Redesigning of Logistics Service Using LSP Lifecycle Model

Abstract

Logistics Service Providers (LSPs) have become a success factor that can increase value added throughout an entire supply chain and can offer services according to customer needs. Since the Industrial Revolution, LSPs have become essential for managing supply chains in the global economy. This research has two objectives: the first is to develop a LSP Lifecycle Model (LSLM), and the second is to identify and evaluate the life cycle state of services and to improve mature or declining services by redesigning an appropriate lifecycle transition for each respective LSP. This research generates a new era of LSLM that integrates lifecycle management (Product Lifecycle Management, Service Lifecycle Management, and Product Service System) and the characteristics of logistics service. The LSLM comprises three phases, eight criteria, and fifteen sub-criteria. The three phases are Beginning of Life (BOL), Middle of Life (MOL), and End of Life (EOL). The eight criteria are service innovation, design and testing, long-term relationship, operation performance, financial performance, risk management, evaluation of customer satisfaction, and end of life decomposition. Adapted Axiomatic Design methodology aims to conceive and re-design services in order to enhance the LSLM through the application of Industry 4.0 (I4.0) technologies in order to improve service logistics. I4.0 generated the eight main Customer Needs (CNs), Functional Requirements (FRs) and Design Parameters (DPs), which are implemented in DPs by Big Data Analytics, IoT/IoS, real-time autonomous, and track & trace technology. The LSLM is validated to re-design logistics service for three case study LSPs in Thailand. Lifecycle-stage evaluation was adapted to identify the current status of the LSPs’ lifecycles. Identification and evaluation of the stages of logistics service was performed in order to re-design logistics service using three methodologies, namely, PLC analysis, matrix analysis (GE-Mckinsey, BCG matrix), and fuzzy inference. Four stages were identified: introduction, growth, maturity and decline stage. Evaluation for Case Studies 1, 2, and 3 revealed that all three are in the mature stage of EOL, scoring 61.15, 50.00, and 52.24, respectively. Next, logistics service strategies were implemented based on customer feedback using Quality Function Deployment (QFD). Customer requests were prioritized using the Best-Worst Method (BWM). The study combined the LSLM with I4.0 to improve service logistics. The most important factor for Case Study 1 according to BWM analysis was “Design service to meet customer requirements,” with an Importance Rate (IMP) of 0.3361. The most relatively important technical indicator shows the functional requirement of the “design and test service criterion to satisfy customer requirements” is 41.74. The re-design implemented for Case Study 1 includes text response, real-time tracking, and real-time monitoring of moving operations. For Case Study 2, analysis showed that the most important criterion is “Long-term relationship,” with a weight of 0.3328. The most relatively important technical indicator according to analysis is “Implement CRM initiation,” with a weight of 30.71. I4.0 is applied by developing an Internet of Things (IoT)/Internet of Services (IoS) and a real-time autonomous service to foster a long-term relationship. For Case Study 3, analysis of the importance rate (IMP) revealed that the most important criterion is “Long-term relationship”, with a weight of 0.3340. The most relatively important technical indicator is “Implement CRM initiation,” with a weight of 19.46. I4.0 is applied by developing an IoT/IoS and a real-time autonomous service to foster a long-term relationship. Recommended I4.0 tools are application text response, real-time tracking, and real-time monitoring of moving operation. The case studies showed the implementation of service logistics strategies with the feasibility solution of I4.0. Some examples of the advancement of I4.0 are the application of self-learning automation, big data analytics, real-time information, IoT, and smart sensors. The results of this study indicate that I4.0 strategies can be applied to re-design logistics service, but should be applied with caution. Although the developed LSLM and presented methodology can recommend measures for LSP development per their service requirements, there are some limitations. While the first phase generated the LSLM that can respond to and improve customer satisfaction for the entire lifecycle of logistics service and the second phase of identification and evaluation provided contemplative and multi-dimensional analysis of the service of interest, the third phase of new service redesign can only offer suggestions and the results are rather dynamic. Solid validation of the model requires implementation of the measures, which can be even more challenging.