Method for modeling demand indications and application thereof

Abstract

Aspects relate to a method for providing an indicator of demand density, comprising the steps of: determining a first efficiency metric between a first source and a second source, each source being associated with at least one cargo; determining a second efficiency metric between the first destination and the second destination; associating the first destination with the first source to form a first order pair, and associating the second destination with the second source to form a second order pair; determining a first vector associated with the first order pair and a second vector associated with the second order pair; measuring an angle between the first vector and the second vector; and calculating an indicator of the demand density based on a function of the first efficiency metric, the second efficiency metric, and the angle.

Description

Technical Field

[0001] Various aspects of the present disclosure relate to methods for modeling demand indicators and applications thereof. Background Art

[0002] Demand generation is critical to the healthy functioning of any business or marketplace, including electronic marketplaces and e-commerce businesses. In addition to demand generation, demand metrics, such as demand density associated with a location or geographic area, provide important indicators for the downstream fulfillment process and operational efficiency of the marketplace.

[0003] While it is recognized that quantifying demand density is critical, a method to model demand metrics rigorously and at scale does not currently exist.

[0004] There is a need to provide relatively more robust demand indicators that can be used in various trading platforms. Summary of the invention

[0005] The technical solution is dedicated to modeling demand indicators and providing a measure of demand density. Demand indicators provide a useful feedback mechanism to trading platforms such as delivery platforms, transportation service platforms, etc. to assess how dense the generated demand is, and then changes can be made to operating parameters or personnel distribution to meet various orders / transactions, with the goal of improving batch processing efficiency.

[0006] In some embodiments, the demand metric may be expressed as a score between two orders (each order includes a source and a destination). The score of each order relative to other orders on the platform is calculated, and further processing may be performed to identify or determine an inflection point above which there is a positive gain in efficiency. The associated score of the inflection point may be assigned a baseline score that is maintained between any two orders.

[0007] According to one aspect of the present disclosure, a method for providing an indicator of demand is provided, comprising the following steps: determining a first efficiency metric between a first source and a second source, each source being associated with at least one good or service; determining a second efficiency metric between a first destination and a second destination; associating the first destination with the first source to form a first order pair, and associating the second destination with the second source to form a second order pair; determining a first vector associated with the first order pair and a second vector associated with the second order pair; measuring or calculating an angle between the first vector and the second vector; and calculating an indicator of demand based on a function of the first efficiency metric, the second efficiency metric, and the angle.

[0008] In an embodiment, the first efficiency metric is the distance between the first source and the second source or the time taken to travel between the first source and the second source.

[0009] In an embodiment, the second efficiency metric is the distance between the first destination and the second destination or the time taken to travel between the first destination and the second destination.

[0010] In an embodiment, the method further comprises the step of calculating the cosine of the angle to derive an indication of directional homogeneity between the first order pair and the second order pair.

[0011] In an embodiment, the demanded index (DD) is mathematically represented as: DD=w1*first efficiency metric+w2*second efficiency metric+w3*cosine of angle, where w1, w2 and w3 are weights and the sum of w1+w2+w3=1.

[0012] In an embodiment, the method further comprises the following steps: normalizing the first efficiency metric and / or the second efficiency metric; plotting the relationship of DD relative to the first normalized efficiency metric and / or the second normalized efficiency metric over a predetermined time period, and determining an inflection point on the plot corresponding to a value of 1 for the first efficiency metric.

[0013] In an embodiment, the method further comprises the step of summing the DD values ​​and averaging the summed DD values ​​over a parameter to provide an indication of demand density. The parameter may be selected from at least one of: location, merchant, consumer and time period.

[0014] In an embodiment, the method further comprises the step of estimating a batch efficiency based on the indication of demand density.

[0015] According to another aspect of the present disclosure, a system for determining an indicator of demand for goods or services is provided, comprising: at least one location-based sensor for sensing the location of a first source, a second source, a first destination and / or a second destination; a module arranged to communicate data with the at least one location-based sensor, the module being configured to: determine a first efficiency metric between the first source and the second source, each source being associated with at least one good or service; determine a second efficiency metric between the first destination and the second destination; associate the first destination with the first source to form a first order pair, and associate the second destination with the second source to form a second order pair; determine a first vector associated with the first order pair and a second vector associated with the second order pair; measure the angle between the first vector and the second vector; and calculate an indicator of demand based on a function of the first efficiency metric, the second efficiency metric and the angle.

[0016] In an embodiment, the first efficiency metric is the distance between the first source and the second source or the time taken to travel between the first source and the second source.

[0017] In an embodiment, the second efficiency metric is the distance between the first destination and the second destination or the time taken to travel between the first destination and the second destination.

[0018] In an embodiment, the module is further configured to calculate a cosine of the angle to derive an indication of directional consistency between the first order pair and the second order pair.

[0019] In an embodiment, the demanded index (DD) is mathematically represented as: DD=w1*first efficiency metric+w2*second efficiency metric+w3*cosine of angle, where w1, w2 and w3 are weights and the sum of w1+w2+w3=1.

[0020] In an embodiment, the location based sensor is a GPS sensor in the vehicle.

[0021] In an embodiment, the system forms part of an electronic delivery system or vehicle on-demand platform.

[0022] In an embodiment, the module is arranged to be in data communication with a mapping service to obtain map data to determine the distance or time between the first source, the second source, the first destination and the second destination.

[0023] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium comprising instructions is provided, which when executed by one or more processors result in the performance of the described method. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure will be better understood by reference to the detailed description when considered in conjunction with the non-limiting examples and accompanying drawings, in which:

[0025] - Figure 1 is a flow chart of a method for providing an indicator of demand according to various embodiments;

[0026] - Figure 2 An embodiment of a first efficiency metric in the form of a pick-up efficiency associated with two or more sources is shown;

[0027] - Figure 3 An embodiment of a second efficiency metric in the form of a drop-off efficiency associated with two or more destinations is shown;

[0028] - Figure 4 An embodiment of a metric for determining directional consistency is shown;

[0029] - Figure 5 A two-dimensional plot of demand indicators on the x-axis versus time and distance efficiency gains on the y-axis is shown to determine the inflection point that needs to be maintained to achieve efficiency;

[0030] - Figure 6 A schematic diagram of a processor for processing and / or calculating location-based data to derive or determine an indicator of demand is shown in accordance with some embodiments. DETAILED DESCRIPTION

[0031] The following detailed description refers to the accompanying drawings, which illustrate the specific details and embodiments of the present disclosure in a diagrammatic manner. The detailed description of these embodiments is sufficient to enable those skilled in the art to implement the present disclosure. Other embodiments may also be utilized and structural and logical changes may be made thereto without departing from the scope of the present disclosure. The various embodiments are not necessarily mutually exclusive, as some embodiments may be combined with one or more other embodiments to form new embodiments.

[0032] Embodiments described in the context of one of the housing systems, devices, or methods are also valid for the other systems, devices, or methods. Similarly, embodiments described in the context of one system are also valid for the device or method, and vice versa.

[0033] Features described in the context of an embodiment may be correspondingly applicable to the same or similar features in other embodiments. Features described in the context of an embodiment may be correspondingly applicable to other embodiments, even if not explicitly described in these other embodiments. In addition, additions and / or combinations and / or alternatives described for features in the context of an embodiment may be correspondingly applicable to the same or similar features in other embodiments.

[0034] In the context of various embodiments, the articles “a,” “an,” and “the” used with respect to features or elements include reference to one or more features or elements.

[0035] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] As used herein, the term "data" may be understood to include information in any suitable analog or digital form, for example, provided as a file, a portion of a file, a collection of files, a signal or stream, a portion of a signal or stream, a collection of signals or streams, etc. However, the term "data" is not limited to the foregoing examples and may take various forms and represent any information as understood in the art.

[0037] As used herein, the term "demand" broadly includes demand for goods, services, or combinations thereof associated with one or more sources and intended to go to one or more destinations. Examples of sources may include physical stores / venues, mobile stores, or even one or more passengers at a specific location or pick-up point in the context of an on-demand vehicle service. The location of a source at any time may be determined by one or more location-based sensors. One or more destinations may include a physical location to which the goods will be delivered, or a drop-off point for one or more passengers of an on-demand vehicle service. As a non-limiting example, in the context of an electronic trading platform, demand for goods from a source may be fulfilled by placing an order to have the goods delivered to the intended destination. The order is fulfilled when the delivered goods are delivered to the intended destination and payment or other form of consideration is received.

[0038] As used herein, the term "module" refers to or forms part of or includes: an application specific integrated circuit (ASIC); an electronic circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor (shared, dedicated, or group) that executes code; other suitable hardware components that provide the functionality; or a combination of some or all of the above, such as in a system on a chip. The term "module" may include a memory (shared, dedicated, or group) that stores code executed by a processor. A single module or a combination of modules may be considered a device.

[0039] As used herein, the terms "association," "associated," and "associated" indicate a defined relationship (or cross-reference) between two items. For example, a shipment from a source may be associated with one or more intended destinations.

[0040] As used herein, "memory" may be understood as a non-transitory computer-readable medium in which data or information may be stored for retrieval. Therefore, references to "memory" included herein may be understood to refer to volatile or non-volatile memory, including random access memory ("RAM"), read-only memory ("ROM"), flash memory, solid-state storage, tape, hard drive, optical drive, etc., or any combination thereof. In addition, it should be understood that, in this article, the term memory also covers registers, shift registers, processor registers, data buffers, etc. It should be understood that a single component referred to as "memory" or "a memory" may be composed of more than one different type of memory, and therefore may refer to a collection of components including one or more types of memory. It is readily understood that any single memory component may be separated into multiple commonly equivalent memory components, and vice versa. In addition, although the memory may be depicted as being separated from one or more other components (such as in the accompanying drawings), it should be understood that the memory may be integrated within another component, such as on a common integrated chip.

[0041] According to one aspect of the present disclosure and with reference to Figure 1 , there is a method 100 for providing a demand indicator, comprising the following steps: determining a first efficiency metric between a first source and a second source (step 102), each source being associated with at least one good and / or service; determining a second efficiency metric between a first destination and a second destination (step 104), associating the first destination with the first source to form a first order pair, and associating the second destination with the second source to form a second order pair (step 106); determining a first vector associated with the first order pair and a second vector associated with the second order pair (step 108); measuring an angle between the first vector and the second vector (step 110); and calculating an indicator of demand based on a function of the first efficiency metric, the second efficiency metric, and the angle (step 112).

[0042] The indicator of demand may be an indicator of demand for goods and / or services on the electronic platform. Examples include demand for goods such as food / drinks from a specific merchant, demand for on-demand vehicles by one or more passengers located in a specific geographic area.

[0043] The first efficiency metric may be considered a pickup efficiency metric and, in the context of a delivery service, is a measure of how easy it is for a delivery person or partner to pick up multiple orders. In some embodiments, the first efficiency metric may be expressed in terms of the time or distance between two pickup orders assigned to a particular delivery person. In some embodiments, where the context is a vehicle on demand service, the first efficiency metric may be expressed in terms of the time or distance between two passengers at different locations assigned to a rideshare or carpooling service.

[0044] Figure 2 An example of how to derive or determine a first efficiency metric is shown. Figure 2 Three sources are shown in the form of three merchants M1, M2 and M3, each located at a distance from each other. M2 is located 0.5 kilometers (km) from M1 and M3 is located 1 km from M1. Figure 2 Also shown is the destination (i.e., delivery location) associated with each source M1, M2, M3. Three vectors 202, 204, 206, also referred to as order vectors, are formed between each of the sources and destinations. Each order vector 202, 204, 206 includes a magnitude component and a direction component. For example, order vector 202 has a magnitude in the form of time of 30 minutes (ETA minus the current time), and a direction from source M1 to destination location 1. Order vector 204 has a magnitude of 1 hour and a direction from source M2 to destination location 2, and order vector 206 has a magnitude of 1 hour and 15 minutes and a direction from source M3 to destination location 3.

[0045] The second efficiency metric may be considered delivery efficiency, and in the context of delivery services, is a measure of how easily a delivery person or partner delivers multiple orders. In some embodiments, the second efficiency metric may be expressed in terms of the time or distance between two delivery orders assigned to a particular delivery person. In some embodiments, the second efficiency metric may be expressed in terms of the time or distance between two delivery locations associated with two passengers on a ride-sharing or carpooling service.

[0046] Figure 3 An example of how to derive or determine the second efficiency metric is shown. The illustration includes a single source merchant M and multiple destination locations 1, 2, and 3 associated with order 1, order 2, and order 3, respectively. After determining the order vectors 302, 304, and 306, the distance between each delivery location is determined. For example, as shown by reference numeral 308, the distance between location 1 and location 2 is 4 km, and as shown by reference numeral 310, the distance between location 1 and location 3 is 6 km.

[0047] Figure 4 Measures of the angle α between the first vector 402 and the second vector 404 and the angle β between the first vector 402 and the third vector 406 are shown. It will be appreciated that the angle between the second vector 404 and the third vector 406 is β-α. An indication of directional consistency between two order vectors can be provided by the cosine of the angle. For example, the cosine of the angle α has a higher value than the cosine of the angle β, indicating a higher directional consistency between the first vector and the second vector as compared to the first vector and the third vector. Directionally consistent orders can then be grouped or batched to achieve batch processing efficiencies because the incremental work in terms of distance or time for the delivery partners to deliver together is lower than if the order were delivered as two separate orders.

[0048] In some embodiments, the calculation of the demand indicator (DD) according to step 112 can be mathematically expressed in equation (1) as follows:

[0049] DD = w1*first efficiency measure + w2*second efficiency measure + w3*cosine(angle) (1)

[0050] Among them, w1, w2 and w3 are weights, and the sum of w1+w2+w3=1.

[0051] In some embodiments, the cosine of the angle may be normalized to a value in the range of 0 to 1, inclusive. In some embodiments, the weights may be adjusted dynamically based on operational requirements, or may be adjusted at predetermined time intervals.

[0052] The first efficiency metric, the second efficiency metric and / or the directional consistency metric in equation (1) may be normalized. The step of normalizing the first efficiency metric, the second efficiency metric and / or the directional consistency may include dividing the actual value of each efficiency metric by a reference value or a benchmark value.

[0053] It is expected that an indicator of demand can be derived for each source-destination pair. For example, a first DD value can be derived considering a first efficiency metric between sources M1 and M2, destination locations 1 and 2, and a calculation based on directional consistency of angle α. Another second DD value can be derived considering a first efficiency metric between sources M1 and M3, destination locations 1, and location 3, and a calculation based on directional consistency of angle β. The DD values ​​can then be stored as obtained data and statistically independent (e.g., sum, mean, mode, median). In other words, the DD score as expressed in equation (1) quantifies the density between two orders. The DD score can be calculated between all order pairs within a specific time interval. This may depend on the order creation / broadcast time, the estimated arrival time for completing the order (delivery), service agreements, etc.

[0054] Figure 5 A two-dimensional plot of the DD score on the x-axis versus the time and distance efficiency gain on the y-axis is shown to determine the inflection point that needs to be maintained to achieve efficiency. For a given reference order, all orders with scores above the indicated inflection point are identified as dense neighbors of the particular reference order. Dense neighbors can be grouped for subsequent allocation to delivery partners without compromising efficiency. This measure of dense neighbors is averaged over parameters such as time, place, consumer, merchant, etc., giving a measure of demand density with respect to each parameter (and therefore in various dimensions). In other words, the relationship between the score and the gain in time and distance efficiency helps determine the inflection point that needs to be maintained to achieve efficiency.

[0055] Demand density metrics can also be used to estimate batch processing efficiency.

[0056] A demand density indicator can be used in a system for determining an indicator of demand for goods or services, the system comprising: at least one location-based sensor for sensing the location of a first source, a second source, a first destination and / or a second destination; a module arranged to communicate data with the at least one location-based sensor, the module being configured to: determine a first efficiency metric between a first source and a second source, each source being associated with at least one good or service; determine a second efficiency metric between a first destination and a second destination; associate the first destination with the first source to form a first order pair, and associate the second destination with the second source to form a second order pair; determine a first vector associated with the first order pair and a second vector associated with the second order pair; measure an angle between the first vector and the second vector; and calculate an indicator of demand based on a function of the first efficiency metric, the second efficiency metric and the angle.

[0057] The location sensor may be a GPS sensor. In an embodiment where the first source and the second source are associated with passengers at two separate pick-up locations, the GPS sensor may be on the passenger's mobile phone and the location information transmitted to the vehicle as needed. In another embodiment, the first source and the second source may be two physical stores that sell a particular good such as food or beverages.

[0058] In some embodiments, it is contemplated that only one source provides various goods. A first order from a source is intended for delivery to a first location, and a second order from a source is intended for delivery to a second location different from the first location. In such a case or scenario, a zero weight may be assigned to the weight w1 of the first efficiency metric, while higher weight values ​​may be assigned to weights w2 and w3. In some embodiments, it is also contemplated that goods from multiple sources may be intended to the same destination. In such a case or scenario, a zero weight may be assigned to the weight w2 of the second efficiency metric, while higher weight values ​​may be assigned to weights w1 and w3.

[0059] Figure 6 A server computer 600 is shown according to various embodiments. The server computer 600 includes a communication interface 602 (e.g., configured to receive input data from one or more cameras or image capture devices). The server computer 600 further includes a processing unit 604 and a memory 606. The memory 606 can be used by the processing unit 604 to store, for example, data to be processed, such as location-based data associated with a source and a destination and directional data associated with each angle between two order vectors. In some embodiments, the server computer 600 can be arranged to communicate data with one or more geographic mapping services / servers to obtain map data to facilitate deriving or calculating the metric represented in equation (1). The server computer 600 can be configured to perform Figure 1It should be noted that the server computer system 600 may be a distributed system including multiple computers. The memory 606 may include a non-transitory computer-readable medium.

[0060] The methods described herein, such as method 100, may be performed, and the various processing or computing units and devices and computing entities described herein may be implemented by one or more circuits. In an embodiment, a "circuit" may be understood as any kind of logic implementation entity, which may be hardware, software, firmware, or any combination thereof. Thus, in an embodiment, a "circuit" may be a hard-wired logic circuit or a programmable logic circuit, such as a programmable processor, such as a microprocessor. A "circuit" may also be software implemented or executed by a processor, such as any type of computer program, such as a computer program using virtual machine code. According to alternative embodiments, any other type of implementation of the functions described herein may also be understood as a "circuit".

[0061] Although the disclosed contents have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims. Therefore, the scope of the present disclosure shall be subject to the appended claims, and all changes within the meaning and equivalent range of the claims shall be included within the scope of the present invention.

Claims

1. A method for providing an indicator of demand, comprising the following steps: determining a first efficiency metric between a first source and a second source, each source being associated with at least one good or service; determining a second efficiency metric between the first destination and the second destination; associating the first destination with the first source to form a first order pair, and associating the second destination with the second source to form a second order pair; determining a first vector associated with the first order pair and a second vector associated with the second order pair; measuring an angle between the first vector and the second vector; as well as An indicator of the demand is calculated based on a function of the first efficiency metric, the second efficiency metric, and the angle.

2. The method according to claim 1, wherein: The first efficiency metric is the distance between the first source and the second source or the time taken to travel between the first source and the second source.

3. The method according to claim 1 or 2, wherein: The second efficiency metric is the distance between the first destination and the second destination or the time taken to travel between the first destination and the second destination.

4. A method according to any one of the preceding claims, further comprising the step of calculating the cosine of the angle to derive an indication of directional consistency between the first order pair and the second order pair.

5. The method according to claim 4, wherein: The demand index (DD) is mathematically expressed as: DD=w1*first efficiency metric+w2*second efficiency metric+w3*cosine of the angle, Among them, w1, w2 and w3 are weights, and the sum of w1+w2+w3=1.

6. The method according to claim 5, further comprising the steps of: normalizing the first efficiency metric and / or the second efficiency metric; plotting DD versus normalized first efficiency metric and / or normalized second efficiency metric over a predetermined time period; And determining an inflection point on the plot corresponding to a value of 1 for the first efficiency metric.

7. The method of claim 6, further comprising the step of summing the DD values ​​and averaging the summed DD values ​​over a parameter to provide an indication of demand density.

8. The method according to claim 7, wherein: The parameter is selected from at least one of the following: location, merchant, consumer and time period.

9. A method according to claim 7 or 8, further comprising the step of estimating batch processing efficiency based on said indication of demand density.

10. A system for determining an indicator of demand for a good or service, comprising: at least one location-based sensor for sensing the location of the first source, the second source, the first destination, and / or the second destination; A module is arranged to communicate data with the at least one location-based sensor, the module being configured to: determining a first efficiency metric between the first source and the second source, each source being associated with at least one good or service; determining a second efficiency metric between the first destination and the second destination; associating the first destination with the first source to form a first order pair, and associating the second destination with the second source to form a second order pair; determining a first vector associated with the first order pair and a second vector associated with the second order pair; measuring an angle between the first vector and the second vector; as well as An indicator of the demand is calculated based on a function of the first efficiency metric, the second efficiency metric, and the angle.

11. The system according to claim 10, wherein: The first efficiency metric is the distance between the first source and the second source or the time taken to travel between the first source and the second source.

12. The system according to claim 10 or 11, wherein: The second efficiency metric is the distance between the first destination and the second destination or the time taken to travel between the first destination and the second destination.

13. A system according to any one of claims 10 to 12, wherein: The module is further configured to calculate a cosine of the angle to derive an indication of directional consistency between the first order pair and the second order pair.

14. The system according to claim 13, wherein: The demand index (DD) is mathematically expressed as: DD=w1*first efficiency metric+w2*second efficiency metric+w3*cosine of the angle, Among them, w1, w2 and w3 are weights, and the sum of w1+w2+w3=1.

15. A system according to any one of claims 10 to 14, wherein: The location-based sensor is a GPS sensor in the vehicle.

16. The system according to any one of claims 10 to 15, further forming part of an electronic delivery system or a vehicle on-demand platform.

17. The system according to claims 10 and 11, wherein: The module is arranged to be in data communication with a mapping service to obtain map data to determine the distance or time between the first source, the second source, the first destination and the second destination.

18. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors, cause performance of the method of any one of claims 1-9.

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