Shipment Size Research Articles

Logistics Model for Indonesia’s National Freight Model System: From a Deterministic to a Stochastic Framework

One of the components of a new model, called INTRAMOD, for Indonesia’s domestic freight transport is the logistics model. The logistics model describes shipment size choice and the choice between five different transport chain alternatives involving four main modes: truck, train, vessel, and plane. This paper presents the work to forecast the disaggregate transport chain and shipment size choices for Indonesia’s domestic shipments by applying a deterministic and a stochastic approach. Using a standard economic order quantity model with a consolidation assumption, a deterministic approach is used to determine the transport chain and shipment size, minimizing total logistics cost. As an alternative for this, a stochastic model aims to improve the logistics choice modeling by employing data on the manufacturer’s revealed preferences and stated preferences about only the transport chain choice. The chosen specification for the stochastic approach is utilizing the multinomial logit model. Using the demand elasticities for all alternatives with respect to changes in its transport cost, a comparison will be made between the two approaches. In addition, it is concluded that the deterministic model is susceptible to sticky and flip-flop behaviors. In contrast, this characteristic is absent from the stochastic approach.

An Innovative Approach to Optimize Inbound Delivery Scheduling at Distribution Centers

Distribution centers (DCs) run more efficiently if their inbound deliveries are spread out evenly over the days of the week so that volume is aligned with receiving capacity. However, delivery dates are often scheduled at the time of placing purchase orders without an efficient process for spreading deliveries evenly, and there has been little research on how to optimally do this. This paper terms this process Master Purchase Receipt Scheduling (MPRS) and introduces a novel MPRS algorithm developed by New Horizon Soft, LLC using data from a major U.S. restaurant chain, where the algorithm is now in use. The approach optimizes order delivery dates, accounting for vendor shipping calendars, shipment sizes, and transportation uncertainty. Results from a Monte Carlo simulation validate the effectiveness and robustness of the algorithm. By addressing the complexities of DC operations, this innovation promises to reduce receiving bottlenecks, increase efficiency and capacity utilization, and foster additional research in this vital area.

Supply Shocks in Global Value Chains and Export Performance: Evidence from Bangladesh Using Triple Difference-in-Differences

In this paper, we evaluate how supply shocks in global value chains, such as the lockdown in China during the coronavirus pandemic, affected firm-product-level export performance in Bangladesh, which is a least-developed country where domestic inputs are inadequate due to capacity constraints. Transaction-level customs data on the universe of Bangladeshi apparel manufacturing firms are used in the analysis. We apply a triple difference-in-differences approach to compare the woven apparel exports of the firms that relied on China for inputs before and after the lockdown with those of knit apparel and firms that depended on the rest of the world as a comparison group. The results show that firms exposed to the Chinese input supply shock experienced a 36.5% decline in woven apparel exports after the lockdown. A plausible explanation for the decline in exports is the reduced shipment size driven by the constrained production capacity due to the shortage of inputs from China. We also find that exposed firms whose exports were highly concentrated in the European Union and the United States incurred an additional 18% decline compared to other firms.

Impact of COVID-19 on Manufacturing Overhead Cost: A Case of an Electronic OEM Company in Guangdong, China

This research analyzes the data of the manufacturing overhead costs (MOHC) of a plant in an electronic OEM company (located in Guangdong, China) during and after the COVID-19 pandemic. A paired t-test and a multiple variable regression analysis are conducted. MOHC and other indications are visualized by time sequence diagrams to facilitate the identification of the difference between mid- and post-pandemic. Through diving deeper into the effect of COVID-19 on the MOHC, Unit MOHC, and shipment, the overall influence of COVID-19 on the entire electronic OEM industry can be inferred. It is concluded that COVID-19 has influenced MOHC, Unit MOHC, shipment, and employee size, which may bring forth a paradigm-shifting experience on how people view the influence of COVID-19 on the entire electronic OEM industry and pave the way for further research afterward. This paper fills the research gap by focusing on one plant in one company, avoiding the difference in MOHC among different companies and plants.

Optimising railcar transfer chain via fuzzy programming and a simulated annealing algorithm

With the accelerated changes of trade and economic structure, the fluctuation of shipment size is increasing in railway transportation. Railway companies are facing continuous challenges about how to optimise railcar transfer chain to achieve the balance between the workload of railway network and daily changing transportation demands. In this paper, elastic capacity constraints are designed to solve the number fluctuation of railcars in shipments. We define available capacity belts to describe the elastic capacities of railway network, then fuzzy theory is introduced. A membership function is designed to designate the satisfaction degree for the number of railcars, and a non-linear integer programming model is developed. We test the model with two numerical examples from the 2019 Railway Applications Section Problem Solving Competition, and a simulated annealing algorithm is employed to solve the problem. In the experiment with 16 yards, the model generated 1304 variables. Furthermore, as the scale of the railway network increases, the number of variables exhibited exponential explosive growth. In the experiment with 32 yards, the model generated 76,037 variables and determined 365 direct train services, resulting in an operating cost of 245,014,388 car-hours. The results of the experiments effectively verify the effectiveness of the model.

Shipment size and vehicle choice modeling for road freight transport: A geographical perspective

Shipment size and freight transport mode choice have been modeled previously in many studies. There are several studies on the road transport level of this subject as well. This study investigates shipment size and road freight vehicle choice from a geographical point of view. An industrial region in a province may be neighboring another province, a large metropolitan area. Then, freight vehicle choice patterns in that industrial region may differ from the rest of the province it is part of. In this research, data were obtained from Kocaeli province in Türkiye. Modeling techniques include Analysis of Covariance (ANCOVA) for shipment size choice and Discrete Choice approaches for vehicle choice. This study investigates the effects of neighboring using dummy variable and market segmentation approaches. Both approaches show that one region of Kocaeli, neighboring Istanbul, has different vehicle choice mechanisms from the rest of Kocaeli. Therefore, in shipment size and freight vehicle choice modeling or freight transportation master plans for a region, neighboring effects of other large metropolitan areas should be considered. If necessary, separate models may be required for such regions. Effects of several variables on shipment size and vehicle choice patterns are also given.

Analysis of the Impacts of Freight Subsidisation on Transport Mode Selection: An Assessment of the Tasmanian Freight Equalisation Scheme

The objective of this research is to identify the key selection criteria that form the basis of a shipper’s selection of freight transport mode. The study is undertaken in the context of sea freight. The study is localised to Tasmanian shippers who are, or are potentially, recipients of freight subsidisation through the Tasmanian Freight Equalisation Scheme. Data collected through an internet-based survey is described, and influential mode choice selection criteria are reported with the use of descriptive statistics. The findings indicate that Tasmanian shippers are conscious of cost, service, and cargo characteristics.Median responses indicate that cost, service, and cargo are equally significant in the selection process. The findings indicate that cost and cost flexibility are influential cost-based criteria. The findings also indicate that transit time, service capacity, and service flexibility are influential service-based criteria. Shipment size, cargo weight, and cargo volume are also identified as influential cargo-based criteria.This study provides insight into mode choice selection for Tasmanian shippers. In this function, it may assist existing air and sea carriers operating in the Tasmanian market to evaluate their performance and strategy by providing an analysis of shippers’ transport mode choice process. Tasmanian shippers themselves may benefit from the potential expansion of their existing freight transport mode choice process. New shippers of freight into and out of Tasmania may benefit from the perspectives of existing shippers of freight into and out of Tasmania.

Temporal stability of shipment size decisions related to choice of truck type

The choice of shipment size is a vital decision in logistics and has a strong indirect influence on freight transport demand, via the choice of mode and truck type choice. Through time, shipment sizes can change as a result of new decisions in the logistics process or due to conditions external to the supply chain. This study investigates the temporal stability of shipment size choices, relating these to the choice of truck types. It uses repeated cross-sectional data for the years 2015, 2017, and 2019 collected from cordon and business establishment surveys in Addis Ababa city, Ethiopia. The integrated choice and latent variables (ICLV) and latent growth (LG) models were used to assess the time-dependent patterns of choosing shipment sizes, both at the level of the entire freight system as well as the specific truck types. The model results reveal that shipment size decisions are temporally unstable where, in our case, shipment sizes exhibited a declining trend.

Data Fusion Approach to Identify Distribution Chain Segments in Freight Shipment Databases

Understanding the logistic determinants of freight trips is an important goal in freight transport modeling. Freight shipments move between nodes in the supply chain for different logistic purposes, including production, storage, transshipment, and consumption. A key problem with data availability is that databases generally do not identify these purposes, given the commercial sensitivity of the data. In addition, including information on senders and receivers of the shipments is often prohibitively costly. Therefore, one of the challenges of transport data analysis is to identify freight trip purposes using data fusion, linking information about the main function of logistics nodes to trips in existing databases. This paper proposes a data fusion approach to enrich big truck shipment databases with firm registry data. We use the national freight shipment micro-database from the Netherlands which includes shipment, vehicle, and tour information. Although our presentation here uses formats and methods of accounting for freight data used in the Netherlands, it can be readily replicated for conditions in other countries, as long as similar data sets on shipment data and firm registry are available. The enriched, new database contains transport and firm data for more than 2 million observed trips with information on the vehicle used, shipments carried, and sender/receiver firm. An initial descriptive analysis provides unique empirical insights into the logistic determinants of freight trips. These include the share of national trips that use intermediate nodes, typical changes in shipment sizes, and the role of distribution centers for (de)consolidation of shipments.

Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

Modeling U.S. cattle movements until the cows come home: Who ships to whom and how many?

Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one year’s between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network.

Examining the Impacts of Highway Proximity on Freight Flow Patterns of Establishments

Highway proximity is essential to the economic growth of establishments and productivity of freight operations. The present study is motivated by the discernible gap in knowledge about the potential impacts of highway proximity on freight flow patterns of establishments. This knowledge gap impedes accurately estimating the potential benefits associated with infrastructure investments, especially related to freight-specific facility planning in developing countries. This paper investigates highway proximity impacts on freight establishments’ travel and shipment patterns using a comparative experiment approach. A total of 17 explanatory variables reflecting the freight flow pattern of an establishment have been considered. Proximity impact is evaluated by comparing the means of these explanatory variables among establishments closer to the highway, termed the “Influence” group, and those farther from the highway, defined as the “Control” group. Spatial and kernel analyses are carried out to determine the cut-off distance for demarcating samples into Influence and Control groups. The propensity score matching technique is subsequently used to create matched samples for a one-to-one comparison between the Influence group and Control group establishments. Results suggest that the Influence group typically consists of older establishments with larger business areas, generates many small-sized trips, and is associated with lower tonnage generation, expenditure, and shipment size than the Control group. The study findings are expected to have important implications on the infrastructure investment decision process and planning of freight operations.

The Effects of Cognitive and Skill Learning on the Joint Vendor–Buyer Model with Imperfect Quality and Fuzzy Random Demand

This study investigates the optimization of an integrated production–inventory system that consists of an original equipment manufacturer (OEM) supplier and an OEM brand company. The cognitive and skill learning effect, imperfect quality, and fuzzy random demand are incorporated into the integrated two-echelon supply chain model to minimize the total cost. We contribute to dividing the learning effect into cognitive learning and skill learning, we build a new learning curve to resemble the real complexity more closely and avoid the problem that production time tends towards zero after production is stable. In total, five production–inventory models are constructed. Furthermore, a solution procedure is designed to solve the model to obtain the optimal order quantities, and the optimal shipment size. Additionally, the symbolic distance method is used to deal with the inverse fuzzification. Then numerical analysis shows that the increase of the cognitive learning coefficient and skill learning coefficient will reduce the total cost of the production–inventory system. With the increase of the cognitive learning coefficient, the gap between the total cost of cognitive learning and skill learning, and that of Wright learning, correspondingly decreases consistently. However, with the increase of the skill learning coefficient, there is a consistent corresponding increase. The total cost of cognitive learning and skill learning shows hyperbolic characteristics. The important insights of this study for managers are that employees’ knowledge plays an important role in reducing costs in the early learning stage and humanistic management measures should be taken to reduce employees’ turnover. Compared with the skill learning training for production technicians, we should pay more attention to the training of cognitive learning.

Joint and sequential models for freight vehicle type and shipment size choice

Joint and sequential models for freight vehicle type and shipment size choice

Accounting for Heterogeneity of Travel Time Reliability Valuation in Shippers’ Freight Mode Choice

Designing efficient strategies to adjust freight transportation mode structure requires in-depth understanding of shippers’ mode choice behavior. This paper presents an empirical study to investigate preference heterogeneity in value of reliability (VOR) for hinterland leg transportation mode choice. A stated preference survey using a D-efficient design approach is carried out in the corridor from Yiwu to the port of Ningbo to collect data on shippers’ behavior. Two model specifications including the Base Model and the Heterogeneous Model are developed to analyze these data. Mixed logit is applied to estimate the parameters of models. The estimation results of the base model reveal significant preference heterogeneity in shippers’ VOR. We then calculate the mean and variation of overall VOR. In addition, the potential factors leading to heterogeneity in VOR are identified. Results imply that commodity characteristics including shipment size, value, and weight could partially explain the shippers’ heterogeneity in VOR. Based on these factors, eight sub-groups of container shippers are obtained, and the mode shares of railway under different levels of railway reliability are estimated for each sub-group. Results show that improvement in the level of reliability is important to increase the mode share of rail, especially in the sub-group where shipments are light and of high value. The findings of this paper can be used for demand forecasting and transportation policy making.

Patterns of export shipments

PurposeThis paper studies the frequency and the average size of export shipments at the firm–product–destination level.Design/methodology/approachThis paper sets up a theoretical model about the shipping frequency and the shipment size of exports to a given market from individual exporters. Then it confronts the predictions of the model with trade data.FindingsThis paper shows that patterns of export shipments are determined by (1) a product's export potential in a market, which is the exports revenue when the firm delivers the exports of the product to the market in a single shipment in each period; and (2) factors related to the firm's efficiency of managing shipments of the product exported to the market.Originality/valueThis paper reveals some important patterns of export shipments. It provides a theoretical model that explains the patterns.

Improving Preferential Market Access through Rules of Origin: Firm-Level Evidence from Bangladesh

In countries where there exist limited opportunities to source inputs locally, rules of origin undermine access to preferential trade agreements for final goods exporters. I analyze the 2011 revision to the rules of origin associated with the European Union’s Generalized System of Preferences, which allowed apparel producers in least developed countries to use internationally sourced textiles in exported products. Using transaction-level data on Bangladeshi apparel firms, I find that the rules of origin effectively cut the preferential margin by three-fourths. Liberalizing the rules of origin resulted in firm-level revenue growth, which was driven by the intensive margin through increased shipment sizes and quality upgrading. (JEL F13, F14, L67, O14, O19)

A three-layer supply chain integrated production-inventory model with idle cost and batch shipment policy

The paper describes an integrated/centralised supply chain model consisting of one supplier, one manufacturer and one retailer within a finite time horizon. The manufacturer produces, at a finite rate, in each lot. The lot production rate in a batch increases with a rate λ in successive batch and the produced items are supplied to the retailer. The objective of the proposed model is to optimize the average total profit under the consideration of the proportional increase in the size of successive shipments within a batch production run and the production time of the supplier. The corresponding average profits of the supplier, the manufacturer and the retailer and the average total profit of integrated model are obtained. The results obtained in the numerical examples clearly establish that it is always beneficial in terms of profit when the size of the successive shipment is a variable. Therefore, size of the successive shipment should be variable in order to get more profit. A sensitivity analysis of the optimal solution with respect to changes of the parameter values is also carried out to strengthen the proposed model.

Building latent segments of goods to improve shipment size modelling: Confirmatory evidence from France

Freight transport demand models are generally based on administrative commodity type segmentation which are usually not tailored to behavioral freight transport demand modelling. Recent literature has explored new approaches to segment freight transport demand, notably based on latent class analysis, with promising results. In particular, empirical evidence from road freight transport modelling in Germany hints at the importance of conditioning and handling constraints as a sound basis for segmentation. However, this literature is currently sparse and based on small samples. Before it can be accepted that conditioning should be integrated in the state-of-the-art doctrine of freight data collection and model specification, more evidence is required. The objective of this article is to contribute to the issue. Using detailed data on shipments transported in France, a model of choice of shipment size with latent classes is estimated. The choice of shipment size is modelled as a process of total logistic cost minimization. Latent class analysis leverages the wide range of variables available in the dataset, to provide five categories of shipments which are both contrasted, internally homogenous, and directly usable to update freight collection protocols. The groups are: "‘standard temperature-controlled food products"’, "‘special transports"’, "‘bulk cargo"’, "‘miscellaneous standard cargo in bags"’, "‘palletised standard cargo"’. This segmentation is highly consistent with the empirical evidence from Germany and also leads to better estimates of shipment size choices than administrative segmentation. As a conclusion, the finding that conditioning and handling information is essential to understanding and modelling freight transport can be regarded as more robust.

Estimation Method to Determine Shipment Size of Iron Ore Trade in Maritime Transport

Estimation Method to Determine Shipment Size of Iron Ore Trade in Maritime Transport