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2.1 Agent-based modelling application fields in relation to supply chains / 42
3.1 Data sources used for each primary case / 61
3.2 Information regarding the interviewees / 62
3.3 Main forms of remote interview (source: King and Horrocks, 2010) / 65
3.4 Steps for data analysis in this study (adapted from the general guideline in Bryman, 2012, Gillham, 2000, Charmaz, 2006) / 71
3.5 Examples of code information / 72
3.6 Tactics for evaluating the research quality / 74
4.1 Key stakeholders in logistics industry / 87
4.2 Performance indicators for measuring service levels / 96
4.3 Example of full-scale information sharing in PC manufacturing collaboration(source: Case 3) / 104
5.1 Fill-rate in baseline configuration (random SC) / 152
5.2 Imbalance index for fill-rate under random SC / 154
5.3 Capacity utilization under the random SC / 155
5.4 Imbalance index for capacity utilization under scenario-R / 158
5.5 Cumulative KPIs under random SC / 159
5.6 Imbalance index for cumulative KPIs under the random SC / 161
5.7 Customer total profits and imbalance under the random SC / 162
5.8 Fill-rate under the performance-based SC / 164
5.9 Change percentages for fill-rate under the performance-based SC / 164
5.10 Imbalance index for fill-rate under the performance-based SC / 167
5.11 Change percentages for fill-rate imbalance under the performance-based SC / 167
5.12 Capacity utilization under the performance-based SC / 168
5.13 Change percentages for utilization under the performance-based SC / 168
5.14 Imbalance index for capacity utilization under the performance-based SC / 170
5.15 Change percentages for utilization imbalance under the performance-based SC / 170
5.16 Cumulative KPIs under the performance-based SC / 171
5.17 Change percentages for cumulative KPIs under the performance-based SC / 172
5.18 Imbalance index for cumulative KPIs under the performance-based SC / 174
5.19 Change percentages in imbalance index for cumulative KPIs under the performance-based SC / 175
5.20 Customer total profits and imbalance under the performance-based SC / 177
5.21 Change percentages for customer total profits and imbalance under the performance-based SC / 177
5.22 Fill-rate under the relation-based SC / 178
5.23 Change percentages for fill-rate under the relation-based SC / 179
5.24 Imbalance index for fill-rate under the relation-based SC / 181
5.25 Change percentages for fill-rate imbalance under the relation-based SC / 181
5.26 Capacity utilization under the relation-based SC / 182
5.27 Change percentages for capacity utilization under the relation-based SC / 183
5.28 Imbalance index for capacity utilization under the relation-based SC / 186
5.29 Change percentages for utilization imbalance under the relation-based SC / 186
5.30 Cumulative KPIs under the relation-based SC / 187
5.31 Change percentages for cumulative KPIs under the relation-based SC / 188
5.32 Imbalance index for cumulative KPIs under the relation-based SC / 191
5.33 Change percentages in the imbalance index for cumulative KPIs under the relation-based SC / 192
5.34 Customer total profits and imbalance under the relation-based SC / 194
5.35 Change percentages for customer total profits and imbalance under the relationbased SC / 194
6.1 Summary of new contributions / 216
1.1 Differences between vertical and horizontal collaboration in logistics and transport (Mason et al. 2007) / 5
1.2 Logistics cost comparison between China, USA, Japan (in GDP percentage) (source: China Logistics Information Centre) / 9
2.1 Example of a discrete event simulation model of a supply chain design / 36
2.2 Example of a system dynamics model of a supply chain design / 37
2.3 Example of an agent-based model of a supply chain design / 39
3.1 Network view of interviewed companies in PC supply chain / 63
3.2 Example of comparing and analysing data from different sources / 74
3.3 Stages in a simulation study — adapted from Robinson et al. (2010) / 79
4.1 Key elements for developing logistics horizontal collaboration / 83
4.2 Structures of logistics horizontal collaboration / 89
4.3 An example of a hybrid horizontal collaboration structure / 92
4.4 Example of cost reduction in a French retail collaboration (source: Case 10) / 93
4.5 Capacity utilization improvement through collaborative freight bundling between Hammerwerk and JSP (source: Case 8) / 94
4.6 CO 2 emissions comparison between the modes of transport (source: Hindley, 2013) / 99
4.7 Example of CO 2 emissions through collaborative transport (source: Case 8) / 99
4.8 Intensity of Collaboration: adapted from Lambert et al. (1999); Cruijssen (2006) / 101
4.9 The shipper mode / 106
4.10 Example of food distribution collaboration (source: Case 11) / 107
4.11 The common LSP mode / 109
4.12 The LSP mode / 110
4.13 Collaboration on front/back empty hauls / 111
4.14 Collaboration on shared warehouse / 113
4.15 Example of collaboration through a shared warehouse (source: Case 5) / 114
4.16 Modal split in EU 28 (source: Eurostat) / 115
4.17 Collaboration on freight modal shift / 117
4.18 Collaboration on freight modal shift / 118
4.19 A screenshot of the coordinated capacity allocation process in rail transport collaboration (source: Case 2) / 119
4.20 Collaboration on freight modal shift / 120
4.21 Collaborative group purchasing / 122
4.22 Collaborative service network / 123
5.1 Class diagram of the supply chain framework / 129
5.2 Sequence diagram of agent actions in the supply chain process / 131
5.3 Sequence diagram for the equal capacity sharing model / 134
5.4 Sequence diagram for the proportional capacity sharing strategy / 136
5.5 Sequence diagram for the excess capacity sharing strategy / 138
5.6 The key agent action rules in the simulation model / 145
5.7 Experimental settings across models / 145
5.8 Screenshot of the model implemented in Netlogo / 147
Underutilized capacity, long shipping lead time, high costs and lack of sufficient scale are showcases of logistics inefficiencies that have troubled many supply chain operations. Logistics horizontal collaboration (LHC) is believed to be an innovative approach to tackle the increasing logistics challenges. This kind of collaborative logistics is quickly gaining momentum in practice but relevant contributions in literature are scarcely seen. So far it remains unclear how LHC could be structured and operated given the limited understanding of the various characteristics and forms of LHC between companies.Furthermore, the explicit impact of LHC on the participating partners,as well as on the supply chain system is understudied. Very few studies have explored the process of collaboration and how it links to performance behaviours.
Case studies and agent-based simulation are employed in this thesis to study the research gaps identified above. Case studies are initially conducted to examine the key elements which can support the design of LHC, and to make a classification of models for collaboration. These are followed by agent-based simulation to model a typical collaboration process and work out what benefits would emerge if participating in horizontal collaboration and how the collaboration can produce the impacts on the supply chain operations for individuals and the system as a whole.
The case studies suggest that “collaboration structures”“collaboration objectives”“collaboration intensity”, and “collaboration modes” are the four key elements critical to the design of a LHC project. Each element represents an important aspect of the collaboration and exhibits different characteristics and forms. Based on these key elements, several typologies are derived which together provide a comprehensive view to explain the different types of LHC in practice. The simulation modelling demonstrates that LHC can significantly benefit the logistics efficiency in terms of capacity utilization and customer service in the sense of order fill-rate, and such beneficial effects are consistently observed in different supply chain environments. In particular, LHC can produce better logistics performance in a relationship-based supply chain network where downstream customers can support upstream shippers with more stable and predictable demands. On the other hand, information sharing in the collaboration, for the most part, does not facilitate the higher collaboration gains for partners. Specifically, sharing either the demand or supply information in the horizontal collaboration is not helpful in increasing collaboration gains. Hence there is a difference for the value of information sharing in the context of horizontal collaboration as opposed to vertical collaboration, the latter of which is often justified as providing more beneficial gains. The research findings provide insights for practitioners and scholars about how to develop a type of collaboration project or study, as well as enabling a better understanding of the dynamic collaboration effects.
Zhu Jie
June 2021