The social process of Big Data and predictive analytics use for logistics and supply chain management

Challenges in Adoption of Business Analytics by Small Retailers

Business analytics are transforming every industry in modern times, and the retail sector is not an exception to this. However, the adoption of analytics among small and medium enterprises is quite limited. There is a decent amount of research that has been done in the recent past on identifying the barriers of digitalization for small and medium enterprises. But any such work is missing in the case of analytics. Also, the small retailer, in particular, has been left untouched by the existing research studies when it comes to identifying what prohibits him from the use of analytics for making business decisions. Using a scientific way of data collection from small and mid-sized retailers and from retail industry experts, this study explores why the local retailers are reluctant to use analytics in their work and what can be done to make them more comfortable with the use of analytics. The study also proposes a novel framework for adoption of data analytics and titles it “SPEFSERT Framework.”

A large-scale real-world comparative study using pre-COVID lockdown and post-COVID lockdown data on predicting shipment times of therapeutics in e-pharmacy supply chains

Purpose

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

Design/methodology/approach

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

Findings

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

Research limitations/implications

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

Practical implications

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

Originality/value

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

Supply chain and logistics optimization management for international trading enterprises using IoT-based economic logistics model

Under the development trend of economic globalization, the financial difficulties of the supply chain may cause a serious crisis to the enterprises in the whole supply chain. It is necessary to alleviate the transformation pressure faced by international trading enterprises (ITE), to enhance the core competitiveness of ITE. This paper takes the SCLM (Supply Chain and Logistic Management) of ITE as the research object. Based on the analysis of the existing SCLM experience of ITE and the Internet of Things (IoT) technology, this paper puts forward the economic analysis model of the IoT-based SCLM. Specific data of some ITE prove the effectiveness of the proposed model. Further optimization is carried out through process analysis. The results show that the proposed SCLM economic analysis model based on IoT can effectively maximize the interests of enterprises and shorten the supply chain process by adding information groups. The introduced IoT-based method reduces the Average Queue Time (AQT) of vehicles by nearly 7 times and the Average Waiting Time (AWT) by nearly 4 times. After the IoT technology is applied to the SCLM of ITE, this paper significantly improves the cooperation efficiency of various departments, optimizes the relationship between customers and suppliers, and improves the core competitiveness of the enterprise. Therefore, this paper has important reference value for promoting the development of the small and micro machinery industry in China.

The Effects of Logistics Websites’ Technical Factors on the Optimization of Digital Marketing Strategies and Corporate Brand Name

In a world overwhelmed with unstructured information, logistics companies increasingly depend on their websites to acquire new customers and maintain existing ones. Following this rationale, a series of technical elements may set the ground for differentiating one logistics website from another. Nevertheless, a suitable digital marketing strategy should be adopted in order to build competitive advantage. In this paper, the authors attempt to respond by implementing an innovative methodology building on web analytics and big data. The first phase of the research collects data for 180 days from 7 world-leading logistics companies. The second phase presents the statistical analysis of the gathered data, including regression, correlations, and descriptive statistics. Subsequently, Fuzzy Cognitive Mapping (FCM) was employed to illustrate the cause-and-effect links among the metrics in question. Finally, a predictive simulation model is developed to show the intercorrelation among the metrics studied as well as various optimization strategies. Research findings reveal a significant correlation between the logistics websites’ technical factors and the growth of the corporate brand name.

Big data analytics in supply chain management: a systematic literature review

Purpose

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

Design/methodology/approach

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

Findings

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

Research limitations/implications

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

Originality/value

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

On relating big data analytics to supply chain planning: towards a research agenda

Purpose

This paper aims to examine how the extant publication has related big data analytics (BDA) to supply chain planning (SCP). The paper presents a conceptual model based on the reviewed articles and the dominant research gaps and outlines the research directions for future advancement.

Design/methodology/approach

Based on a systematic literature review, this study analysed 72 journal articles and reported the descriptive and thematic analysis in assessing the established body of knowledge.

Findings

This study reveals the fact that literature on relating BDA to SCP has an ambiguous use of BDA-related terminologies and a siloed view on SCP processes that primarily focuses on the short-term. Looking at the big data sources, the objective of adopting BDA and changes to SCP, we identified three roles of big data and BDA for SCP: supportive facilitator, source of empowerment and game-changer. It bridges the conversation between BDA technology for SCP and its management issues in organisations and supply chains according to the technology-organisation-environmental framework.

Research limitations/implications

This paper presents a comprehensive examination of existing literature on relating BDA to SCP. The resulted themes and research opportunities will help to advance the understanding of how BDA will reshape the future of SCP and how to manage BDA adoption towards a big data-driven SCP.

Originality/value

This study is unique in its discussion on how BDA will reshape SCP integrating the technical and managerial perspectives, which have not been discussed to date.

Environmental uncertainty and cross-buying in logistics outsourcing arrangements: a resource dependence perspective

Purpose

This study follows the tenets of the resource dependence theory (RDT) to investigate the effects of four dimensions of industry-level environmental uncertainty – munificence, dynamism, complexity and innovative intensity – on a shipper's cross-buying (i.e. outsourcing across multiple service categories) in logistics outsourcing arrangements.

Design/methodology/approach

Negative binomial regression was used to test the hypotheses with a sample of US manufacturers. Measures were developed through information acquired from a proprietary database of 3PL companies obtained through Armstrong and Associates, Inc. and publicly available industry measures from the US Manufacturing Census and Compustat.

Findings

The findings indicate that individual dimensions of environmental uncertainty exhibit distinct influences on shippers' cross-buying in their logistics outsourcing arrangements. Specifically, the growth and initial innovative intensity of shippers' industries lead to an increased number of logistics service categories outsourced to 3PLs, while industry dynamism and exceptionally high innovative intensity drive the opposite effect.

Practical implications

These findings provide valuable guidance to 3PLs with respect to decisions related to the acquisition of specialized transportation, storage, information systems and personnel assets to serve specific industries. The findings highlight industry conditions that are more likely to lead shippers to outsource across a wider array of logistics service categories and, as a result, potentially yield higher customer retention and profit margins.

Originality/value

While extant 3PL literature posits that shippers' individual strategic orientations and capabilities impact their outsourcing strategy, this study contributes to the literature by providing a theoretical-based empirical examination of the industry-level influencers of such behavior.

Logistics integration in the supply chain: a resource dependence theory perspective

Firms have strategically used cooperative linkages to establish competitiveness. In this study, we incorporated the resource dependency theory view to assess how trust, satisfaction, and commitment affect firms’ decisions on logistics integration. Also, we examined the link between logistics integration and supply chain performance. The study collected data from 250 South Korean manufacturers for analysis. The results revealed positive impacts of trust, satisfaction, and commitment on logistics integration between manufacturing firms and logistics service providers that enhances logistics service capabilities of the firms. Furthermore, our study showed that building a strategic relationship for logistics services helps the manufacturing firms improve their business and operations performances in their supply chain. Implications and suggestions for future research are discussed.