Leveraging Data Analytics for Last-Mile Visibility and Route Optimization: Benefits & Best Practices
Data analytics has revolutionized many industries, and the logistics and transportation sector is no exception. With the rise of e-commerce and consumer expectations for fast and efficient delivery, companies are increasingly turning to data analytics to improve last-mile visibility and route optimization.
We’ve curated this list of expert strategies and insights to provide insight into the best ways to leverage data analytics for last-mile visibility and route optimization. We’ve also included expert tips and best practices for leveraging data analytics effectively in the last mile
In this article:
Understanding the Role of Data Analytics for Last-Mile Visibility and Route Optimization
Data analytics plays an integral role in last-mile visibility and route optimization. Here are a few of the ways data analytics can be leveraged to improve these processes:
Real-time Tracking
One of the most powerful ways to leverage data analytics for last-mile visibility is through real-time tracking of shipments. By collecting and analyzing data on the location of trucks, delivery vehicles, and packages, companies can gain valuable insights into the status of their deliveries. This information can be used to optimize routes, adjust delivery schedules, and provide customers with accurate ETA updates.
Predictive Analytics
Predictive analytics utilizes historical data to forecast future events, such as delivery times, traffic patterns, and customer demand. By analyzing past delivery data, companies can identify trends and patterns that can help them make informed decisions about route planning, resource allocation, and inventory management.
Route Optimization
Route optimization involves finding the most efficient routes for delivering goods to customers. By using data analytics to analyze factors such as traffic patterns, road conditions, delivery windows, and customer preferences, companies can optimize their delivery routes to minimize delivery times and costs.
Demand Forecasting
Demand forecasting is another way to leverage data analytics in last-mile delivery. By analyzing data on customer orders, delivery volumes, and seasonal trends, companies can predict future demand for their products and services. This information can be used to better allocate resources, plan delivery schedules, and adjust inventory levels to meet customer demand more effectively.
Customer Insights
Data analytics can also provide valuable insights into customer behavior and preferences. By analyzing data on customer demographics, order histories, and feedback, companies can better understand their customers' needs and expectations. These insights can then be used to tailor delivery services, offer personalized recommendations, and enhance the overall customer experience.
Below, we’ll explore the benefits of data analytics in last mile logistics and provide expert tips and best practices for leveraging data analytics effectively in the last mile.
Benefits of Leveraging Data Analytics in Last-Mile Delivery
1. Data analytics can integrate data from multiple sources.
“Data analytics models can derive insights from large volumes of data collected from various sources such as messages from carriers, satellite imagery, customer preferences, weather forecasts, and more. Powered by machine learning and statistical techniques, these analytical models can help find patterns in the data and use them to gain visibility into customer behaviour, shipment trends, and more. This will play a key role in helping logistics business leaders identify their shipment patterns and create better customer experiences.” - Highway 905
2. Data analytics offer predictive capabilities.
“Traditional route planning often relies on guesswork and manual efforts, which can hinder productivity and efficiency. Machine learning transforms this process by enabling smarter driver assignments.
Machine learning can also be used to analyze past data to match drivers with routes that suit their historical performance and preferences. This approach not only enhances efficiency but also leverages the driver’s strengths and experiences.” - Wise Systems
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3. Leveraging big data in the supply chain can uncover inefficiencies.
“One of the key benefits of big data in supply chain and logistics management is the reduction of unnecessary costs. Using the right dashboard and data visualizations, it’s possible to hone in on any trends or patterns that uncover inefficiencies within your processes. Interacting with powerful data sets will empower you to drive down operational costs by optimizing delivery routes, predicting machinery or delivery vehicle maintenance, and weaving the whole supply chain together fluently.” - Datapine
4. Data analytics can streamline record-keeping and back-office management.
“The data-driven automation led by Big Data technology will benefit goods tracking and customer service and help automate the entire supply chain and the logistical process by streamlining record-keeping and back-office operations. All the paperwork and paper trails can be reduced to digital footprints shared automatically across locations and personnel in real-time.
From monitoring fuel uses to transportation timing to the hiring process to tracking the quality of deliverables, every aspect of the business operation can be streamlined with data-centric processes. The logistics companies and management can easily track their performance and output through key metrics in real-time and plan measures to improve them.” - Datafloq
5. You can improve delivery times with data analytics in the last mile.
“Last-mile analytics can help you monitor and track your delivery performances in real-time using metrics such as delivery time, driver behaviour, customer feedback, and delivery status. This can help you ensure that your deliveries are on time and meet the expectations of your customers. You can also use this data to predict and prevent potential issues and disruptions such as driver shortages, weather events, rush-hour snarl-ups, or vehicle breakdowns.” - TransVirtual
6. Data analytics reveals opportunities for performance improvement.
“Business intelligence and analytics solutions can help your business identify gaps between planned vs. actual delivery sequences. You can use these insights to track expected vehicle mileage and compare it to the actual distance traveled, identify operational inefficiencies, and monitor your on-time performance rate.
Analytics technologies reveal where opportunities for performance improvement lie. You can identify coaching opportunities for drivers, better manage customer relationships, and optimize overall efficiency, for instance. However, making these business improvements requires tracking and monitoring the right data.” - Wise Systems
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7. Data analytics can help improve courier performance.
“Couriers play a crucial role in the final stage of getting packages to recipients. To understand their performance and identify areas for improvement, transparency is key.? Analytics assist in highlighting issues in real-time and recognizing patterns in historical data. Ultimately, applying analytics leads to helping couriers achieve more stops per route, minimizing idle delivery times, and evenly spreading workload amongst couriers.” - Mily Tech
8. Data analytics eliminates blind spots in distribution networks.
“Driven by significant advances in sensor technology, mobile computing, mobile data and vehicle connectivity, real-time visibility is no longer limited to aggregate information on stationary items at distribution and warehousing facilities.
It is now possible to achieve real-time visibility for in-transit volumes down to the level of individual items or shipments. This new level of detail and dynamism enables companies to substantially reduce the number of blind spots in their distribution networks.
Previously, these blind spots prevented them from dynamically adjusting their last-mile distribution approach to changing market dynamics, or to proactively mitigate the effects of disruptions to their last-mile operations.” - Supply Chain 247
9. Data analytics can help boost customer satisfaction.
“AI-powered chatbots and virtual assistants provide instant responses to customer inquiries around the clock. These tools can handle a wide range of queries, from tracking deliveries to processing returns or exchanges, reducing wait times and improving the customer experience.
AI can also analyze customer data, including past orders, preferences, and interactions, to offer personalized recommendations and services. For instance, it can suggest convenient delivery times or locations based on the customer’s history.” - Wise Systems
Schedule a demo today to discover how Customer Portal from Wise Systems can help you transform your customer communications and boost customer satisfaction.
10. Data analytics can synchronize your operations.
“Marrying the strengths of machine learning, vast data? troves, and? sophisticated algorithms, predictive analytics offers a beacon of hope. It promises not just to predict but to optimize, ensuring that logistical decisions are data-driven, proactive, and aligned with the ever-evolving ground realities. This potent combination is not just about ensuring that packages reach on time; it's about preempting challenges,? dynamically? recalibrating? routes,? and? ensuring that the entire? logistical orchestra operates in harmonious synchrony.” - Optimizing the Last Mile: Advanced Predictive Analytics for Delivery Time Estimation in Supply Chain Logistics via ResearchGate
11. Data analytics can be used to verify performance and reliability.
“It’s now possible to track the whereabouts of every vehicle to provide updates over a likely time of delivery. For the overachievers, this information can even be used to prove their reliability in the securing of new contracts.
The end goal should be for data to create a more open logistics industry where a group’s all-round performance is put beyond doubt. Again, for the most efficient groups - regardless of their size - it could prove a great signal of worth.” - Eleks
12. Data analytics can help to overcome urban logistics challenges.
“Identifying common obstacles in urban last-mile delivery means acknowledging the tight grip of traffic congestion and the often frustrating limitations on access to certain areas. How can deliveries reach high-rise apartments swiftly when elevators are slower than molasses and roads are as jammed as your favorite radio station’s top hits? Enter the heroics of technology.
Examples of digital solutions lifting up distribution in dense cityscapes are everywhere. For instance, crowd-sourced delivery platforms allow passengers and commuters to deliver parcels en route to their destinations, making every trip a potential delivery run. And smart lockers? They stand guard in convenient locations, offering secure and accessible points for customers to pick up their goods at their leisure.” - NetworkON
13. Data analytics helps you do more with your data.
“Enriched data quality is the most important benefit of ML in logistics. Natural Language Processing (NLP) and Machine Learning (ML) help businesses obtain, store, and analyze data faster and more efficiently, drawing connections between the key points and learning simultaneously as the data evolves.
Moreover, the capabilities of ML in logistics allow businesses to use collected data effectively during shipping to avoid risks, come up with better shipment methods and routes, and reduce costs. With all the capabilities of machine learning in logistics and supply chain, companies can utilize machine learning datasets to learn how many people they need to perform certain operations, for how long, and where, even if they need to operate multiple locations.” - Intellisoft
14. Data analytics enables companies to respond to unexpected factors.
“AI systems learn from historical data and continuously improve their forecasts over time. AI-supported predictive analyses adjust shipping patterns, optimize routes and loads, estimate delivery times, and anticipate consumer behavior.
In addition to the large amount of historical data, real-time data from sources such as traffic reports or weather forecasts can be integrated to dynamically adapt delivery processes and respond to unexpected circumstances and hazards. In combination with tracking technology, companies can provide their customers with a better service on the last mile.” - DHL Freight Connections
15. Data analytics makes it possible to optimize resource utilization.
“Optimization helps save a logistics company money while avoiding late shipments, which can give them a bad reputation and impact their future bottom line. When managing any delivery system, everything is a juggling act. You cannot overcommit resources like vehicles, nor can you fail to meet the need.
If you flood a delivery route with an excessive number of vehicles and resources, then you risk spending too much money and cutting into your profitability. Even if you have the additional funds, the assets will prove more useful somewhere else.
Holding back vehicles might delay delivery and your client relationship will suffer and your brand integrity starts to slip.” - Extensiv
16. Data analytics can help reduce environmental impacts.
“By optimizing routes and delivery processes, machine learning contributes to reducing the carbon footprint of delivery operations. This is increasingly important in the context of global environmental concerns.
In fact, consumers are increasingly conscious of the impact of their purchasing habits on the environment. This gives companies that adopt sustainable practices and take steps to reduce their carbon footprint a competitive advantage.” - Wise Systems
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Tips & Best Practices for Leveraging Data Analytics in the Last Mile
17. Identify your key performance indicators (KPIs).
“Sit down with your management team and figure out which KPIs matter most to your particular business. These could include such things as delivery speed, accuracy of delivery, and customer satisfaction with your service. These KPIs will provide a sound foundation for configuring your last-mile data analytics system.” - TransVirtual
18. Make smart use of available data.
“Over the past decade, the explosion of cloud-based services, such as Google Maps, has yielded a wealth of actionable information to aid in supply chain logistics. This data is increasingly being leveraged by conventional shippers, retailers and a growing stable of innovative startups that deal exclusively in last-mile delivery. One of those startups, Deliv, is a same-day delivery service that stores and online retailers can offer and, much like Uber, relies on app-connected freelance drivers to handle the back and forth.
These companies combine a plethora of publicly available cloud data with internal information to devise more efficient routes and delivery schedules to optimize package delivery over the last mile.
Part of the solution is teaching companies to better use the information that is already available.” - Forbes
19. Automate route planning and dispatch.
“Automating route planning and driver dispatch can improve delivery route optimization and driver allocation.
Automated route planning software is a management system that tracks each vehicle's location using hardware units. Once a manager adds drivers and their routes to the system, the software optimizes each route, then sends real-time vehicle information to the manager's dashboard. The system can take into account variables like traffic or weather so drivers use the most efficient routes.
Automating driver dispatch could prevent the mistakes brought about by human error.
Automation reduces the chance that a human dispatcher will tell a driver incorrect information because the dispatcher is receiving the latest route data, Larabie said.” - TechTarget
20. Use predictive analytics for accurate delivery windows.
“Uncertainty about when a package will arrive can be a source of frustration for customers. Enter AI with its predictive analytics prowess, transforming the guessing game into a precision art. Leveraging this technology, AI foresees accurate delivery windows by delving into a diverse array of factors – historical data, traffic patterns, and even external events that might throw a curveball into delivery schedules.
Here’s the magic at work: AI analyzes the historical data of deliveries, learning from patterns and trends. It understands the ebb and flow of delivery demands on different days and times. But AI doesn’t stop there; it’s forward-thinking. Predictive analytics means it can anticipate future scenarios based on the current state of affairs. If there’s a special event causing traffic or a weather event brewing, AI factors it in, adjusting its predictions accordingly.” - RTS Labs
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21. Use data to drive rapid decision-making.
“New machine learning based predictive analytics tools will gain in importance as companies realize last-mile decisions must be accomplished more quickly, emphasizes Transportation Insight’s Richardson.
‘IoT data itself is not as important as what you do with it,’ he said, ‘Don’t make a lot of investments in something that is not going to be actionable for the business.’
When it comes to last-mile logistics, ‘you have to be able to do things very quickly,’ he said. Improved machine learning implementations can help. ‘You need valid data, but you can’t take a lot of time for data cleansing.’” - IoT World Today
22. Provide access to data on an as-needed basis.
“It is crucial for the data being held about deliveries to be kept secure. Drivers should only be able to access information about pickups and dropoffs while they are delivering. While a certain level of information is necessary to allow the driver to get from point A to point B efficiently, the need for this information ends once the dropoff has been made. Restricting this information on a ‘need to know’ basis will ensure the security of everyone involved.” - Inside Big Data
23. Leverage data analytics to reduce damage rates.
“Another area that Wayfair is improving through its cost-cutting plan is product damage rates. It has worked with suppliers to identify product weak points and improve packaging to more effectively prevent damage, Shah said. The company also uses damage rates data ‘to compose better search results for shoppers,’ he added. This has helped Wayfair drive its overall incidence rate down by more than 15% since last summer.” - Supply Chain Dive
24. Validate and correct delivery addresses with AI.
“AI tools can automatically validate and correct addresses during the order process. AI algorithms are trained to recognize patterns in address data, enabling them to validate addresses by comparing input data against a comprehensive, constantly updated database of valid addresses, helping to prevent packages from being sent to incorrect or non-existent addresses.
AI systems can also identify errors such as misspellings, incorrect postal codes, and missing information. Over time, AI models learn from corrections and successful deliveries, improving their ability to predict and correct address errors automatically. This reduces the risk of failed deliveries due to address errors and ensures a smoother delivery process.” - Wise Systems
Schedule a demo today to discover how the Wise Systems delivery automation platform can help you improve delivery accuracy and enhance performance.
25. Perfect your order delivery rate.
“The number of orders that are delivered to the right place with a complete order that is damage free and has all the correct paperwork included. While it’s not common to reach perfection, it’s a good indicator to monitor over time to understand the effectiveness of improvements or quickly identify any troubling trends.” - ParcelLab
26. Improve end-user service levels.
“Customers have specific expectations regarding last-mile deliveries, as previously stated. These expectations are largely responsible for customers spending so much time tracking their items from the time they are picked up until they arrive.
As a result, one of the most common data analytics applications are to improve end-user service levels. This means customers are happier, which leads to increased sales for the company.” - Datafloq
27. Group deliveries to cut costs.
“When a customer places an order on the Amazon store, Amazon immediately reserves inventory for it. Within minutes, Amazon’s logistics models evaluate thousands of options for fulfilling the order. Fulfillment location is one variable: a single warehouse may assemble the order if it has all the items; if not, the order can be split into multiple shipments. Amazon also reserves capacity within its transportation network to ensure the order can be delivered on time.
At that point, a decision is made about how the order will be fulfilled, but it won’t necessarily be the final one. There will be opportunities to revisit the decision before the shipment process starts. If a neighbor places an order later that day, for example, the logistics plan may be updated so that the same carrier delivers both orders.
Different programs may work together to make all of that happen. One program will consolidate orders by the customer’s address, for example, so that a carrier visits the address only once if possible. Another will consolidate the orders by buildings and another by groups of buildings.” - Amazon Science
28. Ensure data health.
“Most data is unstructured and needs to be refined to attain the necessary quality for further analysis.
First, the data must undergo an audit. Good data health means the organization’s data is complete, valid, easily discoverable, and high quality. This is a prerequisite for transforming it into business value.
Weak inputs are sometimes easy to spot—for example, inconsistencies such as different units or periodically missing data. For large volumes of data, examining every single record can be daunting. A good approach is using a platform that provides automated data audits or has both integration and governance abilities.” - Mily Tech
29. Improve your on-time delivery rate.
“While late deliveries typically get more bad press, early deliveries can also cause customer frustration. If a customer is not home to receive a package and then has to reschedule a delivery or pick-up it up elsewhere, it can impact satisfaction and drive up customer service contacts.” - ParcelLab
30. Use real-time data to improve efficiency.
“Real-time data is a valuable resource for companies involved in last mile delivery. Collecting and analysing data from various sources such as GPS devices, sensors and mobile applications allows companies to get a real-time overview of their delivery operations. This allows them to track the location of their vehicles, monitor delivery status and make the necessary adjustments to ensure timely and efficient delivery.” - Thomas Arno Hellmuth-Sander via LinkedIn
31. Be mindful of ethics considerations.
“...while the applications of analytics are important, we need to take care on how to apply them. An important? consideration? in this? regard? needs to? be? given to? data? privacy,? ethics, use? of? right tools, presenting? the? right? findings, etc.
Barton? and? Court? (2012)? emphasized? that? the? advanced? analytics techniques need to be applied in appropriate manner to make them for us. Many other researchers in the field of data science have also conveyed the need for ethics, data privacy in the use of data analytics (Monreale et al, 2014, Buttarelli, 2015 and Fargo et al, 2020).” - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics via ResearchGate
32. Leverage the local knowledge and expertise of your distribution workforce.
“For instance, the analysis of high-resolution GPS traces in conjunction with telemetry data and transactional records can provide relevant insights on the availability and suitability of local infrastructures such as roads and parking bays for last-mile delivery.
The data can reveal behavioral patterns of drivers and delivery crews that have local knowledge about their route territory and know better than any algorithm or data source where to park, which shortcut to take, or which congestion hotspot to avoid.
Extracting this knowledge without having to disrupt crew member workflows can achieve significant improvements in route planning and more effective delivery instructions.” - Supply Chain 247
33. Leverage AI for load optimization.
“AI algorithms can optimize how packages are loaded into delivery vehicles, ensuring that space is used efficiently and the order of deliveries minimizes the need for rearrangement. This ensures that each delivery vehicle is loaded to its optimal capacity, reducing the number of trips required to deliver the same amount of goods. This decreases the overall fuel consumption and emissions associated with delivery operations.” - Wise Systems
Learn how Wise Systems’ Dynamic Optimization Engine (DOE) leverages AI to optimize last-mile delivery. Request a demo today.
34. Leverage data analytics to minimize the cost of transport.
“At present, the Vehicle Routing optimization (VRO) is still one of the important topics
which, if more examined, could be used to reduce environmental pollution. The VRO is
represented by many AI applications, with the aim of designing the lowest cost routes to
meet customer demand, including the solution of the shortest route problem, the traveling?salesman problem and the driving problem.
The complexity of all operations varies according to specific customers. The main essence of VRO is to calculate a route under specific conditions, which ultimately minimizes the total cost of transport, by reducing, for example, the total distance traveled, the number of vehicles used or the total time of Transport.” - Use of artificial intelligence in last mile delivery via SHS Web of Conferences
35. Consider a hybrid model for adaptability.
“...the? modular nature of hybrid models allows? for? continuous adaptation. As new? data streams become available or as delivery dynamics change, individual components (like the decision tree or the neural network) can be tweaked or replaced, ensuring that the model remains contemporary and effective.
The consistent rise in the adoption of hybrid models across the logistics sector is testament to their efficacy. These models, with their potent blend of depth and simplicity, are setting new benchmarks in prediction accuracy.
Their emergence underscores a broader? theme in predictive analytics: the? idea? that a collaborative, multi-methodological approach often surpasses the capabilities of singular, isolated models. As the complexities of last-mile delivery continue to escalate, such integrated solutions will likely become the mainstay of predictive endeavors.” - Optimizing the Last Mile: Advanced Predictive Analytics for Delivery Time Estimation in Supply Chain Logistics via ResearchGate
36. Leverage big data to ensure route safety.
“Big data can also be used for ensuring the route safety. Union Pacific Railroad, for instance, utilises real-time data analytics in order to better predict and mitigate accident risks. IoT sensors, which placed along with the rail network, are able to identify wheel defects, like flat spots and worn bearings. The data is transferred to central servers, where internal analysts process it and determine whether to immediately pass it to dispatchers, who can stop the train if necessary.” - Eleks
37. Incorporate driver know-how into route planning.
“Given a delivery driver and a set of package destinations, the Last Mile team’s software tries to find the most efficient delivery route.
Drivers, however, frequently deviate from those computed routes. Drivers carry information about which roads are hard to navigate, when traffic is bad, when and where they can easily find parking, which stops can be conveniently served together, and many other factors that existing optimization models don’t capture.” - Amazon Science
38. Leverage data analytics to inform resource allocation.
“Allocating resources is an integral part of the business. This means managers have to ensure that there are enough vehicles of the right type and load capacity while keeping tabs on personnel shifts to ensure there is enough manpower to keep operations running smoothly. Traditionally, managers would rely on available historical data or worse – gut feel from past personal experiences. Given the modern era’s highly demanding customers, past practices not rooted in data and technology can only take businesses so far.
Integrated data from functional areas such as personnel, transport dispatch, and distribution networks can provide a holistic view of what a given period of time entails in terms of resources needed. Visibility on incoming shipments, customer delivery schedules, vehicle availability up to personnel shift schedules will allow efficient allocation of resources to match expected workload in near-real time.” - Monstarlab
39. Monitor driver performance.
“Monitoring driver performance and driving could help improve last-mile delivery. Unsafe drivers can negatively affect company expenses and become a public relations problem.
Driver performance is not limited to their being on time, the number of deliveries they make and their turnaround time, Sardar said. Safety and efficiency are also crucial aspects of the job.
This has become more of a concern as e-commerce has increased in popularity.
Companies have hired people who are not necessarily experienced professional drivers and are under tremendous time pressure to make their deliveries, said Susan Beardslee, principal analyst at ABI Research, a market foresight advisory firm located in Oyster Bay, N.Y.
Drivers who speed endanger other motorists and pedestrians, are at higher risk for accidents and are more likely to damage a delivery vehicle. Aggressive driving could also increase fuel consumption, which affects overall fuel costs.” - TechTarget
40. Leverage data analytics to assess and manage risks.
“Risk management is the process of identifying, assessing, and mitigating the potential risks that can affect the delivery operations, such as accidents, thefts, damages, delays, and complaints. By using data analytics techniques such as probability analysis, scenario analysis, and decision trees, transportation managers can estimate the likelihood and impact of various risks, and develop contingency plans and preventive measures. Risk management can help transportation managers reduce the negative consequences of the risks, and increase the resilience and reliability of their delivery operations.” - What data analytics techniques can help improve last-mile delivery? via LinkedIn
Final Thoughts
Tune in to our latest Feature Spotlight to discover how simplified route visualizations and real-time fleet visibility optimize your last-mile. Schedule a demo with us today to learn more about Dispatcher from Wise Systems here! https://t.co/dMPjyYSEZv pic.twitter.com/u1kpYbKkd6
Data analytics is vital in last-mile delivery, opening up a realm of possibilities from last-mile visibility to route optimization, improved customer service, predictive capabilities, and more. The right software solution can help you harness the potential of data analytics to streamline and optimize every facet of your last-mile operations.
The Wise Systems delivery automation platform, for example, offers a suite of solutions for every stage of delivery, from routing and dispatch to performance management and customer communication. Request a demo today to discover how Wise Systems can help you leverage data analytics for last-mile visibility and route optimization.
Frequently Asked Questions
How do I optimize my last-mile delivery?
Optimizing last-mile delivery involves several strategies aimed at enhancing efficiency and customer satisfaction. Start by leveraging route optimization software to plan the most efficient delivery routes, reducing fuel costs and delivery times.
Implement real-time tracking systems to monitor deliveries and provide customers with accurate updates. Consider using local distribution hubs or micro-fulfillment centers to shorten delivery distances.
Additionally, adopting flexible delivery options such as same-day or next-day delivery can cater to customer preferences. Integrating data analytics to forecast demand and manage inventory effectively also can significantly improve the overall last-mile delivery process.
How do you measure last-mile delivery?
Measuring last-mile delivery involves tracking several key performance indicators (KPIs), such as:
What are the key success factors for last-mile delivery?
Key success factors for last-mile delivery include efficiency, reliability, and customer satisfaction:
What are the new technologies for last-mile delivery?
Several new technologies are revolutionizing last-mile delivery. Autonomous delivery vehicles, including drones and robots, are becoming more common, offering efficient and contactless delivery options.
Artificial intelligence (AI) and machine learning are used for route optimization, predictive analytics, and demand forecasting, improving delivery efficiency and reducing costs. Internet of Things (IoT) devices enable real-time tracking and monitoring of deliveries, enhancing transparency and customer satisfaction.
Additionally, mobile apps and platforms facilitate better communication between delivery personnel and customers, providing real-time updates and flexible delivery options.
This article was originally published on the Wise Systems blog. Check out our other blogs here for more insights on last-mile visibility, route optimization, and more.