Purpose
The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?
Design/methodology/approach
This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.
Findings
The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.
Originality/value
While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.
Given the high priority accorded to research collaboration on the assumption that it yields higher productivity and impact rates than do non-collaborative results, research collaboration modes are assessed for their benefits and costs before being executed. Researchers are accountable for selecting their collaboration modes, a decision made through strategic decision making influenced by their environments and the tradeoffs among alternatives. In this context, by using bibliographic information and related internal data from the Korea Institute of Machinery and Materials (KIMM, a representative Korean government institute of mechanical research), this paper examines the suggested yet unproven determinants of research collaboration modes that the SCI data set cannot reveal through a Multinomial Probit Model. The results indicate that informal communication, cultural proximity, academic excellence, external fund inspiration, and technology development levels play significant roles in the determination of specific collaboration modes, such as sole research, internal collaboration, domestic collaboration, and international collaboration. This paper refines collaboration mode studies by describing the actual collaboration phenomenon as it occurs in research institutes and the motivations prompting research collaboration, allowing research mangers to encourage researchers to collaborate in an appropriate decision-making context.
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