臺灣博碩士論文加值系統

近年來企業利用複雜且大規模的供應鏈運作來滿足世界各地的需求，然而要在此環境下建造出相關的作業是非常具有挑戰性的，尤其是在以下所發生的事件下，如全球疫情造成供應鏈中斷、全球氣候危機與廢棄物問題等。藉此，本研究透過延遲策略的概念，發展供應鏈網路平台，來探討相關議題。儘管供應鏈的延遲問題已被討論有一段時間，但其中仍缺乏以供應鏈網路建模，結合延遲策略以探討環境問題、運營不確定性與中斷等風險的研究，更不用說結合延遲策略與逆向供應鏈之研究。因此，本研究提出考量延遲策略的供應鏈網路建模，並分為兩個部分：正向供應鏈網路與逆向供應鏈網路。研究採用混合整數線性規劃及兩階段隨機規劃兩種方法建立模型。
在正向供應鏈部分，本研究開始建構跨多層之供應網絡模式，以滿足多變化的產品需求。接著，在供應鏈網路中以模組來發展預測與延遲策略，以實現大量客製化與規劃推與拉策略。此外，模型在全球化供應鏈環境下，探討環境政策、需求與供應鏈不確定性及中斷問題。最後，發展四種預測與延遲策略優化以上情境。研究數據顯示，當平衡風險成本與作業成本時，延遲策略可以降低供應鏈的總成本；也突顯出在遵守嚴格的環境政策下，它有幫助適應波動之全球供應鏈的能力。
在逆向供應鏈部分，本研究為結合延遲策略至逆向供應鏈領域中的先驅之一。首先，以物料清單結構與回收品質，對產品回收、再製造與料料回收等作業建模。此外，制定三種不同延遲程度的預測與延遲策略，以適應預測與逆向供應鏈的作業規畫。最後，在考量供應與需求不確定與中斷下，優化逆向供應鏈以進行設施配置與流程選擇。研究結果顯示，採用正確的延遲策略可優化逆向供應鏈，可以降低逆向供應鏈的風險成本與操作成本。在高度不確定性或嚴格的環境政策下，其可在提供所需效率與反應能力上取得平衡，以改善經濟表現。此外，在逆向供應鏈實施延遲策略比沒實施延遲策略表現得更為突出，展示延遲策略可以改善逆向供應鏈系統。
總體而言，延遲提供一種全面優化的方法，可適當模擬現實世界供應鏈的各種複雜情況與大規模問題，並可進一步面對環境問題、多種不確定及供應鏈帶來的中斷。此外，在逆向供應鏈領域，延遲的實施可以協助系統的不確定性風險，改善廢棄物管理問題。本研究展示延遲策略的發展及延伸是適合處理現代供應鏈的議題，是塑造未來可持續供應鏈發展的基礎。

The modern business traits have urged the utilization of complex and large-scale supply chain operations to fulfill the worldwide demand. However, shaping up these operations for such a business environment is very challenging, especially in the wake of events, such as the global pandemic disruption, global climate crisis, and waste problems. This research revisits the concept of postponement strategy in developing the supply chain network (SCN) as a platform to cope with these situations. In the supply chain field, postponement has been studied for some time. However, there is still a lack of studies of SCN modeling with postponement for the purpose of addressing environmental issues, operational uncertainties and disruption, let alone, integrating postponement with reverse SCN. Therefore, this research proposes two parts of SCN modeling with postponement strategies: the forward SCN (FSCN) and the reverse (SCN) RSCN. Mixed-integer linear programming and two-stage stochastic programming are used to formulate the models in this research.
On the FSCN part, the research highlights are as follows. To begin with, this research models the operations performed across a multi-tiered SCN, to fulfil multi-variant product demands. Then, speculation and postponement (SP) strategies with modularization are developed into the SCN models to enable the mass customization and operations planning of push-pull strategy. Furthermore, the models are set under the globalized supply chain environment while addressing environmental policies, the demand and supply uncertainties, and disruption. Finally, four SP strategies are formulated into the models to be optimized by considering abovementioned points. Results show that these strategies can consistently reduce the SCN’s total cost by balancing between risk and operational costs. Therefore, it highlights postponement strategies’ capability to anticipate the volatile global supply chain environment while adhering to stricter environmental policies.
On the RSCN part, this research has become one of the pioneers in integrating postponement strategies into the RSCN research field. The highlights of the RSCN part are as follows. To start with, the RSCN is modeled to perform the takeback, remanufacturing and recycling operations of multi-variant products with BOM structure and takeback quality issue. In addition, related to the operations planning of the RSCN, three SP strategies with different degrees of postponement are developed to accommodate the forecast and RSCN operations planning. Finally, the proposed models are used to optimize the RSCN configuration by adjusting to various supply and demand uncertainties and disruptions. Results show how RSCN optimization using the right postponement strategies can mitigate the risk and operational costs. It balances required efficiency and responsiveness to improve economic performances even under high uncertainty levels or stricter environmental policies. Moreover, the RSCN performs better with postponement strategies than without, demonstrating how postponement can improve the RSCN system.
Overall, postponement allows a comprehensive optimization approach to properly model the variety of real world’s SCN problems and further address dire environmental issues, uncertainties and disruption. In addition, postponement implementation in the RSCN field can anticipate the system’s uncertainty risks while improving waste management issues. This research shows how the development and extension of postponement strategies suit the modern supply chain issues and is fundamental in shaping future sustainable supply chain development.

Table of Contents
摘要 i
ABSTRACT iii
Table of Contents v
List of Table xiv
Table of Figures xix
Chapter 1 Introduction 1
1.1 Modern business condition in the globalized and technology advancement environment 1
1.1.1 Uncertainty problems in the supply chain network 1
1.2 Needs of addressing the environmental issues in the supply chain operations 2
1.2.1 The business development trend into sustainable business model 3
1.2.2 Environmental effects of uncertainties and disruption risks 3
1.2.3 Management of the reverse flow of the supply chain 4
1.3 Postponement concepts role in supply chain system development 5
1.4 Relevancy of supply chain network design development 6
1.5 Research formulation 7
1.5.1 Research motivations and aims 8
1.5.2 Dissertation outline 9
1.5.2.1 Introduction 9
1.5.2.2 Literature review 10
1.5.2.3 Dissertation research modeling development framework 10
1.5.2.4 Forward supply chain modeling development 11
1.5.2.5 Reverse supply chain modeling development 11
1.5.2.6 Conclusion 12
Chapter 2 Review on Postponement Literature 16
2.1 Postponement concept and related concepts 16
2.1.1 Postponement and speculation strategy 16
2.1.2 Precondition of postponement concept implementation 18
2.1.3 Decoupling point 19
2.1.4 Modularization (design for postponement) 20
2.2 Postponement and other supply chain strategies and concepts 21
2.3 Supply chain modeling studies 23
2.3.1 Development of supply chain quantitative modeling with postponement strategy consideration 23
2.3.2 Research opportunities in forward and reverse supply chain with postponement consideration 25
Chapter 3 Global Green Supply Chain Network Design Model with Postponement Strategies 29
3.1 Introduction 29
3.2 Literature review 31
3.3 Model development 36
3.3.1 Modular design and modular process concept 36
3.3.2 Speculation-postponement strategy 38
3.3.3 Mathematical model 41
3.3.3.1 Single-period model 42
3.3.3.2 Multi-period model 48
3.4 Notebook computer supply chain case study 51
3.5 Result and discussion 54
3.5.1 Comparison of the four postponement strategies results with the real-world situation 54
3.5.2 Comparison of the four speculation-postponement strategies under BAU policy 54
3.5.3 Comparison of the four speculation-postponement strategies under various environmental policies 57
3.5.3.1 The effect of environmental policies on the supply chain network configuration of every speculation-postponement strategy 57
3.5.3.2 The impact of the current carbon tax in supply chain performance 63
3.5.3.3 The impact of combining carbon tax and carbon cap on the supply chain performance 64
3.5.4 Comparison of the four speculation-postponement strategies under various demand’s standard deviation levels 65
3.6 General insight of postponement implementation in the globalized green supply chain network 68
3.7 Chapter summary 70
Chapter 4 Supply Chain Network Design Model with Postponement Strategies under Uncertain Demand Consideration 72
4.1 Introduction 73
4.2 Literature review 76
4.3 Model development 82
4.3.1 Modular product design or architecture 83
4.3.1.1 Integrating supply chain network with the modular product architecture 84
4.3.1.2 Modular design and modular process definition modeling into transition matrix 85
4.3.2 Speculation and postponement strategies 86
4.3.2.1 Speculation strategy 86
4.3.2.2 Formulated speculation-postponement strategies 87
4.3.2.3 The supply chain network design’s decision and planning 90
4.3.3 Globalization factors 91
4.3.4 Mathematical model 93
4.3.5 Two-stage stochastic model 93
4.3.6 Two-stage stochastic SCND model formulation 95
4.3.7 Sample average approximation 102
4.4 Numerical example 103
4.4.1 SAA scheme 106
4.4.2 Speculation-postponement (SP) strategy performances 107
4.4.3 Comparison of global SCND of various speculation-postponement under various situations 109
4.4.3.1 The demand uncertainty effect toward the performance of speculation-postponement strategies 110
4.4.3.2 The demand uncertainty effect toward speculation-postponement strategies and their service level 113
4.5 General insight of postponement in addressing global supply chain and demand uncertainty issues 117
4.6 The consideration of environmental issues into the model 118
4.6.1 Supply chain performance under various SP strategies and demand uncertainty levels, when performing business as usual operations 122
4.6.2 Supply chain performance under various SP strategies and demand uncertainty levels, when performing under various carbon trading scenarios 123
4.7 General insight of postponement in improving greenness under demand uncertainties 125
4.8 Chapter Summary 126
Chapter 5 Supply Chain Network Design Model Development with Postponement Strategies under Uncertain Demand and Supply, and Disruption Consideations 128
5.1 Introduction 128
5.2 Literature review 131
5.2.1 SCND studies related to uncertainty and disruption 131
5.2.2 Product structure, modular product architecture, and postponement concepts 132
5.3 Model development 134
5.3.1 The supply chain network design’s decision and planning 135
5.3.2 Modular product and process architecture definitions 135
5.3.1 Speculation and postponement strategies to model push-pull supply chain operations 137
5.3.2 Consideration of uncertainty factors 138
5.4 Mathematical model formulations 140
5.4.1 Resilience indicators formulation 152
5.4.2 Uncertainty and disruption scenario generations 153
5.5 Numerical example 154
5.5.1 SCN performances under various SP strategies and uncertainties 158
5.5.1.1 Comparison of SP configuration under uncertainty sources independently 159
5.5.1.2 Comparison of SP configuration under various uncertainty combinations 160
5.5.1.3 Effect of uncertainty and disruption toward the feasibility of the SCN 171
5.5.1.4 Resiliency analysis in SP configuration 171
5.5.1.5 Analysis of contingency planning effect in mitigating risk 174
5.5.1.6 The discussion of uncertainty and SP strategies influence the greenness of the SCN 177
5.6 General insight of postponement in anticipating demand and supply uncertainties and disruption 178
5.7 Chapter summary 179
Chapter 6 Developing a Reverse Supply Chain Profit Model for Remanufacturing Products with Consideration of Production Postponement Strategies 181
6.1 Introduction 181
6.2 Literature review 183
6.3 Model development 187
6.3.1 Speculation-postponement strategies for reverse supply chain network 189
6.3.2 Service level, inventory level, and forecasts 190
6.3.3 Model development 190
6.4 Numerical Analysis 195
6.4.1 The effect of SP strategies on the RSC profit under various service levels 196
6.4.2 The effect of quality gap 201
6.4.3 Strategical analysis 201
6.5 General insight of postponement implementation in the reverse supply chain 204
6.6 Chapter summary 204
Chapter 7 Reverse Supply Chain Model with Postponement Strategies in Anticipating Uncertain Demand 206
7.1 Introduction 206
7.2 Literature review 209
7.2.1 Development trend in reverse supply chain studies 209
7.2.2 The uncertainty and complexities barriers in reverse supply chain 209
7.2.3 Postponement concept implementation in reverse supply chain 211
7.2.4 Supply chain network studies with postponement concepts 212
7.2.5 Remark on the reverse supply chain studies 213
7.3 Methodology 216
7.3.1 Reverse supply chain network definition 216
7.3.2 Reverse supply chain network flow 217
7.3.3 Speculation-postponement strategy 219
7.3.4 Mathematical model formulation 223
7.3.5 The sample average approximation method 228
7.4 Numerical example 229
7.4.1 Case study 229
7.4.2 RSCN model optimization’s performance analyses 230
7.4.3 Comparison of RSCN performance under different SP strategies 231
7.4.4 Comparison of RSCN performance under different SP strategies on various demand uncertainty levels 234
7.4.5 RSCN under various demand uncertainty and takeback regulation 234
7.4.6 Insights of SP strategies and uncertainty effect 237
7.5 General insight of postponement implementation in the reverse supply chain with demand uncertainty 238
7.6 Chapter summary 239
Chapter 8 Reverse Supply Chain Network Design Model with Postponement Strategies in Anticipating Uncertain Supply 241
8.1 Reverse supply chain modeling development 242
8.2 Literature review 244
8.3 Model development 245
8.3.1 Speculation-postponement strategies for reverse supply chain network 249
8.3.2 Uncertainty and disruption definitions 251
8.3.3 Model development 252
8.3.3.1 Proposed RSCN formulation 252
8.3.3.2 The formulation of push and pull RSCN mapping models 262
8.3.3.3 RSCN optimization scheme 266
8.4 Numerical Analysis 267
8.4.1 Result and discussions 271
8.4.1.1 SP strategies overall performance 274
8.4.2 The effect of the demand uncertainty, supply uncertainty and disruption to the RSCN under different SP strategies 275
8.4.3 The cost breakdown analyses of SP strategies under different uncertainties levels scenarios 277
8.4.4 Discussion about the overall SP strategies performance on the RSCN 283
8.4.5 Recycling performance of the RSCN 284
8.5 General insight of postponement implementation to anticipate demand and supply uncertainties and disruption in the reverse supply chain 285
8.6 Chapter summary 286
Chapter 9 Conclusion 287
9.1 Concluding remarks 287
9.2 Future works 291
REFERENCES 293


List of Table
Table 3.1 Review of related past studies on postponement and green supply chain network (part 1) 34
Table 3.2 Review of related past studies on postponement and green supply chain network (part 2) 35
Table 3.3 The possible process configurations in each supply chain tier type 37
Table 3.4 The list of defined items in the supply chain network 52
Table 3.5 The labor cost and carbon tax values in each country 53
Table 3.6 Parameter Information used for transportation 53
Table 3.7 The performance comparison of SP strategies under BAU condition of the single-period model 56
Table 3.8 The performance comparison of SP strategies under BAU condition of the multi-period model 56
Table 3.9 Possible scenario combinations of various carbon tax and carbon cap policies 57
Table 3.10 The comparison of BAU and several scenarios of emission policies (single-period model) 62
Table 3.11 The comparison of BAU and several scenarios of emission policies (multi-period model) 63
Table 4.1 Review of related past studies on postponement supply chain network (part 1) 80
Table 4.2 Review of related past studies on postponement supply chain network (part 2) 81
Table 4.3 The possible process configurations in each supply chain tier 84
Table 4.4 The list of defined items in the supply chain network 103
Table 4.5 Relationship mapping between the produced item and preceding items that assemble them 104
Table 4.6 The potential nodes that can be utilized in the SCND model 104
Table 4.7 The labor costs in each country 105
Table 4.8 Demand related parameters around various regions 105
Table 4.9 Parameters of the supply chain network 106
Table 4.10 Result of the varying sample of SAA under M=10, N'=1000, for solving the SCND under FG-SP strategy 106
Table 4.11 Result comparison between SP strategies 108
Table 4.12 Parameter setting of various cases to be tested in the global supply chain network 109
Table 4.13 Result comparison between different SP strategies under various demand uncertainty levels 112
Table 4.14 The total forecast-driven production (speculation planning) quantity (in thousands unit items) of every SP strategy under various service level strategies and demand uncertainty level 114
Table 4.15 Parameter setting of various scenarios in the green global supply chain network 122
Table 5.1 The process configurations in each supply chain tier 137
Table 5.2 The list of defined items in the SCN 155
Table 5.3 Configured SCN performance with different SP strategies under the default uncertainties level (d:10% s:10% r:05%) 158
Table 5.4 Considered settings in generating the scenario 159
Table 5.5 Configured SCN performance with different SP strategies under the deterministic scenario (d:00% s:00% r:00%) 162
Table 5.6 Heat map of SCN profit with different SP strategies and under various demand, supply, and disruption uncertainty levels (the benchmark is the profit of FG d:00%s:00%r:00%) 166
Table 5.7 Heat map of SCN profit (mean, upper and lower bounds) with different SP strategies and under various demand, supply, and disruption uncertainty levels (the benchmark is the profit of FG d:00%s:00%r:00%) 167
Table 5.8 Heat map of SCN supply chain cost and risk cost with different SP strategies and under various demand, supply, and disruption uncertainty levels (the benchmark is the profit of FG d:00%s:00%r:00%) 169
Table 5.9 Supply chain operations comparison between SCN configuration under highest level uncertainy (d:20s:20r:10), middle level of uncertainty (d:10%s:10%r:05%), and low/no uncertainty (d:00%s:00%r:00%) 170
Table 5.10 Supply chain performance between SCN configuration under highest level uncertainty (d:20s:20r:10), SCN configuration under no uncertainty but then exposed to the highest level of uncertainty (d:00s:00r:00T) 172
Table 5.11 Supply chain performance difference summary between SCN configuration under highest level uncertainy (d:20s:20i:10), SCN configuration under no uncertainty but then exposed to the highest level of uncertainty (d:00s:00r:00T) 172
Table 5.12 Supply chain operations comparison between SCN configuration under highest level uncertainty (d:20s:20i:10), SCN configuration under no uncertainty but then exposed to the highest level of uncertainty (d:00s:00r:00T), and SCN configuration under no uncertainty (d:00s:00r:00) 173
Table 5.13 Supply chain performance between SCN configuration under highest level uncertainty (d:20s:20r:10) with the SCN configuration under the same condition but without the contingency purchasing (d:20s:20r:10NP) 175
Table 5.14 Supply chain performance difference summary between SCN configuration under highest level uncertainty (d:20s:20i:10), with the SCN configuration under the same condition but without the contingency purchasing (d:20s:20r:10NP) 175
Table 5.15 Supply chain operations comparison between SCN configuration under highest level uncertainty (d:20s:20i:10), with the SCN configuration under the same condition but without the contingency purchasing (d:20s:20r:10NP) 176
Table 5.16 Emission comparison between varieties of scenarios 178
Table 6.1 Related studies on the reverse supply chain network 186
Table 6.2 Transition matrix configuration for the RSC processes 191
Table 6.3 Data used in product 1 (best quality) 195
Table 6.4 Data of IEOU, EOU and REOU 195
Table 6.5 Cost of various process in the RSC 196
Table 6.6 The settings considered in the numerical analyses 196
Table 6.7 The average performance indicator for various SP strategies 198
Table 7.1 Related studies on the supply chain network (Part 1) 214
Table 7.2 Related studies on the supply chain network (Part 2) 215
Table 7.3 Performance comparison between Stochastic (SAA) and MVP solutions for SP strategies under various uncertainty levels 230
Table 7.4 Inventory quantity for SP strategies (base case) 233
Table 8.1 Reverse supply chain network facilities/nodes description 248
Table 8.2 General flow in the RSCN system 248
Table 8.3 Item description 248
Table 8.4 Process definition in the reverse supply chain network 249
Table 8.5 RSCN node description 268
Table 8.6 Disruption setting 268
Table 8.7 Item definition in the RSCN 269
Table 8.8 Process description and cost 270
Table 8.9 Transportation parameter settings 270
Table 8.10 Scenarios parameter setting 271
Table 8.11 Notation for cost analyses 277
Table 8.12 The recycling performance of the RSCN 285


Table of Figures
Figure 1.1 General research framework 13
Figure 1.2 Forward supply chain postponement research framework and scope development diagram, for: (a) Chapter 3 (1-1), (b) Chapter 4 (1-2), (c) Chapter 5 (1-3) 14
Figure 1.3 Reverse supply chain postponement research framework and scope development diagram, for: (a) Chapter 6 (2-1), (b) Chapter 7 (2-2), (c) Chapter 8 (2-3) 15
Figure 2.1 Speculation-postponement strategy and a continuum of standardization-customization (Yang and Burns (2003)) 18
Figure 3.1 Various product mix of the modularized product 36
Figure 3.2 Supply chain structure and its decoupling point for different speculation strategies 38
Figure 3.3 Illustration of the shipment in the global supply chain network 39
Figure 3.4 Illustration of flow in the global supply chain network 40
Figure 3.5 Average percentages of the technology utilized by four SP strategies under various carbon tax and carbon cap policies (single-period model) 58
Figure 3.6 Average percentages of the technology utilized by four SP strategies under various carbon tax and carbon cap policies (multi-period model) 58
Figure 3.7 Total cost comparison for four SP strategies under various carbon tax and carbon cap policies (in thousand $) (single-period model) 59
Figure 3.8 Total cost comparison for four SP strategies under various carbon tax and carbon cap policies (in thousand $) (multi-period model) 60
Figure 3.9 Total emission comparison for four SP strategies under various carbon tax and carbon cap policies (in ton eq.CO2) (single-period model) 60
Figure 3.10 Total emission comparison for four SP strategies under various carbon tax and carbon cap policies (in ton eq.CO2) (multi-period model) 61
Figure 3.11 Average total cost performance of every SP strategy under various demand’s standard deviation levels (single-period model) 66
Figure 3.12 Average total emission performance of every SP strategy under various demand’s standard deviation levels (single-period model) 66
Figure 3.13 Average total cost performance of every SP strategy under various demand’s standard deviation levels (multi-period model) 67
Figure 3.14 Average total emission performance of every SP strategy under various demand’s standard deviation levels (multi-period model) 67
Figure 4.1 General framework of the proposed supply chain network design model with postponement and modularization concepts 82
Figure 4.2 Various product mix of the modularized product 83
Figure 4.3 Possible configuration of supply chain network with modular architecture design (‘possible process configuration information’ refers to Table 4.3) 85
Figure 4.4 Illustration of transition matrix concept usage in describing the: (1) item and process relationship (the left table), and (2) the process availability in each node in the supply chain network 86
Figure 4.5 Supply chain structure and its decoupling point for speculation-postponement (SP) strategies: (a) BC SP, (b) SFG1 SP, (c) SFG2 SP, and (d) FG SP 89
Figure 4.6 Illustration of the speculation/forecast planning and demand pooling considering demand uncertainty 91
Figure 4.7 Relationship between globalization related parameters to the model and demand uncertainty 92
Figure 4.8 Illustration of the configured global supply chain network (utilizing SFG1-SP strategy) 93
Figure 4.9 Profit comparison between speculation-postponement (SP) strategies’ performance 107
Figure 4.10 The SCN configuration comparison between different SP strategies under various demand uncertainty (CV) level: (a) 5%, (b) 10%, (c) 20%, (d) 30% 111
Figure 4.11 Profit comparison between different SP strategies under various demand uncertainty levels 112
Figure 4.12 Comparison of forecast-driven production (speculation planning) quantity of the SFG1-SP strategy under various service level policies and demand uncertainty levels 114
Figure 4.13 Profit comparison of different SP strategies under various service level policies for 10% demand uncertainty level 115
Figure 4.14 Profit comparison of utilizing different SL policies for various SP strategies under: (a) 5%, (b) 10%, (c) 20%, and (d) 30% demand uncertainty levels 116
Figure 4.15 Profit and emission of SCN under different SP strategies and demand uncertainty level when there is no carbon trading (business as usual or BAU scenarios) 122
Figure 4.16 Profit and emission performances of SCN with various SP strategies under various demand uncertainty levels and different carbon cap allowances and carbon credit values 124
Figure 5.1 Supply chain network design with postponement strategy framework 134
Figure 5.2 Modular product assembly alternatives 136
Figure 5.3 The schematic of (a) the configured SCN model and (b) the illustration of the configured SFG1 SP supply chain network with push and pull operations under uncertainty and disruption considerations 141
Figure 5.4 Comparison of SCN performance configured with different SP strategies and under the default uncertainties level (d:10% s:10% r:05%) 157
Figure 5.5 Performance comparison of the SCN configuration with different SP strategies, under the demand uncertainty only scenarios, supply uncertainty only scenarios, and disruption only scenarios 160
Figure 5.6 Comparison between the deterministic, middle, and high level of uncertainties between four SP strategies on (a) capacity utilization level and (b) centralization level 163
Figure 5.7 Comparison between speculation-postponement strategies’ performances of SAA solutions on (a) profits and (b) costs and revenue 164
Figure 5.8 The SCN profits comparison of all SP strategies under various demand, supply uncertainties within the disruption probability of (a) 00%; (b) 05%; (c) 10% 165
Figure 6.1 The reverse supply chain network system 188
Figure 6.2 Process, product, and items relationship 188
Figure 6.3 Speculation-postponement strategy and a continuum of push-pull operations 189
Figure 6.4 RSC profit comparison for various SP strategies, service levels under three quality gaps: (a) no difference; (b) slight difference; (c) big difference 197
Figure 6.5 Cost comparison of RSC of various SP strategies, minimum service level under quality gap: (a) q1; (b) q2; (c) q3 200
Figure 6.6 Performance mapping of the best SP strategy under various components sharing level, service level, and quality gap scenarios 201
Figure 6.7 Profit comparison between DSP with different IEOU utilization estimations and other SP strategies under various service levels and three quality gaps: (a) no difference; (b) slight difference; (c) big difference 203
Figure 7.1 The integrated reverse supply chain network system: (a) network flow definition (b) relationship between RSC processes and items 218
Figure 7.2 A general model of speculation-postponement strategies integration in the reverse supply chain network 222
Figure 7.3 RSCN performance (SAA solution) of every SP strategy case (under base case) at: (a) profit, (b) total cost, (c) forecast penalty and inventory costs 232
Figure 7.4 RSCN’s performances under different demand uncertainty levels and takeback rate levels; (a) profit and product takeback; (b) inventory and forecast penalty costs 236
Figure 8.1 Mapping of the process, product, and items relationship within the RSCN system 247
Figure 8.2 Speculation-postponement strategy and a continuum of push-pull operations 250
Figure 8.3 Conceptualization of the disruption toward the potential nodes to be configured in the RSCN 252
Figure 8.4 RSCN profits (in thousands $) under various disruption and uncertainties levels 272
Figure 8.5 Heat map of the profit ratio between uncertainties scenarios and benchmark scenario 274
Figure 8.6 The boxplot comparison of profit between different SP strategies (the average performance under various uncertainty scenarios) 275
Figure 8.7 The interaction effect plot of demand uncertainty, supply uncertainty, and disruption effects toward the SP strategies 276
Figure 8.8 The other and risk costs under various uncertainties levels scenarios 278
Figure 8.9 The push and pull costs under various uncertainties levels scenarios 279
Figure 8.10 amount of recycling revenue of the RSCN 285

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