Cloud Computing in Logistics
Cloud computing in logistics is proving to be something of a godsend for the industry. The ability to share and collect data wirelessly from any location complements the operational realities of the sector perfectly. The cloud is making it easier for firms to get a handle on their operations and improve performance.
Unfortunately, many companies still aren’t taking advantage of the technology. Data suggest that 82 percent still haven’t adopted cloud-based solutions and a further 26 percent can’t see the benefits of making the switch.
The Benefits And Applications Of Cloud Computing in Logistics
However, as we discuss below, the cloud offers firms tremendous benefits.
Real-Time Inventory Management
Imagine being able to monitor your inventory levels from any location in real-time. In the past, it wasn’t possible. But with a combination of NFC tagging and cloud services, it is now feasible. Real-time inventory management allows you to respond to supply and demand shocks quickly and adjust your lead times and processes to accommodate.
It is worth pointing out that the costs of failing to manage inventory correctly can be enormous. Mismanagement of merchandise famously caused Walmart to lose over $3 billion.
Logistics companies traditionally charged fees based on predefined variables, such as the distance freight had to travel. However, that approach is inefficient. Prices should reflect demand and their cost to you. Raising prices when demand is high and lowering them when it is low supports your bottom line, as does being able to adjust your prices to reflect the risks (such as adverse weather).
Until recently, approaches like this were just theoretical. However, today, cloud solutions offer tools that enable better pricing decision-making. They make it possible to view all information relating to demand, market conditions and weather from a single dashboard, allowing you to make pricing adjustments on the fly.
Greater Operational Visibility
According to Statista, making on-time deliveries and responding to last-minute changes are two of the three biggest issues facing logistics companies. 21 and 16 percent of them highlighted them as concerns respectively. With legacy technology, companies find it challenging to adjust their processes and ensure that they make deliveries on-time.
Greater operational visibility afforded by the cloud can correct this. Systems can track lorry locations, warehouse capacities, traffic conditions, weather and incoming demand in real-time. Firms can then use this data to learn more about their current operational position. Over time, they can analyse data to predict likely outcomes of making certain choices.
Create Merge-In-Transit Models
A merge-in-transit model is a distribution method that involves consolidating shipments from multiple distributors in a single location or predefined time sequence. In the past, it was notoriously difficult to achieve and often only worked for highly predictable patterns of distribution. However, with help from the cloud, more logistics companies are discovering that they can now do it routinely.
Cloud-based solutions make it possible to create realistic merge-in-transit models. Firms are integrating point of sale data and then forwarding it to “decoupling points” further upstream, allowing them to separate both orders and flows of goods, reducing uncertainty when putting together the final load.
Cloud-enable merge-in-transit models offer many benefits. These include:
- An increase in flexibility because of simplifications in the receiving and depositing process, eliminating many individual shipments and receipts
- A reduction in capital investment because of lower inventory requirements
- Greater customer service due to wider availability of products and suppliers
- A reduction in administration costs since products require less processing time and storage
- The elimination of unnecessary transportation by avoiding double shipments of goods from suppliers
Improved Pattern Recognition And Forecasting
Logistics companies work in tandem with other firms in the supply chain such as producers and retailers. Thus, demand for their services is derived. Orders for lorries and inventory vary according to production constraints and consumer purchases.
Historically, logistics companies used backward-looking forecasting methods. These evaluated demand patterns from previous years and then used them to anticipate demand for their services in the future.
However, as the COVID-19 pandemic has shown, backward-looking thinking doesn’t always work. Sometimes crises can disrupt long-term trends, throwing off projections.
Cloud computing permits the use of machine learning systems that can anticipate demand based on multiple data streams. These software tools can take into account POS data, weather conditions, factory output, supply chain managers, raw material supplies, and border restrictions from multiple sources. Crunching the numbers, they process the information centrally, producing outputs that allow operatives to make actionable business-relevant decisions.
If you would like to discuss how your logistics operation might best utilise the Cloud please drop us a line. We also work with many technology companies to help them move their applications for the logistics industry into the Cloud.