With the trends towards servitization and digital innovations in supply chains (SCs), a number of SC leaders have started to commercialize their SC capabilities as services provided to business customers. In order to efficiently organize multiple suppliers’ resources and customize the service offerings, some of these leaders have developed a “supply chain as a service” model (hereafter SCaaS), in which different functions of a SC, are grouped into service modules to enable plug-and-play agility in meeting the varying needs of business customers. Although SCaaS is emerging as an evolution of the market for cloud services (as with other “X as a service” models like Software as a Service, Platform as a Service, and Infrastructure as a Service), supply chain management (SCM) researchers have not systematically studied the SCaaS phenomenon, which has evolved from a cloud computing application to a new business model at the ecosystem level.
This study explores how a SCaaS has emerged and how it works by instigating three complementary research questions: (1) how do a firm form its SCaaS through the interactive implementation of supply chain innovations (SCIs) and business model innovations (BMIs) over time; (2) what are the roles and activities that SCaaS incorporate, and how these roles and activities are organized to serve the business customers; and (3) what is the detailed service operation process of SCaaS for satisfying a specific customer demand. To address these questions, this study adopts a longitudinal case study approach to investigate a SCaaS formed by Haier COSMO, a company which connects together customized orders, third-party R&D solution providers, intelligent manufacturing factories, and other SC service providers, to provide mass customized SC services to business customers.
This study makes contributions to both the SCM and the service innovation literature. It expands our knowledge of SCI-driven BMIs and echoes with recent calls to refocus SCM on the perspectives of value co-creation and service ecosystem. The study also reveals new insights into how to apply digital technologies to enhance SC capabilities, and how to apply these SC capabilities to support new business models. The findings provide important managerial insights for firms to design and implement new business models in today’s trends towards open innovation and value co-creation with ecosystem participants.
Keyword
business model innovations (BMIs)
;
longitudinal case study
;
service modularity
;
supply chain as a service model (SCaaS)
;
supply chain innovations (SCIs)
Purpose—The third-party logistics (3PL) firms increasingly rely on information technology (IT) to improve the supply chain process and firm performance in the context of the globalized and fiercely competitive market. The purpose of this study is to investigate how logistics IT adoption as a standardized resource affects firm performance. Moreover, we explore the mediating role of customer collaboration and the moderating role of government policy support between logistics IT adoption and firm performance from the resource-based view and socio-technical perspective.
Design/ methodology/ approach— Survey data acquired from a sample of 235 3PL firms in China were analyzed using partial least squares structural equation modeling (PLS-SEM).
Findings—The empirical results show that logistics IT adoption has a positive effect on both financial and operational performance by strengthening customer collaboration. Additionally, government policy support amplifies the positive effect of customer collaboration on operational performance, rather than on financial performance.
Originality/value—This study offers rich empirical insights to the growing body of SCM and 3PL literature. And the findings contribute to our understanding of the technological and developmental issues of 3PL firms both theoretically and practically
Keyword
information technology adoption
;
financial performance
;
operational performance
;
customer collaboration
;
government policy support
Since customer choice rules would greatly affect the performance of retail facilities, they
should be considered when a chain wants to locate a new facility in a competitive market.
In the existing studies, customers’ choice behavior is usually considered as homogeneous,
which means that all customers patronize facilities with one kind of customer choice rules:
the deterministic rule, the probabilistic rule or the multi-deterministic rule. However, it
is not in line with reality as we have investigated people’s choice behavior on convenience
stores by questionnaire surveys, and survey results show that different customers may patronize facilities with different choice rules. In order to study competitive facility location
problems in which customers’ choice behavior is heterogeneous, we classify customers as
three types by customer choice rules, the relative proportions of which are calculated based
on questionnaires. A customer classification based competitive facility location model in
the plane is proposed in which location and quality of the new facility are to be determined
in order to maximize the profit of the locating chain. Since the model is non-convex and
discontinuous, and location problems in practice are usually large-scale, four kinds of heuristic algorithms instead of exact algorithms are designed for obtaining a satisfactory solution
including Particle Swarm Optimization, Tabu Search, Simulated Annealing and Genetic
Algorithm. Numerical experiments show that Particle Swarm Optimization performs best
both in computation efficiency and solution precision. Comparisons among location results
employing different customer proportions reveal that customer proportion significantly affects location results. Most importantly, the locating chain may lose large profit once the
customer proportion is wrongly estimated. Maximum profit loss is more than 20% in our
cases.
The case consists of both A and B minor cases, which focus on decision-making optimization and service innovation business models related to logistics and big data. Case A – standing in the present and looking into the past – is a descriptive one, which mainly represents the service platform of fourth-party logistics, and is targeted at the main progress of the past 12 years made by Yiliu Technology Co., Ltd (hereinafter referred to as Yiliu Tech). It starts from opening up physical data and business process data via various software and hardware technologies. Then, through the analysis and application of logistics big data of different areas, it provides innovative data-based services including user portraits, intelligent loading and scheduling, route optimization, and logistics finance to logistics companies, carriers/fleets, drivers, and other participants on the platform.
Case B, as a decision-making case of “standing in the present and looking into the future,” mainly describes how would Yiliu Tech innovate its service or business model for more stakeholders in the logistics ecosystem in the face of the transformation and upgrading of China’s logistics industry, and the strategic investment from Cainiao Network. Faced with the four possible development directions proposed in the case, how would Yiliu Tech choose and how to implement its choice?
Keyword
big data and business intelligence
;
Business ecosystem
;
Business Model
;
fourth party logistics
;
logistics transparency
The case consists of both A and B minor cases, which focus on decision-making optimization and service innovation business models related to logistics and big data. Case A – standing in the present and looking into the past – is a descriptive one, which mainly represents the service platform of fourth-party logistics, and is targeted at the main progress of the past 12 years made by Yiliu Technology Co., Ltd (hereinafter referred to as Yiliu Tech). It starts from opening up physical data and business process data via various software and hardware technologies. Then, through the analysis and application of logistics big data of different areas, it provides innovative data-based services including user portraits, intelligent loading and scheduling, route optimization, and logistics finance to logistics companies, carriers/fleets, drivers, and other participants on the platform.
Case B, as a decision-making case of “standing in the present and looking into the future,” mainly describes how would Yiliu Tech innovate its service or business model for more stakeholders in the logistics ecosystem in the face of the transformation and upgrading of China’s logistics industry, and the strategic investment from Cainiao Network. Faced with the four possible development directions proposed in the case, how would Yiliu Tech choose and how to implement its choice?
Keyword
big data and business intelligence
;
Business ecosystem
;
Business Model
;
fourth party logistics
;
logistics transparency
International Journal of Production Research
, 2018
, 56
(18)
, 6240-6258
SCISCIEScopusABDC-A
Abstract
This study explores the conjunct roles of a series of formal and informal control mechanisms exerted, respectively, by client and vendor in offshore-outsourced project performance. Using a sample of 203 offshore projects executed by vendors in China, the results indicate that client process control enhances (or complements) the effect of vendor outcome control, yet impairs (or substitutes) the effect of vendor process control. Conversely, client outcome control enhances (or complements) the effect of vendor process control, yet impairs (or substitutes) the effect of vendor outcome control. Further, for the two informal control mechanisms, the results indicate that client relational control enhances (or complements) the effects of both vendor process and outcome control, whereas vendor clan control only enhances (or complements) the effect of client outcome control on offshore-outsourced project performance. These findings not only contribute new insights for the organisational control and the outsourcing literature, but also provide managerial guidance for client and vendor managers on how to exert and fine-tune their control mechanisms to promote project performance.
