In a volatile industry in which parcel volumes fluctuate so wildly, capacity isn’t always fully utilized, which is costly. But big CEP operators with a global or national overview can improve the symbiosis of their distribution network to cope better with demand fluctuations.
This will enable them to avoid suboptimization and uncoordinated processes while making the network more optimized to save money internally: fewer costs equals more earnings.
Most of the tech needed to achieve this is already here, but there is some way to go to achieve this, warns Thierry Golliard, director of innovation and venturing at Swiss Post:
“The industry has improved many related aspects but still has a long journey ahead to offer this seamless experience we’re all aiming for.”
Firstly, investment is required in new systems. Only with them in place can aggregated data and forecast models transform your business, as data’s revolutionary role in the industry is now undeniable.
Reacting to dynamic circumstances
A ‘dynamic parcel network’ (DPN) integrates the latest advances in automation, software and artificial intelligence to connect, digitalize and optimize the delivery process across the entire network.
By bringing together all the components involved in a parcel’s journey, from the point of shipment to the end consumer’s doorstep, the DPN will enable a CEP business to respond in real time to dynamic circumstances.
While the connectivity is ambitious, it’s fast becoming achievable because developments in software intelligence are moving faster than previously predicted, enthuses Golliard.
“An increasing number of different platforms linked to transport and logistics, provided by postal operators and other organizations, will integrate with one another. The resulting network could ultimately enable a better use and sharing of logistics capacities,” he says.
Furthermore, the technology is becoming more and more affordable.
Predicting peaks – and scaling them
Already other industries, including telecom suppliers and energy companies, have embraced intelligent network operations that predict end consumers’ needs, enabling the suppliers to scale up and down accordingly.
Digitalizing the entirety of a parcel’s physical journey is not as easy as supplying electricity to a home, but digital intelligence can be used to connect parcel networks and the systems that operate within them.
After all, CEP companies can anticipate their peak times with the same level of certainty that energy companies expect the onset of winter: whether it’s Christmas, Black Friday or Apple releasing its latest iPhone.
This agility to keep up with peaks of demand is key, according to Christian Østergaard, lead visionary – senior group strategist of IT production/IoT/AI at PostNord. He comments, “We need to be increasingly flexible, scalable and fast – to enable this, we will use data and especially generative AI more than ever before. Combining our own operational data with demographics, like behaviors in different areas, can give you an overall optimization.”
The telling contribution of digital twins
Data relating to the end consumer, and from elsewhere on the network, can be easily mined and used to form digital twins – virtual copies of the future that enable operators to visualize countless scenarios – in 3D.
They can predict peak and low capacity, arrival profiles, fill rates and parcel types, all while assisting with volumetric detections, security analyses and productivity tracking.
Certainly, the CEP operators busy digitalizing their processes have become much better at predicting volumes and workflows, using time efficiently, achieving sustainability goals and cutting costs.
The days of using forecasts based on siloes and spreadsheets are long gone, along with the frequent data input errors. Now the automatically entered data is reviewed as part of the bigger picture, yielding new insights into how best to operate.
Digital twins have helped the operators to optimize their sortation systems and line-haul transportation, establishing an ecosystem that can visualize KPIs, create fact-based operations and analyze and predict important criteria.
The increased digitalization is also having an impact in the last mile where forecasting tools can detect whether customers will be home and predict parcel volumes and estimated time of departures.
Realizing the predictive potential of the data
In a DPN, the whole operation will be overseen from one central point: all the components will be aggregated, as well as the data. It’s only with a comprehensive overview of the complete operation that the predictive potential of the data can be realized, and the right decisions to route parcels can be made.
“The potential for improved processes thanks to interconnectivity and the automated use of data is huge,” says Østergaard. “Internet of Postal Things (IoPT) has emphasized how data can yield many insights, while AI will also play an important role in improving processes – both business and operational.”
Today’s networks are still manually structured, but the DPNs will be fully automated in their structure. It won’t be a case of a DPN being dynamic or not, but how dynamic it is.
Three steps to becoming dynamic
There are three steps to becoming a dynamic operation:
- Aggregate data from multiple sources
- Build forecast models using the latest technologies, such as digital twins
- Implement equipment changes
There will be no room for holy cows in the DPN – everything the industry thought it knew about optimal processes is up for discussion.
Data will indicate what should be done, and the focus will change every day – a future that excites Anett Berger Sørli, investment director at Bring Ventures and SVP at Next Studio. “The most exciting part of digitalization and using data is how it can boost efficiency,” he explains. “With real-time data analysis and automation, tasks can be streamlined, mistakes reduced and customers will have better experiences. It’s thrilling to envision a future where every aspect of our logistics network, from route optimization to package tracking, becomes smarter and more responsive, ultimately delivering better services and minimizing the environmental impact.”
Saving on time, cost and labor
CEP operators are finding that they can dramatically save costs by delaying deliveries until there is an adequate capacity, but also easily deal with peak scenarios. Time will continue to be a parameter, but it won’t be as important as before. If it makes sense to delay Tuesday deliveries until there are enough parcels to fill the truck, they will be pushed to Wednesday.
To leverage the situation, the DPN might suggest lowering the price on Monday to attract more custom. So, cost will determine how long a parcel takes to be delivered, but increasingly the carbon footprint will have a say too.
In the last mile, one of the most efficient and cost-effective ways to make deliveries more efficient is automated sequencing. Data can ensure the parcels are loaded according to the order they will be delivered, saving on time, route distance, emissions, labor and cost – both of fuel and wages.
Optimized routes using real-time data help drivers navigate congestion and weather. But most importantly they learn from experience, points out Østergaard. “For example, it might be the best place to park your vehicle to minimize the time spent on the delivery,” he adds. “The more data we generate, the more detailed the distribution model will become, making it easier to deliver on the next occasion. Meanwhile, our own data is important – for example, an end consumer will benefit from when and where and knowing if they can fit it on their bicycle. Overall, data enables more transparency for the whole of the parcel’s journey.”
Takeaway
A future of seamless deliveries rendered by DPNs is within touching distance for the CEP industry. Almost all the tech needed to achieve this realistic goal is available, but the major players will need to invest in the necessary innovation first – and it is far more affordable than they might think.
This article was originally published by Beumer Group. Read the original here.