After two years in which the pandemic was the force shaping the immediate future, logistics companies working in e-commerce are now facing a ‘new normal’ of an unpredictable global economy, disrupted supply chains and labour shortages.
Amidst this evolving environment, the growth in e-commerce continues, presenting its own challenges for e-commerce fulfilment operators, such as reverse logistics. In order to stay ahead, fulfilment centres must make adjustments and one of the most cost-effective ways is through the use of real-time data.
There is no doubt that innovations in AI, automation and advanced analytics are becoming more commonplace in logistics. DHL’s Logistics Trend Radar, which tracks innovations in logistics concludes:
“More technology visionaries than ever before are beginning to understand the vast, ripening opportunities in the logistics industry to develop and apply their innovative solutions around the world.”
Indeed, many logistics operators are starting to integrate automated technologies into their strategic plans and include statistical systems into their systems. They’re also starting to get familiar with collecting, analysing and using data to predict future events and returns volumes.
But there’s a fundamental shift that’s happening and taking automation even one step further. That is the capture of data in real time to make informed and immediate decisions.
Real-time analysis of data means that fulfilment managers can react straight away, rather than basing their reactions on data collected a week or a month ago. Using real-time analysis, in tandem with analyses of historical trends, operators can proactively maintain throughput and optimal efficiency throughout their operations.
Yet, it has to be said, many fulfilment professionals generally still struggle to systematically and effectively make use of their data. For the majority of operators, data use relates more to their customers and end consumers than their sortation processes. In overlooking their valuable sortation data, many facilities end up running at just 60 or 70 percent of their theoretical automated handling capacity.
If e-commerce fulfilment facilities start to use their sortation data in real time, they can be so much more efficient. They can utilise their billions of data points to examine their sortation and material handling to proactively maintain throughput.
How is this done?
It starts with digitalisation, which is the process of collecting enormous amounts of information – generated from both equipment and operations of the fulfilment centre – which is stored as data. Understanding and working with this data then becomes an extremely valuable tool for improving the throughput and efficiency.
Some e-commerce fulfilment operations have integrated fully autonomous solutions throughout their facilities. But even small to midsize operations can take advantage of data and automation. They have valuable data points in their sorters, scanners, scales, chutes and conveyors from which they can begin automatising in ways that enable efficiencies and future automation growth.
The goal of a material handling system is to condense these billions of data points to unearth issues that must be resolved or reveal opportunities to achieve greater efficiency, regardless of the fulfilment centre’s data maturity.
While fulfilment handling systems can be complicated, the processes within the systems are not: the fulfilment operator must induct an item, then get the item out of the system in the most efficient way.
Using their data points, e-commerce logistics providers can begin to unlock proactive decision-making abilities through real-time data analytics which present facts that help them understand what’s happening in their processes. Data brings to light contributing events and their impacts, allowing managers to counteract the event immediately.
Material handling systems integrate with warehouse management systems and subsystems to provide operational support at all levels, from basic machine controls to computing complex statistics.
Its software can monitor and control the material flow from the moment an item enters the system until it is dispatched to ensure optimum capacity and efficiency.
Each control module within the software is based on proven and robust IT architecture that has evolved over the past 20 years. But where in the past ‘islands of automation’ could not communicate with each other without human intervention and a lot of data crunching in spreadsheets, today’s logistics systems enable machines to communicate with each other at high levels, enabling managers to align their operations in all areas of their fulfilment operations.
Future innovations point to self-learning machines that utilise real-time data to self-optimise the system.
This means that while present data analytics is moving fulfilment operations from the responsive to the predictive, the future will actually lie in prescriptive analytics – where material handling systems tell operators the action they need to take to avoid future problems.
The future will also include greater integration with third-party data, allowing operators to further optimise their processes, from sourcing materials to last-mile delivery.
A material handling system based on real-time data analytics will also empower skilled associates to determine where potential backlogs may occur to preemptively respond and mitigate potential system downtime. Managers are able to make determinations about efficiency, machine communication and operator performance.
It is clear that if e-commerce fulfilment centres want to prepare for operations of the future, they need to start utilising their data in all aspects of their handling processes. Fortunately, they already have mountains of data available to them to start digitalising their productions. Adopting digital software and know-how, fulfilment centres can take their digitalisation efforts even further, with analysis in real time, letting the intelligence of their automated systems direct the processes and reduce the need for human intervention.
