# US 2013/0159059 A1 · Freight market demand modeling and price optimization

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2013/0159059 A1
- Document ID: US20130159059A1
- Status: Published application
- Kind: A1
- Application number: 13/331,538
- Filed: 2011-12-20
- Published: 2013-06-20
- Earliest claimed: 2011-12-20
- Assignees: Sap SE
- Inventors: Denis Malov
- Web page: https://patentgrep.com/patent/US20130159059A1

## CPC

- G06Q 30/02: Marketing; Price estimation or determination; Fundraising
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Various embodiments herein include at least one of systems, methods, and software for freight market demand modeling and price optimization. Some such embodiments include acquiring historical data regarding hauled loads, bid loads that were not hauled, data representative of at least one of current and expected conditions, and data representing business goals. The acquired data may then be mapped to market segments and a statistical, spot load demand model is generated for each market segment based on a number of factors included in the mapped data including at least a load price factor. A demand and price forecast model may next be generated for each market segment based on the generated model and the data representative of at least one of current and expected conditions. For each market segment, a pricing element may then be determined based on the respective market segment model and forecast in view of the business goals.

## Claims

### Claim 1

A computerized method comprising:
acquiring data including historical load data, data of given price quotes that were not accepted, data representative of at least one of current and expected conditions including data representative of current and forecasted weather conditions, and data representing business goals;
mapping, by executing instructions on at least one processor, the acquired data to market segments;
generating, by executing instructions on the at least one processor, a statistical model for each market segment based on the data mapped thereto, the statistical model generated based on a number of factors included in the mapped data, number of factors including at least a load price factor and the data representative of current and forecasted weather conditions, the model providing a spot load demand model;
generating, by executing instructions on the at least one processor, a demand and price forecast, for each market segment, based on the generated model and the data representative of at least one of current and expected conditions; and
for each market segment, determining, by executing instructions on the at least one processor, a pricing element based on the respective market segment model and forecast and the data representing business goals.

### Claim 2 (depends on 1)

The method of claim 1, wherein the data representing business goals includes data representing at least one of business rules and key performance indicators.

### Claim 3 (depends on 1)

The method of claim 1, wherein retrieving data sets from at least one database into the memory includes retrieving data representative of historical and current market conditions.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
identifying a market segment with too little data mapped thereto for the data to provide statistical significance to the market segment; and
performing a clustering analysis with regard to the identified market segment.

### Claim 5 (depends on 1)

The method of claim 1, wherein the statistical model is a Log-linear model that models demand G as a time t dependent variable for a number k of factors x based on the formula:
G

(
t
)
=
Exp
(
∑
k

β
k

x
k

(
t
)
)
.

### Claim 6 (depends on 1)

The method of claim 1, wherein data representative of the at least one of current and expected conditions includes data representative of at least one assumption with regard to an expected condition and load capacity factors.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
receiving a pricing request with regard to a set of load data;
identifying a market segment based on data included in the set of load data; and
responding to the request with a pricing element selected based on the identified market segment.

### Claim 8

A non-transitory computer-readable storage medium, with instructions stored thereon which when executed by at least one processor causes a computer to;
acquire data including historical load data, data of given price quotes that were not accepted, data representative of at least one of current and expected conditions including data representative of current and forecasted weather conditions, and data representing business goals;
map the acquired data to market segments;
generate a statistical model for each market segment based on the data mapped thereto, the statistical model generated based on a number of factors included in the mapped data, number of factors including at least a load price factor and the data representative of current and forecasted weather conditions, the model providing a spot load demand model;
generate a demand and price forecast, for each market segment, based on the generated model and the data representative of at least one of current and expected conditions; and
for each market segment, determine a pricing element based on the respective market segment model and forecast and the data representing business goals.

### Claim 9 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, wherein the data representing business goals includes data representing at least one of business rules and key performance indicators.

### Claim 10 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, with further instructions stored thereon which when executed by the at least one computer processor further cause the computer to:
identify a market segment with too little data mapped thereto for the data to provide statistical significance to the market segment; and
perform a clustering analysis with regard to the identified market segment.

### Claim 11 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, wherein the statistical model is a regression model.

### Claim 12 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, wherein data representative of the at least one of current and expected conditions includes data representative of at least one assumption with regard to an expected condition and load capacity factors.

### Claim 13 (depends on 8)

The non-transitory computer-readable storage medium of claim 8, with further instructions stored thereon which when executed by the at least one computer processor further cause the computer to:
receive a pricing request with regard to a set of load data;
identify a market segment based on data included in the set of load data; and
respond to the request with a pricing element selected based on the identified market segment, the pricing element being one of two or more pricing elements that contribute to a total carrier cost for hauling a load as defined at least in part by the load data.

### Claim 14 (depends on 13)

The non-transitory computer-readable storage medium of claim 13, with further instructions stored thereon which when executed by the at least one computer processor further cause the computer to:
map the received load data to the identified market segment;
regenerate the statistical model for at least each market segment for which the received load data is mapped;
regenerate the demand and price forecast, for at least each market segment for which the received load data is mapped; and
for at least each market segment for which the received load data is mapped, re-determine the pricing element based on the respective market segment model and forecast and the data representing business goals.

### Claim 15

A system comprising:
at least one computing device including at least one processor and at least one memory device;
a data acquisition module stored in the at least one memory device and executable by the at least one processor to acquire data including historical load data, data of given price quotes that were not accepted, data representative of at least one of current and expected conditions including data representative of current and forecasted weather conditions, and data representing business goals;
a data preparation module stored in the at least one memory device and executable by the at least one processor to map the acquired data to market segments;
a data analysis module stored in the at least one memory device and executable by the at least one processor to generate a statistical model for each market segment based on the data mapped thereto, the statistical model generated based on a number of factors included in the mapped data, number of factors including at least a load price factor and the data representative of current and forecasted weather conditions, the model providing a spot load demand model;
a demand forecasting module stored in the at least one memory device and executable by the at least one processor to generate a demand and price forecast, for each market segment, based on the generated model and the data representative of at least one of current and expected conditions; and
an optimization module stored in the at least one memory device and executable by the at least one processor to determine, for each market segment, a pricing element based on the respective market segment model and forecast and the data representing business goals.

### Claim 16 (depends on 15)

The system of claim 15, wherein the data preparation module is further executable by the at least one processor to:
identify a market segment with too little data mapped thereto for the data to provide statistical significance to the market segment; and
perform a clustering analysis with regard to the identified market segment.

### Claim 17 (depends on 15)

The system of claim 15, wherein the statistical model generated by the data analysis module is a Gaussian model.

### Claim 18 (depends on 15)

The system of claim 15, wherein data representative of the at least one of current and expected conditions acquired by the data acquisition module includes data representative of at least one assumption with regard to an expected condition and load capacity factors.

### Claim 19 (depends on 15)

The system of claim 15, further comprising:
at least one network interface device; and
a load pricing module stored in the at least one memory device and executable by the at least one processor to:
receive, via the at least one network interface device, a pricing request with regard to a set of load data;
identify a market segment based on data included in the set of load data; and
respond to the request, via the at least one network interface device, with a pricing element selected based on the identified market segment, the pricing element being one of two or more pricing elements that contribute to a total carrier cost for hauling a load as defined at least in part by the load data.

### Claim 20 (depends on 15)

The system of claim 15, further comprising:
an adjustment module stored in the at least one memory device and executable by the at least one processor to:
call the mapping module to map the received load data to the identified market segment;
call the data analysis module to regenerate the statistical model for at least each market segment for which the received load data is mapped;
call the demand forecasting module to regenerate the demand and price forecast, for at least each market segment for which the received load data is mapped; and
call the optimization module to re-determine the pricing element, for at least each market segment for which the received load data is mapped, based on the respective market segment model and forecast and the data representing business goals.