How supply chain business intelligence, data and analytics can improve the parcel delivery experience

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Parcel shippers are turning to supply chain business intelligence, data and analytics to identify ways to improve the parcel delivery experience and uncover new ways to generate efficiencies. A continuing increase in the number of data sources available to inform parcel shipping as well as expansions in business analytics capabilities have increased the adoption rates of digital supply chain technology, and in particular, business intelligence and machine learning, to improve strategic decision making.

The incorporation of new external data to augment transportation management data within business intelligence (BI) platforms is a particularly exciting trend. Here are three examples of how this is going to help e-commerce retailers make informed parcel shipping decisions.

  • Predicting how major weather events will impact parcel carrier performance

External weather data feeds can allow shippers to model and evaluate the extent to which major weather events such as hurricanes or snowstorms have impacted past carrier performance. With these data analyses, organizations can predict what will happen when similar future weather events occur. Taking things to another level, transportation management systems (TMS) for parcel shipping with geographic carrier hub and spoke networking capabilities are able to use machine learning algorithms to best route shipments to appropriate shipping points. They can optimally meet the service level agreement expectation of end customers and minimize or ‘bypass’ weather related risks. By analyzing how carrier partners have performed at the service level compared with their competition, shippers would be able to make more informed decisions about which carriers, services and origins to choose for orders moving through weather-impacted regions.

This would be incredibly valuable to e-commerce companies. It goes way beyond standard carrier information, which typically only tells you information such as ‘a delivery from A to B takes two days and costs €10 (US$12)’.

  • Using social media as an early warning of delivery incidents

Carriers are expanding the availability of geo-data during delivery processes, and its consumption and interpretation is likely to provide key last-mile delivery insights. As consumer demand for delivery event visibility expands, and carrier technology evolves, it will be critical to expose and share delivery events. Using social media data as an early warning of incidents that could disrupt parcel deliveries is a new practice that carriers could, and likely will, deploy.

Another fascinating application is anomaly detection through social media activity tracking. It’s the power of the people. For example, a large spike in unusual activity on a social platform in a particular location or region could be a signal that there may be a disruption affecting carrier networks or delivery processes. Algorithms already recognize these spikes in activity, which often occur before carriers are aware or traditional news media reports them. The actual incident might be a major accident, an unplanned protest march, or even a riot. While the technology may not be able to immediately identify the specific nature of the event, social media spikes can be used as a trendline against carrier performance in that region during the same time. Marking these incidents as impactful or not can help machines learn through these incidents over time. Which carriers were most affected? What were the impacts?

  • Comparing the impact of traffic flows on parcel delivery journeys

Similar to the weather example, BI platforms can benefit by integrating available road traffic data against in-transit carrier parcel deliveries. For example, historical benchmarking of traffic data can enhance confidence levels about potential delivery delays or failures. This enables shippers to assess how traffic flows may affect deliveries, allowing them to proactively notify end customers of potential delays.

Co-mingling client data remains a challenge
In an ideal world, parcel TMS BI platforms could provide even more accurate insights if they were able to consolidate both carrier visibility and performance data, as well as these types of external data, across different customers. However, currently most of the key global parcel carriers’ contracts prevent co-mingling of data for analyses.

If it were possible to secure clients’ and carriers’ permission to aggregate performance data across a parcel TMS vendor’s largest customers, this bigger data sample would produce more robust analytics, accurate KPIs and broader trend results. It remains to be seen if this will ever happen.

But data ownership and usage are a topic for another day. Suffice it to say, we continue to see an increase in the use of external data feeds to augment BI systems within parcel transportation management software. And a properly integrated supply chain technology stack, complete with BI, will support strategic decision-making by providing insights that enable greater flexibility to adapt to today’s rapidly changing supply chain landscape while keeping costs in check.

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