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The opportunity to anticipate delivery failures, shortages or delays in company’s upstream supply chains at an early stage facilitates to take preventive countermeas-ures to mitigate potential damage. However, data-driven predictive technologies such as machine learning (ML) are rarely examined in supply chain risk management (SCRM). The purpose of the following paper is to present a framework of design principles for the application of ML in SCRM. The foundation of this framework is an action design research (ADR) project, which is performed in collaboration with the SCRM department of an automotive company. A predictive ML model is developed and evaluated in collaboration with the company. Based on the findings and observa-tions made during the project, general design principles are derived and grouped by the three interrelated elements of organisation, development and operation, which are to be considered when applying ML in SCRM. Finally, the derived elements and the corresponding design principles are discussed and justified with reference to the literature.
To increase maturity within purchasing and supply management (PSM), future purchasing skills are needed based on the technological development towards Industry 4.0. Past research, eg, the work of Bals, Schulze, Kelly, and Stek (2019), started to address this issue based on literature review and interview studies. However, a detailed description of these skills is missing. Utilizing a real-time Delhi study with 45 experts within the PSM field, nine future purchasing skills have been elaborated. Identified skills connect to the maturing and emerging technologies within purchasing and provide a guideline towards Industry 4.0 in purchasing based on a human-centric perspective.
Nowadays, the human-centric discipline of purchasing and supply management (PSM) is of strategic importance for firms’ success. Within the discipline, scholars address PSM professionals’ skills and provide practitioners with academic insights. Due to changes in the industry environment, changes in the working environment and the task of purchasing professionals are assumed. This paper aims to contribute to the PSM professional skills literature by defining current PSM professionals’ skill gaps as the difference between the acquired skill level and perceived skill importance. Findings show that current PSM professionals feel to be underqualified to abstract the full potential of professional relationships, as buyer-supplier relationships, due to current PSM professionals’ skill gaps.
The global development towards the Fourth Industrial Revolution, the so-called Industry 4.0, is steaming forwards. Where cyber-physical systems connect the physical and digital world, allowing for demand identification, without the need for direct human intervention. Further, Artificial Intelligence supports various parts of operative and strategic purchasing. The new purchasing environment forces purchasing professionals to develop new skills. Research is needed to identify appropriate skill sets. Based on a World-Café method with 82 purchasing professionals, a list of 32 essential future skills in purchasing is composed. Further, the identified skills are ranked and assigned to the roles of the direct and indirect material purchasers.
Specifying roles in purchasing and supply management in the era of Industry 4.0: A Delphi study
(2021)
New technologies and systems within the field of purchasing and supply management (PSM) call forth responsibilities and require expertise. Moving towards Industry 4.0 in purchasing, increasing attention on specialization within talent and skills, where human capital is needed to exploit the full potential of technologies. Based on an internet-based real-time Delhi study with 47 experts within the PSM field, six future purchasing roles have been defined and elaborated. These future roles connect to the maturing and emerging technologies within the purchasing field and provide a guideline to further develop towards Industry 4.0 in purchasing based on a human-centered evolutionary approach.