A design method for supply chain traceability systems with aligned interests

Dai, Hongyan; Ge, Ling; Zhou, Weihua

doi:10.1016/j.ijpe.2015.08.010

Cited by 46 publications

(26 citation statements)

References 37 publications

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“…For instance, tracking and tracing systems can be implemented at a smaller unit level (e.g. truck level to batch level) and for a wider scope covering more participating supply chain firms (Dai, Ge, and Zhou 2015). The same trend applies to inventory and resources tracking on a manufacturing shop floor and warehouses and significantly improves the level of information detail (Zhong et al 2017;Fang et al 2020).…”

Section: The Multi-level Framework From the Data-information-decisionmentioning

confidence: 99%

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

Viet

Behdani

Bloemhof-Ruwaard

et al. 2020

International Journal of Production Research

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While many supply chain decisions could take advantage of big data, firms struggle with investments into supply chain analytics since they are not able to assess the application areas and benefits of these initiatives. In this paper, we provide a multi-level perspective to assess the value of supply chain data. We develop a framework that highlights the connections between data characteristics and supply chain decisions with different time horizons (i.e. short-or long-term) as well as different supply chain levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the 4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage of the opportunities offered. We use the Dutch floriculture sector as a case study for our framework in which we highlight four data analytics applications to improve logistics processes. In the applications, we demonstrate how the data is used to support the decisions at different time horizons and supply-chain levels. We find that each of the big data's Vs is required differently according to the decisions' characteristics. Based on the findings, applications in other industries and promising directions for future research are discussed.

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Section: The Multi-level Framework From the Data-information-decisionmentioning

confidence: 99%

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

Viet

Behdani

Bloemhof-Ruwaard

et al. 2020

International Journal of Production Research

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“…Cloud is a computing paradigm in which tasks are assigned to a combination of connections, software and services accessed on a network (Dai et al, 2015). This network of servers and connections is collectively known as "the cloud".…”

Section: Cloudmentioning

confidence: 99%

“…IoT identifies inputs and goods (Kim & Kim, 2016), BDA identifies consumption patterns (Waller & Fawcett, 2013), cloud stores data related to operations (Dai et al, 2015), blockchain guarantees validity of commercial transactions (Bocek et al, 2017), 3D printing allows local printing of components, avoiding logistical traffic (Waller & Fawcett, 2014) and, finally, crowdsourcing improves lead-time (delivery time) through the sharing of road assets (Castillo et al, 2018). b) Transport: IoT (Lu et al, 2018), blockchain (Kshetri, 2018) and crowdsourcing (Carbone et al, 2018) deal with the tracking, inviolability and agility of transport, streamlining the entry and exit logistics; c) Storage: IoT (Lu et al, 2018), blockchain (Bocek et al, 2017) and BDA (Wang, 2016) can ensure that inputs and goods are being stored in locations the closest to points of consumption as possible, improving lead-time; d) Information: this new activity incorporated into the model proposed by Lambert (1975) has the function of managing all the complexity of data and operational, managerial and strategic information generated by these data.…”

Section: Conceptual Model For Logistics 40mentioning

confidence: 99%

An exploratory study on emerging technologies applied to logistics 4.0

2020

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The concept of Logistics 4.0 works closely to that of Industry 4.0. While Industry 4.0 proposes a disruptive change in manufacturing, Logistics 4.0 advocates a transformation in the way organizations buy, manufacture, sell, and deliver products. The objective of this paper is to identify, in Brazilian companies, the degree of interest in the investment in six emerging technologies applicable to logistics, according to scientific literature, as well as to identify the current perception of data quality of these companies. To achieve these objectives, an online survey was conducted. The research showed that the technologies that most interest Brazilian companies are Internet of Things (IoT) and cloud computing, both with 82% of investment intention. The two technologies that least interested companies are crowdsourcing and 3D printing, both with 68% investment disinterest among respondents.

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“…However, the implementation of effective drug traceability systems faces many challenges, and it is essential to identify the critical success factors (CSFs) for such systems. There are theoretical gaps in the study of the development of traceability in different pharmaceutical supply chains, such as the creation of value by the implementation of traceability systems, as discussed by [5,6]. In references [5,7], the authors argue that the primary reason for adopting traceability may be to respond to legal claims, and that companies that use new technologies do not make the most of the potential operational benefits of traceability.…”

Section: Introductionmentioning

confidence: 99%

“…There are theoretical gaps in the study of the development of traceability in different pharmaceutical supply chains, such as the creation of value by the implementation of traceability systems, as discussed by [5,6]. In references [5,7], the authors argue that the primary reason for adopting traceability may be to respond to legal claims, and that companies that use new technologies do not make the most of the potential operational benefits of traceability. For these reasons, it is necessary to understand the potential values which can be obtained with traceability, and to understand the CSFs to reach these values.…”

Section: Introductionmentioning

confidence: 99%

Critical Success Factors of a Drug Traceability System for Creating Value in a Pharmaceutical Supply Chain (PSC)

Silva

Mattos

2019

IJERPH

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The general objective of this study was to identify and prioritize the critical success factors required for the adoption of a system to create value for pharmaceutical supply chain stakeholders, and the pharmaceutical supply network as a whole, by using a multi-perspective framework that combines elements of the technology–organization–environment (TOE) contexts for enterprises. The methodology is based on a literature review and expert interviews following the analytic hierarchy process (AHP). This paper identifies and prioritizes 18 critical success factors from three categories: technological, organizational, and environmental. From a practical point of view, this research contributes to the literature by providing expert insight on the topic of drug traceability, especially in terms of how possible values can be captured by companies.

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A design method for supply chain traceability systems with aligned interests

Cited by 46 publications

References 37 publications

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

An exploratory study on emerging technologies applied to logistics 4.0

Critical Success Factors of a Drug Traceability System for Creating Value in a Pharmaceutical Supply Chain (PSC)

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