Shipment Consolidation Research Articles

Optimal shipment consolidation and dynamic pricing policies for perishable items

Considering a firm who operates multiple stores to sell perishable items with limited inventory, we build a multi-location inventory model to explore the optimal dynamic pricing and shipment consolidation policies. The optimal consolidation and continuous-time dynamic pricing policies are obtained by solving the corresponding optimization problem based on optimal control theory. The optimal strategies derived show that the firm should take penetration pricing policy and select a subset of stores with high market potentials. Compared to cases of static pricing and non-consolidation, both dynamic pricing and shipment consolidation policies can enhance the firm’s profitability, while they exert substitutable effects. Furthermore, the heterogeneity of stores, price sensitivity of demands, deteriorating property of items, holding cost and delivery cost all highlight the importance of consolidation. Additionally, the optimal dynamic price exhibits non-monotonicity with respect to deterioration rate and holding cost.

The Flexible Scheduled Service Network Design Problem

The scheduled service network design problem (SSNDP) can support planning the transportation operations of consolidation carriers given shipment-level service commitments regarding available and due times. These available and due times impact transportation costs by constraining potential consolidation opportunities. However, such available and due times may be changed, either because of negotiations with customers or redesigned internal operations to increase shipment consolidation and reduce transportation costs. As changing these times can lead to customer service and operational issues, we presume a carrier seeks to do so for a limited number of shipments. We propose a new variant of the SSNDP, the flexible scheduled service network design problem, that identifies the shipments for which these times should be changed to minimize total transportation and handling costs. We present a solution approach for this problem that outperforms a commercial optimization solver on instances derived from the operations of a U.S. less-than-truckload freight transportation carrier. With an extensive computational study, we study the savings potential of leveraging flexibility and the operational settings that are fertile ground for doing so.

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.

Minimizing Total Logistics Cost for Long-Haul Multi-Stop Truck Transportation

Whenever there is sufficient demand, companies generally prefer the full truckload (TL) option for long-distance transport, resulting in large and less frequent shipment operations that can be costly if inventory carrying costs are high. Less than truckload (LTL) is another option for transport when carrying costs are high and/or there is insufficient demand. Shipment consolidation provides another option that combines many of the benefits of both TL and LTL. Shipment consolidation is a cost-effective transport solution that combines different size shipments into a single truckload. Combining many loads as a single load brings together economies of scale and potential savings. Traditional routing techniques that minimize distance are not suitable for shipments that have different origins and destinations because it can be beneficial to travel further to minimize overall transport and inventory cost, or what is termed total logistics cost (TLC). Effective consolidation of multi-stop routes to minimize TLC requires routing procedures that are more computationally intensive to find beneficial combinations of loads into consolidated shipments. In this study, we have developed a saving-based procedure to determine consolidated route sequences that minimize the TLC of shipments. Twenty-one data sets were produced using real city coordinates and population densities in North Carolina to demonstrate the effectiveness of the procedure. The solutions of the proposed method are compared with the solutions of the traditional Clarke and Wright (C-W) algorithm. Although the traditional C-W algorithm provides very fast solution times, the proposed method has produced much better solution values.

Modeling Freight Consolidation in a Make-to-Order Supply Chain: A Simulation Approach

Shipment consolidation is one of main initiatives to reduce CO2 emissions and transportation cost. It reduces the number of shipments per customer and reduces transportation costs by using larger shipments. This paper investigates the temporal consolidation process in a central consolidation center in a make-to-order supply chain. This research was motivated by a case study of a design furniture company that has many suppliers and customers in large parts of Europe. Simulation was used to check the effect of a new and a special time-based temporal consolidation on the response time in outbound logistics. A soft delivery deadline that is less than the average lead time was used because of the long lead time. Arena Software was used to model the supply chain in order to find the best circumstances to use consolidation. Results showed that temporal consolidation could be more effective when order preparation time is with larger variability. The useful waiting is more when there is at least one order every four days. A formula that approximates the percent of reduced shipments was found. Furthermore, many shipments can be reduced without severely affecting the average response time. The value of the study is that it investigates consolidation problems in a high-mix low-volume environment that was overlooked by previous research.

Freight delivery in a Physical Internet Supply Chain: an applied optimisation model with peddling and shipment consolidation

The delivery of goods (freight) accounts for the major cost and emissions in logistics systems worldwide. The effective management of the supply chain requires logistical efficiency and sustainability. Several logistics performance measurements such as distances driven and fuel efficiency are also indicators of sustainability. As an emerging paradigm, the Physical Internet (PI) addresses logistical, societal, and sustainability goals. However, the question remains whether some aspects of logistical efficiency in traditional logistics could be incorporated into the PI paradigm. To this effect, we examine how peddling, a consolidation strategy in traditional logistics, can enhance PI supply chain. Specifically, we formulate optimisation models and study the case of a Mexican automotive supply chain from both the PI and traditional logistics perspectives by comparing three different configurations; namely, Model P built on PI, Model S based on conventional standard peddling, and Model H, a hybrid. The hybrid Model H is optimised in a two-phase solution framework. We show via the case of a Mexican automotive supply chain that Model H outperforms others on average distance travelled and truck utilisation. Thereby, it is concluded that appropriate peddling and shipment consolidation policies may enhance PI supply chain logistics performance.

The impact of shipment consolidation strategies for green home delivery: a case study in a Mexican retail company

The growth of e-commerce has increased the number of home deliveries that need to be made in retail operations. Strategies such as reducing routing distance using optimisation models have been the most popular approaches to enhancing the efficiency of the delivery process and reducing its environmental burden. However, recently, customer purchasing behaviour has become relevant in understanding demand trends and increasing the effectiveness of the supply chain process. Customers are subject to different incentives to drive them to wait longer for their home deliveries. These incentives allow companies to pool and ship in more cost-efficient and energy-friendly alternatives. In this article, we present a methodology that uses a mixed-integer-linear programming model to evaluate the impact of extending delivery slots. We use real data from one of the largest retail companies in Mexico to analyse the impact of different time slot extensions for three different criteria: distance, transport costs and CO2 emissions. Our results show that the common approach of minimising distance is not recommended for any of the scenarios under study. On average, having up to 4 days for deliveries leads to savings of 57% in total distance, 61% in total costs and 56% in fuel consumption and/or CO2 emissions.

Time and Quantity Based Hybrid Consolidation Algorithms for Reduced Cost Products Delivery

In today’s competitive business environment, the cost of a product is one of the most important considerations for its sale. Businesses are heavily involved in research strategies to minimize the cost of elements that can impact on the final price of the product. Logistics is one such factor. Numerous products arrive from diverse locations to consumers in today’s digital era of online businesses. Clearly, the logistics sector faces several dilemmas from order attributes to environmental changes in this regard. This has specially been noted during the ongoing Covid-19 pandemic where the demands on online businesses have increased several fold. Consequently, the methodology to optimise delivery cost and its impact on environmental focus by reducing CO2 emissions has gained relevance. The resultant strategy of Shipment Consolidation that has evolved is an approach that combines one or more transport orders in the same vehicle for delivery. Shipment Consolidation has been categorized in three order scheduling approaches: Time based consolidation, Quantity based consolidation, and a Hybrid (Time-Quantity) based consolidation. In this paper, a new Hybrid Consolidation approach is presented. Using the Hybrid approach, it has been shown that order delivery can be facilitated by taking into account not only the order pick up time, but also the total order quantity. These results have shown that if a time window is available in respect of the order delivery time, then the order can be delayed from pickup to consolidate it with other orders for cost optimization. This hybrid approach is based on four consolidation principles, two of which work on fixed departure and two, on demand departure. Three of these rules have been implemented and tested here with an application case study. Statistical analysis of the results is illustrated with different planning evaluation indicators. The Result analyses indicate that consolidation of orders is increased with each implemented rule hence motivating us towards the implementation of the fourth rule. Testing with bigger data sets is required.

ANALYTICAL RESULTS ON THE SERVICE PERFORMANCE OF STOCHASTIC CLEARING SYSTEMS

Stochastic clearing theory has wide-spread applications in the context of supply chain and service operations management. Historical application domains include bulk service queues, inventory control, and transportation planning (e.g., vehicle dispatching and shipment consolidation). In this paper, motivated by a fundamental application in shipment consolidation, we revisit the notion of service performance for stochastic clearing system operation. More specifically, our goal is to evaluate and compare service performance of alternative operational policies for clearing decisions, as quantified by a measure of timely service referred to as Average Order Delay ($AOD$). All stochastic clearing systems are subject to service delay due to the inherent clearing practice, and $\textrm {AOD}$ can be thought of as a benchmark for evaluating timely service. Although stochastic clearing theory has a long history, the existing literature on the analysis of $\textrm {AOD}$ as a service measure has several limitations. Hence, we extend the previous analysis by proposing a more general method for a generic analytical derivation of $\textrm {AOD}$ for any renewal-type clearing policy, including but not limited to alternative shipment consolidation policies in the previous literature. Our proposed method utilizes a new martingale point of view and lends itself for a generic analytical characterization of $\textrm {AOD}$, leading to a complete comparative analysis of alternative renewal-type clearing policies. Hence, we also close the gaps in the literature on shipment consolidation via a complete set of analytically provable results regarding $\textrm {AOD}$ which were only illustrated through numerical tests previously.

An analytical investigation of alternative batching policies for remanufacturing under stochastic demands and returns

This article examines a fundamental lot-sizing problem which arises in the context of a make-to-order remanufacturing environment. The problem setting is characterized by a stochastic used-item return process along with a stochastic remanufactured-item demand process faced by a remanufacturer. We explicitly take into account for all relevant costs, including the fixed costs (associated with remanufacturing of used-items and dispatching of remanufactured-item orders in batches) and inventory-related cost (associated with used-item inventory holding costs and remanufactured-item order waiting costs). We propose five batching policies inspired by shipment consolidation practice (three periodic policies and two threshold policies). For the purpose of computing policy parameters, we develop analytical models that are aimed at minimizing the long-run average expected total cost of the remanufacturer. Since the underlying cost expressions are not analytically tractable, we propose easily computable approximations that lead to closed-form expressions for obtaining policy parameters. A careful numerical investigation demonstrates that the resulting policy parameters are highly effective approximations. Then, we extend the policies by considering disposal options when needed. For this extension, an effective parameter-based approximation approach is developed for computational purposes, and additional numerical experiments demonstrate the effectiveness of the proposed approach.

Shipping Consolidation Across Two Warehouses with Delivery Deadline and Expedited Options for E-commerce and Omni-channel Retailers

Problem definition: Shipment consolidation (i.e., shipping multiple orders together instead of shipping them separately) is commonly used to decrease total shipping costs. However, when the delivery of some orders is delayed, so they can be consolidated with future orders, a more expensive expedited shipment may be needed to meet shorter deadlines. In this paper, we study the optimal consolidation policy focusing on the trade-off between economies of scale due to combining orders and expedited shipping costs, in the setting of two warehouses. Academic/practical relevance: Our work is motivated by the application of fulfillment consolidation in e-commerce and omni-channel retail, especially with the rise of so-called on-demand logistics services. Sellers have the flexibility to take advantage of consolidation by deciding when to ship the orders and from which warehouse to fulfill the orders, as long as the orders’ deadlines are met. Methodology: We use Dynamic Programming to study the optimal policy and its structure. We also conduct extensive simulation tests to evaluate the performance of heuristics that are based on structures of the optimal policies. Results: The optimal policies and their structures are characterized. Using the insights of these structural properties, we propose two easily implementable heuristics that perform within 1%–2% of the optimal solution and outperform other benchmark consolidation methods in numerical tests. Managerial implications: Consolidation is shown to significantly reduce the outbound shipping costs. Retailers can take advantage of it to effectively improve the standard policies by simply applying the threshold-form heuristics we propose.

Achieving sustainable development of supply chain by incorporating various carbon regulatory mechanisms

Nowadays, sustainability issues have received considerable attention in supply chain management because of the governmental requirements as well as expectations of the people. This paper introduces a novel supply chain network design problem to cover three dimensions of sustainability, namely economic, environmental, and social. The advantage of the presented model stems from considering the booming development aligned with reduction in environmental impact. In this paper, to achieve the mentioned benefits and to derive a more sustainable supply chain, a novel model in the presence of the most commonly used carbon policies is proposed. This paper, addresses sustainable development through imposing proper carbon regulatory mechanisms. Main contribution of this study is to consider the effect of imposing carbon policies on environmental advantages as well as improving the regional development level in a supply chain network design problem. Moreover, the shipment consolidation decisions are utilized to reduce cost as well as environmental impact. In addition, a novel mixed uncertainty approach is proposed to capture the uncertain emission parameters. The numerical examples and a case study are analyzed to evaluate the performance of the proposed models. It is concluded that, a high-growth economy with low-carbon can be made and also almost global well-being of people is ensured by applying the proposed model. Some managerial insights are provided for the enterprises of supply chains to make the most appropriate sustainable decisions. Finally, proper carbon emission policies are suggested based on the region sustainability characteristics.

Vehicle Routing with Shipment Consolidation

The Vehicle Routing Problem with Split Deliveries (VRPSD) is a decision model that is increasingly used in practice to enhance the efficiency of last-mile distribution, an important task which all manufacturing, distribution, and retail firms must perform. In its current form, however, the VRPSD is missing an important cost saving opportunity by not allowing interactions among multiple vehicles that visit the same customer. In this paper, we introduce an extended version of the VRPSD, called the Vehicle Routing Problem with Shipment Consolidation (VRPC), a model that considers not only split deliveries, but also shipment exchanges (transfers) between different vehicles at certain customer locations (a process referred to as mid-route shipment consolidation). We conduct a series of numerical experiments, as well as a case study, to show that VRPC solutions match or outperform those of the VRPSD. Results indicate that up to 10 percent savings may be attained through the VRPC in lieu of the VRPSD, which implies that the efficiency of last-mile delivery may be improved by allowing mid-route shipment consolidation at select customer locations.

Understanding the usage patterns, practices and decision process of third party logistics outsourcing in Bangladesh

PurposeBangladesh is becoming one of the emerging destinations for global outsourcing. However, little is known about the usage patterns of third party logistics (3PL) outsourced services. The purpose of this paper is to examine the extent of 3PL usage, the organisational factors affecting 3PL usage, impacts of 3PL and the future trend of 3PL usage in Bangladesh.Design/methodology/approachA survey questionnaire is used to collect data against a number of items pertaining to the extent of 3PL use and its impact on performance. A total of 1,000 organizations were randomly selected from the Federation of Bangladesh Chambers of Commerce and Industry (FBCCI) database. The survey resulted in 243 responses, out of which 150 organisations have adopted 3PL services. A chi-square test is conducted to assess statistical significance of the hypothesized relationships among the variables relating to 3PL decision-making process and a regression analysis is conducted to assess the strength of those variables to predict the impact of 3PL usage.FindingsOverall, the results reveal that 3PL services are extensively adopted by businesses (63 per cent) in Bangladesh. Freight forwarding, order fulfilment and shipment consolidation are the top three most frequently outsourced 3PL services. However, the extent of 3PL use varies between manufacturing and service organisations. Results also suggest that logistics system performance is the key predictor of the overall satisfaction of 3PL users.Research limitations/implicationsThe key limitation of this study is that both manufacturing and services firms are collectively analysed. Future research will separately investigate the outsourcing 3PL services requirements for two categories of firms. Characterisation of 3PL usage patterns and their drivers has practical significance and strategic implications for logistics practitioners and decision-makers who opt for global outsourcing.Originality/valueThe implementation in the context of Bangladesh to provide an evidence base to formulate business strategies for increasing the adoption of 3PL usage is novel.

Reducing Carbon Emission through Container Shipment Consolidation and Optimization

Reducing Carbon Emission through Container Shipment Consolidation and Optimization

Controlling distribution inventory systems with shipment consolidation and compound Poisson demand

We consider a one-warehouse-multiple-retailer inventory system where the retailers face stochastic customer demand, modelled as compound Poisson processes. Deliveries from the central warehouse to groups of retailers are consolidated using a time based shipment consolidation policy. This means that replenishment orders have to wait until a vehicle departures, which increases the lead time for the retailers and therefore also the safety stock. Thus, a trade-off exists between expected shipment costs and holding costs. Our aim is to determine the shipment intervals and the required amount of safety stock for each retailer and the warehouse to minimize total cost, both for backorder costs and fill rate constraints. Previous work has focused on exact solutions which are computationally demanding and not applicable for larger real world problems. The focus of our present work is on the development of computationally attractive heuristics that can be applied in practice. A numerical study shows that the proposed heuristics perform well compared to the exact cost minimizing solutions. We also illustrate that the approaches are appropriate for solving real world problems using data from a large European company.

The two-level lot-sizing problem with outbound shipment

We consider the two-level lot-sizing problem for the integrated replenishment and dispatch plan of a third-party logistics company and distribution center. Each outbound shipment for a dispatch involves cargos that incur a stepwise transportation cost. To increase the effect of shipment consolidation, the distribution center allows backlogging. This problem has the characteristics of capacitated and uncapacitated lot-sizing. A forward dynamic program is applied to exploit the beneficial characteristic of the uncapacitated problem. The forward dynamic program is implemented using Zangwill partitions to decrease the number of computational parameters with legitimacy checks to guarantee optimality. Employing these techniques, we provide an O(T4) algorithm, which serves as an improvement on the O(T6) algorithm of previous research. Moreover, computational results from actual implementations show that our algorithm is significantly faster than that of prior research.

A perishable product shipment consolidation model considering freshness-keeping effort

Based on Quality-based policy and Quantity-and-Quality-based policy proposed in this paper, the corresponding perishable products shipment consolidation stochastic models by integrating freshness-keeping effort decision are formulated. For each policy, analytical results give conditions and expressions of adopting these policies and freshness-keeping effort. Numerical experiments show: (i) the positive effect of fresh investment on cost, (ii) the changing tends of policy parameters, freshness-keeping efforts and cost based on all parameters, (iii) the superiority of Quantity-and-Quality-based policy over Quality-based policy in most operational environment and (iv) the cost improvement under Quantity-and-Quality-based policy than other existing shipment consolidation policies.

A Machine Learning Approach to Shipment Consolidation

This research analyzes the current approach used by a client of DHL LLP for the transportation of shipments from suppliers to production sites. As a result of this analysis, several improvements are proposed that can be used to reduce the costs of transportation in the logistics network. Focus is put on shipment consolidation, rather than on the rerouting of shipments. In this paper, two consolidation methods are introduced. Cluster analysis groups together suppliers that are geographically close and generally ship to the same production sites, and a time-based policy that introduces a maximum waiting time for shipments before they are released. While only marginal improvements are obtained when applying these techniques indepen- dently, a combination of the two provides a powerful synergy. A trade-off between the savings and proportion of on-time shipments arises, when a maximum waiting time is introduced. The potential savings, therefore, depends on the company’s tol- erance for late shipments. While the proposed techniques work in theory, practical and organizational challenges emerge when applying them in the real world.

Shipment consolidation with two demand classes: Rationing the dispatch capacity

We analyze the problem faced by a logistics provider that dispatches shipment orders (parcels or larger packages) of two order classes, viz. expedited and regular. Shipment orders arrive according to a compound Poisson process for each class. Upon an arrival, the logistics provider may continue consolidating arriving orders by paying a holding cost. Alternatively, the provider may dispatch, at a fixed cost, a vehicle containing (a portion of) the load consolidated so far. In addition, the provider must specify the composition of each dispatch by allocating (rationing) the volume of the vehicle between expedited and regular shipment orders. We model this problem as a continuous-time Markov Decision Process and minimize the expected discounted total cost. We prove the existence of quantity-based optimal threshold policies under particular conditions. We also structurally analyze the thresholds of these optimal policies. Based on these structural properties, we develop an efficient solution approach for large problem instances which are difficult to solve using the conventional policy-iteration method. For two real-life applications, we show that the quantity-based threshold policies derived using the proposed approach outperform the time policies used in practice.