The past two years have brought about significant disruptions to global supply chains. In October 2021, for example, hundreds of container ships lined up outside busy ports for several weeks, waiting to unload. The result: empty shelves in stores and long waits for a wide range of products, from computer chips to cars.
Moving forward, the hope is that some of these disruptions will ease as businesses work to make their supply chains more resilient, e.g., by diversifying their manufacturing networks, multi-sourcing their suppliers, or creating inventory buffers. However, these levels of systemic change will take time to make an impact and will do little to solve either the immediate problems caused by the current labor shortages and pent-up demand, or the ongoing issues associated with getting items from distribution centers to stores and customers (e.g., high turnover rates of staff and driver shortages).
Instead, logistics and supply chain leaders should look to the latest technology innovations, powered by artificial intelligence (AI) to help with these challenges. While these solutions won’t solve all of the current problems, they will allow employers to implement some key, workforce-focused changes that will make their operations more efficient and their employees more productive.
Optimizing Labor Efficiency with AI
Supply chains are powered by people, so having the right person, with the right skills, at the right place, and at the right time is critical for businesses to meet their service levels and customer expectations. This makes accurate demand forecasting and an optimized labor plan key components for labor efficiency.
Supply chain organizations are well-practiced at using data and performance benchmarks to determine demand forecasts and workforce needs, e.g., how many pallets and cases they can expect on each truck, and how many operators they will need to support the logistics. The problem is that the past two years’ worth of data is atypical due to COVID-19.
Workforce management tools that use AI and Machine Learning (ML) enable supply chain leaders to leverage historic datasets to help uncover longer-term trends. In addition, the more sophisticated ML-based models can be self-learning, taking into account external factors such as weather, local events, and more, to more accurately determine where demand is likely to come from and what it means for a business in terms of resourcing needs.
Supply chain operations yield huge volumes of data that can help managers adapt to a volatile landscape. For example, factors such as truck delivery days have a significant impact on workforce needs. The latest AI-powered workforce management platforms can factor in thousands of data points, such as delivery channels and dates, so logistics and supply chain professionals are able to schedule the right worker at the right time at the right location.
Significantly, machine learning-based models are self-learning – the more data that is fed into the model the better it becomes. Organizations can therefore rapidly improve their demand forecasting capabilities, quickly making up for the lack of useful data from 2020/2021.
AI-Enabled Employee Engagement
With the US in the grip of a “great resignation” – some 95% of workers say they’re considering taking a new job – ensuring a winning employee experience is more important than ever.
If logistics and supply chain leaders are to attract and retain the people they need, they first need to understand what makes workers happy, outside of higher pay. We recently surveyed 1,000 hourly employees in North America to get an idea of what some of these factors might be. We found that three major factors contribute to overall employee satisfaction: schedule empowerment, improved communication, and early access to wages. Some of the most pertinent findings include:
● 59% of respondents cite scheduling issues as a reason to quit a job
● 39% say they would leave their current job if their employer was a poor communicator
● Nearly 75% say they would like the option to receive pay early to help avoid financial emergencies.
Insights like these can help logistics and supply chain organizations build a leading employee experience. For instance, employers could look to arm their workers with the tools to better control their work schedules, providing options to access pay quickly, and all the while ensuring that effective communications channels are in place.
Resilient Workforce, Resilient Supply Chain
2022 will present a fresh set of challenges for logistics and supply chain leaders as the world continues to be reshaped by the fallout of the coronavirus pandemic. Companies that draw on the power of AI to optimize labor efficiency while engaging with employees will put themselves on a good footing to mitigate the worst of the challenges facing the sector.