Understanding the role of big data analytics for coordination of electronic retail service supply chain

Water resources allocation considering water supply and demand uncertainties using newsvendor model-based framework

A novel newsvendor model-based framework for regional industrial water resources allocation that considers uncertainties in water supply and demand was proposed in this study. This framework generates optimal water allocation schemes while minimizing total costs. The total cost of water allocation consists of the allocated water cost, the opportunity loss for not meeting water demand, and the loss of the penalty for exceeding water demand. The uncertainties in water demand and supply are expressed by cumulative distribution functions. The optimal water allocation for each water use sector is determined by the water price, the unit loss of the penalty and opportunity loss, and the cumulative distribution functions. The model was then applied to monthly water allocation for domestic, industrial, and agricultural water use in two counties of Huizhou City, China, whose water supply mainly depends on Baipenzhu Reservoir. The water demand for each water use sector and the monthly reservoir inflow showed good fits with the uniform and P-III distributions, respectively. The water demand satisfied ratio for each water use sector was stable and increased for the optimal water allocation scheme from the newsvendor model-based framework, and the costs were lower compared with the actual water allocation scheme. The novel framework is characterized by less severe water shortages, lower costs, and greater similarity to actual water use compared with the traditional deterministic multi-objective analysis model, and demonstrates strong robustness in the advantages of lower released surplus water and higher water demand satisfied ratio. This novel framework yields the optimal water allocation for each water use sector by integrating the properties of the market (i.e., determining the opportunity loss for not meeting water demand) with the government (i.e., determining the water price and the loss of the penalty for exceeding water demand) under the strictest water resources management systems.

The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

Purpose

In this age of digitalization, organizations are generating large data from the organizations' manufacturing processes that are valuable for capturing a competitive edge. Chinese small and medium enterprises (SMEs) can bring organizations radical innovation by investing in innovation projects (i.e. big data use; BDU) using the SMEs' scarce resources. Thus, the authors' research aims to predict Chinese SMEs' radical innovation (RI) through BDU using the theoretical lenses of the resource-based view. Moreover, the authors' study also pursues to realize the undermining mediating process of business strategy alignment (BSA) and the buffering role of information sharing in BDU–RI linkage.

Design/methodology/approach

In total, 297 Chinese SMEs entrepreneurs and managers were recruited from the Yangtze River Economic Belt, China using a survey approach. In Mplus 7.4, the authors tested the proposed hypotheses.

Findings

The sample of 297 shows that BDU is directly and indirectly (via BSA) related to RI. Further, information sharing moderates the linkage between BDU and BSA and BSA and RI. The association between BSA and RI is only significant and stronger when information sharing is high.

Practical implications

This research is beneficial for SME entrepreneurs/managers to enhance the understanding of BDU, eliminate challenges of BSA via BDU and align business strategies to bring RI to Chinese SMEs.

Originality/value

SMEs always search for new ways to enhance SMEs' productivity using scarce resources. This is the first research that advances big data and innovation literature by predicting firm RI through BDU using a resource-based view. Moreover, this study is novel because the study investigates the mediation role of BSA and the moderating role of information sharing in the linkage between BDU and firm RI in Chinese SMEs.

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

Purpose

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

Design/methodology/approach

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

Findings

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

Research limitations/implications

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

Practical implications

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

Originality/value

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.