Using big data analytics to improve decision-making in apparel supply chains

Bănică, Logica; Hagiu, Alina

doi:10.1016/b978-0-08-100571-2.00004-x

Cited by 21 publications

(17 citation statements)

References 2 publications

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“…Finally, Zhong et al (2015) stated how information from Big Data can be used to create effective logistic plan, production plan and scheduling. Confirming the importance of the analysis of the data collected, Banica and Hagiu (2016) stated that fashion companies that invest in implementing solutions for gathering, processing and analysing internal data, combined with external consumer and market data, have a competitive advantage in the marketplace.…”

Section: Literature Reviewmentioning

confidence: 98%

Managerial and Industry 4.0 solutions for fashion supply chains

Braglia

Marrazzini

Padellini

et al. 2020

JFMM

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PurposeThe purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.Design/methodology/approachA Lean Manufacturing tool, the 5-Whys Analysis, has been used to find out the root causes associated with the problem identified from a data analysis of production orders of a fashion-luxury company. A case study, which explains the methodology and illustrates the capability of the tool, is provided.FindingsThis tool can be considered a suitable instrument to identify the causal factors of inefficiencies within luxury supply chains, suggesting potential countermeasures able to eliminate the problems previously highlighted. In addition, enabling technologies that deal with Industry 4.0 are associated with the root causes to enable further improvement of the supply chain.Practical implicationsThe effectiveness and practicality of the tool are illustrated using an industrial case study concerning an international Italian signature in the world of fashion-luxury footwear sector.Originality/valueThis framework provides practitioners with an operative tool useful to highlight where the major inefficiencies of fashion-luxury supply chains take place and, at the same time, individuates both the root causes of inefficiencies and the corresponding corrective actions, even considering Industry 4.0 enabling technologies.

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Section: Literature Reviewmentioning

confidence: 98%

Managerial and Industry 4.0 solutions for fashion supply chains

Braglia

Marrazzini

Padellini

et al. 2020

JFMM

View full text Add to dashboard Cite

show abstract

“…There have been instances in research trying to use the power of big data with advanced analytics and artificial intelligence to improve perceived value and satisfaction by focusing on the product offering. More of it is associated with improving the manufacturing operations or providing limited garment customization options (Fogliatto et al, 2012;Shang et al, 2013;Banica and Hagiu, 2016). There has been research focusing on the success factors of e-commerce, which shows personalization and improving the customer support system throughout the shopping process and during order fulfillment can benefit e-commerce businesses (Lei [Murray] et al, 2018;Bielozorov et al, 2019;Sharma and Aggarwal, 2019;Mushtaq et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Toward a conceptualization of personalized services in apparel e-commerce fulfillment

Jain

Sundström

2021

RJTA

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Purpose Today, customers’ perceived value does not only depend on the products, but also on the services provided by a firm. In e-commerce, it is important to shift the focus beyond the product and discuss the value of personalized services in the context of e-commerce fulfillment. Therefore, the purpose of this paper is twofold: to develop a conceptual framework proposing satisfaction through personalized services as a middle-range theory; and to suggest foundational premises supporting the theoretical framework, which in turn shape middle-range theory within the context of apparel e-commerce fulfillment. Design/methodology/approach In this theory-driven paper, the authors apply the scientific circle of enquiry, as it demonstrates the role of theorizing with the help of middle-range theory and empirical evidence and as such provides a methodological scaffolding that connects theory formulation and verification. The authors synthesize literature related to customer perceived value (CPV) and satisfaction, followed by abduction focusing on understanding the empirical domain as it occurred in practice from company cases. The presented case studies are based on semi-structured interviews with three Swedish online retailers within the apparel industry. The theory-driven analysis results in suggestions of foundational premises. Findings Based on the theoretical foundations and empirical generalizations, three propositions are suggested. The premises regarding satisfaction through personalized service applied in the domain of apparel e-commerce fulfillment are: to ensure customer satisfaction requires a value co-creation perspective using data during the pre-purchase phase; to ensure customer satisfaction and retention require added-value perspective during the post-purchase phase of the shopping journey; and to ensure satisfaction and convenience require an added-value perspective at the last mile. Practical implications The apparel firms lose a substantial amount of revenue because of poor online customer satisfaction, leading to e-commerce not reaching its full potential. To enhance customer value, online retailers need to find a resort in advanced technologies and analytics to address customer satisfaction, and it is suggested that retailers shift their focus beyond the products and find ways to improve personalized service offerings to gain market advantage, improve fulfillment, drive sales and increase CPV. Originality/value To consider personalized services as a source for improving e-commerce fulfillment and CPV, the main contribution of this study is conceptual as it presents a theoretical model developed from general theory, middle-range theory and verified with empirical claims.

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“…The problem in utilizing Hadoop in big data analytics is the incorporation between Hadoop and the previous ERP systems of the organizations, a likely scenario in [9] point to an integrated architecture that integrates Big Data technologies in real systems, companies should be agile when they merge the old infrastructure with the new one.…”

Section: Interpretationsmentioning

confidence: 99%

“…Another example is Caesars Corporation who analyzed health-insurance data for 65,000 employees and their families about how they used medicalservices and used these data to deal with specific drugs companies. The authors in [9] build a big-data infrastructure for their project; they presented a new database called "NoSQL" for storing big data, and implemented it on Hadoop for gathering structured and unstructured data. The first architecture layer designed to collect any type of data whether it is structured or unstructured the second one is processing the previous collected data using Hadoop and the last one is analyzing Big Data by using analytical business and modelling tools.…”

Section: Utilizing Hadoop In Big Data Analyticsmentioning

confidence: 99%

“…Hadoop is "an open source software platform that enables processing of large data sets in a distributed computing environment", the authors in [9] discussed some concepts according to big data, the rules for building, organizing and analyzing huge data-sets in the business environment, they offered 3 architecture layers and also they indicate some graphical tools to explore and represent unstructured-data, the authors specified how the famous companies could improve their business for example Google, Twitter and Facebook show their attention in processing big data within cloud-environment Many companies get the benefit from collecting and analyzing data from comments on social media. Zynga is a game maker who collected data from customer service and he used these collected data to design new version for his game.…”

Section: Utilizing Hadoop In Big Data Analyticsmentioning

confidence: 99%

See 1 more Smart Citation

The Importance of Big Data Analytics in Business: A Case Study

Alsghaier¹,

Akour²,

Shehabat³

et al. 2017

AJSEA

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Abstract:Companies all over the world try to get the benefits from accessing to information that is available in social media to improve their performance and increase their revenue, processing heterogeneous type of data to extract the valuable data is a problem that many organizations try to solve. One of the most important trends is in general known as "Big Data", technology for Storing, Processing and analyzing data, companies are Managing data in order to use it in new levels and direct decision makers to make agile decisions in real time, Big Data trend have the capability to guide a revolutionary transformation in research, invention, and business marketing. In this research we highlight some aspects of Big Data and its importance on organizations' business performance and how companies can use the famous open source platform Hadoop to process data to gain the competitive advantage.

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Using big data analytics to improve decision-making in apparel supply chains

Cited by 21 publications

References 2 publications

Managerial and Industry 4.0 solutions for fashion supply chains

Managerial and Industry 4.0 solutions for fashion supply chains

Toward a conceptualization of personalized services in apparel e-commerce fulfillment

The Importance of Big Data Analytics in Business: A Case Study

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