Part 3 of 3. This article is based on the findings in the report, Understanding the Impacts of Industry 4.0 on Manufacturing Organizations and Workers, prepared for the Smart Factory Institute and written by Chris Cunningham, PhD, UC Foundation Professor of Psychology, and Scott Meyers, Graduate Assistant, Psychology Department & Smart Factory Institute, from the Industrial and Organizational Psychology Department at the University of Tennessee at Chattanooga.
All the effects of Industry 4.0 on the manufacturing industry and workforce create opportunities and challenges for the broader management of talent within these organizations. Failing to respond to these talent management challenges will prevent manufacturing organizations from realizing the benefits of Industry 4.0 advancements. Only organizations that effectively leverage their people-based organizational resources such as workforce talent, technical expertise, and training and development opportunities to create and sustain organizational capabilities will thrive (Avitia-Carlos et al., 2019; Babatunde, 2020; Shet & Pereira, 2021). To do this, manufacturers will need to actively attract, retain, and develop emerging and existing talent. These critical talent management considerations can be summarized into the following functional categories: talent recruitment, acquisition, selection, and placement; talent training and development; and ongoing talent management. In this third of a three-part series, we will explore ongoing talent management.
If you missed Parts 1 (Talent Recruitment, Acquisition, Selection, and Placement) and 2 (Talent Training and Development), click the respective links to get caught up.
Ongoing Talent Management
An incredibly important talent management challenge brought about by Industry 4.0 is how to manage and generally keep properly recruited, selected, and trained smart workers motivated and engaged in their work. Beyond managing the massive changes associated with adopting Industry 4.0 technologies and practices, traditional talent management is likely to undergo its own evolution. Talent management under Industry 4.0 is less characterized by administrative functions and more defined by complex and strategic decision-making. This is made possible by the automation of many administrative functions using advanced Industry 4.0 technologies, which also are likely to result in wider distribution of power and responsibilities across organizations and teams (Dzwigol et al., 2020; Wesselink et al., 2017). Traditional approaches and methods for managing talent will also change as advanced data analytics methods (leveraging AI and ML) make available new insights and potentially may decrease some of the most common talent management decision-making tasks (Yunus, 2020). Even as these advanced technologies streamline or otherwise optimize traditional talent management decisions and processes, it is important to emphasize that there will be commonalities between traditional talent management and talent management under Industry 4.0. For example, although all talent within manufacturing organizations will need to continuously learn and develop under Industry 4.0, managers will still need to stay a step ahead of their workers so they can help to support and guide organizational talent even as required technologies in the organization change over time. Managers will also still need to be able to identify valuable talent and accurately deploy it in the most appropriate positions within the organization (Shet & Pereira, 2021). Managers will also continue to carry much of the responsibility for ensuring workers are staying competent through ongoing training and development, though Industry 4.0 technologies do hold potential to put more of the power for ongoing professional development into the hands of workers directly. Finally, managers will also need to lead by example by building and sustaining a positive learning culture in the organization (Shet & Pereira, 2021). This will ultimately keep the workforce current and competitive within Industry 4.0.
Continual evaluation and management of workforce competence will be a related and critical talent management responsibility for manufacturing organizations seeking to maintain a competitive advantage. New technology developments, technology integrations, and process improvements should be consistently examined. Organizational competency models and critical worker requirements should be regularly reviewed and potentially updated. Manufacturers should understand the current state of labor skill and requirement changes. Organizations can assess workforce using learning/competency management systems, formal reviews, self-assessments, interviews, observations, surveys, employee performance monitoring, testing, and assessment centers (Hernandez-de-Menendez et al., 2020).
Along with ongoing management of workforce development, talent management professionals will need to put even more time and effort than before into the assignment of talent to particular roles. Initially, these decisions will be based on the KSAOs and competencies possessed by applicants at their time of hire and reskilled existing workers at the time new technologies are introduced and/or work roles are redesigned. Over time, however, it will likely be an important component to a broad talent management strategy to rotate and shuffle talent throughout a variety of work roles. This type of cross-training can be an especially effective way to keep highly trained talent engaged and open to continuous learning and development. This type of talent management strategy can also help to ensure an organization’s workforce is as adaptive, flexible, and resilient as possible.
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