Exploration of Big Data in Procurement - Benefits and Challenges

Emergence of Big Data had positive implications in various industries and businesses. Big Data analytics provides the ability to harness massive amount of data for decision making purposes. One of the important use case of Big Data analytics is in supply chain management. Increased visibility, enhanced bargaining position in negotiations, better risk management and informed decision making are examples of benefits gained from Big Data analytics in supply chain. Although there are advances in analytics application throughout supply chain management, sourcing applications are lagging behind other functions of supply chain. The purpose of this study is to analyse use cases of exploiting Big Data for purchasing and supply purposes, in order to help companies having more visibility over the supply market. Data collection in this study was carried out through the use of semi-structured interviews which then were coded and categorized for comparison. The results pointed out that big data aids in identifying new suppliers. Additionally, having transparency over n-tier suppliers for managing risks were important for companies. Most of the companies are using descriptive analytics. However, they expected to have predictive analytics to become aware of market situation and gain better position in negotiations. Furthermore, this research showed that to prevent supply disruptions, the Big Data analytics should send timely warnings to managers. The main expectations from Big Data analytics are gaining transparency, automation of data collection and analysis, prediction, availability of new data sources, more efficient KPIs and better representation of data. The main hurdle in Big Data initiative is unintegrated and non-homogenous internal data

Big data analytics and application for logistics and supply chain management

This special issue explores big data analytics and applications for logistics and supply chain management by examining novel methods, practices, and opportunities. The articles present and analyse a variety of opportunities to improve big data analytics and applications for logistics and supply chain management, such as those through exploring technology-driven tracking strategies, financial performance relations with data driven supply chains, and implementation issues and supply chain capability maturity with big data. This editorial note summarizes the discussions on the big data attributes, on effective practices for implementation, and on evaluation and implementation methods

Impacts of Competitive Uncertainty on Supply Chain Competence and Big Data Analytics Utilization: An Information Processing View

Research advancements in big data analytics have invoked tremendous attention from both academics and industries. Many researchers refer that the adoption and application of big data analytics could lead to performance impact to organizations, and therefore further affect organizational adoption intention of this technology. However, few studies discuss the association between business strategy and big data analytics adoption under uncertainty such as pandemics or disasters. Furthermore, the role of firms’ functional activities such as supply chain operations has seldom been addressed in the adoption considerations of big data analytics under abnormal situations. In this research, empirical data from enterprises were collected and analyzed to assess the impact of competitive strategy uncertainty on big data analytics adoption and the possible effect of supply chain competence in the linkage. The results supported positive effects of strategy practices and supply chain competence on big data analytics utilization. The implications for management decisions are then elaborated

Big Data Analytics and Data Visualization in Shaping Supply Chain Industry: A Review

Technology is changing the way we live and organize our days. As the number of smart city projects grows, enhancing Supply Chain Management is a top objective in each smart city program. The study below describes how big data analytics and visualization tools have shaped the supply chain industry today. The different applications identified from big data analytics in the supply chain industry are reviewed as their impact and influence within the industry. The supply chain sector is shown to experience several challenges. Risks and unpredictability are shown to be the main problems. Big data analytics is, however, shown to be an effective tool for effective decision-making. Technology Acceptance Model is shown to inform and guide the entire research process

An Analysis of the Potential Applications of Big Data Analytics (BDA) in Supply Chain Management: Emerging Market Perspective

Big Data is defined as the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions. Big Data can be utilized to gain critical and fundamental insights towards optimizing the supply chain decisions more effective and efficient. In the recent years, therefore, researchers and practitioners have tried to measure the capabilities of Big Data to optimize Supply Chain Management (SCM) efficiency. This research attempts to provide a clear understanding of Big Data applications on Supply Chain Management in emerging markets, especially in Bangladesh, primarily focusing on four key areas: reducing inventory cost, attaining cost leadership, improving customer service and enhancing speed of delivery. To investigate the potential application of Big Data in supply management, a qualitative research has been conducted. Ten in-depth interviews and a case study have been conducted to collect the relevant information from the supply chain experts of the selected firms. Thematic analysis and Hermeneutic iterative methods of analyses have been used. The results indicate that the supply chain of both physical products and services can be benefited from Big Data analytics. The study also revealed that Big Data can be applied in SCM for operational and development purposes including value discovery, value creation and value capture. This study would help the decision makers and practitioners of Supply Chain Management of diverse fields to adopt Big Data to improve the organizations performance and sustainability. Keywords: Big Data analytics, Supply Chain Management, applications, emerging markets

Adoption of supply chain analytics in SMEs: an exploratory study

Objective Given the extant knowledge in the literature of the intersection among big data, analytics, and supply chain management, this thesis is aimed to explore the adoption of supply chain analytics in the SMEs. More specifically, the thesis’ main objectives are to investigate under what situations the SMEs adopt supply chain analytics and provide the recommendations for SMEs in adopting supply chain analytics. Summary Based on the content analysis of interviews with solution providers from different countries, the thesis has explored the main motivations behind the adoptions from SMEs, and the necessary existing resources and the challenges for SMEs to adopt supply chain analytics. Given such findings, a framework for future research on the factors that affect the adoption of supply chain analytics in SMEs is proposed and detailed recommendations for such companies are also discussed. Conclusions In conclusion, the adoption of supply chain analytics in SMEs is still in modest rate due to certain barriers and complex required resources for SMEs in adopting such practices. The decisions to adopt supply chain analytics in SMEs depends on factors such as perceived benefits, dynamic environment, data-driven culture, necessary resources, and challenges of the adoptions. The thesis recommends that SMEs should firstly build basic awareness of analytics, and technical capability related to data management before adopting supply chain analytics. Then, SMEs also need to emphasize on change management and adopt alignment strategy to optimize the benefits gained from analytics adoptions

A Proposed Architecture for Big Data Driven Supply Chain Analytics

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out. Keyword: Big Data, Analytics, Cloud, Architecture, Protocols, Supply Chain Management, Security, Privacy.Comment: 24 pages, 4 figures, 3 table

Big Data Analytics for Supply Chain Innovation

As supply chain management (SCM) involves a network of organizations in different processes and activities through linkages upstream and downstream (Christopher, 1992) in the sourcing, flow and control of raw materials, inventory in production, and finished product, SCM organizations are inundated with data, including streaming data from multiple sources. Innovation in today’s multi-channel, multi-modal complex supply chain world is very important for organizations to create next generation of superior, well-coordinated supply chain systems. This paper examines varied sources of Big Data in Supply chain world and presents research into ways in which Big Data Analytics, tools and technologies, platforms and Big Data frameworks can be used in Supply Chain Innovation

A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research

Big data has become a global phenomenon with companies in almost all industries trying in some way to identify and exploit this is untapped asset. The big data application in supply chain management (SCM) has also caught the management’s attention, and with the high influx of data being generated at different points in supply chain can be used to stimulate data driven decisions, build supply chain flexibility, adaptability and agility. With the inception and wide adoption of the Industry 4.0 technologies like Internet of things (IoT), cloud computing (CC), Smart manufacturing (SM), Artificial intelligence (AI), the need for integration of big data and analytics has been felt more than ever. The purpose of this survey is to investigate the applications of predictive analytics in different supply chain areas and provide a classification based on the different techniques/algorithm used at various supply chain areas, detect gaps and propose the future direction for research. The review also investigates the application of big data analytics (specifically, predictive analytics) along with these disruptive technologies in the SCM areas. The survey review indicated that manufacturing and demand forecasting are the two major areas with the most predictive analytics application, whereas the clustering, regression, and artificial neural networks are the more commonly used algorithms. The new SCM areas identified for Big data analytics applications integrated with the emerging technology are smart manufacturing and intelligent logistics management. Furthermore, the immediate need for future studies in other SCM areas like product development and inventory management are pointed out due to its immense potential benefits for the supply chain management

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers