Labour is a critical component of any logistics operation. Very often it represents the largest proportion of expenses for large organisations – at least those with hourly workforces. Reducing the costs of labour for the logistics industry is a pressing concern. Given that customer demands are increasing, and services must now be more flexible than before many find that labour costs are only rising. Those who continue to assess and deploy labour using traditional methods will risk profitability.
Luckily, analysing workforce data has never been easier for logistics companies. Using this data to analyse and inform decisions seems an obvious solution to the problem of rising labour costs. Labour demands, like many parts of the supply chain, are moveable and subject to rapid change.
The results are not just anecdotal. A study from 2011 by Aberdeen claims that “organisations that integrate workforce and business performance data into analytics tools are three times as likely to achieve Best-in-Class (top 20%) performance results as those who don’t.”
Real-time, accurate and highly-detailed information on a workforce can be transformational for logistics companies, offering unrivalled visibility and opportunity for improvements. This will quickly highlight any glaring oversights or worrying trends that may be impacting on labour costs. Spotting and resolving issues before they can impact profitability will be an essential ability for logistics companies in the 21st century.
Combining this workforce data with operational data can reveal deeper and more actionable insights. Being able to match labour capacity with operational demands, reallocating resources where necessary. This will allow logistics companies to maintain their flexibility and services whilst saving on unnecessary labour wastage. With bottom lines becoming tighter and tighter for the industry, any savings that can be found are significant.
One logistics company has found improvements through analysing historical labour data. Using these insights, they have optimised their combinations of full-time and part-time staff to match seasonal and operational demands. This optimisation has saved them over $1million in labour overtime in the first year alone.
An increase in automation, especially in warehousing operations, is in full flow. The increasing availability of Machine Learning and AI tools means more and more logistics companies are turning to automation to reduce labour costs. Fully automated loading and unloading systems are already available – although this will require some investment in machinery the costs over time are much lower.
The analytics behind these tools is now becoming so sophisticated that many tasks previously thought too complex are becoming automated. Trailer loading and unloading, and package delivery especially will benefit from increased automation. The savings in labour costs here are immediately obvious – although initial investments may be large these prices will scale as warehouses embrace the full power of analytics and automation.
Controlling labour costs is critical for logistics companies and protecting margins. Making data-driven decisions on labour demand and matching this to operational demands can drive significant improvements. Fully embracing analytics and the increasing role of automation will not only lead to lower labour costs but improved productivity and profitability as well. Logistics operations must adapt to reflect the fast-changing environment they are in.