Shipment Patterns Research Articles

Defining and testing the value proposition of outside-in planning processes

Traditional planning processes are inside-out. The focus is on mining the patterns of orders and shipments and synchronising demand and supply based on enterprise data. The problem with this approach is with product proliferation and item complexity, demand latency increases, making the order signal out of step with the market by weeks and months. In addition, as supply chain leaders attempt to better align supply chains with markets in the face of months after months of disruption, historical patterns of orders and shipments are irrelevant. The reason is simple. In a volatile world, history is not a good signal to drive decisions. To find a solution, o9 Solutions and Supply Chain Insights partnered with a group of business leaders and academics to test the use of multiple streams of demand data and determine the value of using market, or channel, data versus driving demand off of order or shipment patterns. In this paper, we share insights and the journey. While the opportunity is tremendous and the value clear, the biggest issue for technologists and business leaders is unlearning conventional paradigms. As the use of market data makes many of the traditional planning assumptions obsolete. Here the reader will learn the lessons from the testing and gain insights to adapt their planning processes to be more outside-in.

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.

A complementarity model and algorithm for multi-commodity flow supply chain network equilibrium with random demands

This research aims to establish a nonlinear complementarity model and method for multi-commodity supply chain network equilibrium problems consisting of manufacturers and retailers in which the demands associated with the retail outlets are random. This work is different from previous research in that it focuses on a condition in which a manufacturer cannot sell all of its products. Based on this, the optimizing behaviour of the various decision-makers is modelled, the equilibrium conditions of the manufacturers and the retailers are derived, respectively, and nonlinear complementarity model of this problem is established. Qualitative properties of the equilibrium pattern in terms of existence, boundedness and global uniqueness are provided under milder conditions. Neither strict complementarity nor global Lipschitz continuity are required, and non-singular assumption is replaced by local error bound. A modified smoothing Levenberg–Marquardt algorithm to solve this model is proposed, and its global convergence and quadratic convergence rate are established, respectively. Finally, the model is illustrated through several numerical examples for which equilibrium prices and product shipments are computed under a variety of scenarios. Numerical results indicate the validity of the model, the availability of the algorithm, as well as the variation characteristics of equilibrium price and product shipment pattern.

Identifying large freight traffic generators and investigating the impacts on travel pattern: A decision tree approach for last-mile delivery management

Large urban freight traffic generators (LTGs) are large specialized buildings or landmarks housing multiple establishments and generate a significant truck trips at both disaggregate and aggregate levels. Identification of LTGs and quantifying their relationship with freight travel characteristics helps policymakers formulate necessary logistical interventions and reduce externalities from freight activity. Hence, this study proposes a methodology for identifying LTGs and exploring their interactions on freight travel, expenditure pattern, shipment pattern, and other establishment characteristics. A decision-tree approach called chi-squared automatic interaction detector (CHAID) algorithm is used to map these interactions. Results suggest that LTGs are distinctly associated with multiple variables such as shipment size, shipper expenditure, commodity classification, and business age characteristics. Business age is the best predictor across all models. These associations vary based on LTG definitions. Implications of this study would augment the efforts on interlinking LTGs with urban freight demand modeling systems and enable sustainable city logistics initiatives and last mile delivery management.

Cost savings with hemin versus givosiran for the treatment of patients with acute intermittent porphyria (AIP)

Objective Since 1983, hemin has been FDA-approved for acute intermittent porphyria (AIP) attacks. In 2019, FDA approved givosiran for the treatment of adults with acute hepatic porphyria. The objective of this research was to estimate and compare the total cost of AIP-related healthcare for patients treated with hemin or givosiran. Methods A microsimulation cost model was developed to estimate the annual economic impact of hemin versus givosiran treatment for patients with AIP from the U.S. healthcare payer perspective. Hemin treatment costs were calculated from the Hemin Shipment Data in which patients were defined as receiving acute attack treatment or prophylaxis treatment based on shipment patterns. Three separate hemin subpopulations were considered: one attack per year, multiple attacks per year, and hemin prophylaxis. Treatment costs for givosiran (with hemin for acute attacks) were simulated based on Phase III trial efficacy results applied to individual treatment histories in the Hemin Shipment Data. Other healthcare utilization was also considered. Outcomes were annualized and expenditures inflated to 2019. Results For all patients with AIP, the average annual total cost of care with hemin was 78% lower (difference = $482,113; 95% CI=$373,638–$594,778) than the average annual total cost of care with givosiran. Average annual total cost of care with hemin was between 46% and 92% lower than givosiran for the three hemin subpopulations: one attack per year (difference = $545,219; 95% CI=$436,584–$657,239), multiple attacks per year (difference = $459,366; 95% CI=$350,291–$574,403), and hemin prophylaxis (difference = $311,950; 95% CI=$191,898–$435,893). Cost savings with hemin were robust to one-way and probabilistic sensitivity as well as scenario analyses. Conclusions Hemin is expected to provide cost savings compared to givosiran for all AIP patients and subpopulations. Lower annual total costs of care with hemin range from $311,950 to $545,219 less depending on whether the patient uses hemin prophylactically or for acute treatment attacks.

Analysis of Labor Needs at Perawang Port 2013 - 2018

Shipment of container has become a phenomenon in the world of logistics it is seen as more efficient and effective means of transportation. Changes in shipment patterns from break bulk cargo to containers also occur in the port of Perawang along with the construction of bridges in Siak Sri Indrapura so that large vessels can no longer enter and carry out loading and unloading activities at the Perawang port, and this has resulted in companies having to invest in mechanical equipment because ships that enters the perawang private jetty is no longer supported by the crane on the ship.
 Based on data owned by PT. Indah Kiat Perawang container shipments continue to increase from year to year but not accompanied by an increase in the number of labor. This study aims to see the effect of Container Shipment, Mechanical Equipment Investment and Labor Wages on Labor Demand at perawang port. This study uses secondary data with data from 12 semesters from 2013 to 2018. With independent variables namely Container Shipment, Investment and Wages. The analysis technique used is multiple regression. The results of the research shows that container shipment and investment has no significant effect on labor demand, whilts wages has a significant and negative effect on labor demand

Estimating and exploring the proportions of inter- and intrastate cattle shipments in the United States

Mathematical models are key tools for the development of surveillance, preparedness and response plans for the potential events of emerging and introduced foreign animal diseases. Creating these types of plans requires data; when data are incomplete, mathematical models can help fill in missing information, provided they are informed by the data that are available. In the United States, the most complete national-scale data available on cattle shipments are based on Interstate Certificates of Veterinary Inspection, which track the shipment of cattle between states; data on intrastate cattle shipments are lacking. Here we develop four new datasets on intrastate cattle shipments in the U.S., including an expert elicitation survey covering 19 states and territories and three state-level brand inspection data sets. The expert elicitation survey provides estimates on the proportion of shipments that travel interstate over multiple regions of the U.S. These survey data also identify differences in shipment patterns between regions, cattle commodity types, and sectors of the cattle industry. These survey data cover more states than any other source of intrastate data; however, one limitation of these data is the small number of participating experts in many of the states, only seven of the 19 responding states and territories had a group size of three or larger. The brand data sets include origin and destination information for both intra- and interstate shipments. These data, therefore, also provide detailed information on the proportion of interstate shipments in three Western states, including the temporal and geographic variation in shipments. Because the survey and brand data overlap in the Western U.S., they can be compared. We find that in the Western U.S. the expert estimates of the overall proportion of cattle shipments matched the brand data well. However, the experts estimated that there would be larger differences in beef and dairy shipments than the brand data show. This suggests the cattle industries in the West may be sending similar proportions of commodity specific cattle shipments over state lines. We additionally used the expert survey data to explore how differences in the proportion of interstate shipments can change predictions about cattle shipment patterns using the example of model-guided suggestions for targeted surveillance in Texas. Together these four data sets are the most extensive and geographically comprehensive information to date on intrastate cattle shipments. Additionally, our analyses on predicted shipment patterns suggest that assumptions about intrastate shipments could have consequences for targeted surveillance.

A Study on the Day-of-The-Week Effect of Apple Shipment in China

본 연구는 중국 농수산물 도매시장의 거래량에 요일효과가 존재하는지 여부를 실증 분석한 것 이다. 본 연구에서는 중국 농업부에서 한국 농림수산식품 교육문화정보원으로 전송된 16개 도매시장에서 거래된 사과의 거래량 및 가격 정보 중 최근 5년여 간의 거래 데이터를 1년 내내 수집할 수 있는 광동산두 부산물도매시장과 북경순의 석문채소 도매시장의 후지사과를 분석 대상으로 하였다. 광동산두 부산물도매시장에서는 요일별 거래량에서 유의미한 차이를 보여 요일 효과가 나타남이 확인되었으나, 북경순의 석문채소도매시장에서는 요일별 거래량에 차이를 보이지 않아 요일효과가 나타나지 않음이 확인되었다. 이와 같은 연구 결과는 실무적으로는 한국 사과가 광동산 두 부산물 도매시장에서는 월요일과 금요일에 사과를 출고시킬 때 높은 가격을 획득할 수 있는 기회가 될 수 있음을 시사한다. 학술적으로는 다른 산업에 비해 상대적으로 연구결과가 축적되지 않은 농수산물시장에 대한 요일효과를 검증했다는 공헌점이 있다. 특히, 한국이 아닌 중국의 농수산물 거래량에 대한 구매시점효과를 실증 분석했다는데 학술적 의미가 더 있다.The practical cases and academic researches on the day-of-the-week effect are well documented in various industry. However, studies on agricultural products in terms of the day-of the-week effect are limited in China. The purposes of this study are to analyze the day-of-the–week effect and to figure out the trade shipment patterns in the wholesale market of apple in China. For this purpose, the study chose the Shantou wholesale market of byproducts in Guangdong and the Shunyi Shimen wholesale market of vegetables in Beijing for analysis subjects. The trade shipments data of these two markets for the last five years were available from the information about the volumes and prices of apples from the Chinese Ministry of Agriculture. The empirical results showed that there were significant differences in the trade volumes according to the day of the week in Shantou wholesales market, which confirms the day-of-the-week effect. However there were no differences in the trade volumes according to the-day-of-the-week in Beijing. This research can contribute to introduce constructs and empirical results such as purchase timing effect and the day-of-the-week effect for the agricultural industry in China.

Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S.

Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk of infection. Risk-based surveillance in U.S. livestock is limited because the locations of high-risk herds are often unknown and data to identify high-risk herds based on shipments are often unavailable. In this study, we use a novel, data-driven network model for the shipments of cattle in the U.S. (the U.S. Animal Movement Model, USAMM) to provide surveillance suggestions for cattle imported into the U.S. from Mexico. We describe the volume and locations where cattle are imported and analyze their predicted shipment patterns to identify counties that are most likely to receive shipments of imported cattle. Our results suggest that most imported cattle are sent to relatively few counties. Surveillance at 10 counties is predicted to sample 22–34% of imported cattle while surveillance at 50 counties is predicted to sample 43%–61% of imported cattle. These findings are based on the assumption that USAMM accurately describes the shipments of imported cattle because their shipments are not tracked separately from the remainder of the U.S. herd. However, we analyze two additional datasets – Interstate Certificates of Veterinary Inspection and brand inspection data – to ensure that the characteristics of potential post-import shipments do not change on an annual scale and are not dependent on the dataset informing our analyses. Overall, these results highlight the utility of USAMM to inform targeted surveillance strategies when complete shipment information is unavailable.

Mapping U.S. cattle shipment networks: Spatial and temporal patterns of trade communities from 2009 to 2011

The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41–95% of shipments in monthly networks from 2009 and 41–66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management.

Implementation of Hybrid Genetic Algorithm for CLSC Network Design Problem—A Case Study on Fashion Leather Goods Industry

The implementation of closed loop supply chain system is becoming essential for fashion leather products industry to ensure an economically sustainable business model and eco-friendly industrial practice as demanded by the environmental regulations, consumer awareness and the prevailing social consciousness. In this context, this research work addresses a closed loop supply chain network problem of fashion leather goods industry, with an objective of minimizing the total cost of the entire supply chain and also reducing the total waste from the end of life product returns. The research work commenced with a literature review on the reverse and closed loop supply chain network design problems of fashion and leather goods industry dealt in the past. Then, the identified CLSCND problem is solved using a mathematical model based on Mixed Integer Non-Linear Programme (MINLP) and then a suitable Hybrid Genetic Algorithm (HGA) developed for the CLSCND is implemented for obtaining optimum solution. Both the MINLP model and HGA are customized as per the CLSCND problem chosen and implemented for the industrial case of an Indian Fashion Leather Goods Industry. Finally, the solutions obtained for MINLP model in LINGO 15 and for HGA in VB.NET platform are compared and presented. The optimum solution obtained from the suitable HGA is illustrated as an optimum shipment pattern for the closed loop supply chain network design problem of the fashion leather goods industry case.

Strategic response to pollution taxes in supply chain networks: Dynamic, spatial, and organizational dimensions

This paper presents a model of the strategic behavior of firms operating in a spatial supply chain network. The manufacturing and retailing firms engage in an oligopolistic, noncooperative game by sharing customer demand such that a firm’s decisions impact the product prices, which in turn result in changes in all other firms’ decisions. Each firm’s payoff is to maximize its own profit and we show that, in response to such changes in prices and to exogenous environmental taxes, the manufacturing firms may strategically alter a variety of choices such as ’make-buy’ decisions with respect to intermediate inputs, spatial distribution of production, product shipment patterns and inventory management, environmental tax payment vs recycling decisions, and timing of all such choices to sustainably manage the profit and the environmental regulations. An important implication is that effects of a tax depends on the oligopolistic game structure. With respect to methods, we show that this dynamic game can be represented as a set of differential variational inequalities (DVIs) that motivate a computationally efficient nonlinear complementarity (NCP) approach that enables the full exploitation of above-mentioned salient features. We also provide a numerical example that confirms the utility of our proposed framework and shows substantial strategic reaction can be expected to a tax on pollution stocks.

Constrained optimisation of distribution network using new hybrid PSO optimiser

The efficient and effective movement of goods from raw material sites to processing facilities, component fabrication plants, finished goods assembly plants, distribution centres, retailers and customers are critical in today’s competitive environment. These in turn depend on facility location decisions taken during strategic planning. These facilities are expensive to construct and difficult to modify. Inefficient locations for production and assembly plants as well as distribution centres will result in excess costs being incurred throughout the lifetime of the facilities, no matter how well the production plans, transportation options, inventory management, and information sharing decisions are optimised in response to changing conditions. This paper addresses design of network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. If a facility is located at a candidate site, a known fixed location cost is incurred. There is a known unit shipment cost between each candidate site and each customer location. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the customers to minimise the combined facility location and shipment costs subject to a requirement that all customer demands be met. To optimise this, the distribution network model is mathematically represented and solved using hybrid PSO algorithm.

Buyer-Vendor Integrated system – the Technique of EOQ Dependent Shipment Size to Achieve Steady Level and Cost Minimization

Study of buyer-vendor integrated system, in general, has two major features; determining delivery schedule with supply quantity in each shipment and minimization of total incremental cost. Researchers in this area concentrate on latter part, which probably may not justify both the features. Many models developed so far, without considering both the features, begin with some pre-determined shipment pattern and establish cost minimization but may not establish stability in supply or shipment size that may vary in reality. In fact along with cost minimization stability in shipment size should also be a dominant feature of the doubly effective inventory model. Stable supply within a normal limit of small fluctuation will allow the carrying charges to be considered constant. The importance of the model lies in making all shipment size dependent on EOQ. The shipment size gets stable after two or three shipments and achieves optimization of total incremental cost.

Bi‐objective optimization of distribution scheduling using MOPSO optimizer

PurposeThe paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.Design/methodology/approachTo optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.FindingsThis paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.Originality/valueVery few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

Trade responses to geographic frictions: A decomposition using micro-data

A large literature has shown that geographic frictions reduce trade, but has not clarified precisely why. In this paper, we provide some insight into why such frictions matter by examining what parts of trade these frictions reduce most. Using data that track manufacturers’ shipments within the US on an exceptionally fine grid, we find that the pattern of shipments is extremely localized. Shipments within 5-digit zip codes, which have a median radius of just 4 miles, are 3 times larger than shipments outside the zip code. We decompose aggregate shipments into extensive and intensive margins, and show that distance and other frictions reduce aggregate trade values primarily by reducing the number of commodities shipped and the number of establishments shipping. Extensive margins are particularly important over very short distances. We examine trade in intermediate goods as an explanation for highly localized shipments and the dominant role of the extensive margin and find evidence consistent with this hypothesis. In another significant finding, we find no evidence of state-level home bias when distances are measured precisely and trade is observed over a very fine grid.

Differential variational inequalities and shipper dynamic oligopolistic network competition

In this paper, we show that the differential Cournot–Nash game describing dynamic oligopolistic network competition may be articulated as a differential variational inequality involving both control and state variables. We exploit this formulation to establish necessary conditions, an existence theorem and a nonconvex functional mathematical programming formulation. We show by example that, despite its nonconvexity, this mathematical program may be solved by the multi-start global optimization scheme found in the off-the-shelf software package GAMS when used in conjunction with the commercial solver MINOS. We also present a detailed numerical example. For that example, freight trip tables, formed ex post from the shipment patterns that are solutions to the dynamic oligopolistic network competition model we present, exhibit substantial temporal fluctuations. These observations are significant for they suggest that shippers must be extremely astute and capable of dramatically and rapidly altering production and distribution schedules if they are to compete in the final goods market successfully. If distribution services are provided by separate freight carriers, those carriers must be able to survive in a feast-or-famine environment.

In, Out, Within, and Through: Geography of Truck Freight in the Lower 48

The 1993 Commodity Flow Survey (CFS) proved that most freight transport on U.S. highways is interstate. The nation’s highway system functions principally to link the economies of states to form an integrated national economy rather than to serve the economies of individual states. Data from the 1997 CFS allow comparisons with the 1993 data to determine ( a) whether the predominant pattern of interstate shipments continues as a stable phenomenon; ( b) how fast the within-, from-, to-, and through-state components of truck freight are growing; and ( c) whether important regional trends are evident. The shipment of freight by truck in the United States was even more predominantly interstate in 1997 than in 1993. In 1997, interstate shipments made up 76 percent of the total tonmiles shipped by truck; in 1993 it was 73 percent. The proportions of truck freight shipments originating from, destined to, passing through, or occurring entirely within a state vary significantly from state to state. Individual state distributions remained relatively stable from 1993 to 1997, with the exceptions of dramatic increases in interstate truck ton-miles in small New England states and substantial but less-dramatic increases in border states, such as New York, Michigan, Washington, California, Texas, and Florida. These findings could have important implications for highway revenue allocations because trucks carrying freight play a significant role in damage to highway pavements and structures.

Environmental Impact Assessment of Transporting Hazardous Waste Generated by Maquiladora Industry in U.S.-Mexico Border Region

Hazardous waste shipments from U.S.-owned industries in the northern part of Mexico near the border with the United States are a growing problem. Today, the Mexican Environmental Agency requires all U.S. industries to return the waste produced by their plants to the United States. Currently, there is no database on the amount of hazardous waste transported from these firms, the pattern of shipments (from what origins to what destinations), and the nature of the risks to the population and environment along the shipment routes. In addition, there is a growing need to develop a risk assessment model and framework to focus on the transport of hazardous waste in the United States–Mexico border region, given the anticipated changes resulting from implementation of the North American Free Trade Agreement. Results of the data collection and analysis task and the risk assessment model formulation task are presented. The methodology is demonstrated in a case-study area of the United States–Mexico border region, namely, the Arizona-Sonora border area, and should be a valuable tool for evaluating various transport risk management scenarios of importance to the border area.

Spatial Competition Amongst Hierarchically Organized Corporations: Prices, Profits, and Shipment Patterns

In recent years, there have been computational and theoretical advances in the analysis both of the equilibrium and of the disequilibrium properties of pricing models in which spatial markets are dominated by autonomous firms engaged in oligopolistic competition. In this paper I develop an approach to the modeling of spatial pricing that transcends the unrealistic institutional simplification that firms are autonomous and independent of corporate organizational structures. Specifically, I hypothesize that competition between corporations takes place at two spatial scales. At the intraurban scale, corporations compete for market share through their franchise sites, where market share is contingent upon the nature and degree of competition between franchises, the spatial structure of the urban market, and the costs of production to the franchise. At the intraurban scale, competition is defined in terms of the strategies of the individual corporations as they adjust their delivered prices to urban markets in response to changes in their costs of production and distribution, the interurban transportation network, and the achieved market share in each urban market. I demonstrate that, for a general corporate objective, there exists at least one spatial price equilibrium and that the stability conditions of this model are identical for two price-setting scenarios: a partial adjustment model and a Bertrand game. For the specific corporate objective of total-profit maximization, I examine the qualitative properties of the hierarchical model.