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The art of (mis)loading deliveries

Publication: Goods Movement 2030, an Urban Freight Blog
Publication Date: 2024
Summary:

Imagine the frustration of searching for a misplaced item, like your house keys or wallet, before leaving for a night out. Now, picture a FedEx or Amazon delivery driver halfway through a tight morning route, struggling to locate a parcel due by 9 a.m. while parked right outside the customer’s address.

These misloads — where shipments are accidentally loaded onto the wrong delivery route or vehicle — not only cause stress and lost time for the delivery driver but also result in significant negative economic and environmental impacts. Misloads can also lead to customer dissatisfaction, erode trust in the delivery company, and necessitate additional vehicle travel miles to rectify the mistake. Despite this, little is known about the frequency of human errors in last-mile delivery and how they affect the overall supply chain. In this post, we define the concept of misloading and unpack some of these questions to better understand its implications and identify potential solutions.

What is misloading?

Misloading is generally considered an error in the Load Planning Problem (LPP). An LPP is a discrete optimization problem that considers a logistic network structure (set of nodes, or logistics terminals, and links, routes connecting terminals served by a given fleet of trucks) and the demand for freight (quantity, origin, and destination). The objective is to determine the optimal sequence of terminals that a load of freight should traverse to minimize handling costs and maintain a specified level of service. The outcome of an LPP is a “load plan,” which details a unique strategy to handle each shipment at every point in the system (Powell & Sheffi, 1983).

A shipment misload is a deviation from the load plan, which could occur due to intentional or unintentional actions. For example, during a ridealong I performed on a parcel delivery route in downtown Seattle (Dalla Chiara et al., 2020), the driver chose to deliver a bulky carpet earlier in the morning instead of the afternoon ahead of schedule in the morning rather than the afternoon, in order to create space inside the vehicle to safely and efficiently move around and retrieve packages from the shelves. Such intended deviation from the load plan improved the efficiency of the overall route. Conversely, unintended misloads often occur due to human errors (a shipment is misplaced on the wrong vehicle or route) or machine errors (a shipment is incorrectly labeled).

Based on the stage in the supply chain where they occur, misloads can also be classified as hub-to-hub or preload misload. Hub-to-hub misloading occurs when the mis-shipment is during a package transfer between two depots (for example, a package mistakenly sent to Vancouver, B.C., Canada, instead of Vancouver, WA, USA). Preload misloading happens at the last-mile facility — the last leg of a supply chain, where shipments are scanned, sorted, and loaded into delivery vehicles either by a driver or a preloader. At this stage, the a shipment may be placed on the wrong route, either due to human or upstream label errors.

Frequency of misloaded packages

Misloading is often reported as a misloading rate (or its corresponding order accuracy rate) calculated by dividing the number of misloads by the total number of deliveries during a given time period.

The misload rate varies across industry sector, leg of the supply chain (whether hub-to-hub or preload), and even geographical location of logistics facilities. In the fast-moving goods sector, hub-to-hub misloads rate are reported to range from 0.01% to 0.1%, while preload misload rates have been reported between 0.1% and 0.3%.

While this may seem relatively small, misloading occurs daily due to the vast scale of delivery operations. For example, with a 0.2% misload rate, approximately one in 500 parcels is misloaded. Considering that a typical parcel delivery van handles around 250 packages per route, on average, every two vehicles would contain one misloaded package. Even with a lower misload rate of 0.1% (one in 1,000 packages), there would still be one misloaded package for every four delivery vehicles. In Seattle, where approximately 900 parcel delivery vehicles enter the greater downtown area daily (Giron-Valderrama & Goodchild, 2020), this equates to more than 200 misloaded packages every day. These figures highlight the frequency of misloading incidents despite their seemingly low percentage, and underscore the impact on operational efficiency and customer service.

We note that the misload rate increases the closer we get to the last mile of a delivery journey in the fast-moving consumer goods sector. From the data above, the misload rate quadrupled from the hub-to-hub to the last-mile segment (from 0.05% to 0.2%). This reflects increased manual labor, reduced automation, and increased complexity in handling smaller, non-standard parcels.

Quantifying the impact of misloading

Quantifying the economic and environmental loss of a misloaded package involves first understanding how drivers respond to these errors.

 

A preload misload is typically identified when a driver has either a missing package they are supposed to deliver or an additional package that does not belong on their assigned route. What happens next will depend on procedures implemented by the facility and other operational factors. In the case of a missing package deemed “critical,” the driver would typically alert nearby routes where the misloaded package is likely to have been placed). The driver might meet the other driver halfway, or the other driver may make the additional delivery. A “non-critical” package may be returned to the facility and rescheduled for delivery the following day. In either case, misloads result in additional miles traveled and the loss of driver time.

Quantifying the negative impacts of misloading is a difficult task. Transportation science often uses simulation tools to test different scenarios that are difficult to measure empirically by generating mathematical models. In this case, a misloading simulator takes as input the existing delivery demand and misload rate, calculates the optimal load plan, and outputs the total vehicle miles traveled (VMT) and total route time under scenarios both with and without misloads. By running simulations with varying parameters (different demands and misload rates), the misload simulator can provide a sufficiently precise estimate of how the misloads affects route performance.

According to the previous section, misloading can cause three possible scenarios, depicted in the figure below. In all three scenarios, we identified two routes — the red route carrying the misloaded shipment, the blue route missing the misloaded shipment — and the full node representing the final destination of the misloaded shipment.

  • Scenario A simulates the case of a misloaded non-critical package; in this scenario, the impact of misload is the additional VMT and time the driver spends on the blue route to reach the customer without being able to complete the delivery, as the shipment was misloaded on the vehicle carrying out the red route.
  • Scenario B simulates the case of a misloaded critical package, where the driver of the red route is required to spend extra time and VMT to make an additional delivery.
  • Scenario C simulates the case of a misloaded critical package, in which the driver of the blue route needs to spend additional time and VMT to meet the driver on the red route and retrieve the misloaded package.

The shape and length of delivery routes are extremely heterogeneous and vary among carriers, business sectors, and contexts. For instance, if we consider the case of a typical parcel delivery carrier delivering in downtown Seattle, a route averages 7.2 miles, with 24 stops, and an average distance of 0.3 miles per stop. A beverage company delivering in downtown Seattle typically has a 15-mile route with 11 stops and an average of 1.4 miles per stop (Dalla Chiara et al., 2021). Considering the simplest scenario to simulate (scenario A) and assuming the above-discussed misload rate of one misloaded shipment every two routes, a single misload would result in an additional 0.6 miles of travel, representing 4% of the total VMT. In the case of the beverage distributor, a single misload would leads to an additional 2.8 miles traveled, constituting 9% of total VMT.

Addressing misloading

Despite their statistical infrequency, misloads occur daily, affecting delivery times, increasing VMT, and eroding customer trust. Delivery companies strive to meet and exceed their misload target rates, but often struggle to identify effective solutions.

Addressing misloads involves a multifaceted approach that combines improved training and the adoption of advanced technologies. Developing clear procedures and providing training for drivers and preloaders can reduce human errors in labeling, sorting, scanning, and loading, as well as in detecting and correcting misloads. The Service Awareness Label Training (SALT) practice helps improve error detection. SALT involves placing fake misloaded packages in the system to assess employees’ ability to identify them.

Recent advancements in tracking technologies are creating new opportunities for delivery companies to reduce misloading. Since the introduction of scanning (the first item marked with a Universal Product Code was scanned in 1974 in a supermarket in Troy, OH, Weightman, 2015), most parcels are now scanned at key checkpoints, reducing human errors, generating a wealth of data that can be used to optimize the supply chain, and providing customers with real-time location and status information about their parcels.

Radio-frequency identification (RFID) technology, which allows multiple simultaneous scans, has allowed for substantial efficiency gains throughout the supply chain (Fan et al., 2015), enabling seamless tracking and reducing manual effort. While cost has historically been a major obstacle to full deployment (Bottani and Rizzi, 2008), 2022 seemed to be a tipping point in RFID implementation at scale (Swedberg, 2022). For instance, UPS launched a smart package initiative starting in 2022, deploying an RFID-based system through its facilities (Garland, 2022). The system involves placing RFID scanners on wearable devices and on delivery vehicle rear doors to automate preloading and eliminate manual scanning — and, therefore, the likelihood of misloads. Also beginning in September 2022, global retailer Walmart mandated that suppliers across several departments include RFID tags on all products shipped to its warehouses.

What’s next?

While the impact of misloading has been viewed mostly from a customer service perspective, its broader economic and environmental impacts are often overlooked. Implementing technologies like RFID can reduce misload rates, yet companies must weigh the cost and benefits of such investments. Quantifying the benefits of reducing misloads, such as decreasing VMT, lowering vehicle emissions, and improving drivers’ efficiency (among other potential efficiencies, for instance, Brewster, 2024) is important to guide companies in making informed decisions and optimize strategies.

Acknowledgements

The author would like to acknowledge IMPINJ for their technical and financial support and the experts and practitioners who provided content for this article.

References

Measuring the Sustainability Impact of Misloaded Packages

The Urban Freight Lab and RFID device manufacturer Impinj are joining forces to create a conceptual framework aimed at assessing the repercussions of misloaded packages on Vehicle Miles Traveled (VMT) and emissions. Misloaded packages (packages placed on an incorrect delivery vehicle) can cause drivers to deviate from their intended routes miles to rectify the error, increasing both VMT and emissions. This collaborative effort will analyze the consequences of such incidents in order to optimize delivery efficiency, minimize environmental impacts, and contribute to more efficient and environmentally sustainable urban freight practices.

Background
Impinj, a leader in the manufacturing of radio frequency identification (RFID) devices, has developed a Misloaded Packages Carbon Calculator, a model that quantifies the environmental impact of misloaded packages. The Urban Freight Lab (UFL) is an internationally recognized laboratory with research experience in measuring behaviors and impacts of last-mile delivery systems.

Objective
The current project proposes a collaboration between Impinj and the UFL to:

  • Explore the operational and sustainability impacts of misloaded packages across different industry segments and communicate findings through a blog post.
  • Introduce a novel conceptual model framework based on the IMPINJ carbon calculator that could be implemented in a future project to estimate the marginal change in Vehicle Miles Traveled (VMT) and emissions from changes in the misload rate.

Project Outputs
The UFL team will output the following deliverables:

  • A presentation at the 2023 Impinj Executive Forum to introduce the Impinj-UFL collaboration and the model framework for the misload package carbon calculator
  • A blog post reporting on the operational impact of misloaded packages across different industry sectors, and reflection on the sustainability implications of changing the misload rate (percent of misload packages experienced in a typical day)
  • A conceptual model framework based on Impinj misload packages carbon calculator that take into account different behavioral responses to handle misload packages and different industry sectors

Tasks
The UFL team will complete the following tasks:

  1. The UFL research team will meet with Impinj executives and visit the facilities to learn how RFID technology can be leveraged to reduce misload rates and draft a preliminary list of Impinj customers UFL can interview.
  2. The UFL will present at the 2023 Impinj Executive Forum.
  3. Through Impinj introduction, the UFL team will reach out and schedule at least four interviews with practitioners to document the operational, behavioral and sustainability impacts of misload packages. Interviews will be conducted to cover different sectors, including urban, suburban, and long-haul deliveries.
  4. The UFL will write a draft blog post documenting the results from the interviews, discuss the potential environmental impact of reducing misload rates across different industry sectors, proposed a conceptual model framework on how companies can estimate the marginal change in Vehicle Miles Traveled (VMT) and emissions from changes in the misload rate.

UPS E-Bike Delivery Pilot Test in Seattle: Analysis of Public Benefits and Costs (Task Order 6)

The City of Seattle granted a permit to United Parcel Service, Inc. (UPS) in fall 2018 to pilot test a new e-bike parcel delivery system in the Pioneer Square/Belltown area for one year. The Seattle Department of Transportation (SDOT) commissioned the Urban Freight Lab (UFL) to quantify and document the public impacts of this multimodal delivery system change in the final 50 feet of supply chains, to provide data and evidence for development of future urban freight policies.

The UFL will conduct analyses into the following research questions:

  1. What are the total changes in VMT and emissions (PM and GHG) to all three affected cargo van routes due to the e-bike pilot test in the Pike Place Market and neighboring areas?
  2. What is the change in the delivery van’s dwell time, e.g. the amount of time the van is parked, before and after introducing the e-bike?
  3. How does the e-bike system affect UPS’ failed first delivery (FFD) attempt rate along the route?
  4. If UPS begins to stage drop boxes along the route for the e-bike (instead of having to replenish from the parked trailer) what are the impacts to total VMT and emissions?
  5. How do e-bike delivery operations impact pedestrian, other bike, and motor traffic?
Paper

Economic Analysis of Onboard Monitoring Systems in Commercial Vehicles

 
Download PDF  (1.01 MB)
Publication: Transportation Research Record
Volume: 2379
Pages: 64-71
Publication Date: 2013
Summary:
Onboard monitoring systems (OBMSs) can be used in commercial vehicle operations to monitor driving behavior, to enhance safety. Although improved safety produces an economic benefit to carriers, understanding how this benefit compares with the cost of the system is an important factor for carrier acceptance.
In addition to the safety benefits provided by the use of OBMSs, operational improvements may have economic benefits. This research provides, through a benefit-cost analysis, a better understanding of the economic implications of OBMSs from the perspective of the carrier. In addition to the benefits of reduced crashes, the benefits associated with reduced mileage, reduced fuel costs, and the electronic recording of hours of service (HOS) are considered. A sensitivity analysis demonstrates that OBMSs are economically viable under a wide range of conditions.
The results indicate that for some types of fleets, a reduction in crashes and an improvement in HOS recording provides a net benefit of close to $300,000 over the 5-year expected life span of the system. Furthermore, when additional benefits, such as reduced fuel consumption and reduced vehicle miles, are explored, the operation-related benefits can be upward of seven times more than the safety-related benefits.
This research also shows that net positive benefits are possible in large and small fleets. The results can be used to inform policies that motivate or mandate carriers to use such systems and to inform carriers about the value of system investment.

 

Authors: Dr. Anne Goodchild, Kelly A. Pitera, Linda Ng Boyle
Recommended Citation:
Pitera, Kelly, Linda Ng Boyle, and Anne V. Goodchild. "Economic Analysis of Onboard Monitoring Systems in Commercial Vehicles." Transportation Research Record 2379, no. 1 (2013): 64-71. 
Paper

An Analytical Model for Vehicle Miles Traveled and Carbon Emissions for Goods Delivery Scenarios

 
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Publication: European Transport Research Review
Volume: 10
Publication Date: 2018
Summary:

This paper presents an analytical model to contrast the carbon emissions from a number of goods delivery methods. This includes individuals travelling to the store by car, and delivery trucks delivering to homes. While the impact of growing home delivery services has been studied with combinatorial approaches, those approaches do not allow for systematic conclusions regarding when the service provides net benefit. The use of the analytical approach presented here, allows for more systematic relationships to be established between problem parameters, and therefore broader conclusions regarding when delivery services may provide a CO2 benefit over personal travel.

Methods

Analytical mathematical models are developed to approximate total vehicle miles traveled (VMT) and carbon emissions for a personal vehicle travel scenario, a local depot vehicle travel scenario, and a regional warehouse travel scenario. A graphical heuristic is developed to compare the carbon emissions of a personal vehicle travel scenario and local depot delivery scenario.

Results

The analytical approach developed and presented in the paper demonstrates that two key variables drive whether a delivery service or personal travel will provide a lower CO2 solution. These are the emissions ratio, and customer density. The emissions ratio represents the relative emissions impact of the delivery vehicle when compared to the personal vehicle. The results show that with a small number of customers, and low emissions ratio, personal travel is preferred. In contrast, with a high number of customers and low emissions ratio, delivery service is preferred.

Conclusions

While other research into the impact of delivery services on CO2 emissions has generally used a combinatorial approach, this paper considers the problem using an analytical model. A detailed simulation can provide locational specificity, but provides less insight into the fundamental drivers of system behavior. The analytical approach exposes the problem’s basic relationships that are independent of local geography and infrastructure. The result is a simple method for identifying context when personal travel, or delivery service, is more CO2 efficient.

Authors: Dr. Anne Goodchild, Erica Wygonik, Nathan Mayes
Recommended Citation:
Goodchild, Anne, Erica Wygonik, and Nathan Mayes. "An analytical model for vehicle miles traveled and carbon emissions for goods delivery scenarios." European Transport Research Review 10, no. 1 (2018): 8.
Paper

Urban Form and Last-Mile Goods Movement: Factors Affecting Vehicle Miles Travelled and Emissions

 
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Publication: Transportation Research Part D: Transport and Environment
Volume: 61 (A)
Pages: 217-229
Publication Date: 2018
Summary:

There are established relationships between urban form and passenger travel, but less is known about urban form and goods movement. The work presented in this paper evaluates how the design of a delivery service and the urban form in which it operates affects its performance, as measured by vehicle miles traveled, CO2, NOx, and PM10 emissions.

This work compares simulated amounts of VMT, CO2, NOx, and PM10 generated by last-mile travel in several different development patterns and in many different goods movement structures, including various warehouse locations. Last-mile travel includes personal travel or delivery vehicles delivering goods to customers. Regression models for each goods movement scheme and models that compare sets of goods movement schemes were developed. The most influential variables in all models were measures of roadway density and proximity of a service area to the regional warehouse.

These efforts will support urban planning for goods movement, inform policies designed to mitigate the impacts of goods movement vehicles, and provide insights into achieving sustainability targets, especially as online shopping and goods delivery become more prevalent.

Authors: Dr. Anne Goodchild, Erica Wygonik
Recommended Citation:
Wygonik, Erica and Anne Goodchild. (2018) Urban Form and Last-Mile Goods Movement: Factors Affecting Vehicle Miles Travelled and Emissions. Transportation Research. Part D, Transport and Environment, 61, 217–229. https://doi.org/10.1016/j.trd.2016.09.015
Paper

Delivery by Drone: An Evaluation of Unmanned Aerial Vehicle Technology in Reducing CO2 Emissions in the Delivery Service Industry

 
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Publication: Transportation Research Part D: Transport and Environment
Volume: 61
Pages: 58-67
Publication Date: 2018
Summary:

This research paper estimates carbon dioxide (CO2) emissions and vehicle-miles traveled (VMT) levels of two delivery models, one by trucks and the other by unmanned aerial vehicles (UAVs), or “drones.”

Using several ArcGIS tools and emission standards within a framework of logistical and operational assumptions, it has been found that emission results vary greatly and are highly dependent on the energy requirements of the drone, as well as the distance it must travel and the number of recipients it serves.

Still, general conditions are identified under which drones are likely to provide a CO2 benefit – when service zones are close to the depot, have small numbers of stops, or both. Additionally, measures of VMT for both modes were found to be relatively consistent with existing literature that compares traditional passenger travel with truck delivery.

Authors: Dr. Anne Goodchild, Jordan Toy
Recommended Citation:
Goodchild, Anne, and Jordan Toy. "Delivery by Drone: An Evaluation of Unmanned Aerial Vehicle Technology in Reducing CO2 Emissions in the Delivery Service Industry" Transportation Research Part D: Transport and Environment 61 (2018): 58-67.
Student Thesis and Dissertations

Preparing Cities for Package Demand Growth: Predicting Neighborhood Demand and Implementing Truck VMT Reduction Strategies

Publication Date: 2018
Summary:

E-commerce has empowered consumers to order goods online from anywhere in the world with just a couple of clicks. This new trend has led to significant growth in the number of package deliveries related to online shopping. Seattle’s freight infrastructure is challenged to accommodate this freight growth. Commercial vehicles can already be seen double parked or parked illegally on the city’s streets impacting traffic flow and inconveniencing other road users. It is vital to understand how the package demand is growing in the neighborhoods and what freight trips reduction strategies can cities implement to mitigate the freight growth. The purpose of the research is to analyze Vehicle Miles Traveled (VMT) reduction strategies in the neighborhoods with different built environment characteristics. First, the impact of individual factors on person’s decision to order goods online for home delivery is analyzed. A predictive model was built that estimates online order probability based on these factors. This model is then applied to synthetic Seattle population to produce estimated demand levels in each neighborhood. Second, two VMT reduction strategies were modeled and analyzed: 1) decreasing number of trucks needed to deliver neighborhoods’ package demand and 2) package locker implementation. Based on packages demand and built environment characteristics, two neighborhoods were chosen for a case study. ArcGIS toolbox was developed to generate delivery stops on the route, ArcGIS Network Analyst was used to make a delivery route and calculate VMT. It was found that VMT reduction strategies have different effects on the delivery system in two neighborhoods. Delivering neighborhoods’ demand in a smaller number of trucks would save slightly more VMT in a dense urban area compared to suburban one. Moreover, since the traffic perception by different road users varies by neighborhood, VMT reduction strategies will be more critical to implement in dense urban areas. Locker implementation strategy will also be more effective in VMT reduction in a dense urban area due to high residential density.

Authors: Polina Butrina
Recommended Citation:
Butrina, Polina (2018). Preparing Cities for Package Demand Growth: Predicting Neighborhood Demand and Implementing Truck VMT Reduction Strategies. University of Washington Master's Degree Thesis.
Thesis: Array
Chapter

Comparison of Vehicle Miles Traveled and Pollution from Three Goods Movement Strategies

Publication: Sustainable Logistics: Transport and Sustainability (Emerald Group Publishing Limited)
Volume: Volume 6
Pages: 63-82
Publication Date: 2014
Summary:

This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Purpose: To provide insight into the role and design of delivery services to address CO2, NO x , and PM10 emissions from passenger travel.Methodology/approach: A simulated North American data sample is served with three transportation structures: last-mile personal vehicles, local-depot-based truck delivery, and regional warehouse-based truck delivery. CO2, NO x , and PM10 emissions are modeled using values from the US EPA’s MOVES model and are added to an ArcGIS optimization scheme.Findings: Local-depot-based truck delivery requires the lowest amount of vehicle miles traveled (VMT), and last-mile passenger travel generates the lowest levels of CO2, NO x , and PM10. While last-mile passenger travel requires the highest amount of VMT, the efficiency gains of the delivery services are not large enough to offset the higher pollution rate of the delivery vehicle as compared to personal vehicles.

Practical implications: This research illustrates the clear role delivery structure and logistics have in impacting the CO2, NO x , and PM10 emissions of goods transportation in North America.

Social implications: This research illustrates the tension between goals to reduce congestion (via VMT reduction) and CO2, NO x , and PM10 emissions.

Originality/value: This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Authors: Dr. Anne Goodchild, Erica Wygonik
Recommended Citation:
Wygonik, Erica, and Anne Goodchild. "Comparison of vehicle miles traveled and pollution from three goods movement strategies." Sustainable Logistics, pp. 63-82. Emerald Group Publishing Limited, 2014.