Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method

Mehdi Keshavarz-Ghorabaee

Conbad Kavous University

Maghsoud Amiri

Allameh Tabataba'i University

Mohammad Hashemi-Tabatabaei

Allameh Tabataba'i University

Mohammad Ghahremanloo

Shahrood University of Technology

Introduction to Big Data Analytics

Big Data Analytics (BDA) is a term that refers to the processes of investigating large amounts of data with diverse variables to uncover hidden patterns, unknown correlations, and other useful information or knowledge (Zhong et al. 2016).

BDA has many advantages over traditional database management. Lack of information in various manufacturing and service areas has been one of the major challenges for various organizations over the years. By using BDA, organizations can improve their performance and ultimately gain competitive advantages. In the management of the supply chain of large organizations and companies, the flow of information is also large. Information of transactions and activities that are performed throughout the supply chain is invaluable for business owners. This information can help them in future decisions on issues such as demand forecasting, production planning, and so on.

The main purpose of BDA is to help companies make better business decisions, enabling users to analyze large volumes of data from various sources such as databases, the Internet, mobile and location records, as well as information recorded by sensors. To analyze such enormous data which have different formats, there is a wide range of technologies for analyzing Big Data (BD), enormous data that form a set of open-source software frameworks capable of supporting analyzing large amounts of data sets in cluster systems (Zakir, Seymour, and Berg 2015).

Today, the effective and efficient use of BDA is a key factor for the success of manufacturing companies in the global market (Y. Wang and Hajli 2017). The widespread use of digital technologies has led to the emergence of Big Data Business Analytics (BDBA) as an important business capability to give companies a better means to derive value from the increasing volume of data and achieve a powerful competitive advantage. BA is the study of the skills, technologies, and practices used to assess strategies and operations at the organization level to gain insight into and guide the career planning of an organization. These evaluations include evaluation of strategic management, product development, customer service delivery through evidence-based data, statistical analysis and operations, predictive modeling, forecasting, and optimization techniques (H. Chen, Chiang, and Storey 2012). The BDBA comprises two dimensions: BD and Business Analysis (BA). BA refers to the ability to process data with the following characteristics (3V): high velocity, high variety, and high volume. It has more processing capabilities than traditional data management approaches (C. L. P. Chen and Zhang 2014).

In recent years, BDBA has become a fast-growing and effective way for business organizations to keep their competitive advantages in a dynamic business environment.

One of the important applications of BDBA is its use in strategic management: formulating the strategies and aligning the organizational strategies and supply chain. Organizational strategy is very important because it presents the overall direction of the organization and guides the operations and strategies of an organization’s supply chain. Therefore, supply chain operations and strategies must be consistent with organizational strategies. BDBA can complement strategic management by adding advanced forecasting insights to strategy implementation processes.

BDBA can complement strategic management by introducing advanced forecasting insights into strategy implementation processes. On the other hand, the amount of data generated and communications over the Internet is increasing dramatically; thus, some challenges arise for organizations that wish to use this vast volume of data. Organizations wish to use this data because BD can provide unique insights about market trends, customer purchase patterns, maintenance cycles, cost-cutting approaches, as well as more targeted business decisions (G. Wang et al. 2016).

The purpose of BDBA is to gain insights from data using statistics, mathematics, econometrics, simulation, optimization, or other methods to support business organizations to make better decisions (Accenture Global Operations Megatrends Study 2014). In the strategic stage of supply chain planning, BDBA plays a key role and it is used to support firms to make strategic decisions in terms of resources, supply chain network design, and product design and development. In the operational planning phase, BDBA is applied to assist management in supply chain performance decisions, which often include planning of demands, inventory, and logistics activities (C. L. P. Chen and Zhang 2014).

Many companies around the world use BDA to identify and predict their customers’ behavior and achieve flexibility in relationships, logistics, and support. They can also effectively manage demand or cost fluctuations.

BDA has been used in Logistics and Supply Chain Management (LSCM) due to the sensitivity and importance of historical data and information. LSCM faces significant challenges that can potentially lead to inefficiency and wasting of resources in supply chains, including delayed deliveries, increased fuel costs, inappropriate suppliers, and increased unsatisfied customer expectations (Barnaghi, Sheth, and Henson 2013).

To achieve the goal of this chapter, which is to prioritize the barriers to BDA in the supply chain and logistics, the hierarchical best-worst method (HBWM) is used. This method has been introduced in Tabatabaei et al. (2019) and performs well for hierarchical decision-making. The reason for choosing HBWM in this study is that it requires less data for decision-making and offers more consistent comparisons. This method will be described in Section 3.3.

In this chapter, we examine the barriers and challenges of BDA in the supply chain management. These barriers and limitations are identified using experts’ opinions and reviewing the literature. Then, we determine their significances using the MCDM method. The results of the study reveal the problems and issues that organizations and companies face in analyzing BD and identify the most important factors. The study also shows that organizations can achieve competitive advantage by employing appropriate strategies and removing barriers.

The rest of the chapter is organized as follows: Section 3.2 reviews the literature on the barriers to BDA; Section 3.3 describes the methods used to prioritize the barriers; in Section 3.4, the barriers are prioritized based on experts’ opinions and hierarchical decision-making; conclusions of the chapter are provided in Section 3.5.

Barriers to BDA: Background

In this section, we will examine the barriers to implementing and using BDA. Many studies have been conducted to identify these barriers and various criteria and subcriteria have been defined for this purpose. Here, we intend to identify and analyze some of these barriers that are more relevant to supply chain management and logistics. Given the importance of BD and BDA, the purpose of this section is to identify and examine the major barriers to BDA adoption in supply chains and logistics.

A study examining organization and technology management practices at 330 state-owned companies in North America revealed that many organizations were not ready to use BD to improve organizational performance (McAfee and Brynjolfsson 2012), and they needed to overcome several barriers (or challenges) in this regard. These barriers include the need to acquire new skills by employees and upgrade IT infrastructure as well as instill new managerial practices or new organizational culture across the organization (Manyika 2011).

In order to examine the barriers to BDA, we searched for research literature using a variety of keywords, such as the “barriers to BDA,” “challenges of using BDA,” and the like. Few researchers have investigated and identified the barriers to BDA in the supply chain management and logistics. For example, a study was conducted to examine and identify the major barriers to the use and adoption of BDA in manufacturing supply chains in Bangladesh, and these barriers were prioritized based on the literature and experts’ opinions (Moktadir et al. 2019). In another study, techniques for removing barriers to acceptance and use of BDA were also discussed using qualitative analysis (Alharthi, Krotov, and Bowman 2017). A qualitative framework was also used to examine the challenges of using BDA in the telecommunications industry in South Africa (Malaka and Brown 2015). A conceptual framework was used to review articles related to the threats and opportunities of using BDA for international development (Hilbert 2016). An in-depth review process was carried out according to the literature review to accurately examine and identify the barriers to the acceptance and use of BD, and the transient and permanent barriers were categorized and discussed (Brohi, Bamiah, and Brohi 2016).

Twenty-six factors influencing BDA acceptance and utilization were identified and evaluated and integrated into a conceptual framework that encompasses technology, organization, and environment. The factors were identified by reviewing the literature during the period 2009-2014, and the results of the article enriched the literature on BD (Sun et al. 2018). Factors and challenges affecting the acceptance of BD in Korean companies were identified. Importance of factors was determined using the analytic hierarchy process and opinions of experts in Korea. The results showed that understanding the benefits of BD and technological capabilities are the most important factors influencing BD use in this country (Park, Kim, and Paik 2015).

In order to identify and rank important factors in BD acceptance and predict the impact of BD acceptance on the performance of manufacturing companies, important factors were identified: DEMATEL-ANFIS hybrid approach and then these factors were categorized based on technological, Organizational, and environmental dimensions. Data were collected from 234 industry executives involved in decision-making about IT provision in the Malaysian manufacturing companies. The results of the research showed that technological factors have the most influence on BD adoption and corporate performance (Yadegaridehkordi et al. 2018).

In this chapter, based on the research literature and opinions of various supply chain experts and managers, the barriers to the acceptance and use of BDA in supply chain and logistics management were selected. Therefore, it was found that given the

Barriers of BD and BDA

FIGURE 3.1 Barriers of BD and BDA.

specific international conditions and relations between countries, political barriers should also be considered as one of the important factors influencing the acceptance and usage of BDA. It should be noted, however, that the barriers to using and accepting BDA in different countries can vary depending on different cultures, different laws and policies, and so on. The barriers examined in this study have been selected through extensive discussions with supply chain managers and business owners in Iran and may be subject to change for use in other countries. These barriers are categorized into five main categories: technological barriers, specialized and human-centered barriers, data-based barriers, organizational barriers, and political barriers, which we briefly refer to as TEDOP. Each category contains several barriers in a specific area (Figure 3.1). A brief description and references for each factor can be seen in Table 3.1.

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