Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph

By Keah Choon Tan, Yuanzhu Zhan, Guojun Ji, Fei Ye, Ching-Ter Chang. 2015

403 citations recorded by the source corpus.

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm׳s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/133b5143a6e54a64067637349287d89bdf403c0b

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In Information Systems

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In Management Science and Operations Research

Citations in the corpus

Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “Significant time and effort are required to sort the information obtained and identify viable and relevant information (Tan et al., 2015).”
Artificial intelligence in supply chain management: A systematic literature review (2021, positive): “…in several manners, such as controlling and monitoring warehouses, food supply chains and sustainability in supply chains (Ting et al., 2014; Wang and Yue, 2017), improving knowledge management and marketing (Shaw et al., 2001), and enhancing supply chain innovation capabilities (Tan et al., 2015).”
Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors (2017, background): “With the primary goal to assist companies in making better business decisions, Big Data Analytics allow users to analyse huge volumes of data from different sources such as the database, the Internet, mobile phone records and locations, as well as sensor-captured information (Tan et al. 2015).”
Big data analytics in supply chain management: A state-of-the-art literature review (2017, background): “, in structured, semi-structured, and unstructured formats (Tan et al., 2015).”
Big Data and supply chain management: a review and bibliometric analysis (2016, background): “With regards to social media, a leading eyeglasses manufacturer, SPEC, collects and analyses Big Data from social media (i.e., tweets, Google, Facebook, etc.) to generate new product ideas (Tan et al. 2015).”
Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018, background): “Tan et al. (2015) proposed an approach to assist firms to capture their innovation potential with big data analytics.”
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “The SPEC, a leading eyeglasses manufacturer in China, analyzed the big data that were derived from customer feedback to provide ideas for new product innovations (Tan et al., 2015).”
Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2017, background): “Concerns with the trustworthiness of the social media and web scraped data are also raised (Tan et al., 2015).”
Impact of Industry 4.0 on supply chain performance (2020, background): “2014; Christensen, Olesen, and Kjaer 2005) • Rapid product design and prototyping and reduced time to market (Kache and Seuring 2017; Tan et al. 2015; Baur and Wee 2015) Fulfilment, procurement, and logistics • Real-time traceability and interoperability of resources (Qiu et al.”
Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, result): “Our findings also concur with earlier research that proved adopting big data analytics enhances supply chain innovation’s capability [92].”
Role of big data analytics in developing sustainable capabilities (2019, background): “Decision makers use big data analytics to devise strategies that translate into better ﬁ rm performance through innovation, competitiveness and values creation (de Camargo Fiorini et al., 2018; Fosso-Wamba et al., 2017; Tan et al., 2015).”