime costs)

Results: future case studies
Previous analysis where based on data concerning the year 2017. As aforementioned, the variable
production costs of 3D printing per piece in period 0 (cAM(0)), is the most sensitive input parameter
towards the results. Therefore, we perform an extra analysis which reveals the full potential of 3D
printing, without changing parts.
Assuming the variable production costs of 3D printing per piece in period 0 (cAM(0)), decrease as
expected, we will produce the sunshade after 3 years from now (starting production in 2020) only
by means of 3D printing (also the initial production phase). The protection cover will be produced
only by means of 3D printing after 3 years from now (starting production in 2023). This will result in
higher cost savings.

Protection cover for the STIR

Sunshade for the STIR

Agenda
 Thales
 SINTAS-project
 Research goal
• Results
 Case studies

Conclusions
Research question:
“How can Thales Hengelo use 3D printing, in order to decrease Life Cycle Cost of one-off parts?”
• This is dependent upon input parameters, however:
 3D printing can be used to decrease average life cycle costs, if it can be used during the after-sales phase
 3D printing is at this point in time to expensive to apply during the initial production phase, however this will change in
the future and can lead to cost savings up to more than 16% based on two case studies.
• Other conclusions
 For parts that are subjected to downtime costs, it is relevant to reconsider to put them in inventory. Extra cost savings of
more than 8% were realized with respect to the protection cover for the STIR.

Recommendations
• Current after-sales supply chain
 Reconsider storing parts which cause downtime in anyway. (Take into account costs of downtime for other parties in the
supply chain).
 Start spin-offs regarding the production of parts by means of 3D printing.
• Further research
 A framework/methodology towards identifying parts applicable for production by 3D printing
 Impact of 3D printing on the physical characteristics of parts (failure behaviour)
 Benefits of 3D printing regarding the integration of functions of different parts and integration of subassemblies into one
part

Questions?

170614 MSc Project Thales (Michel Reimert)

Recommended

Recommended

More Related Content

Similar to 170614 MSc Project Thales (Michel Reimert)

Similar to 170614 MSc Project Thales (Michel Reimert) (20)

More from SINTAS

More from SINTAS (20)

Recently uploaded

Recently uploaded (20)

170614 MSc Project Thales (Michel Reimert)