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Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.

This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.

First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.

The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.

The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.

This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.

While big data creates business value, knowledge on how value is created remains limited and research is needed to discover big data’s value mechanism. The purpose of this paper is to explore value creation capabilities of big data through an alignment perspective.

The paper is based on a single case study of a service division of a large Danish wind turbine generator manufacturer based on 18 semi-structured interviews.

A strategic alignment framework comprising human, information technology, organization, performance, process and strategic practices are used as a basis to identify 15 types of alignment capabilities and their inter-dependent variables fostering the value creation of big data. The alignment framework is accompanied by seven propositions to obtain alignment of big data in service processes.

The study demonstrates empirical anchoring of how alignment capabilities affect a company’s ability to create value from big data as identified in a service supply chain.

Service supply chains and big data are complex matters. Therefore, understanding how alignment affects a company’s ability to create value of big data may help the company to overcome challenges of big data.

The study demonstrates how value from big data can be created following an alignment logic. By this, both critical and complementary alignment capabilities have been identified.

The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective.

A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles.

By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data.

This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence.

Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data.

Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

This research paper determines how service supply chains can create value with big data, by building cross-departmental processes. Based on the study’s results, the critical alignment capabilities for successful big data value creation are: IT-process alignment; IT-performance alignment; performance-process alignment; human-IT alignment; and human-process alignment. Additionally, overarching and underlying strategic and organizational alignment capabilities also impacted this value creation. The human impact on employees of big data-led process creation shouldn’t be underestimated.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.