for supply chain network design. Presentation at the. 27th European Conference on Operational Research (EURO). Matthias Kannegiesser, Hans-Otto Günther, ...
Time-to-Sustainability as optimization strategy for supply chain network design
Matthias Kannegiesser, Hans-Otto Günther, Niels Autenrieb
Presentation at the 27th European Conference on Operational Research (EURO)
Glasgow, 14 July, 2015 Technical University Berlin Department of Production Management
Seoul National University Dept. of Industrial Engineering
Agenda
Problem introduction: Sustainability in network design Time-to-Sustainability (TTS) optimization strategy Numerical results
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Summary
2
Sustainability in network design aims for a long-term triple bottom line in the supply chain network. Sustainability in network design
Illustrative example
Sustainability triple bottom line meets ...
...supply chain network design
Economic Raw materials
Part Supplier
Manufacturer
x
x
Retailer
End consumer
Import
x Sustainability
Domestic
Demand
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Social
Environment
• Triple-bottom-line perspective on corporate performance • Measured with economic, social and environmental key performance indicators • Setting long-term targets e.g. to reduce CO2e emissions, maintaining cost competitiveness and improve social conditions CO2e: Carbon dioxide equivalents
Manufacturing
Logistics
Retail
Transport lane
• Long-term decision on network structures • Focus on locations and lanes decisions driven by future demand vs. anticipated costs • Discrete open/close vs. continuous capacity increase/ decrease decisions 3
Various research areas can be related to sustainable supply chain and network design. Literature review
Min. cost or max profit (after tax) of supply chain network with projected demand Sustainable supply chain - Seuring (2013) - Brandenburg et al. (2014) - Taticchi et al. (2014) Optimizing multiple sustainability objective trade-off incl. weighting factors
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Goal Programming (GP) - Charnes et al. (1955) - Charnes and Cooper (1961) - Schniederjans (1995)
Research Gaps • Economic objectives only • Single objective function • Max. profit/min. cost paradigm, vs. sustainability balance • Assuming sustainability to be conflicting trade-offs • Multi-objective requires subjective weighting factors • Single-period focus, aspect of time & transition missing • Economic objectives only • Single objective function • Single-period focus, aspect of time & transition missing
Illustration multi-objective trade-off optimization
economic value
Conventional SC network design - Meixell and Gargeya (2005) - Goetschalckx and Fleischmann (2008) - Melo et al. (2010) - Corominas et al. (2015)
Selection
Potential optimum situation, with trade-off line chosen by society
Actual situation
total environmental value
Flexible method, multiple economic goals can be modeled, delta to target optimized
SC: Supply Chain
4
How can decisions for supply chain network design towards sustainability can be effectively supported?
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Research questions
1
How to support the aspect of time in sustainability transformation in long-term network design?
2
How to deal with multiple incompatible sustainability objectives and indicators in quantitative optimization strategies?
3
How to deliver insights to decision makers to answer, if and how sustainability objectives can be achieved?
5
Agenda
Problem introduction: Sustainability in network design Time-to-Sustainability (TTS) optimization strategy Numerical results
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Summary
6
Sustainability optimization models for network design can be structured along an optimization framework. Sustainable supply chain optimization framework and strategies Alternative optimization strategies • Min. Costs/max. profit with sustainability constraints • Multi-objective trade-off optimization • Minimize time-to-sustainability
Framework
Sustainability key performance indicators
Optimization strategies in objective function
External scenario drivers Demographics Globalization
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Resources & Environment
1. Value Chain
External scenario drivers Consumer patterns
Integrated value chain model
Process Model
Transport Model
Network definition
Productin-use model
Approach
Technology Regulations & Activism
LP-Model
2. KPIs 3. Targets & Time 4. SubSystem 5. Opti.Strategy 6. Results
Demarcation of the relevant value chain and definition of the aims of the investigation Identification of the factors of interest and definition of the KPIs Setting of targets and timeframe Modeling of the relevant sub-system incl. data Selection of the optimization strategy Detailed analysis of the obtained results
KPI: Key Performance Indicator Model details in M. Kannegiesser, H.-O. Günther: Sustainable development of global supply chains – part 1: sustainability optimization framework. Flexible Services and Manufacturing, No. 1-2 (2014), 24-47; External scenario drivers based on Laudicina (2005)
7
TTS minimizes the time to reach sustainability targets in all KPIs steady-state in the supply chain network. Minimize Time-to-Sustainability (TTS) principle Illustrative example Sustainability KPIs Sustainability target met Environmental targets e.g. CO2 emissions sustainable corridor
Social targets e.g. job redundancies
Actual emissions
Actual job redundancies
Actual costs Economic targets e.g. total costs
sustainable corridor Sustainability target not met
0%
20% 0%
Sustainability steady state 110%
sustainable corridor
0%
Time
Minimize time to sustainability /15/2015 5:40 PM
40%
Minimum TTS (steady state) KPI: Key Performance Indicator Note: corridor boundary values expressed as ratio to baseline value in percent
8
The standard TTS checks for each KPI if a period is already sustainable steady state (=1) or not (=0). Time-to-sustainability variants (1) – Standard TTS Parameters Basek Baseline (reference) value of KPI k Targetk Target value of KPI k as percentage of the
Equations Sustainability targets for all KPIs and periods
M kt KPI kt Targetk Basek
k K , t T
baseline value
UBk Upper bound of KPI k as percentage of the
baseline value
A period is sustainable if all succeeding periods are also sustainable
kt k ,t 1 Decision variables KPI kt
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kt
Actual value of KPI k in period t =1, if the target for KPI k has not been achieved by period t (0, else)
zk
Time to sustainability for KPI k
Z
Overall time to sustainability
Time-to-sustainability
zk kt
k K
tT
Minimize the overall time-to-sustainability
min Z
TTS: Time-to-Sustainability
k K , t T : t 1 with k1 1
s.t. Z zk
k K
9
Characteristically for TTS is the check of periods to reach sustainability targets steady state.
period sustainability [0=Yes, 1=No]
Standard TTS result – Illustrative example
1
0
period 1
2
3
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CO2 emissions
TTS: Time-to-Sustainability
4
5 costs
6
7
8
9
10
labor dismissals
10
The total TTS reduces integers by focusing on total sustainability of a period across all KPIs. Time-to-sustainability variants (2) – Total TTS Parameters Basek Baseline (reference) value of KPI k Targetk Target value of KPI k as percentage of the baseline value
UBk Upper bound of KPI k as percentage of the
baseline value
Equations Sustainability targets for all KPIs and periods
kt KPI kt Targetk Basek
k K , t T
M
k K , t T
tot t
kt
A period is sustainable if all succeeding periods are also sustainable
ttot ttot1 Decision variables KPI kt
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Actual value of KPI k in period t
t T : t 1 with 1tot 1
Time-to-sustainability
Z ttot tT
tot t
kt Z
=1, if the entire set of sustainability targets for KPI k has not been achieved by period t (0, else)
Minimize the overall time-to-sustainability
Total overflow with respect to KPI k in period t
min Z Overall time to sustainability
TTS: Time-to-Sustainability
11
The overflow TTS minimizes penalized period overflows compared to target values without integers. Time-to-sustainability variants (3) – Overflow TTS Parameters
Basek
Baseline (reference) value of KPI k
Targetk Target value of KPI k as percentage of the baseline value
UBk
Upper bound of KPI k as percentage of the baseline value Penalty parameter
Decision variables KPI kt
kt /15/2015 5:40 PM
Z
kt
Actual value of KPI k in period t Normalized total overflow with respect to KPI k in period t Overall time to sustainability
Equations Normalized sustainability achievement for all KPIs and periods
kt
KPI kt Targetk Basek UBk Targetk Basek
k K , t T
Linear vs. exponential penalty linear
kt t
exponential
kt t
k K , t T
Time-to-sustainability
Z kt kt kK tT
Minimize the overall time-to-sustainability
min Z
Overflow penalty with respect to KPI k in period t
TTS: Time-to-Sustainability
12
The overflow TTS outperforms the other variants specifically on solving times. Time-to-sustainability variants – Comparison Solving times by TTS variants Comparison of TTS variants Criteria
Method flexibility
CO2 reduction targets
Standard TTS
Total TTS
Overflow TTS
-50%
High
High
High
-40% -30%
Validity of results
Low
Low
High -25%
Objectivity of results
High
High
High
Solving time
Long
Medium
Short
Dependency on data complexity
High
High
Low
-20% Overflow TTS exp. Overflow TTS lin. Total TTS Standard TTS
-10% -5%
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0
1.000
2.000
3.000
4.000
5.000
solving time [sec.]
TTS: Time-to-Sustainability
13
Agenda
Problem introduction: Sustainability in network design Time-to-Sustainability (TTS) optimization strategy Numerical results
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Summary
14
We evaluated the TTS variants with a numerical data basis from the automotive industry. Overview numerical data basis Illustrative excerpt
End-to-end to modeling of the automotive industry supply chain network
Focus on the long-term sustainable development of the industry supply chain towards 2030 under the paradigm of powertrain electrification
Key geographic focus on network in Europe/Germany and China
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Three types of cars: small, compact and premium
Industry supply chain network illustration
Five powertrain options andReverse three sizes of cars supply chain forward flow reverse flow Recycler
Five powertrain options incl. old/new ICE, EV, PHEV, HEV
Data from industry sources e.g. for projected demand, productivity, technology, energy data Focus on economic, environmental and social sustainability performance: total costs, CO2e emissions and avoided job dismissals
Waste disposal Scrap Yard
Forward supply chain Parts suppliers
Raw material supplier
Use Customers Export
Component suppliers
OEMs
Distributors
Customers
ICE: Internal Combustion Engine, EV: Electric Vehicle, PHEV: Plug-in Hybrid Electric Vehicle, HEV: Hybrid Electric Vehicle For more details on the case refer to: M. Kannegiesser, H.-O. Günther, O. Gylfason, Sustainable development of global supply chains - part 2: investigation of the European automotive industry. Flexible Services and Manufacturing, No. 1-2 (2014), 48-68. and H.-O. Günther, M. Kannegiesser, N. Autenrieb, The role of electric vehicles for supply chain sustainability in the automotive industry. Journal of 15 Cleaner Production, 90 (2015).
Overflow TTS comes closest to sustainability targets outperforming other variants. Results by TTS variants average emissions [tonne CO2eq/vehicle] 24
Total make + use Emissions per vehicle by TTS variants - S1: 25% emission reduction target -
23 22
Upper limit Sustainability target Standard TTS Total TTS Overflow TTS Linear Overflow TTS Exp
Minimize overflow
21 20 19 18
Minimize TTS
17 16
15 14 2
3
4
5
6
7
8
9
10
11
12
13
14
15
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Period
16
Looking at different KPIs in parallel, emission reduction is the critical objectives. Overflow TTS results – CO2 vs. Costs vs. Jobs comparison
CO2 emissions per new vehicle (t)
25
20.000
Costs per new vehicle (EUR)
2.500
Job dismissals (1.000)
18.000 2.000
20 16.000 15
12.000
10
Emissions per Vehicle S1 Emissions per Vehicle S2 Max. Level 25%-Target (S1: Scen. 1) 35%-Target (S2: Scen. 2)
5
0 2
3
4
5
6
7
8
9 10 11 12 13 14 15
Period
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1.500
14.000
1.000 Max. Level
10.000 8.000
Costs per New Vehicle EUR (S1) Costs per Vehicle EUR (S2)
500
Job dismissals (S1)
Job dismissals (S2)
Sust. Cost Level (5% Baseline) 6.000
Max. Level
0 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Period
2 3 4 5 6 7 8 9 10 11 12 13 14 15
Period
17
TTS allows to analyze the impact on different reduction targets on reduction paths. Analyzing CO2 emissions reduction scenarios – make vs. use CO2 per vehicle delta to target [t] 5,0
S1: 25% CO2 reduction target - absolute savings to target -
4,0
3,0
3,0
2,0
2,0
1,0
1,0
0,0
0,0 3
4
CO2 delta to target % 50%
5
6
7
8
40%
30%
30%
20%
20%
10%
10%
0%
0% 3
4
5
6
7
8
9 10 11 12 13 14 15
3
4
5
CO2 delta to target % 50%
40%
2
- absolute savings to target -
Make CO2-Emissions Use CO2-Emissions
2
9 10 11 12 13 14 15
- relative savings to target
S2: 30% CO2 reduction target
5,0
4,0
2
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CO2 per vehicle delta to target [t]
6
7
8
9 10 11 12 13 14 15
- relative savings to target -
2
3
4
5
6
7
8
9 10 11 12 13 14 15 18
Underlying supply chain structures can be analyzed in more details Analyzing impact on supply chain structures – excerpt by location Development of process units in China Mio. process units 400
Development of process units in Germany Mio. process units
Overflow TTS exp. 400
Overflow TTS lin. 350
Overflow TTS exp. Overflow TTS lin.
Total TTS
350
Total TTS
Standard TTS 300
250
250
200
200
150
150 100
100 2
3
4
5
6
7
8
9
Periods /15/2015 5:40 PM
Standard TTS
300
10 11 12 13 14 15
2
3
4
5
6
7
8
9
10 11 12 13 14 15
Periods
19
There is not much result difference between linear or exponential penalties in the specific case. Analyzing impact of linear vs. exponential penalties on overflow TTS results Penalty factor log. scale
CO2 TTS overflow exp. vs. lin. %
10000
105%
1000
100
100%
10
linear penalty factors expon. penalty factors CO2 - TTS overflow exp. vs lin.
1
95% 2
3
4
5
6
7
8
9
10
11
12
13
14
15
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Periods
Potential area for further research to understand effect of penalties as „sustainability interest rates“ on results 20
Agenda
Problem introduction: Sustainability in network design Time-to-Sustainability (TTS) optimization strategy Numerical results
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Summary
21
TTS provides new insights for sustainability-driven decision support in supply chain network design. Summary Research questions
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1
How to support the aspect of time in sustainability transformation in long-term network design?
2
How to deal with multiple incompatible sustainability objectives and indicators in quantitative optimization strategies?
3
How to deliver insights to decision makers to answer, if and how sustainability objectives can be achieved?
Findings and areas for further research • TTS can explicitly model the time aspect in network design and the transition towards reaching sustainability targets. • TTS can model multiple sustainability objectives without subjective weighting with the same approach. • Thanks to Overflow TTS, the model is LP and hence reaches fast solution times even with multiple KPIs and industry data sets. • TTS allows to simulate various sustainability target value scenarios and reveals, if and when these targets are all feasible steady-state. • Underlying restructuring transitions in the supply chain network can be analyzed in all scenarios.
TTS approach applicable to all types of network design problems. 22
Related publications: • M. Kannegiesser, H.-O. Günther: Sustainable development of global supply chains – part 1: sustainability optimization framework. Flexible Services and Manufacturing, No. 1-2 (2014), 24-47.
• M. Kannegiesser, H.-O. Günther, O. Gylfason: Sustainable development of global supply chains - part 2: investigation of the European automotive industry. Flexible Services and Manufacturing, No. 1-2 (2014), 48-68.
• H.-O. Günther, M. Kannegiesser, N. Autenrieb: The role of electric vehicles for supply chain sustainability in the automotive industry. Journal of Cleaner Production. No. 90 (2015), 220-233.
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Minimum TTS (steady state)
• M. Kannegiesser, H.-O. Günther, N. Autenrieb: The time-tosustainability optimization strategy for sustainable supply network design. Journal of Cleaner Production, (2015), http://dx.doi.org/10.1016/j.jclepro.2015.06.030,.
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