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Stank, Theodore; Esper, Terry; Goldsby, Thomas J.; Zinn, Walter; Autry, Chad
2019 International Journal of Physical Distribution & Logistics Management
doi: 10.1108/ijpdlm-03-2019-0076
The digital advances in modern industry are accelerating changes in the broad social, economic, political and business environments within which supply chain management (SCM) is practiced. Given this extraordinary contextual upheaval, the conduct of research to identify, define, understand and explain how the digital revolution will impact key SCM concepts is imperative. The purpose of this paper is to introduce a theoretically grounded Digitally Dominant Paradigm (DDP) framework that demonstrates how digital concepts and insights can be infused into existing elements of best-practice SCM, in order to help guide future research.Design/methodology/approachMiddle-range theorizing is proposed as a means to explore the ways in which researchers can explain supply chain phenomena (i.e. build theory) in the age of digitalization.FindingsAn example of how a DDP framework can be applied to a well-entrenched logistics/supply chain concept is provided, and the authors conclude by identifying exemplary research propositions for future exploration.Originality/valueThe broad goal of the paper is to spark forward-looking supply chain scholarship based upon development of a DDP of SCM.
Handfield, Robert; Jeong, Seongkyoon; Choi, Thomas
2019 International Journal of Physical Distribution & Logistics Management
doi: 10.1108/ijpdlm-11-2017-0348
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/approachThis 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.FindingsThe 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/valueWhile 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.
Lechler, Sabrina; Canzaniello, Angelo; Roßmann, Bernhard; von der Gracht, Heiko A.; Hartmann, Evi
2019 International Journal of Physical Distribution & Logistics Management
doi: 10.1108/ijpdlm-12-2017-0398
Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions.Design/methodology/approachAiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts.FindingsRT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself.Originality/valueThis paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.
Morenza-Cinos, Marc; Casamayor-Pujol, Victor; Pous, Rafael
2019 International Journal of Physical Distribution & Logistics Management
doi: 10.1108/ijpdlm-03-2018-0151
The combination of the latest advancements in information and communication technologies with the latest developments in AutoID technologies, especially radio frequency identification (RFID), brings the possibility of high-resolution, item-level visibility of the entire supply chain. In the particular case of retail, visibility of both the stock count and item location in the shop floor is crucial not only for an effective management of the retail supply chain but also for physical retail stores to compete with online retailers. The purpose of this paper is to propose an autonomous robot that can perform stock-taking using RFID for item-level identification much more accurately and efficiently than the traditional method of using human operators with RFID handheld readers.Design/methodology/approachThis work follows the design science research methodology. The paper highlights a required improvement for an RFID inventory robot. The design hypothesis leads to a novel algorithm. Then the cycle of development and evaluation is iterated several times. Finally, conclusions are derived and a new basis for further development is provided.FindingsAn autonomous robot for stock-taking is proven feasible. By applying a proper navigation strategy, coupled to the stream of identifications, the accuracy, precision, consistency and time to complete stock-taking are significantly better than doing the same task manually.Research limitations/implicationsThe main limitation of this work is the unavailability of data to analyze the actual impact on the correction of inventory record inaccuracy and its subsequent implications for the supply chain management. Nonetheless, it is shown that figures of actual stock-tacking procedures can be significantly improved.Originality/valueThis paper discloses the potential of deploying an inventory robot in the supply chain. The robot is called to be a key source of inventory data conforming supply chain management 4.0 and omnichannel retail.
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