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  • Subject area(s): Marketing
  • Price: Free download
  • Published on: 14th September 2019
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  • Number of pages: 2

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1.0 Background and Rationale

Digital supply chain management, through the Internet of Things (IoT) analysis and monitoring of cloud systems, the broad concept of goods, vehicles and other assets will improve the way the supply chain operates, which is the primary mission logistics company for many people.

The key to these advances is the ability to reflect and respond to what is happening in the real world supply chain, tracking the condition or location of the goods, or tightening the sensor or real-time traffic data to discover trends - and triggering appropriate changes. In general, the digital supply chain is about how the system understands the content being developed and is smart enough to change the chain's physical processes to achieve optimal performance.

The company selected in the analysis of this study is CX Logistics, established in 1988 in Malaysia and is headquartered in Seoul, with total sales of $ 5.72 billion in 2015 and more than 5,000 employees worldwide. CX Logistics connects the world through 78 regional bases in 22 countries, providing global integrated logistics services through a wide range of logistics networks. As an integrated logistics service provider, the CX's vision continues to drive the development of the global logistics industry and designs optimized supply chain management (SCM) to provide valuable assets to customers around the world in a timely, safe and convenient way.

2.0 Brief Literature Review

The purpose of this study is to develop a research framework to improve the understanding of digital supply chain and Internet of Things (IoT), to assist the researcher on the key structure in theoretical and empirical investigation, and to explore its impact on overall supply chain performance. This paper aims to extend the study:

a) Digitizing the supply chain with the support of logistics integrated system.

b) How the Internet of Things (IoT) will make freight operational performance more efficient?

The research framework developed in this study can be further refined or extended to various theoretical models, enabling researcher to test the effectiveness and relationships of key components and the impact on supply chain performance and ultimately create a coherent SCM theory. In the literature analysis phase, researcher will identify manifestations of digitalization in logistics, emerging or new technologies, and trends in a structured way.

This paper provides theoretical perspectives, case studies, and outlines research plans to address these challenges and gain insight into best practices for multinational digital supply networks. Chaos theory is a way to improve the degree of understanding the ambiguity of the supply chain, and how the chaos theory provides valuable insights into the effective management of the supply chain network. If researcher can effectively apply chaos theory to enhance the understanding of variables, such as digital supply chain impact to supply chain operations, optimization management techniques can be developed and implemented to improve supply chain performance levels.

Supply chain management is driven by information (Sherman, 2012). The organizations need to turn their supply chains into demand and product networks, focusing on integrated business planning to create real-time supply chain environments that provide end-to-end visibility. Real-time vehicle management is critical to supporting supply chain execution systems and minimizing associated logistics risks (Doukidis et al., 2005).

Companies embracing digital logistics will realize the transition from supply excellence, enterprise logistics management and supply chain leader supply chain integration and collaborative development capabilities. Through the IoT, the company will get the desired visibility, but also to find business efficiency, protection of goods, reduce the ability of the accident. As the IoT integration into existing processes becomes clearer, more businesses and organizations will strive to expand today's logistics supply chain capabilities. Various logistics information systems have been well stored and processed with various data and information to support day-to-day logistics (Kumar, 2007).

3.0 Research Methodology

A comprehensive literature review will be conducted to establish a classification framework for the digital supply chain and IoT. This paper summarizes the literature review of digital supply chain field by content analysis.

3.1 Material collection

The review focuses on the publications (through database searches) have also been found in supply chain management, operations management, marketing, and logistics journals. An initial keyword search for articles containing any of the terms of the phrase "digital supply chain" and “IoT” (limited to citations and abstracts of periodicals) revealed that there were more than 10,000 articles present in the Google Scholar. Quality control is achieved by restricting search to peer-reviewed publications.

3.2 Descriptive analysis

Assess the formal aspects of the material, such as the number of publications per year, providing a background for subsequent theoretical analysis. To provide a basis for subsequent content analysis, the formal aspects of the literature review papers will be evaluated in order to provide an in-depth analysis of the literature of each article selected in this review. Information on the distribution of various journal articles will be evaluated and presented in the next chapter and analyzed.

3.3 Category selection

The main analytical categories are derived from Stuart et al.'s research process model (2002): research purposes, data collection methods, data analysis methods and quality measures. According to the requirements of the literature review, the category data collection is supplemented by the collection of publications and the time period.

In the main process step of content analysis proposed by Mayring (2008), the category method for data analysis is supplemented by the type of data analysis, descriptive analysis criteria, and the main analysis categories / parameters used to build the content. Therefore, qualitative content analysis can realize the role of data analysis technology in digital supply chain research in many ways, thus supplementing some empirical data collection methods.

3.4 Material evaluation

The theory-based classification scheme with predefined categories and well-defined categories improves the reliability of coding and combines it with the intense discussion within the research team to improve the internal validity of the results.

The background of the content analysis results and the theory-led abstraction allow a generalization of the results to a general extent, thus generalizing the external validity (Avenier, 2010). In addition, the transparency and reproducibility of the study design are ensured by carefully recording the entire study process. Finally, as described above, the interdependence of data analysis, which is primarily responsible for interpreting the underlying content, is sought through "interpreted discourse coherence" (Duriau et al., 2007).

4.0 Resources

Determine the resources required to complete the project and understand each stage of the project. Distinguishing facts and opinions is important not only when researcher reads and evaluates literature, but also when writes a review of nursing need to clearly distinguish between previous studies (facts) and how researcher thinks this applies to researcher own research (opinion). Most journals are published from the following sources;

• International Journal of Operations & Production Management

• International Journal of Physical Distribution & Logistics Management

• International Journal of Production Research

• Supply Chain Management: An International Journal

• International Journal of Logistics Management

• The Asian Journal of Shipping and Logistics

• Journal of Supply Chain Management

5.0 Constraints

It can be noted that some of the limitations of the current study, as well as some of the future research direction. Any study is limited to the factors included. The factors identified in the literature on supply chain integration, capturing information flow, product and material flows, and long term relationships between supply chain partners as suggested by Handfield and Nichols (1999).

Therefore, the establishment of a supply chain model, clearly the supplier of uncertain factors in all factors into the supply chain environment (Kachitvichyanukul et al., 2015). According to Branch (2009), supply chain management is successful when coordinating the flow of information and goods between customers and networks between suppliers, manufacturers and distributors.

Internet of things concept and technology has been used in the field of logistics problems. If understand the supply chain digitization as a fully integrated planning and production solution sequence, working together to create a more visible and agile supply stream, then it is clear why this topic is the primary task of seeking to remain competitive.

6.0 Research Contribution/Implication

In the digital age, with the full development of digital technology, companies can create more customer value by manipulating time, space and things more creatively (Chakravarty, 2014). The digitization and integration of wired and wireless telecom technologies is conducive to the convergence of space for the integration and connectivity of the network (Malecki et al., 2008).

In any industry the firm with the fastest response to customer demands has the potential to achieve an overwhelming market advantage (Kumar et al., 2009). The ability to share information in digital form and transfer it from one company to another gives them a great opportunity to optimize supply chain preparation (Sadler, 2007). This digital supply chain not only improves efficiency, but also leads to improved customer experience and revenue growth. The IoT supply chain plays a very important role in modern logistics to speed up the application of things by provide higher accuracy and identification of potential problems in the supply chain process.

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