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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

Technical Report

Development and Analysis of a GIS-Based Statewide Freight Data Flow Network

 
Download PDF  (4.92 MB)
Publication: Washington State Department of Transportation
Publication Date: 2009
Summary:
In the face of many risks of disruptions to our transportation system, this research improves WSDOT’s ability to manage the freight transportation system so that it minimizes the economic consequences of transportation disruptions.
Faced with a high probability that major disruptions to the transportation system will
harm the state’s economy, the Washington State Department of Transportation
(WSDOT), in partnership with Transportation Northwest (TransNow) commissioned
researchers at the University of Washington and Washington State University to
undertake freight resiliency research to:
  • Understand how disruptions of the state’s freight corridors change the way
    trucking companies and various freight-dependent industries route goods,
  • Plan to protect freight-dependent sectors that are at high risk from these disruptive
    events, and
  • Prioritize future transportation investments based on the risk of economic loss to
    the state
To accurately predict how companies will route shipments during a disruption,
this research developed the first statewide multimodal freight model for Washington
State. The model is a GIS-based portrayal of the state’s freight highway, arterial, rail,
waterway and intermodal network and can help the state prioritize strategies that protect industries most vulnerable to disruptions.
The report features two case studies showing the model’s capabilities: the potato growing and processing industry was chosen as a representative agricultural sector, and diesel fuel distribution for its importance to all industry sectors. The case studies are found in sections 5.2 and 5.3 in the report and show how the statewide freight model can:
  • Predict how shipments will be re-routed during disruptions, and
  • Analyze the level of resiliency in various industry sectors in Washington State
The two case studies document the fragility of the state’s potato growing and processing
sectors and its dependence on the I-90 corridor, while showing how the state’s diesel
delivery system is highly resilient and isn’t linked to I-90.
As origin-destination data for other freight-dependent sectors is added to the model,
WSDOT will be able to evaluate the impact of freight system disruptions on each of
them. This will improve WSDOT’s ability to develop optimal strategies for highway
closures, and prioritize improvements to the system based on the relative impact of the
disruption.
This research addressed several technical areas that would need to be resolved by any
organization building a state freight model. First, the researchers had to decide on the
level of spatial and temporal detail to include in the statewide GIS freight model. This
decision has significant consequences for data resolution requirements and results.
Including every road in Washington would have created a cumbersome model with a
large number of links that weren’t used. However, in order to analyze routing during a
disruption all possible connections must exist between origin and destination points in the model. While the team initially included only the core freight network in the model,
ultimately all road links were added to create complete network connectivity.
Second, as state- and corridor-level commodity flow data is practically non-existent, data
collection for the two case studies was resource intensive. Supply chain data is held by
various stakeholders and typically not listed on public websites, and it isn’t organized by
those stakeholders for use in a freight model. In most cases it’s difficult to assure data
quality. The team learned that the most difficult data to obtain is data on spatially or
temporally variable attributes, such as truck location and volume. So they developed a
method to estimate the importance of transportation links without commodity flow data.

Third, the freight model identified the shortest route, based on travel time, between any
origin and destination (O/D) pair in the state, and the shortest travel-time re-route for
each O/D pair after a disruption. The routing logic in the model is based on accepted
algorithms used by Google Maps and MapQuest. Phase III of the state’s freight
resiliency research was funded by WSDOT and will result in improved truck freight
routing logic for the model in 2011.
The two case studies showed how the state’s supply chains use infrastructure differently,
and that some supply chains have built flexibility into their operations and are resilient
while others are not, which leads to very different economic consequences. The results
of these case studies significantly contributed to WSDOT’s understanding of goods
movement and vulnerability to disruptions.
In the future, Washington State will need corridor-level commodity flow data to
implement the research findings and complete the state freight model. In 2009, the
National Cooperative Freight Research Program (NCFRP) funded development of new
methodology to collect and analyze sub-national commodity flow information. This
NCFRP project, funded at $500,000, will be completed in 2010 and provide a mechanism for states to accurately account for corridor-level commodity flows. If funds are available to implement the new methodology in Washington State, the state’s freight
model will use the information to map these existing origin destination commodity flows
onto the freight network, evaluate the number of re-routed commercial vehicles, and their increased reroute distance from any disruption. This will allow WSDOT to develop
prioritized plans for supply chain disruptions, and recommend improvements to the
system based on the economic impact of the disruption.
This report summarizes 1) the results from a thorough review of resilience literature and resilience practices within enterprises and organizations, 2) the development of a GIS-based statewide freight transportation network model, 3) the collection of detailed data regarding two important industries in Washington state, the distribution of potatoes and diesel fuel, and 4) analysis of the response of these industries to specific disruptions to the state transportation network.
The report also includes recommendations for improvements and additions to the GIS model that will further the state’s goals of understanding the relationship between infrastructure availability and economic activity, as well as recommendations for improvements to the statewide freight transportation model so that it can capture additional system complexity.
Authors: Dr. Anne GoodchildDr. Ed McCormack, Eric Jessup, Derik Andreoli, Kelly Pitera, Sunny Rose, Chilan Ta
Recommended Citation:
Goodchild, Anne V., Eric L. Jessup, Edward D. McCormack, Derik Andreoli, S Rose, Chilan Ta and Kelly Pitera. “Development and Analysis of a GIS-Based Statewide Freight Data Flow Network.” (2009).
Paper

An Evaluation of Logistics Sprawl in Chicago and Phoenix

 
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Publication: Journal of Transport Geography
Volume: 88
Publication Date: 2018
Summary:

This paper evaluates whether or not there is a sprawling tendency to the spatial patterns of warehouse establishments in the Chicago and Phoenix metropolitan areas. The trend of warehouses to move away from the urban centers to more suburban and exurban areas is referred to as “Logistics Sprawl”. To measure sprawl, the barycenter of warehousing establishments was compared to the barycenter of all other industry establishments in the region between the years of 1998 and 2013 for Chicago; 1998 and 2015 for Phoenix. This shows that logistics sprawl is a behavior experienced by warehouses in the Chicago area, but not in the Phoenix area. This paper discusses if logistics sprawl is a national trend or a regional behavior by comparing these results to the previous case studies of the Atlanta, Los Angeles, and Seattle metropolitan areas.

Authors: Dr. Anne Goodchild, Melaku Dubie, Kai C. Kuo
Recommended Citation:
Dubie, Melaku, Kai C. Kuo, Gabriela Giron-Valderrama, and Anne Goodchild. (2018) An Evaluation of Logistics Sprawl in Chicago and Phoenix. Journal of Transport Geography, 88, 102298–. https://doi.org/10.1016/j.jtrangeo.2018.08.008
Paper

Mapping Urban Freight Infrastructure for Planning: A Demonstration of a Methodology

Publication: Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2018
Summary:

Urban transportation infrastructure includes facilities such as loading docks and curb space which are important for freight pick-up and delivery operations. Information about the location and nature of these facilities is typically not documented for public or private urban freight stakeholders and therefore cannot be used to support more effective private sector operations or public sector planning and engineering decisions. Consequently, there is considerable value in performing an accurate inventory and evaluation of the system. In response to this urban freight challenge, the Seattle Department of Transportation (SDOT) contracted with the Supply Chain Transportation and Logistics Center (SCTL) at the University of Washington to develop a process to address the lack of information regarding the capacity for freight and parcel load and unload operations in dense urban areas of Seattle. This works focuses on the development of a data collection method for documenting private urban freight infrastructure that does not require prior permission, is ground-truthed, and can be completed within reasonable cost and time constraints. This paper presents the methodology, which consists of a survey form, survey collection app, data quality control process, data structure and a proposed typology for off public right of way freight loading / unloading infrastructure based on basic physical infrastructure characteristics. The data collection process methodology is applied to three Seattle urban centers. The method was then revised and improved for a second data collection effort in two additional urban centers.

Recommended Citation:
Machado-León, Jose Luis, Gabriela del Carmen Giron-Valderrama, Anne Goodchild, and Edward McCormack. Mapping Urban Freight Infrastructure for Planning: A Demonstration of a Methodology. No. 18-06171. 2018.
Paper

Smart Growth and Goods Movement: Emerging Research Agendas

Publication: Journal Urbanism: International Research on Placemaking and Urban Sustainability
Volume: 2-Aug
Pages: 115-132
Publication Date: 2015
Summary:

While recent urban planning efforts have focused on the management of growth into developed areas, the research community has not examined the impacts of these development patterns on urban goods movement. Successful implementation of growth strategies has multiple environmental and social benefits but also raises the demand for intra-urban goods movement, potentially increasing conflicts between modes of travel and worsening air quality. Because urban goods movement is critical for economic vitality, understanding the relation between smart growth and goods movement is necessary in the development of appropriate policies.

This paper reviews the academic literature and summarizes the results of six focus groups to identify gaps in the state of knowledge and suggest important future research topics in five sub-areas of smart growth related to goods movement: (1) access, parking, and loading zones; (2) road channelization and bicycle and pedestrian facilities; (3) land use; (4) logistics; and (5) network system management.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Erica Wygonik, Alon Bassok, Daniel Carlson
Recommended Citation:
Wygonik, Erica, Alon Bassok, Anne Goodchild, Edward McCormack, and Daniel Carlson. "Smart Growth and Goods Movement: Emerging Research Agendas." Journal of Urbanism: International Research on Placemaking and Urban Sustainability 8, no. 2 (2015): 115-132.
Paper

Sustainable Urban Goods Movement: Emerging Research Agendas

Publication: Journal of Urbanism
Volume: 8(20)
Pages: 115-132
Publication Date: 2014
Summary:

While recent urban planning efforts have focused on smart growth development and management of growth into developed areas, the research community has not examined the impacts of these development patterns on urban goods movement. Successful implementation of growth strategies has multiple environmental and social benefits, but it also raises the demand for intraurban goods movement, potentially increasing conflicts between modes of travel and worsening air quality. Because urban goods movement is critical for economic vitality, and as policies are developed to manage urban goods movement, understanding the relationship between smart growth and goods movement is necessary. This paper reviews the academic literature and summarizes the results of guided interviews to identify the existing gaps in the state of knowledge and suggest important future research topics. Little research exists that directly examines the relationship between smart growth and goods movement; therefore, smart growth is dissected into sub-areas that relate to goods movement, and these areas are individually examined. These five key sub-areas are 1) access, parking, and loading zones; 2) road channelization, bicycle, and pedestrian facilities; 3) land use; 4) logistics; and 5) network system management. The existing state of knowledge is discussed in each of these areas and identify specific areas of concern determined from guided interviews. With these inputs, important areas of future research are identified.

Authors: Dr. Anne GoodchildDr. Ed McCormack, Erica Wygonik, Alon Bassok, Daniel Carlson
Recommended Citation:
Wygonik, Erica, Alon Bassok, Anne V. Goodchild, Edward McCormack and Daniel Fred Carlson. “Sustainable Urban Goods Movement: Emerging Research Agendas.” (2012).
Paper

Current State of Estimation of Multimodal Freight Project Impacts

 
Download PDF  (0.50 MB)
Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: 2410
Pages: 141-149
Publication Date: 2014
Summary:

As available data have increased and as the national transportation funding bills have moved toward objective evaluation, departments of transportation (DOTs) throughout the United States have begun to develop tools to attempt to measure the effects of different projects. Increasingly, DOTs recognize that the freight transportation system is necessarily multimodal. However, no DOTs have clearly stated objective tools with which to evaluate multimodal freight project comparisons.

This paper fills that gap by summarizing the existing academic literature on the state of the science for the estimation of freight project impacts and by reviewing methods currently used by selected DOTs nationwide. These methods are analyzed to identify common themes to determine potential avenues for multimodal project evaluation.

Authors: Dr. Anne Goodchild, Erica Wygonik, Daniel Holder, B. McMullen
Recommended Citation:
Wygonik, Erica, Daniel Holder, B. Starr McMullen, and Anne Goodchild. "Current State of Estimation of Multimodal Freight Project Impacts." Transportation Research Record 2410, no. 1 (2014): 141-149. 
Paper

Double-Cycling Strategies for Container Ships and Their Effect on Ship Loading and Unloading Operations

 
Download PDF  (0.22 MB)
Publication: Transportation Science
Volume: 40(4)
Pages: 473-483
Publication Date: 2006
Summary:

Loading ships as they are unloaded (double cycling) can improve the efficiency of a quay crane and container port. This paper describes the double-cycling problem, and presents solution algorithms and simple formulae to determine reductions in the number of operations and operating time using the technique. We focus on reducing the number of operations necessary to turn around a row of a ship. The problem is first formulated as a scheduling problem, which can be solved optimally. A simple lower bound for all strategies is then developed. We also present a greedy algorithm that yields a simple and tight upper bound. The gap between the upper and lower bounds is so small that the formula for either bound is an accurate predictor of crane performance. The analysis is then extended to double cycling when ships have deck hatches. Results are presented for many simulated vessels, and compared to empirical data from a real-world trial. The research demonstrates that double cycling can create significant efficiency gains in crane productivity, typically reducing the number of cycles by about 20% and the operational time by about 10% when double cycling only below deck.

Authors: Dr. Anne Goodchild, C. Daganzo
Recommended Citation:
Goodchild, Anne V., and Carlos F. Daganzo. "Double-Cycling Strategies for Container Ships and Their Effect on Ship Loading and Unloading Operations." Transportation Science 40, no. 4 (2006): 473-483. 
Paper

Building Resilience into Freight Transportation Systems: Actions for State Departments of Transportation

 
Download PDF  (0.22 MB)
Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: 2168
Pages: 129-135
Publication Date: 2010
Summary:

The management of transportation systems for resilience has received significant attention in recent years. Resilience planning concerns the actions of an organization that reduce the consequences of a disruption to the system the organization manages. Little exploration has been made into the connections between resilience planning and the actions of a state department of transportation (DOT) that contribute to resilience of a freight transportation system. Conclusions are presented from collaborative research between the Washington State DOT Freight Systems Division (WSDOT FSD) and researchers at the University of Washington. Activities of the WSDOT FSD that contribute to resilience are identified, and one such activity undertaken by WSDOT to improve communication with system users is described. This and other activities can be undertaken by other DOTs that want to improve the resilience of their freight transportation systems at relatively low cost.

Authors: Dr. Anne GoodchildBarbara Ivanov, Chilan Ta
Recommended Citation:
Ta, Chilan, Anne V. Goodchild, and Barbara Ivanov. "Building Resilience into Freight Transportation Systems: Actions for State Departments of Transportation." Transportation Research Record 2168, no. 1 (2010): 129-135.
Paper

A Description of Commercial Cross Border Trips in the Cascade Gateway and Trade Corridor

Publication: Transportation Letters: The International Journal of Transportation Research
Volume: 1(3)
Pages: 213-225
Publication Date: 2009
Summary:

This paper describes commercial vehicle delay, transportation patterns and the commodity profile at the Western Cascade Gateway, the main border crossing between Southwest British Columbia, Canada, and Northwestern Washington, United States. Using five data sources for comparison—a probe vehicle border crossing time data set, a detailed border operations survey data set, loop detector volume counts, manifest sampling, and data from the Bureau of Transportation Statistics, the transportation, trade, and delay patterns can be synthesized to provide a more complete description of regional freight transportation. This context can be used to consider the impact delay has on regional supply chains, and in developing appropriate freight transportation policy solutions for the border.

Authors: Dr. Anne Goodchild, Susan Albrecht, Li Leung
Recommended Citation:
Goodchild, Anne & Albrecht, Susan & Leung, Li. (2009). A description of commercial cross border trips in the Cascade Gateway and trade corridor. Transportation Letters: The International Journal of Transportation Research. 1. 213-225. 10.3328/TL.2009.01.03.213-225.