On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

Improving service logistics is crucial in order to reciprocate customer needs. The paper aims to validate the Logistics Service Provider (LSP) Lifecycle Model for re-designing logistics service in three LSP case studies in Thailand. The lifecycle-stage evaluation was adapted to identify the current status in its lifecycle. Afterward, logistics service strategies were implemented according to the voice of the customer by Quality Function Deployment (QFD). The study combined the Logistics Service Provider (LSP) Lifecycle Model with the application of Industry 4.0 (I4.0) to improve service logistics. Case studies showed the implementation of the service logistics strategies with the feasibility solution of Industry 4.0.

[1]  Dejan Mirčetić,et al.  Logistics Response to the Industry 4.0: the Physical Internet , 2016 .

[2]  Trung Thanh Nguyen,et al.  A novel technique for evaluating and selecting logistics service providers based on the logistics resource view , 2015, Expert Syst. Appl..

[3]  Seyed Jafar Sadjadi,et al.  Best-worst multi-criteria decision-making method: A robust approach , 2018 .

[4]  Wolfgang Kersten,et al.  The effect of quality management on the service quality and business success of logistics service providers , 2010 .

[5]  T. Pereira,et al.  Industry 4.0 implications in logistics: an overview , 2017 .

[6]  Zhang-peng Tian,et al.  Green Supplier Selection Using Improved TOPSIS and Best-Worst Method Under Intuitionistic Fuzzy Environment , 2018, Informatica.

[7]  João Luiz Kovaleski,et al.  Technology transfer in the supply chain oriented to industry 4.0: a literature review , 2018, Technol. Anal. Strateg. Manag..

[8]  T. Cheng,et al.  Big data analytics and application for logistics and supply chain management , 2018, Transportation Research Part E: Logistics and Transportation Review.

[9]  Ke Wang,et al.  An Uncertain QFD Approach for the Strategic Management of Logistics Services , 2016 .

[10]  M. Nyadzayo,et al.  The antecedents of customer loyalty: A moderated mediation model of customer relationship management quality and brand image , 2016 .

[11]  Zülal Güngör,et al.  A new mixed integer linear programming model for product development using quality function deployment , 2009, Comput. Ind. Eng..

[12]  Jafar Rezaei,et al.  A multiplicative best-worst method for multi-criteria decision making , 2019, Oper. Res. Lett..

[13]  A. Pedrosa,et al.  Logistics innovation development: a micro-level perspective , 2015 .

[14]  H. Gupta,et al.  Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method , 2016 .

[15]  Jasmine Siu Lee Lam,et al.  A quality function deployment approach to improve maritime supply chain resilience , 2016 .

[16]  H. Grupp,et al.  Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies , 2005 .

[17]  Jeongsam Yang,et al.  A fuzzy-based decision-making method for evaluating product discontinuity at the product transition point , 2014, Comput. Ind..

[18]  Elizabeth Chang,et al.  ZBWM: The Z-number extension of Best Worst Method and its application for supplier development , 2018, Expert Syst. Appl..

[19]  Eric W. T. Ngai,et al.  Customer reviews for demand distribution and sales nowcasting: a big data approach , 2016, Annals of Operations Research.

[20]  Robert Sroufe,et al.  Management systems and improving supply chain processes , 2019 .

[21]  Solomon Tesfamariam,et al.  Framework for prioritizing infrastructure user expectations using Quality Function Deployment (QFD) , 2017 .

[22]  Christine Roussat,et al.  Supply Chain Integration: Views from a Logistics Service Provider , 2011 .

[23]  Jafar Rezaei,et al.  Evaluating firms' R&D performance using best worst method. , 2018, Evaluation and program planning.

[24]  Ali Emrouznejad,et al.  Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach , 2012, Expert Syst. Appl..

[25]  J. Rezaei.  Best-worst multi-criteria decision-making method , 2015 .

[26]  Chiung-Lin Liu,et al.  Integration, supply chain resilience, and service performance in third-party logistics providers , 2018 .

[27]  Charbel José Chiappetta Jabbour,et al.  Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations , 2018, Annals of Operations Research.

[28]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[29]  J. Lam,et al.  Environmental sustainability of logistics service provider: an ANP-QFD approach , 2015 .

[30]  M. Skitmore,et al.  Application of a hybrid Entropy–McKinsey Matrix method in evaluating sustainable urbanization: A China case study , 2015 .

[31]  Kumru Didem Atalay,et al.  Quality improvement strategies of highway bus service based on a fuzzy quality function deployment approach , 2016 .

[32]  Sungjoo Lee,et al.  Managing uncertainty to improve decision-making in NPD portfolio management with a fuzzy expert system , 2012, Expert Syst. Appl..

[33]  Yong Lin,et al.  QFD‐based modular logistics service design , 2011 .

[34]  Mohammed Quaddus,et al.  A multi-phased QFD based optimization approach to sustainable service design , 2016 .

[35]  Erik Hofmann,et al.  Industry 4.0 and the current status as well as future prospects on logistics , 2017, Comput. Ind..

[36]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[37]  Milorad Vidovic,et al.  Measuring the quality of logistic service as an element of the logistics provider offering , 2012 .

[38]  Jian Jin,et al.  Prioritising engineering characteristics based on customer online reviews for quality function deployment , 2014 .

[39]  Juergen Jasperneite,et al.  The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0 , 2017, IEEE Industrial Electronics Magazine.

[40]  K. Witkowski.  Internet of Things, Big Data, Industry 4.0 – Innovative Solutions in Logistics and Supply Chains Management ☆ , 2017 .

[41]  Himanshu Gupta,et al.  Evaluating service quality of airline industry using hybrid best worst method and VIKOR , 2017 .

[42]  Riikka Kaipia,et al.  Managing integration in outsourcing relationships - The influence of cost and quality priorities , 2017 .

[43]  Przemysław Zawadzki,et al.  Smart product design and production control for effective mass customization in the Industry 4.0 concept , 2016 .

[44]  Hsing-Pei Kao,et al.  An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming , 2014, Comput. Ind. Eng..

[45]  Abteen Ijadi Maghsoodi,et al.  Hybrid hierarchical fuzzy group decision-making based on information axioms and BWM: Prototype design selection , 2019, Comput. Ind. Eng..

[46]  Christian Kowalkowski,et al.  Servitization and Deservitization: Overview, Concepts, and Definitions , 2016 .

[47]  Morteza Rasti Barzoki,et al.  A group multi-criteria decision-making based on best-worst method , 2018, Comput. Ind. Eng..

[48]  Markus Hartono,et al.  How Kansei Engineering, Kano and QFD can improve logistics services , 2017 .

[49]  Thomas J. Goldsby,et al.  Reflections on 40 Years of theJournal of Business Logistics: From the Editors , 2019, Journal of Business Logistics.

[50]  Yingfeng Zhang,et al.  Smart box-enabled product–service system for cloud logistics , 2016 .

[51]  Jafar Rezaei,et al.  Bioethanol facility location selection using best-worst method , 2019, Applied Energy.

[52]  D. Choi,et al.  Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis , 2018, Sustainability.

[53]  J. Rezaei,et al.  Assessing the social sustainability of supply chains using Best Worst Method , 2017 .

[54]  Fiorenzo Franceschini,et al.  Prioritisation of engineering characteristics in QFD in the case of customer requirements orderings , 2015 .

[55]  Ali Emrouznejad,et al.  Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method , 2018, Journal of Cleaner Production.

[56]  Andreas Schumacher,et al.  A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises , 2016 .