A method for generating carrier haulage data and related electronic device

Abstract

Disclosed is a method, performed by an electronic device, for generating carrier haulage data. The method comprises obtaining historical data associated with one or more previous shipments involving external haulage. The method comprises predicting an external turn time parameter indicative of a turn time of a container handled by external haulage, e.g., by applying a prediction model to the historical data. The method comprises generating, based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service. The method comprises communicating the carrier haulage data to a user for booking of a shipment.

Description

[0001] A METHOD FOR GENERATING CARRIER HAULAGE DATA AND RELATED

[0002] ELECTRONIC DEVICE

[0003] The present disclosure pertains to the field of transport and freight. The present disclosure relates to a method for generating carrier haulage data and related electronic device.

[0004] BACKGROUND

[0005] Shipment of items involves arrival and / or subsequent departure of items at terminal (e.g., port). The shipment is for example carried out by a carrier, such a consignor, of the shipment. Haulage may involve transportation of items to and / or from a terminal, such as a port, e.g., using a truck. Haulage may for example be provided by the carrier service, such as the consignor of the shipment. Alternatively, haulage may be provided by an external haulage service, such as a third-party haulage service.

[0006] SUMMARY

[0007] It may be difficult to effectively anticipate at booking which of the carrier haulage service or the external haulage service is the most efficient to use for transportation. There is a need for an electronic device and a method that may address transparency and prediction of haulage service at booking despite the complexity.

[0008] Accordingly, there is a need for an electronic device and a method for generating carrier haulage data, which mitigate, alleviate, or address the shortcomings existing and provide a prediction of haulage data based on turn time from an external haulage service that is dynamic and has an improved accuracy.

[0009] Disclosed is a method, performed by an electronic device, for generating carrier haulage data. The method comprises obtaining historical data associated with one or more previous shipments involving external haulage. The method comprises predicting an external turn time parameter indicative of a turn time of a container handled by external haulage, e.g., by applying a prediction model to the historical data. The method comprises generating, based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service. The method comprises communicating the carrier haulage data to a user for booking of a shipment. Disclosed is an electronic device comprising memory circuitry, processor circuitry, and an interface, wherein the electronic device is configured to perform any of the methods disclosed herein.

[0010] Disclosed is a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device (optionally with a display and a touch-sensitive surface) cause the electronic device to perform any of the methods disclosed herein.

[0011] It is an advantage of the present disclosure that the disclosed electronic device and method provide carrier haulage data that exploits the predicted turn time of an external haulage service. The disclosed carrier haulage data is advantageously generated by the disclosed technique by exploiting the external turn time parameter of the external haulage service, which is predicted by applying the prediction model to historical data associated with external haulage. This in turn allows the carrier haulage data to be adaptable to variations and thereby to be more accurate, despite the complexity.

[0012] The disclosed carrier haulage data is advantageously communicated to the user at booking of a shipment with the visibility of predicted turn time of an external haulage service, thereby allowing for transparency and mitigating the risk of additional costs, e.g., due to Demurrage and Detention. In other words, the user can gain valuable visibility into the estimated timeframe required for the return of empty containers, thanks to the upfront communication of the carrier haulage data.

[0013] BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features and advantages of the present disclosure will become readily apparent to those skilled in the art by the following detailed description of exemplary embodiments thereof with reference to the attached drawings, in which:

[0015] Figs. 1 A-B are a flow-chart illustrating an exemplary method, performed by an electronic device, for generating carrier haulage data according to this disclosure,

[0016] Fig. 2 is a block diagram illustrating an exemplary electronic device according to this disclosure, and

[0017] Fig. 3 shows an example user interface according to this disclosure.

[0018] DETAILED DESCRIPTION Various exemplary embodiments and details are described hereinafter, with reference to the figures when relevant. It should be noted that the figures may or may not be drawn to scale and that elements of similar structures or functions are represented by like reference numerals throughout the figures. It should also be noted that the figures are only intended to facilitate the description of the embodiments. They are not intended as an exhaustive description of the disclosure or as a limitation on the scope of the disclosure. In addition, an illustrated embodiment needs not have all the aspects or advantages shown. An aspect or an advantage described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced in any other embodiments even if not so illustrated, or if not so explicitly described.

[0019] The figures are schematic and simplified for clarity, and they merely show details which aid understanding the disclosure, while other details have been left out. Throughout, the same reference numerals are used for identical or corresponding parts.

[0020] A container disclosed herein refers to a housing where items to be shipped are enclosed for transport. For example, a container may be seen as a bin. An item disclosed herein refers to an object that is to be placed in a container for transport. For example, an item may be seen as an item of cargo, e.g., an item of freight, e.g., an object to be shipped. Note that the term item may be used interchangeably with cargo. For example, an item may comprise a commodity that can be placed into a container, such as goods, such as consumer goods of large manufacturing corporations, generic consumer goods as shoes, clothing, toys and fast-moving-consumer-goods as packaged foods, beverages, toiletries, and medicines. For example, the items disclosed herein may be seen as commodities that can be packed into e.g., rectangular stackable cartons with variable weights and volumes.

[0021] A commodity disclosed herein may be seen as an object and / or an item that is to be placed in a container for transport. For example, a commodity may be seen as an item of cargo, e.g., an item of freight, e.g., an object to be shipped. For example, the commodities may be seen as items that can be packed into containers. It is noted that in some examples, the term “commodity” may be used interchangeably with the term “cargo”. For example, a commodity may comprise items that can be placed into containers, such as goods. For example, goods can include material, such as oil, crude oil, raw materials, wood, industrial ingredients, plants (such as fruits and vegetables, seeds, beans), food (such as tea, coffee), fertilizers, paper, metal, chemicals, vehicles, mineral fuels, stones, tiles, glass plastic and rubber, etc. For example, goods can include as consumer goods of manufacturing corporations, generic consumer goods as shoes, clothing, toys and fastmoving-consumer-goods as packaged foods, beverages, toiletries, and medicines.

[0022] Examples of commodities include rectangular stackable cartons with variable weights and volumes that are transported in one or more dry containers.

[0023] A shipment may for example be seen as transportation of an item from an origin location to a destination location. For example, the shipment may be seen as shipment of a container comprising the item, e.g., one or more goods. A shipment may involve one or more modalities of transportation such as ocean carrier, land carrier, and / or air carrier. For example, the shipment may be carried out using one or more of: land vehicle, aircraft and seacraft.

[0024] A consignee can for example be seen as an entity, such as a person, that books a shipment. The shipment may for example be delivered to a consignee and / or to a location selected by the consignee. The consignee may for example be seen as a user of the electronic device, such as the electronic device 300 of Fig. 2.

[0025] Demurrage can be seen as the time from which a container arrives at a terminal and / or a port to the time the container is collected from the terminal and / or port to be transported to a recipient and / or destination (e.g., for unloading of at least a part of the container).

[0026] Detention can be seen as the time from which the container is collected from the terminal and / or port (e.g., for unloading) until the container is returned to the terminal and / or port (e.g., after unloading). For example, the returned container may comprise fewer items than prior to transportation from the terminal and / or port towards the recipient. In some examples, the returned container may be empty. Demurrage and / or detention may be referred to herein as D&D.

[0027] A turn time may be seen as a time period from which a loaded container (e.g., a container comprising items) arrives at the port, to the time an unloaded container (e.g., a container not comprising items, e.g., an empty container and / or partly empty container) is returned to the port. For example, the time period between the time that the container arrives at a terminal to be discharged off the vessel and the time that the container returns to the terminal (e.g., after the container has been emptied of items destined to a recipient) may be seen as a turn time. In some examples, the turn time includes the detention time solely. For example, the turn time can be seen as the time period between that the container leaves the terminal to be delivered to the recipient and the time that the container returns to the terminal (e.g., after the container has been emptied of items destined to a recipient). Turn time disclosed herein may comprise demurrage turn time and / or detention turn time. Demurrage turn time is for example the time from which a container arrives at a terminal and / or a port to the time the container is collected from the terminal and / or port to be transported to a recipient and / or destination (e.g., for unloading of at least a part of the container). Detention turn time is for example the time from which the container is collected from the terminal and / or port (e.g., for unloading) until the container is returned to the terminal and / or port (e.g., after unloading). For example, the returned container may comprise fewer items than prior to transportation from the terminal and / or port towards the recipient. In some examples, the returned container may be empty.

[0028] A time extension disclosed herein may be seen as an additional period of time (e.g., days, hours, etc) taken to return a container back to the port. The time extension disclosed herein may for example be seen as an additional period of time (e.g., days, hours, etc) taken to remove a container from a port and / or terminal. In other words, the time extension granted may for example be seen as the extra time which a container may be stored in the port. The time extension may be selected at booking and not be resulting in additional charges after booking. The time extension can be called a free time extension. The time extension may differ depending on the commodity of a container, the consignee associated with the container, the properties of the container (e.g., type and / or size of the container), the port location, the booking type, the container type etc. In some examples, the time extension may be granted by an authority associated with the port. In some examples, the time extension period may be extended. The time extension may for example be selected for one or more containers (e.g., by the consignee).

[0029] A booking request is, for example, based on user input provided by the user. In one or more examples, the booking request comprises a user code, country code, commodity code and contract code. The user code, country code, commodity code and / or contract code can be seen as attributes (e.g., features) of a shipment. The user code is for example indicative of or associated with a user. For example, the user code can be seen as a user identifier associated with a user, such as uniquely associated with the user. The user identifier may for example comprise a code (e.g., comprising one or more letters and / or numbers, e.g., alphanumeric code) indicative of a user identity. The country code is for example indicative of a country. For example, the country code comprises one or more letters and / or numbers, e.g., alphanumeric code indicative of a country. The commodity code is for example indicative of a commodity. For example, the commodity code may be seen as a commodity number (e.g., Harmonized System (HS) code). The commodity code for example comprises a code (e.g., alphanumeric code, a numerical code, a string, etc). The contract code is for example indicative of the contract associated with a shipment. In other words, the contract code can be indicative of the contract (e.g., the agreement) under which the shipment (e.g., the historical shipment) associated with the user was carried out. The contract code for example comprises a code (e.g., alphanumeric code, a numerical code, a string, etc). The contract code may for example be a contract number.

[0030] Haulage can be seen as transport of an item from a first location, such as a port and / or terminal, to a second location, such as a warehouse and / or storage facility. Haulage can for example be seen as the inland transport of a container and / or of an item, such as to and / or from a port. For example, haulage may comprise a transportation of an item using one or more modes of transportation, such as land-based transportation. For example, the haulage of an item may be carried out via a type of truck, lorry, trailer, train, etc. For example, a shipment comprises a section of the shipment / journey that is not the haulage section, but via a cargo ship, cargo aircraft, and another section for haulage, such as a haulage section. For example, haulage of a container and / or of an item may be seen as a haulage section of a journey and / or a shipment. Haulage can for example be seen as preshipment transportation or post-shipment transportation of an item. For example, preshipment transportation of an item may comprise hauling an item to an origin location of a shipment, such as a port and / or terminal. Post-shipment transportation of an item may comprise hauling an item from a destination location of a shipment, such as a port and / or terminal.

[0031] For example, when a container, e.g., comprising an item, is to be shipped from Mumbai to Copenhagen, pre-shipment haulage may comprise transporting the item from a manufacturing and / or storage facility to the Mumbai port, e.g., the origin location of the shipment. The container will then be shipped via a cargo ship to Copenhagen, e.g., the destination port. Post-shipment haulage may then comprise transporting, e.g., via a truck, the item from Copenhagen port to a storage facility and / or final inland destination. The haulage may for example be carried out by the shipment provider, e.g., the carrier, such as the consignor. The shipment provider can be seen as the provider of the shipment and / or transport of the container. When the haulage is carried out by the shipment provider, the haulage is denoted carrier haulage in this disclosure. When the haulage carried out by a service different to the shipment provider, e.g., by an external haulage service, the haulage is denoted external haulage in this disclosure, such as third- party haulage, and / or merchant haulage. In other words, merchant haulage may be seen as haulage not carried out by the carrier provider.

[0032] During the journey of a container, the consignee plays an instrumental role as the consignee receive a loaded container and is responsible for returning the container empty. To facilitate this, the carrier provider (e.g., consignor) provides a certain number of standard time extension days to the consignee for the container return. However, if the consignee exceeds the granted time extension days, the consignee may incur penalties known as Demurrage and Detention (D&D) charges.

[0033] Upon the completion of loading the container, the carrier on behalf of the users assume responsibility for organizing and overseeing the transportation of the container from the port to the desired inland destination through haulage services selected amongst carrier haulage service provided by the carrier, and external haulage services. However, if the haulage services exceed the time extension days allotted for container return, the users ends up bearing the resulting DND charges.

[0034] The present disclosure addresses these challenges by predicting the external turn time parameter for an external haulage service and adapting the carrier haulage data (e.g., including cost and time) to the prediction so that the user gains in transparency when selecting haulage services. The present disclosure allows, inter alia, to anticipate and possibly avoid unforeseen time extensions.

[0035] The present disclosure can be seen as predicting the turn time of the container (by applying the prediction model, such as machine learning & statistical modelling techniques) for an external haulage service (so called external turn time parameter in this disclosure), so that a carrier service with adapted carrier haulage data can be communicated to the user. For example, when the external turn time parameter is above a threshold and the user is likely to attract demurrage and detention penalties, the disclosed technique allows to predict such situation and generate carrier haulage data for communicating the carrier haulage data for the carrier haulage services to the user for booking of such service during the booking of the shipment.

[0036] The present disclosure leverages machine learning algorithms trained on historical data, to generate accurate predictions of container turn time at specific ports when provided by external haulage service and / or carrier haulage services. For example, by analyzing various parameters provided in the historical data, the present disclosure provides to the user personalized insights into the anticipated duration of container processing. Equipped with this information, the users can choose carrier haulage services to mitigate the risks of penalties and optimize logistics planning through data backed decisions.

[0037] It may be appreciated that external haulage can be seen as a first haulage and carrier haulage can be seen as a second haulage. The terms “external haulage” and “first haulage” can be used interchangeably in this disclosure. The terms “carrier haulage” and “second haulage” can be used interchangeably in this disclosure.

[0038] Figs. 1 A-B show a flow-chart illustrating an exemplary method 100, performed by an electronic device. The method 100 may be seen as a method for generating carrier haulage data, e.g., for communicating the carrier haulage data according to this disclosure. The method 100 can be performed by an electronic device, such as the electronic device disclosed herein, such as electronic device 300 of Fig. 2.

[0039] The method 100 comprises obtaining S102 historical data associated with one or more previous shipments involving external haulage. Historical data can for example be seen as previous (e.g., past) data associated with the one or more previous shipments e.g., of a user. The historical data for example comprises data associated with one or more previous shipments involving external haulage for a given period of time, such as for the previous year. The historical data for example comprises data associated with one or more previous shipments involving external haulage for the previous one or more years before the present booking of the shipment by the user. In one or more example methods, the historical data comprises one or more of: historical transaction data associated with previous transactions, historical container movement data indicative of container movement, historical haulage data for external haulage and / or carrier haulage, and historical seasonality factors for external haulage and / or carrier haulage.

[0040] The historical transaction data can for example be seen as historical data associated with a transaction. The transaction may for example be a booking, such as the booking of one or more previous shipments involving external haulage and / or carrier haulage, including booking data and transaction details. Obtaining the historical data for example comprises obtaining, for one or more users, such as users of a given location, historical transactional data associated with one or more previous shipments involving external haulage and / or carrier haulage provided to a plurality of users over a time period.

[0041] The historical container movement data is for example indicative of container movement associated with one or more previous shipments involving external haulage and / or carrier haulage.

[0042] The historical haulage data is for example indicative of external haulage and / or carrier haulage associated with one or more previous shipments. For example, the historical haulage data may be indicative of a historical turn time associated with one or more previous shipments involving external haulage and / or carrier haulage.

[0043] The historical seasonality factors are for example indicative of seasonality associated with one or more previous shipments involving external haulage and / or carrier haulage.

[0044] In some examples, the historical data is extracted from one or more sources, such as one or more databases, that are for example storing booking data and / or container movement data.

[0045] The method 100 comprises predicting S118 an external turn time parameter indicative of a turn time of a container handled by external haulage, optionally by applying S118A a prediction model to the historical data. The external turn time parameter is for example indicative of a time period for the return, by an external hauler, of a container. In other words, the external turn time parameter disclosed herein may be seen as a parameter indicative of a turn time of the container being hauled by an external hauler. For example, the external turn time parameter may comprise a value (e.g., integer, decimal etc.) indicative of the turn time for a container.

[0046] The prediction model can for example be seen as a model configured to predict the external turn time parameter by taking as input the historical data. In other words, the prediction model is for example applied to one or more of: historical transaction data associated with previous transactions, historical container movement data indicative of container movement, historical haulage data for external haulage and / or carrier haulage, and historical seasonality factors for external haulage and / or carrier haulage. The method 100 comprises generating S120, based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service. The carrier haulage service can be seen as a haulage service provided by a carrier, e.g., a consignor, of the shipment. The carrier haulage data can be seen as data indicative of the carrier haulage, such as a carrier haulage value (e.g., cost) and / or a carrier haulage turn time. For example, the carrier haulage data comprises the carrier haulage value (e.g., cost) and / or the carrier haulage turn time. In one or more example methods, generating S120 the carrier haulage data comprises generating S120A, based on the predicted external turn time parameter and a container pickup parameter, the carrier haulage data. For example, the container pickup parameter can be provided (e.g., selected) by the user. The container pickup parameter is for example indicative of a date, such as a date where the carrier haulage may pick up the item, such as the shipped item. In one or more example methods, the carrier haulage data comprising a container return parameter and / or a carrier haulage value. The container return parameter may be indicative of the time period, such as predicted time period, for the carrier haulage to be carried out. The container return parameter is for example indicative of turn time by the carrier haulage service, such as a date where the carrier haulage may return up the item, such as the shipped item. The carrier haulage value may be indicative of the fee and / or cost associated with the selection for the carrier haulage service. In other words, for the carrier haulage service to be provided to the user, the user may pay the fee and / or cost indicated by the carrier haulage value.

[0047] In one or more example methods, the method 100 comprises determining S121 , based on a difference between an external haulage return parameter and a container pickup parameter, an external haulage risk level. The external haulage return parameter can be seen as a parameter providing a predicted date for the return of the container that is determined based on the predicted external turn time parameter. The external haulage risk level is for example indicative of a risk of delay in return of the container, e.g., incurring a D&D fee. In other words, the external haulage risk level may be indicative of the risk that the external haulage may exceed a D&D time period, such as a granted time extension period and / or a selected time extension period. The external haulage risk level may for example be indicated by one or more words, e.g., low risk, medium risk and / or high risk. In some examples, the external haulage risk level may be indicated by a value. The method 100 comprises communicating S122 the carrier haulage data (and optionally the external turn time parameter and / or optionally the external haulage risk level) to a user for booking of a shipment.

[0048] The booking of the shipment may be performed via a user interface (such as the user interface illustrated in Fig. 3). The booking of the shipment can involve a booking request and / or a booking response. A booking request may be seen as request for booking of a shipment. For example, a booking request is provided by a user, possibly via a booking platform. In some examples, the user may be a booking agent. In some examples, the user is a consignee. The user is for example a user of an electronic device, such as an electronic device communicatively coupled with the electronic device disclosed herein (e.g., electronic device 300 of Fig. 2). The booking request for example comprises information associated with the booking. In some examples, the booking request can be seen as a request for shipment of one or more items (e.g., goods).

[0049] In one or more example methods, the prediction model is a machine learning prediction model. In one or more example methods, the machine learning prediction model comprises one or more of: a regression prediction model, a decision tree model, a random forest model and a gradient boosting model.

[0050] A regression prediction model can be seen as a model configured to perform one or more regression techniques, such as linear regression and / or logistic regression. The regression techniques may for example be seen as supervised machine learning techniques.

[0051] A decision tree model can be seen as prediction technique that utilizes a tree-like, such as branching, structure to represent decisions and outcomes, such as possible outcomes. The tree-like structure of the decision tree model may for example be based on the historical data associated with one or more previous shipments involving external haulage.

[0052] The random forest model may be seen as an ensemble learning method for classification, regression and other tasks that operates by constructing a collection of decision trees based on the historical data. For example, the historical data may be subject to bagging and feature randomness for building each individual tree to predict the external turn time parameter. The gradient boosting model may for example be configured to perform one or more gradient boosting techniques. The gradient boosting model may for example be configured to adjust, such as iteratively, one or more predictions associated with the prediction model. In some examples, the gradient boosting model is a light gradientboosting machine (LightGBM). In some examples, the gradient boosting model is an extreme Gradient Boosting (XGBoost).

[0053] In one or more example methods, the method 100 comprises calculating S104, based on the historical data, one or more commodity haulage parameters. In some examples, the historical data is transformed to extract the one or more commodity haulage parameters and / or the consignee haulage parameter(s).

[0054] A commodity haulage parameter can be seen as a parameter indicative of the haulage of a commodity. It may be appreciated that various commodities have various transportation time by haulage. In one or more example methods, the one or more commodity haulage parameters for the at least one commodity are indicative of, for the at least one commodity, at least one of: a proportion of delayed containers for the at least one commodity, a proportion of granted time extension used over granted time extension of shipment for the at least one commodity; and a proportion of selected time extension used over selected time extension of shipment for the at least one commodity. For example, the one or more commodity haulage parameters for each commodity of a plurality of commodities are indicative of, for each commodity, one or more of: a proportion of delayed containers, a proportion of granted time extension used over granted time extension of shipment, and a proportion of selected time extension used over selected time extension of shipment.

[0055] The proportion of delayed containers for a commodity can for example be seen as the proportion of delayed containers of a shipment. For example, the proportion of delayed containers for the commodity can be seen as the proportion of delayed containers over total containers shipped for a given commodity. For example, the proportion of delayed containers for a commodity is the percentile for delayed container returns for each commodity, based on the historical data, e.g., for one year, and for various shipments (e.g., ad hoc shipments, and contract-based shipments).

[0056] The granted time extension may be seen as a time extension granted without being selected, e.g., by a user. The granted time extension can for example be seen as an automatically granted time extension, such as granted by a port authority. For example, the proportion of granted time extension used over granted time extension of shipment is the percentile of granted time extension used over granted time extension of shipment for each commodity, based on the historical data, e.g., for one year, and for various shipments (e.g., contract-based shipments). For example, the percentile of granted time extension used over granted time extension of shipment for each commodity, can be based on consumed vs. granted free time days ratio of shipments for each commodity.

[0057] The proportion of selected time extension used over selected time extension of shipment for the at least one commodity can for example be seen as a proportion of purchased (e.g., by a consignee) time extension used over purchased (e.g., by a consignee) time extension of shipment for the at least one commodity. For example, the proportion of selected time extension used over selected time extension of shipment is the percentile based on time extensions consumed vs. bought ratio of shipments for each commodity, based on the historical data, e.g., for one year, and for various shipments (e.g. ad hoc shipments, and contract-based shipments).

[0058] In one or more example methods, predicting S118 the external turn time parameter comprises determining S118A a commodity risk factor based on the one or more commodity haulage parameters. The commodity risk factor can for example be seen as indicative of a risk, such as for a given commodity, of incurring a fee, such as a D&D fee. In some examples, determining S118A the commodity risk factor comprises determining, per port (such as for each port) a commodity risk factor based on the one or more commodity haulage parameters. In some examples, determining S118A the commodity risk factor can be seen as deriving and / or computing a commodity risk factor based on the one or more commodity haulage parameters. In one or more example methods, determining S118A the commodity risk factor based on the one or more commodity haulage parameters comprises applying S118AA one or more standardization functions to the one or more commodity haulage parameters. In some examples, determining S118A the commodity risk factor based on the one or more commodity haulage parameters comprises applying one or more sigmoidal functions to the one or more commodity haulage parameters. In other words, the one or more standardisation functions for example comprise a sigmodal function.

[0059] In one or more example methods, the method 100 comprises grouping S106 the consignees, based on whether the consignees are associated with a previous shipment. Grouping S106 the consignees can for example be seen as segregating the consignees, based on whether the consignees are associated with a previous shipment (e.g., new vs existing consignees or users). In one or more example methods, a first consignee group comprises consignees associated with one or more previous shipments. The first consignee group can for example be seen as a consignee group comprising existing consignees. In one or more example methods, the second consignee group comprises consignees not associated with a previous shipment. The second consignee group can for example be seen as a consignee group comprising non-existing, such as new, consignees.

[0060] In one or more example methods, the method 100 comprises calculating S108, based on the historical data, one or more consignee haulage parameters. In some examples, calculating S108, based on the historical data, one or more consignee haulage parameters can be seen as transforming, based on the historical data, one or more consignee haulage parameters. In other words, the historical data is used to extract and be transformed into one or more consignee haulage parameters for each consignee. The consignee haulage parameter can be seen as a parameter indicative of the shipment pattern of the consignee which gives insights into the consignee haulage pattern.

[0061] In one or more example methods, the one or more consignee haulage parameters for the at least one consignee are indicative of, for the at least one consignee, at least one of: a proportion of delayed containers for the at least one consignee, a proportion of granted time extension used over granted time extension of shipment for the at least one consignee; and a proportion of selected time extension used over selected time extension of shipment for the at least one consignee. For example, the one or more consignee haulage parameters for each consignee of the plurality of consignees are indicative of at least one of: a proportion of delayed containers for each consignee, a proportion of granted time extension used over granted time extension of shipment for each consignee; and a proportion of selected time extension used over selected time extension of shipment for each consignee.

[0062] The proportion of delayed containers for a consignee can for example be seen as the proportion of delayed containers of a shipment for the consignee. For example, proportion of delayed containers for the consignee can be seen as the proportion of delayed containers over total containers shipped for a given consignee. For example, the proportion of delayed containers for the consignee is a percentile based on proportion of shipments / containers delayed for the consignee.

[0063] The proportion of granted time extension used over granted time extension of shipment for the consignee can for example be seen as a proportion of granted (e.g., by a port authority) time extension used over purchased (e.g., by a consignee) time extension of shipment for the consignee. For example, the proportion of selected time extension used over selected time extension of shipment for the consignee is a percentile based on proportion of consumed vs purchased days by the consignee.

[0064] The proportion of selected time extension used over selected time extension of shipment for the consignee can for example be seen as a proportion of purchased (e.g., by a consignee) time extension used over purchased (e.g., by a consignee) time extension of shipment for the consignee. For example, the proportion of selected time extension used over selected time extension of shipment for the consignee is a percentile based on proportion of consumed vs purchased days by the consignee.

[0065] In one or more example methods, predicting S118 the external turn time parameter comprises determining S118B a consignee risk factor based on the one or more consignee haulage parameters. In some examples, determining S118B a consignee risk factor comprises determining, per port (such as for each port), the consignee risk factor based on the one or more consignee haulage parameters. In some examples, determining S118B a consignee risk factor can be seen as deriving and / or computing a commodity risk factor based on the one or more consignee haulage parameters. The consignee risk factor can for example be seen as indicative of a risk, such as for a given consignee, of incurring a fee, such as a D&D fee. In one or more example methods, determining S118B the consignee risk factor based on the one or more consignee haulage parameters comprises applying S118BA one or more standardization functions to the one or more consignee haulage parameters. In some examples, determining S118B the consignee risk factor based on the one or more consignee haulage parameters comprises applying one or more sigmoidal functions to the one or more consignee haulage parameters.

[0066] In one or more example methods, the method 100 comprises obtaining S110 a transaction quantity parameter associated with a consignee. In one or more example methods, the transaction quantity parameter is indicative of a historical quantity of consignee transactions involving external haulage. The transaction quantity parameter may be indicative of a number of, such as a historical number of external haulages associated with a given consignee. In other words, the transaction quantity parameter is for example indicative of a number of times, such as a historical number of times, that a consignee has used an external haulage service.

[0067] In some examples, obtaining S110 the transaction quantity parameter associated with a consignee comprises receiving and / or retrieving a transaction quantity parameter associated with a consignee. In some examples, obtaining S110 the transaction quantity parameter associated with a consignee comprises generating the transaction quantity parameter associated with a consignee.

[0068] In one or more example methods, the method 100 comprises determining S112 whether the transaction quantity parameter meets a first criterion. In some examples, consignees may be grouped, such as bucketed and / or classified, based on whether the transaction quantity parameter associated with a consignee meets the first criterion. In one or more example methods, the first criterion is based on a first threshold. In some examples, whether the transaction quantity parameter meets the first criterion depends on whether the transaction quantity parameter is greater than or less than the first threshold. For example, when the transaction quantity parameter is greater than or equal to the first threshold, the first criterion can be seen as being met by the transaction quantity parameter. For example, when the transaction quantity parameter is less than the first threshold, the first criterion can be seen as not being met by the transaction quantity parameter. The first threshold is for example a value. For example, the threshold may be a value of 50. In some examples, when the transaction quantity parameter meets the first criterion, the consignee can be seen as a large consignee. In some examples, when the transaction quantity parameter does not meet the first criterion, the consignee can be seen as a small consignee.

[0069] Seasonality may be seen as a characteristic of a time series in which the turn time parameter experiences regular and predictable changes (e.g., patterns) that may recur over a time period (such as every calendar year). For example, seasonality may be seen as seasonal patterns associated with a data set (e.g., a time series) for the turn time.

[0070] The seasonality parameter may be seen as a parameter indicative of seasonality of the turn time, e.g., based on the historical data. In some examples, the seasonality parameter comprises a value (e.g., Boolean, decimal, fraction, integer, etc.) indicative of a characteristic of a time series in which the turn time experiences periodical and / or predictable changes (e.g., patterns) that may recur over a time period (such as every calendar year). For example, seasonality parameter may be indicative of seasonal patterns associated with a data set (e.g., a time series). For example, each seasonality parameter may be associated with a corresponding time period, such as a corresponding season (e.g., a month or quarter of a year). The seasonality parameter may for example be seen as a value indicative of the seasonal impact (e.g., on the revenue per volume) associated with a given month. The seasonality parameter may be seen as indicative of the seasonality in a given month. The seasonality parameter is for example associated with a given port.

[0071] In one or more example methods, the method 100 comprises, upon determining that the transaction quantity parameter meets a first criterion, obtaining S114 a seasonality parameter associated with the consignee. In one or more example methods, the method 100 comprises, upon determining that the transaction quantity parameter meets a first criterion, receiving and / or retrieving a seasonality parameter associated with the consignee. In one or more example methods, the method 100 comprises, upon determining that the transaction quantity parameter meets a first criterion, generating a seasonality parameter associated with the consignee.

[0072] For example, for large consignees, the seasonality parameter is obtained for assessing turn time at the port for the specific commodity, and for any container type / size combination and for the month of discharge.

[0073] In one or more example methods, the method 100 comprises, upon determining that the transaction quantity parameter does not meet the first criterion, refraining S113 from obtaining a seasonality parameter associated with the consignee. The seasonality parameter may for example be refrained from being obtained for small consignees due to a lack of accuracy of the seasonality parameter associated with a small consignee, e.g., due to a lack of data.

[0074] In one or more example methods, predicting S118 the external turn time parameter comprises generating S118C a prediction model-based turn time based on historical data. The prediction model-based turn time is for example the turn time obtained as an output of the prediction model applied to the historical data. In other words, the prediction model- based turn time is an output of S118A. The prediction model-based turn time can for example be seen as a turn time provided by the prediction model. In other words, an output of the prediction model may be the prediction model-based turn time. The prediction model-based turn time is for example indicative of the time from which the container is collected from the terminal and / or port, e.g., by an external haulage provider, until the container is returned to the terminal and / or port, e.g., by the external haulage provider.

[0075] In one or more example methods, generating S118C, based on the historical data, the prediction model-based turn time comprises generating S118CA a set of aggregated data based on historical time extension data with corresponding historical turn time data. In one or more example methods, generating S118C, based on the historical data, the prediction model-based turn time comprises applying S118CB the prediction model to the set of aggregated data. For example, the data of time extension granted and corresponding actual turn time at each port is aggregated unto the set and fit in an ML based regression model for each combination of container type and booking type (e.g., ad hoc, and / or contract-based shipment) to predict the external turn time for a shipment hauled by an external haulage service. The set of aggregated data may for example be generated for a given port, such as for each port. For example, the set of aggregated data may be generated based on historical time extension data and corresponding historical time extension data for a given port. The set of aggregated data may for example be seen as an input to the prediction model. In some examples, the set of aggregated data associated with a given type of booking may be provided as input to the prediction model. For example, the set of aggregated data associated with a given size of the container, such as 20-footer and / or 40-footer, may be provided as input to the prediction model. In some examples, the set of aggregated data associated with a given type of booking, such as ad-hoc booking, (e.g., SPOT booking) and / or contractual booking, may be provided to the prediction model. The historical turn time data can for example be seen as the turn time associated with one or more previous shipments involving external haulage.

[0076] In one or more example methods, predicting S118 the external turn time parameter comprises generating S118D the external turn time parameter based on the prediction model-based turn time and one or more of: the commodity risk factor, the consignee risk factor, and the seasonality parameter. In one or more example methods, communicating S122 the carrier haulage data comprises causing S122A display of a user interface object representative of the carrier haulage data. This is illustrated in Fig. 3.

[0077] The user interface object can for example be seen as an object, such as element, of a user interface. For example, the user interface object may be configured such as to provide information, such as the carrier haulage data, to a user of the electronic device, for selection and / or interaction with the user. In some examples, the user interface object may be configured such that a user of the electronic device may interact with, such as select and / or click, the user interface object.

[0078] The electronic device is for example configured to cause the display of the user interface object representative of the carrier haulage data. For example, the electronic device can be a server device sending data causing the display of the user interface object. For example, the electronic device may be configured to display the user interface object via a display of the electronic device, such as the electronic device 300 of Fig. 2.

[0079] In one or more example methods, the method 100 comprises causing S124 display of a user interface object indicative one or more of: the external turn time parameter, and the external haulage risk level.

[0080] Fig. 2 shows a block diagram of an exemplary electronic device 300 according to the disclosure. The electronic device 300 comprises memory circuitry 301 , processor circuitry 302, and an interface 303. The electronic device 300 is configured to perform any of the methods disclosed in Figs. 1 A-B. In other words, the electronic device 300 is configured for generating and / or communicating the carrier haulage data. In some examples, the electronic device 300 is a server device configured to provide the carrier haulage data.

[0081] In some examples, the electronic device 300 is a haulage data generator and / or provider device.

[0082] The electronic device 300 is configured to obtain (e.g., via memory circuitry 301 and / or interface 303) historical data associated with one or more previous shipments involving external haulage. The electronic device 300 is configured to predict (e.g., via processor 302) an external turn time parameter indicative of a turn time of a container handled by external haulage, by applying (e.g., via processor 302) a prediction model to the historical data.

[0083] The electronic device 300 is configured to generate (e.g., via processor 302), based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service.

[0084] The electronic device 300 is configured to communicate (e.g., via processor 302 and / or interface 303) the carrier haulage data to a user for booking of a shipment.

[0085] The processor circuitry 302 is optionally configured to perform any of the operations disclosed in Figs. 1 A-B (such as any one or more of: S102, S104, S106, S108, S110, S112, S113, S114, S116, S118, S118A, S118AA, S118BA, S118BB, S118C, S118CA, S118CB, S118D, S120, S120A, S120B, S122, S122A, S124). The operations of the electronic device 300 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) that are stored on a non-transitory computer readable medium (e.g., the memory circuitry 301 ) and are executed by the processor circuitry 302).

[0086] Furthermore, the operations of the electronic device 300 may be considered a method that the electronic device 300 is configured to carry out. Also, while the described functions and operations may be implemented in software, such functionality may as well be carried out via dedicated hardware or firmware, or some combination of hardware, firmware and / or software.

[0087] The memory circuitry 301 may be one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, a random-access memory (RAM), or other suitable device. In a typical arrangement, the memory circuitry 301 may include a non-volatile memory for long term data storage and a volatile memory that functions as system memory for the processor circuitry 302. The memory circuitry 301 may exchange data with the processor circuitry 302 over a data bus. Control lines and an address bus between the memory circuitry 301 and the processor circuitry 302 also may be present (not shown in Fig. 2). The memory circuitry 301 is considered a non-transitory computer readable medium. The memory circuitry 301 may be configured to store historical data, external turn time parameter, carrier haulage data, container pickup parameter, container return parameter, carrier haulage value, external haulage risk level, prediction model, one or more commodity haulage parameters, one or more consignee haulage parameters, commodity risk factor, consignee risk factor, first consignee group, second consignee group, transaction quantity parameter, seasonality parameter, prediction model-based turn time, a set of aggregated data, historical turn-time data and / or user interface object in a part of the memory.

[0088] Fig. 3 shows an example user interface according to this disclosure. Fig. 3 shows a user interface 50. The electronic device, such as the electronic device 300 of Fig. 3, may be configured to cause display of the user interface (III) 50.

[0089] The user interface 50 comprises a III object 48 representative of an origin location and a III object 49 representative of a destination location of the shipment. User interface 50 concerns a container HM592448 for shipment with respective user interface objects 54, 56, 60, 66, representative of: a container pickup parameter being 12 September 2023, an external haulage return parameter being 20 September 2023, an external haulage risk level, and a carrier haulage value, (such as a carrier haulage value for a given location, e.g., for a haulage destination) respectively.

[0090] For example, the external haulage return parameter of 20 September 2023 is seen a high risk of having, such as incurring, D&D costs. For example, this can be because the time period between the container pickup parameter represented by 54 and the predicted external haulage return parameter represented by 56 (which is based on the predicted external turn time parameter) is greater than the number of time extension days. In this example, the difference is 4 days.

[0091] The user interface 50 comprises a Ul object representative of a D&D value indicative of 400, e.g., 400 in a given currency. The D&D value can be seen as the D&D penalty corresponding with the number of chargeable days. The D&D penalty may for example be determined, such as set, by a port authority at the destination port, e.g., Ho Chi Minh port.

[0092] The user interface 50 is communicated to the user by display and allows the user to make a booking for this shipment by considering external haulage, the impact of the external haulage on the turn time, and the external haulage risk level. The user interface 50 provides to the user the carrier haulage data via III objects 66. The carrier haulage data comprises e.g., carrier haulage value, and optionally carrier haulage destination(s), and optionally container return parameter based on carrier haulage turn time parameter.

[0093] The user, such as the consignee, may book a shipment, such as a carrier haulage service, by selecting a III object 64 for selecting the carrier haulage service. One or more carrier haulage destinations and / or pre-determined carrier haulage locations are provided via the user interface object 50. The one or more predicted carrier haulage destinations may for example be predicted based on historical data associated with one or more previous shipments involving external haulage.

[0094] The user may select to choose a location, such as input a location different to the provided locations, for which a carrier haulage value may be generated and / or provided, such as provided via user interface object 50.

[0095] Embodiments of methods and products (electronic device) according to the disclosure are set out in the following items:

[0096] Item 1 . A method, performed by an electronic device, the method comprising: obtaining historical data associated with one or more previous shipments involving external haulage; predicting an external turn time parameter indicative of a turn time of a container handled by external haulage, by applying a prediction model to the historical data; generating, based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service; and communicating the carrier haulage data to a user for booking of a shipment.

[0097] Item 2. The method according to item 1 , wherein the prediction model is a machine learning prediction model.

[0098] Item 3. The method according to item 2, wherein the machine learning prediction model comprises one or more of: a regression prediction model, a decision tree model, a random forest model and a gradient boosting model.

[0099] Item 4. The method according to any of the previous items, wherein the method comprises calculating, based on the historical data, one or more commodity haulage parameters, wherein the one or more commodity haulage parameters for the at least one commodity are indicative of, for the at least one commodity, at least one of: a proportion of delayed containers for the at least one commodity, a proportion of granted time extension used over granted time extension of shipment for the at least one commodity; and a proportion of selected time extension used over selected time extension of shipment for the at least one commodity.

[0100] Item 5. The method according to any of the previous items, wherein predicting the external turn time parameter comprises determining a commodity risk factor based on the one or more commodity haulage parameters.

[0101] Item 6. The method according to item 5, wherein determining the commodity risk factor based on the one or more commodity haulage parameters comprises applying one or more standardization functions to the one or more commodity haulage parameters.

[0102] Item 7. The method according to any of the previous items, wherein the method comprises grouping the consignees, based on whether the consignees are associated with a previous shipment, wherein a first consignee group comprises consignees associated with one or more previous shipments, and wherein the second consignee group comprises consignees not associated with a previous shipment.

[0103] Item 8. The method according to any of the previous items, wherein the method comprises calculating, based on the historical data, one or more consignee haulage parameters, wherein the one or more consignee haulage parameters for the at least one consignee are indicative of, for the at least one consignee, at least one of: a proportion of delayed containers for the at least one consignee, a proportion of granted time extension used over granted time extension of shipment for the at least one consignee; and a proportion of selected time extension used over selected time extension of shipment for the at least one consignee. Item 9. The method according to any of the previous items, wherein predicting the external turn time parameter comprises determining a consignee risk factor based on the one or more consignee haulage parameters.

[0104] Item 10. The method according to item 9, wherein determining the consignee risk factor based on the one or more consignee haulage parameters comprises applying one or more standardization functions to the one or more consignee haulage parameters.

[0105] Item 11. The method according to any of the previous items, wherein the method comprises: obtaining a transaction quantity parameter associated with a consignee, wherein the transaction quantity parameter is indicative of a historical quantity of consignee transactions involving external haulage; determining whether the transaction quantity parameter meets a first criterion; and upon determining that the transaction quantity parameter meets a first criterion, obtaining a seasonality parameter associated with the consignee.

[0106] Item 12. The method according to any of the previous items, wherein applying the prediction model to the historical data comprises generating a prediction model-based turn time based on historical data.

[0107] Item 13. The method according to item 12, wherein generating, based on the historical data, the prediction model-based turn time comprises:

[0108] - generating a set of aggregated data based on historical time extension data with corresponding historical turn time data; and

[0109] - applying the prediction model to the set of aggregated data.

[0110] Item 14. The method according to any of the previous items, wherein predicting the external turn time parameter comprises generating the external turn time parameter based on the prediction model-based turn time and one or more of: the commodity risk factor, the consignee risk factor, and the seasonality parameter.

[0111] Item 15. The method according to any of the previous items, wherein generating the carrier haulage data comprises generating, based on the external turn time parameter and a container pickup parameter, the carrier haulage data comprising a container return parameter and / or a carrier haulage value.

[0112] Item 16. The method according to any of the previous items, wherein generating the carrier haulage data comprises determining, based on a difference between an external haulage return parameter and a container pickup parameter, an external haulage risk level.

[0113] Item 17. The method according to any of the previous items, wherein communicating the carrier haulage data comprises causing display of a user interface object representative of the carrier haulage data.

[0114] Item 18. The method according to any of the previous items, wherein the method comprises causing display of a user interface object indicative one or more of: the external turn time parameter, and the external haulage risk level.

[0115] Item 19. The method according to any of the previous items, wherein the historical data comprises one or more of: historical transaction data associated with previous transactions, historical container movement data indicative of container movement, historical haulage data for external haulage and / or carrier haulage, and historical seasonality factors for external haulage and / or carrier haulage.

[0116] Item 20. An electronic device comprising memory circuitry, processor circuitry, and an interface, wherein the electronic device is configured to perform any of the methods according to any of items 1 -19.

[0117] Item 21. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device cause the electronic device to perform any of the methods of items 1 -19.

[0118] The use of the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. does not imply any particular order but are included to identify individual elements. Moreover, the use of the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. does not denote any order or importance, but rather the terms “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. are used to distinguish one element from another. Note that the words “first”, “second”, “third” and “fourth”, “primary”, “secondary”, “tertiary” etc. are used here and elsewhere for labelling purposes only and are not intended to denote any specific spatial or temporal ordering. Furthermore, the labelling of a first element does not imply the presence of a second element and vice versa.

[0119] It may be appreciated that the Figures comprise some circuitries or operations which are illustrated with a solid line and some circuitries or operations which are illustrated with a dashed line. The circuitries or operations which are comprised in a solid line are circuitries or operations which are comprised in the broadest example embodiment. The circuitries or operations which are comprised in a dashed line are example embodiments which may be comprised in, or a part of, or are further circuitries or operations which may be taken in addition to the circuitries or operations of the solid line example embodiments. It should be appreciated that these operations need not be performed in order presented. Furthermore, it should be appreciated that not all of the operations need to be performed. The exemplary operations may be performed in any order and in any combination.

[0120] It is to be noted that the word "comprising" does not necessarily exclude the presence of other elements or steps than those listed.

[0121] It is to be noted that the words "a" or "an" preceding an element do not exclude the presence of a plurality of such elements.

[0122] It is to be noted that the term "indicative of" may be seen as “associated with”, “related to”, “descriptive of’, “characterizing”, and / or “defining”. The terms “indicative of”, “associated with”, “related to”, “descriptive of’, “characterizing”, and “defining” can be used interchangeably. The term “indicative of” can be seen as indicating a relation. For example, weight data indicative of weight may comprise one or more weight parameters.

[0123] It is to be noted that the word "based on" may be seen as “as a function of’ and / or “derived from”. The terms “based on” and “as a function of” can be used interchangeably. For example, a parameter determined “based on” a data set can be seen as a parameter determined “as a function of’ the data set. In other words, the parameter may be an output of one or more functions with the data set as an input. A function may be characterizing a relation between an input and an output, such as mathematical relation, a database relation, a hardware relation, logical relation, and / or other suitable relations.

[0124] It should further be noted that any reference signs do not limit the scope of the claims, that the exemplary embodiments may be implemented at least in part by means of both hardware and software, and that several "means", "units" or "devices" may be represented by the same item of hardware.

[0125] The various exemplary methods, devices, nodes, and systems described herein are described in the general context of method steps or processes, which may be implemented in one aspect by a computer program product, embodied in a computer- readable medium, including computer-executable instructions, such as program code, executed by computers in networked environments. A computer-readable medium may include removable and non-removable storage devices including, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), compact discs (CDs), digital versatile discs (DVD), etc. Generally, program circuitries may include routines, programs, objects, components, data structures, etc. that perform specified tasks or implement specific abstract data types. Computer-executable instructions, associated data structures, and program circuitries represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps or processes.

[0126] Although features have been shown and described, it will be understood that they are not intended to limit the claimed disclosure, and it will be made obvious to those skilled in the art that various changes and modifications may be made without departing from the scope of the claimed disclosure. The specification and drawings are, accordingly, to be regarded in an illustrative rather than restrictive sense. The claimed disclosure is intended to cover all alternatives, modifications, and equivalents.

Claims

CLAIMS1 . A method, performed by an electronic device, the method comprising: obtaining historical data associated with one or more previous shipments involving external haulage; predicting an external turn time parameter indicative of a turn time of a container handled by external haulage, by applying a prediction model to the historical data; generating, based on the predicted external turn time parameter, carrier haulage data associated with a carrier haulage service; and communicating the carrier haulage data to a user for booking of a shipment.

2. The method according to claim 1 , wherein the prediction model is a machine learning prediction model.

3. The method according to claim 2, wherein the machine learning prediction model comprises one or more of: a regression prediction model, a decision tree model, a random forest model and a gradient boosting model.

4. The method according to any of the previous claims, wherein the method comprises calculating, based on the historical data, one or more commodity haulage parameters, wherein the one or more commodity haulage parameters for the at least one commodity are indicative of, for the at least one commodity, at least one of: a proportion of delayed containers for the at least one commodity, a proportion of granted time extension used over granted time extension of shipment for the at least one commodity; and a proportion of selected time extension used over selected time extension of shipment for the at least one commodity.

5. The method according to any of the previous claims, wherein predicting the external turn time parameter comprises determining a commodity risk factor based on the one or more commodity haulage parameters.

6. The method according to claim 5, wherein determining the commodity risk factor based on the one or more commodity haulage parameters comprises applying one or more standardization functions to the one or more commodity haulage parameters.

7. The method according to any of the previous claims, wherein the method comprises grouping the consignees, based on whether the consignees are associated with a previous shipment, wherein a first consignee group comprises consignees associated with one or more previous shipments, and wherein the second consignee group comprises consignees not associated with a previous shipment.

8. The method according to any of the previous claims, wherein the method comprises calculating, based on the historical data, one or more consignee haulage parameters, wherein the one or more consignee haulage parameters for the at least one consignee are indicative of, for the at least one consignee, at least one of: a proportion of delayed containers for the at least one consignee, a proportion of granted time extension used over granted time extension of shipment for the at least one consignee; and a proportion of selected time extension used over selected time extension of shipment for the at least one consignee.

9. The method according to any of the previous claims, wherein predicting the external turn time parameter comprises determining a consignee risk factor based on the one or more consignee haulage parameters.

10. The method according to claim 9, wherein determining the consignee risk factor based on the one or more consignee haulage parameters comprises applying one or more standardization functions to the one or more consignee haulage parameters.11 . The method according to any of the previous claims, wherein the method comprises: obtaining a transaction quantity parameter associated with a consignee, wherein the transaction quantity parameter is indicative of a historical quantity of consignee transactions involving external haulage; determining whether the transaction quantity parameter meets a first criterion; and upon determining that the transaction quantity parameter meets a first criterion, obtaining a seasonality parameter associated with the consignee.

12. The method according to any of the previous claims, wherein predicting the external turn time parameter comprises generating a prediction model-based turn time based on historical data.

13. The method according to claim 12, wherein generating, based on the historical data, the prediction model-based turn time comprises:- generating a set of aggregated data based on historical time extension data with corresponding historical turn time data; and- applying the prediction model to the set of aggregated data.

14. The method according to any of the previous claims, wherein predicting the external turn time parameter comprises generating the external turn time parameter based on the prediction model-based turn time and one or more of: the commodity risk factor, the consignee risk factor, and the seasonality parameter.

15. The method according to any of the previous claims, wherein generating the carrier haulage data comprises generating, based on the external turn time parameter and a container pickup parameter, the carrier haulage data comprising a container return parameter and / or a carrier haulage value.

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