# How AI Is Changing Workforce Planning in Travel and Logistics Companies
September 20, 2024 | Article
Travel and logistics companies face increasingly severe workforce challenges, including frequent shortages, rising costs, and declining productivity. Combining AI tools with new ways of working can enhance efficiency.
**For many travel and logistics providers,** workforce challenges have become a growing source of anxiety. Workforce shortages are frequent, and labor costs are on the rise. Missteps in workforce planning can lead to reduced profit margins and diminished service quality for customers. These workforce issues affect more than three million people employed by the top 40 travel and logistics companies.
## About the Authors
This article is a collaborative effort by Jorge Amar, Neal Parikh, Sal Arora, Scott McConnell, and Taylor Cornwall, representing McKinsey’s Travel, Logistics & Infrastructure Practice.
Companies have limited control over future labor costs and supply, but appropriate strategies can maximize frontline employee productivity, ensuring that the right number of employees are deployed at the right time and place to enhance resilience against unexpected events. In travel and logistics companies (such as airlines, hotels, and logistics service providers), improved workforce management can create significant value in long-term, mid-term, and daily objectives.
Current challenges stem from decisions made over the past few decades. Despite having rich data, many companies have underinvested in infrastructure and optimization tools, leading to ineffective use of this data for better planning. Due to limited visibility, workforce planning often relies on outdated assumptions and methods, frequently resulting in overly simplistic models and spreadsheets managing complex labor usage. The current tools typically lack the precision and flexibility needed to handle today’s complex workforce demands, especially in constantly changing operational environments.
Updated solutions such as generative AI (gen AI) can integrate large discrete datasets to generate new capabilities and greater workforce flexibility. However, tools and analytics alone are not sufficient. Solutions must meet the unique needs of organizations, different business unit goals, and existing technological architecture. Even the best workforce tools may be ineffective without proper process changes and performance management.
Companies should establish comprehensive processes and collaborative management structures to help frontline employees flexibly address daily challenges. Technological advancements can combine with mindset and cultural shifts to drive sustained productivity growth. We have seen some companies improve operational margins, reduce costs, and enhance customer service scores through these methods.
With the accelerating retirement of Baby Boomers, businesses expect to lose decades of expertise. It is time to refresh workforce planning for the next generation of employees—using a new generation of tools and strategies. Companies can consider five themes as guidelines for developing comprehensive workforce management approaches: digital demand reduction, advanced resource and capability planning, recruitment and personnel development, day-of decision-making, and continuous improvement.
## The Industry Faces Major Workforce Challenges
In travel and logistics, workforce challenges are numerous. For instance, many companies have or will soon face labor shortages due to various factors:
– High turnover rates can lead to vacant positions. Logistics employee turnover has risen by 33% compared to previous levels.
– Training planning difficulties contribute significantly to pilot shortages in airlines.
To address these issues, one airline implemented a digital twin solution with two modules: one for generating more streamlined pilot training programs and another for simulating disruptive events like coach illnesses. This reduced training bottlenecks by 5% to 10%, overhiring by 3% to 5%, and planning time by 15% to 20%.
Another airline faced urgent deadlines to recruit a large number of service professionals for ground operations. By adopting data-driven, agile frameworks to transform their recruitment strategies, they achieved their hiring goals while creating a more adaptable system.
### Day-of Decision Making
A typical travel and logistics company operates a distributed network of hundreds or thousands of locations, each needing to function seamlessly and independently. Local teams make hundreds of workforce decisions daily—such as when and where to deploy frontline employees and how to adjust plans during unexpected disruptions. To ensure efficiency, consistency, and alignment with broader corporate goals, organizations must support local leadership in addressing day-of operational challenges through standards, frameworks, and tools.
Clear policies for decision-making are crucial, enabling local teams to replicate these arguments independently. However, decisions should not be made in isolation; frequent meetings help the organization understand the types of challenges faced by local operations and whether current policies and tools adequately support field offices.
Organizations can develop recovery manuals for specific situations. Yet, each day brings its unique workforce challenges due to unplanned employee absences, delays in trains/trucks/planes, adverse weather conditions, and other unexpected obstacles. Leveraging analytical insights—such as real-time data on resource availability and predictive analysis of task requirements—can help forecast demand peaks and troughs. Combining these analytical capabilities with recovery optimization and actual execution allows teams to adapt in real-time to changing demands.
A global logistics company failed to see productivity improvements across its large driver network due to infrequent route changes, leading drivers to rely on the same routes without adjusting for changing environments. By starting to use AI-based daily path optimization to minimize travel distances, driver travel time was reduced by 15%, significantly enhancing productivity.
### Continuous Improvement
Implementing new tools and workflows and hiring outstanding individuals are good first steps but not enough. An organization’s culture—encompassing shared values, beliefs, attitudes, and behaviors—is the engine for true transformation. Continuous improvement capabilities are a crucial part of organizational culture, promoting clear standards, useful performance management, and widespread accountability.
Clear work standards specify time and quality expectations while identifying appropriate personnel to execute tasks. Real-time performance management becomes possible when driven by utilization, productivity, and efficiency metrics displayed on user-friendly digital dashboards for all relevant employees. Managers can use these levers to reward or hold employees accountable. Both monetary and non-monetary incentives are essential for motivating employees and ensuring alignment with organizational goals.
Rapidly understanding the root causes behind over- or underperformance revealed by a metric allows for swift action and correction, leading to continuous improvement. Digital solutions can automate root cause analysis. Leading organizations also use digital analytics to improve feedback and coaching: an AI recommendation engine can send personalized reminders suggesting areas where employees need improvement and alert managers when coaching opportunities arise.
A railway company saw limited usage of its automatic path planning tools despite significant investments, primarily due to frontline employee resistance. Technical challenges and fears about job displacement hindered adoption. The railway company responded with accelerated promotion and targeted interventions. Root cause analysis guided tailored initiatives, resulting in a 40% to 50% increase in training for dispatchers, a 25-30 percentage point rise in the usage rate of automatic path planning, and a tripling of network coverage for this part.
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Navigating transformation requires a strategic approach to change management. This includes building capabilities, supporting process improvements, and rethinking work methods. Without an effective change management strategy, even the best-designed tools and workflows may face resistance or fail to achieve expected outcomes.
Any change management effort can start by creating a compelling change story that clarifies needs and benefits for all affected parties. It should specify what changes are needed and expectations for new ways of working while providing appropriate training so employees can adopt these changes. Leaders can lead by example, reinforcing their importance and helping their teams navigate the transition and intervene when challenges arise.
Companies may have strengths in certain areas of workforce management but still face gaps in other critical areas. To identify these gaps, companies might consider asking themselves:
– Do our policies and workflows effectively simplify tasks and reduce unnecessary burdens on employees?
– Do we possess the appropriate technological infrastructure and skilled team members to leverage AI-driven workforce management tools effectively?
– Are our current recruitment and training channels aligned with operational needs to ensure a ready workforce?
– How effective are we in managing and responding to day-of operational uncertainties?
– Do we have transparent and timely performance metrics that are communicated appropriately to track performance and identify improvement opportunities?
Once organizations identify opportunities, how should they begin? The most innovative companies go beyond point solutions to adopt integrated approaches aimed not only at addressing current pain points but also defining a new performance culture across the organization. These efforts bring data architects and scientists together with frontline leaders and workers to rethink work methods, driving improvements in both performance and quality of life. While broader transformations may take years, proof-of-concept and frontline change initiatives can show impact within months. Success requires comprehensive solutions that combine innovative analytics with operational expertise. But embarking on a journey toward full-scale, data-supported, AI-driven workforce planning is a path that can begin immediately.
## About the Authors
**Jorge Amar** is from McKinsey & Company
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Source: McKinsey & Company
