Companies have the need, and now the ability, to track and better manage workforce well-being risk and its operational implications.
How motivated are your staff? Where are there pockets of anxiety and stress? Are there individuals with material health risk exposure? How are these impacting current operations? How might these impact future operations? Can you afford to not know the answer to these questions?
The COVID-19 pandemic has brought these questions to the front of many leadership teams’ discussions. The uncertainty of the pandemic; its impact on staff morale, anxiety, and productivity; and the consequential impact on operational planning and execution has challenged even the most contingent-ready organization. Most businesses do not have the information to answer the above questions. Without information, they are effectively riding a bike without one pedal.
Resilience is again a crucial capability for all companies to have. The COVID-19 crisis exposed those that lacked resilience and rewarded those that have it. During any crisis, whether driven by a spike in an infectious disease, an extreme weather event, or some other disruption, organizations often have to pivot quickly, reallocating resources to plug gaps in their operational capacity. That requires anticipating what will happen next and knowing which employees are available and at full productivity, not just simply present.
We now have an opportunity, and one can say also an imperative, to change that. Advances in data analytics and machine learning, together with the cultural shifts prompted by the pandemic, mean senior managers can get a much better idea of the physical and emotional state of their workforce and take appropriate, more decisive actions on both their people care agendas and operational responsiveness.
From insight to action
At Kearney, we are helping organizations understand the state of the workforce and predict where their operational gaps will appear, even during periods of rapid change such as now. Our 360° people and operational dynamics (POD) approach captures a unique and complete picture of workforce dynamics. Using artificial intelligence and natural language processing, we analyze structured and unstructured data across 12 disparate sources, ranging from local regulations to operational readiness to social media sentiment, and derive actionable insights and recommend tangible interventions that manage the risk (see figure).
Figure
12 types of data are analyzed, providing clients with a comprehensive and unique picture
A leading energy company in the Middle East used the approach to give its leadership team full transparency on the operational risks they were facing as the COVID-19 pandemic evolved. The company became better able to identify which of their vital staff could be at risk and to intervene to address that from both a people care and a business continuity perspective. The result was higher confidence for the leadership and the risk teams in taking snap decisions with their vital staff, as they were all backed by data and analytics.
Getting the intervention right
360° POD helps organizations know which interventions will make the biggest difference. Over time, the predictive nature of supervised machine learning narrows the range of options to those which have demonstrated to be impactful, against a playbook of well-proven actions including:
Addressing physical health with protective equipment, changes to on-site layout, processes to increase social distancing, and health routines
Addressing mental health with counseling, coaching, and flexible working
Addressing social health with peer-to-peer interactions, recognition, and team-/unit-/ organization-wide events
Addressing self-actualization with information access, role adjustments, training, and pay adjustments
Across all of these actions (and others) is purposeful, engaged leadership, armed with the information needed to know to whom, how, and with which messages to reach out to address feelings of anxiety, fear, isolation, and vulnerability. This becomes especially obvious when 360° POD flags divergent views between what staff are comfortable sharing internally, what they are saying as a group externally in public domains, and the impression of line managers. In a well-performing, high-trust organizational environment, there should be minimal divergence between these.
Throughout, high-quality communications are a must. The workforce needs to know and feel that the organization has their best interests at heart. This is first and foremost about great people care. The fact this also helps organizations manage operational risk is a secondary—though a big secondary—benefit. As some of the data sources will be voluntary in nature, this can only fully work when staff recognize the value of access, transparency, dialogue, and action. They will, when the organization shows and regularly reinforces their commitment to them, and reassures that no data is uniquely identifiable.
Time for a new approach
As a result of the pandemic, both leaders and front-line employees now appreciate the need for greater communication and engagement across organizations. The definition of employee well-being is broadening, and the crisis has underlined the importance of real-time or near real-time information to help businesses respond to a continually shifting environment. In such a fast-changing and unpredictable world, leaders need to know what their staff are thinking and feeling—and take the right actions to support them.
The data analytics capabilities that exist today, which we did not have a few years ago, make this possible. COVID-19 is both a crisis and a catalyst for a new level of people analytics, people engagement, and people care. Kearney believes this should become standard practice for all organizations.
The authors are indebted to Chris Berry, Anna Martynenko, and Arpita Ghosal for their support in the writing of this article, and additionally thank Bharath Thota and Nicolas Deschamps for their valuable contributions.
Source : https://www.kearney.com/leadership-change-organization/article/?/a/how-to-predict-protect-and-pivot