EP3437039A1

EP3437039A1 - Cold chain overall cost and quality software as a service module

Info

Publication number: EP3437039A1
Application number: EP17715362.4A
Authority: EP
Inventors: John Cronin; Steven Matthew PHILBIN
Original assignee: Carrier Corp
Current assignee: Carrier Corp
Priority date: 2016-03-28
Filing date: 2017-03-23
Publication date: 2019-02-06
Also published as: WO2017172484A1; CN109074539B; CN109074539A

Abstract

A system and method for analyzing and selecting a cold chain route of a cold chain system is provided. The method includes determining, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination, receiving, at the cold chain network, collected route data about the determined route, calculating, using the cold chain network, predicted route data based on the collected route data, displaying, using a display, the collected route data and the predicated route data using a graphical user interface (GUI), and receiving, from a user input device, a route selection based on the displayed collected route data and predicated route data.

Description

COLD CHAIN OVERALL COST AND QUALITY SOFTWARE AS A SERVICE

MODULE

BACKGROUND

[0001] The present disclosure relates to a cold chain system and more specifically, to methods, systems and computer program products for providing a route management tool to collect and analyze route information to facilitate route selection.

[0002] A cold chain is a temperature-controlled supply chain. Particularly, a cold chain is an unbroken and uninterrupted series of storage and distribution activities which maintain a given temperature range for product being moved along the chain. For example, a cold chain is used to help extend and ensure the shelf life of products such as fresh agricultural produce, seafood, frozen food, film, fluids, chemicals, pharmaceutical drugs, and other temperature sensitive items.

[0003] Route offering for use in such a supply chain can be provided using a mapping service. However, such mapping services provide routes based on general criteria. Accordingly, a cold chain route manager executes route selection for the cold chain without taking into consideration factors that specifically affect a cold chain.

[0004] Accordingly, there is a desire for a system and method for determining a potentially more optimal route for a cold chain transport vehicle taking into account factors that affect the cold chain.

BRIEF DESCRIPTION

[0005] In accordance with an embodiment, a method for analyzing and selecting a cold chain route of a cold chain system is provided. The method includes determining, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination, receiving, at the cold chain network, collected route data about the determined route, calculating, using the cold chain network, predicted route data based on the collected route data, displaying, using a display, the collected route data and the predicated route data using a graphical user interface (GUI), and receiving, from a user input device, a route selection based on the displayed collected route data and predicated route data.

[0006] In addition to one or more of the features described above, or as an alternative, further embodiments may include storing, in a first database, collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency, and storing, in a second database, collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness, wherein driver effectiveness is determined based on actual route data compared to predicted route data.

[0007] In addition to one or more of the features described above, or as an alternative, further embodiments may include determining, using the cold chain network, a plurality of routes that each form a cold chain from the current location to the destination, receiving, at the cold chain network, collected route data about the determined plurality of routes, calculating, using the cold chain network, predicated route data based on the collected route data, displaying, using the display of the cold transport vehicle, the collected route data and the predicated route data, and receiving, from the user input device, a route selection based on the displayed collected route data and predicated route data.

[0008] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the collected route data consists of weather data and refrigerator data, wherein the weather data includes one or more of temperature, barometric pressure, wind speed, wind direction, cloud cover, storm warnings, humidity, ozone levels, and pollen levels, and wherein refrigerator data includes one or more of temperature values over time, energy usage data, and current temperature value.

[0009] In addition to one or more of the features described above, or as an alternative, further embodiments may include, wherein the predicted route data is at least one selected from a group consisting of weather effects on the refrigerator, predicted weather of different routes, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

[0010] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the collected route data consists of driver data, wherein driver data includes one or more of previous drive time for route, speed average, speed values throughout, drive times, stop lengths, stop locations, and fueling habits.

[0011] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the predicted route data is at least one selected from a group consisting of gas mileage range value, semi-trailer truck route gas mileage, energy efficiency, predicted freshness and quality, and risk of route.

[0012] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the collected route data consists of route data, route delays, route length, and semi-trailer truck data, wherein route data includes route speed limit data, route incline data, gas station mapping data, repair shop mapping data, general mapping data, and road type data, and wherein semi-trailer truck data includes gas tank capacity, tire pressures, mileage, engine temperature, engine sensor data, cabin sensor data, battery data, and generator data.

[0013] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the predicted route data is at least one selected from a group consisting of gas mileage range values, refrigerator efficiency, weather effects on the refrigerator, predicted weather of different routes, semi-trailer truck route gas mileage, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

[0014] In accordance with an embodiment, a route management tool for a cold chain system is provided. The route management tool includes a cold transport vehicle including a display configured to display collected route data and predicated route data using a graphical user interface (GUI), and a user input device configured to receive a route selection based on the displayed collected route data and predicated route data, and a cold chain network configured to determine a route that forms a cold chain from a current location of a cold transport vehicle to a destination, receive collected route data about the determined route, and calculate predicted route data based on the collected route data, the cold chain network including a first database configured to store collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency, and a second database configured to store collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness, wherein driver effectiveness is determined based on actual route data compared to estimated route data.

[0015] In addition to one or more of the features described above, or as an alternative, further embodiments may include, wherein the cold chain network is further configured to determine a plurality of routes that each form a cold chain from the current location to the destination and receive collected route data about the determined plurality of routes.

[0016] In addition to one or more of the features described above, or as an alternative, further embodiments may include, wherein the collected route data consists of weather data and refrigerator data, wherein the weather data includes one or more of temperature, barometric pressure, wind speed, wind direction, cloud cover, storm warnings, humidity, ozone levels, and pollen levels, and wherein refrigerator data includes one or more of temperature values over time, energy usage data, and current temperature value. [0017] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the predicted route data is at least one selected from a group consisting of weather effects on the refrigerator, predicted weather of different routes, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

[0018] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the collected route data consists of driver data, and wherein driver data includes one or more of previous drive time for route, speed average, speed values throughout, drive times, stop lengths, stop locations, and fueling habits.

[0019] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the predicted route data is at least one selected from a group consisting of gas mileage range value, semi-trailer truck route gas mileage usage, energy efficiency, predicted freshness and quality, and risk of route.

[0020] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the collected route data consists of route data, route delays, route length, and semi-trailer truck data, wherein route data includes route speed limit data, route incline data, gas station mapping data, repair shop mapping data, general mapping data, and road type data, and wherein semi-trailer truck data includes gas tank capacity, tire pressures, mileage, engine temperature, engine sensor data, cabin sensor data, battery data, and generator data.

[0021] In addition to one or more of the features described above, or as an alternative, further embodiments may include wherein the predicted route data is at least one selected from a group consisting of gas mileage range values, refrigerator efficiency, weather effects on the refrigerator, predicted weather of different routes, semi-trailer truck route gas mileage, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

[0022] In accordance with another embodiment, a system for cold chain route management, including one or more processors in communication with one or more types of computer readable storage mediums having program instructions embodied therewith is provided. The program instructions executable by the one or more processors to cause the processors to determine, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination, receive, at the cold chain network, collected route data about the determined route, calculate, using the cold chain network, predicted route data based on the collected route data, display, using a display of the cold transport vehicle, the collected route data and the predicated route data using a graphical user interface (GUI), and receive, from a user input device of the cold transport vehicle, a route selection based on the displayed collected route data and predicated route data.

[0023] In addition to one or more of the features described above, or as an alternative, further embodiments may include additional program instructions executable by the one or more processors to cause the processors to store, in a first database, collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency, and store, in a second database, collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness, wherein driver effectiveness is determined based on actual route data compared to predicted route data.

[0024] In addition to one or more of the features described above, or as an alternative, further embodiments may include additional program instructions executable by the one or more processors to cause the processors to wherein the collected route data consists of route data, route delays, route length, semi-trailer truck data, driver data, weather data, and refrigerator data, wherein route data includes route speed limit data, route incline data, gas station mapping data, repair shop mapping data, general mapping data, and road type data, wherein semi-trailer truck data includes gas tank capacity, tire pressures, mileage, engine temperature, engine sensor data, cabin sensor data, battery data, and generator data, wherein driver data includes one or more of previous drive time for route, speed average, speed values throughout, drive times, stop lengths, stop locations, and fueling habits, wherein the predicted route data is at least one selected from a group consisting of gas mileage range values, refrigerator efficiency, weather effects on the refrigerator, predicted weather of different routes, calculation of effects of weather on refrigeration, semi-trailer truck route gas mileage, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route, wherein the weather data includes one or more of temperature, barometric pressure, wind speed, wind direction, cloud cover, storm warnings, humidity, ozone levels, and pollen levels, and wherein refrigerator data includes one or more of temperature values over time, energy usage data, and current temperature value.

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The foregoing and other features, and advantages of the present disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: [0026] FIG. 1 depicts a cloud computing environment according to an embodiment of the present disclosure;

[0027] FIG. 2 depicts abstraction model layers according to an embodiment of the present disclosure;

[0028] FIG. 3 is a block diagram illustrating one example of a processing system for practice of the teachings herein;

[0029] FIG. 4 depicts a block diagram illustrating a system in accordance with an embodiment of the present disclosure;

[0030] FIG. 5 depicts a flow diagram of routing software in accordance with an embodiment of the present disclosure;

[0031] FIG. 6 depicts a flow diagram of route analysis software of routing software in accordance with an embodiment of the present disclosure;

[0032] FIG. 7 depicts a flow diagram of weather effect software of routing software in accordance with an embodiment of the present disclosure;

[0033] FIG. 8 depicts a flow diagram of risk analysis software of routing software in accordance with an embodiment of the present disclosure;

[0034] FIG. 9 depicts a flow diagram of product quality software of routing software in accordance with an embodiment of the present disclosure;

[0035] FIG. 10 depicts a route management tool graphical user interface (GUI) for the routing software in accordance with an embodiment of the present disclosure;

[0036] FIG. 11 depicts a route management toll risk GUI for the routing software in accordance with an embodiment of the present disclosure; and

[0037] FIG. 12 depicts a flow chart of a method of collecting and processing route information for a cold chain system in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0038] It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present disclosure are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

[0039] Cloud computing is a model of service delivery for enabling convenient, on- demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

[0040] Characteristics are as follows:

[0041] On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.

[0042] Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

[0043] Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

[0044] Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

[0045] Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

[0046] Service Models are as follows:

[0047] Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web- based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. [0048] Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.

[0049] Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

[0050] Deployment Models are as follows:

[0051] Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off- premises.

[0052] Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off -premises.

[0053] Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

[0054] Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

[0055] A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.

[0056] Referring now to Fig. 1, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in Fig. 1 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

[0057] Referring now to Fig. 2, a set of functional abstraction layers provided by cloud computing environment 50 (Fig. 1) is shown. It should be understood in advance that the components, layers, and functions shown in Fig. 2 are intended to be illustrative only and embodiments of the disclosure are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0058] Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

[0059] Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

[0060] In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provides pre- arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

[0061] Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and processing of messages across multiple communication systems 96.

[0062] In accordance with exemplary embodiments of the disclosure, methods, systems and computer program products for prioritizing delivery of messages across multiple communication systems are provided. In exemplary embodiments, a messaging system is configured to receive messages for an individual across multiple communication systems utilized by the individual. The messaging system is also configured to determine a priority level associated with each of the messages based on an analysis of the messages and a user profile of the individual. Based on the determined priority level and the user profile, the messaging system delivers the messages to a desired communication device via a desired messaging system. In exemplary embodiments, the user profile is updated by the messaging system upon receiving feedback from the individual, wherein the feedback includes message delivery preferences and message priority preferences of the individual.

[0063] Referring to FIG. 3, there is shown an embodiment of a processing system 100 for implementing the teachings herein. In this embodiment, the system 100 has one or more central processing units (processors) 101a, 101b, 101c, etc. (collectively or generically referred to as processor(s) 101). In one embodiment, each processor 101 may include a reduced instruction set computer (RISC) microprocessor. Processors 101 are coupled to system memory 114 and various other components via a system bus 113. Read only memory (ROM) 102 is coupled to the system bus 113 and may include a basic input/output system (BIOS), which controls certain basic functions of system 100.

[0064] FIG. 3 further depicts an input/output (I/O) adapter 107 and a network adapter 106 coupled to the system bus 113. I/O adapter 107 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 103 and/or tape storage drive 105 or any other similar component. I/O adapter 107, hard disk 103, and tape storage device 105 are collectively referred to herein as mass storage 104. Operating system 120 for execution on the processing system 100 may be stored in mass storage 104. A network adapter 106 interconnects bus 113 with an outside network 116 enabling data processing system 100 to communicate with other such systems. A screen (e.g., a display monitor) 115 is connected to system bus 113 by display adaptor 112, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one embodiment, adapters 107, 106, and 112 may be connected to one or more I/O busses that are connected to system bus 113 via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus 113 via user interface adapter 108 and display adapter 112. A keyboard 109, mouse 110, and speaker 111 all interconnected to bus 113 via user interface adapter 108, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

[0065] In exemplary embodiments, the processing system 100 includes a graphics processing unit 130. Graphics processing unit 130 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 130 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

[0066] Thus, as configured in FIG. 3, the system 100 includes processing capability in the form of processors 101, storage capability including system memory 114 and mass storage 104, input means such as keyboard 109 and mouse 110, and output capability including speaker 111 and display 115. In one embodiment, a portion of system memory 114 and mass storage 104 collectively store an operating system to coordinate the functions of the various components shown in FIG. 3.

[0067] According to one or more embodiments, a route management tool is provided that determines the routing of one or more cold transport vehicles based on predicted route that including for example weather and delays, gas mileage, weather effects on refrigeration, energy efficiencies, and predicted product freshness/quality. A database of refrigeration units and drivers with their respective effectiveness is also used to determine the route.

[0068] Further, according to one or more embodiments, a system to evaluate and manage the risks of a cold storage transport vehicle is also provided. The software collects data of fuel, spare parts, backup energy, nearest repair shops and communicates the data to the distributor. The distributor software system identifies risks associated with the cold chain and allows the distributor to react to the identified risks via a user interface with planning and management tools.

[0069] According to one or more embodiments, a method of collecting and analyzing route information for a cold chain system is provided. The method includes collecting information relating to efficiency, product quality, and risk management. For example, the method collects efficiency information relating to temperature control efficiency, route travel time efficiency, and fuel/battery usage efficiency. Further, product quality information can include a calculated spoilage rate over time as affect by travel conditions including temperature, temperature fluctuations, pressure, humidity, sunlight exposure, air filtration data, and other factors. Further, the method can collect information and process the information to calculate risk values relating to different routes.

[0070] According to one or more specific embodiments, the tool, system, and/or method can be implemented for use with a refrigerated semi-trailer truck to determine what route it should take. Factors such as gas mileage, refrigerator efficiency, weather effects on the refrigerator are not taken into account.

[0071] Further, one or more embodiments allows for a routing manager that decides the routing of cold trucks based on: predicted weather of different routes and delays, route trucks gas mileage, calculation of effects of weather on refrigeration , energy efficiencies, predicted freshness and quality, and risk of route. Further, according to another embodiment, the SaaS implementation for collecting and analyzing route related data for a cold chain system can include and use a unique first database of refrigeration units tracking to fleet so that refrigeration efficiencies are more precisely calculated. Further, according to another embodiment, the SaaS implementation for collecting and analyzing route related data for a cold chain system can include and use a unique second database of drivers' effectiveness in meeting timings etc., which can also be included in the routing manager. According to an embedment, determining an optimal timing can be calculated using a combination of refrigeration efficiency a particular driver's effectiveness.

[0072] According to one or more embodiments, the risk of the possible routes is determined by a SaaS system. For example, software on the SaaS system collects cold transport vehicle data such as fuel, spare parts, and backup energy, as well as route data such as location of repair shops and driving conditions. Further, the software on the system identifies risks associated with the cold chain and displays the risk data in the routing management system to help inform the routing decision. The system also allows the distributor to react to the identified risks via a user interface with planning and management tools. Accordingly, such as system can be implemented for cold chain transport vehicles that have multiple possible routes for their delivery to help the cold chain transport vehicles select one of the routes.

[0073] In accordance with one or more embodiments, an advantage that can be provided is improvement in value from a market perspective because cold chain as a service software's first goal is to manage routing but also to leverage cold chain data (e.g. weather, risks) that affect quality and costs. Another advantage, in accordance with one or more embodiments, includes the ability for the system to help manage risks involved in the cold chain.

[0074] Turning now back to the figures, FIG. 4 depicts a block diagram illustrating a system in accordance with an exemplary embodiment that is specifically called, for example, a route management tool 400. The route management tool 400 includes at least one cold transport vehicle 410 as shown. According to another embodiment the route management tool 400 may include a plurality of cold transport vehicles. Further, the route management tool 400 also includes a cold chain network 420. The cold chain network 420 is communicatively connected to the cold transport vehicle 410 through a cloud resource 405. The cold chain network 420 is configured to help collect and predict route data values for different routes that are determined for the cold transport vehicle 410. These collected and predicted route data values are provided to a user/driver of the cold transport vehicle 410 using a display and a GUI that the user/driver uses to select a route.

[0075] According to one or more embodiments, the cold transport vehicle 410 includes a global positioning service (GPS) device 411, a fuel sensor 412, a backup energy sensor 415, a spare parts database (DB) 413, and a route management tool 414. The GPS device 411, fuel sensor 412, and backup energy sensor 415 can be used to collect route data that is then provided to the cold chain network 420 through the cloud 405. Further, spare parts DB 413 stores information relating to spare parts for the cold transport vehicle 411. The route management tool 414 is configured to determine the routing of cold transport vehicles based on predicted weather and delays, gas mileage, weather effects on refrigeration, energy efficiencies, and predicted product freshness/quality.

[0076] According to one or more embodiments, the cold chain network 420 includes a number of different databases (DB). For example, the cold chain network 420 includes a repair shop DB 421, a traffic DB 422, a weather DB 423, a driver DB 424, and a refrigeration unit DB 429. These databases are used to stored collected and predicted route data related to each of the respect databases. [0077] The cold chain network 420 also includes risk analysis software 428 and a risk GUI 429 configured to evaluate and manage the risks of a cold storage transport vehicle. Specifically, the risk analysis software 428 is configured to generate risk values using route data from the different databases in the cold chain network 420 as well as route data received from the old transport vehicle 410. The risk GUI 429 displays the risk values to a user/driver alongside the calculated route data so that the user can also use the risk values when determining what route to select.

[0078] The cold chain network 420 also includes routing software 426 and route analysis software 427. The routing software 426 is configured to determine one or more routes while the route analysis software 427 is configured to analyze the routes using route data from all the databases as well as route data received from the cold transport vehicle 410.

[0079] FIG. 5 depicts a flow diagram of routing software 500 in accordance with one or more embodiments of the disclosure. The routing software 500 determines different possible routes from current location to delivery destination and saves these routes to the routing DB (operation 510). The routing software 500 then executes the various pieces of software to collect and calculate data about the different routes. Specifically, the routing software 500 executes route analysis software (operation 520), weather effect software (operation 530), risk analysis software (operation 540) and product quality software (operation 550). The software 500 then displays a route management tool on a transport display (which could be part of the vehicle or it could be a separate device held by either the driver or the distribution company) which is populated with data collected and calculated from the previously executed pieces of software (operation 560). The user of the transport display (i.e. the driver or the distribution company) chooses the route based on the information displayed in the route management tool.

[0080] FIG. 6 depicts a flow diagram of route analysis software 600 of routing software in accordance with one or more embodiments of the disclosure. The route analysis software 600 determines the total distances of each route and stored the route data into the routing DB (operation 605). The route analysis software 600 then calculates the amount of gas that would be used to drive each route based on distance and retrieved Average Miles Per Gallon data (operation 610) and the calculated gallons of gas is stored in the routing DB (operation 615). The route analysis software 600 then accesses the traffic DB such that traffic data is retrieved for each of the routes and saved to the routing DB (operation 620). The route analysis software 600 then calculates the amount of delay caused by traffic by summing all of the different traffic data entries pertaining to the route and stored the traffic data in the routing DB (operation 625). The driver DB is then accessed by the route analysis software 600 to retrieve the data on specific drivers on how their actual trip lengths (in time) compare to the estimated trip lengths (operation 630). In one embodiment, the data from each trip can be combined into one value of Actual vs. Estimated Trip Time Length. For example, the value is a quotient of the actual trip time length divided by the estimated trip time length for all of the driver's previous trips. Next the route analysis software 600 calculates the estimated trip time length for each route by dividing the route distance by the average speed limit, adding traffic delay, and multiplying the sum by the retrieved actual vs. estimated trip time Length data (operation 635).

[0081] FIG. 7 depicts a flow diagram of weather effect software 700 of routing software in accordance with an exemplary embodiment. This weather effect software 700retrieves weather data relevant to the possible routes. Weather data includes precipitation (e.g. snow, rain) and ambient temperature. It could also further include data such as sunlight, wind, humidity, or air pressure. The effects of the weather on the energy efficiency of the refrigerated cargo container are calculated, resulting in an energy usage rate. The energy usage rate is multiplied by the estimated trip time length, the result of which is a refrigeration cost. The refrigeration cost is calculated for each of the routes in the routing DB and saved to the routing DB.

[0082] Specifically, the weather effect software 700 accesses the weather DB and retrieve weather data for each of the routes of the Routing DB and save the data to the routing DB (operation 705). The weather effect software 700 also accesses the Refrigeration DB of the Network to retrieve the energy efficiency data for the refrigerated cargo container (operation 710). Further, the weather effect software 700 calculates for each route the average effect of the predicted ambient temperature and precipitation on the energy efficiency of the refrigerated cargo container (operation 715). The weather effect software 700 also calculates the Refrigeration Cost of each route by multiplying the calculated energy efficiency by the estimated trip time length of the route, (operation 720). Finally, the weather effect software 700 saves the calculated refrigeration cost of each route to the routing DB (operation 725)

[0083] FIG. 8 depicts a flow diagram of risk analysis software 800 of routing software in accordance with an exemplary embodiment. This risk analysis software 800 analyzes risk of routes based on the range of the vehicle, the location of gas stations, the location of repair shops, and the fuel level of the back-up energy source. The range of the vehicle is calculated based on fuel tank size and average miles per gallon. The locations of gas stations along each route are retrieved from the gas station DB, and the maximal distance and average distance between gas stations is calculated to inform the user on the risk of running out of gas on each route. The locations of repair shops along each route are retrieved from the repair shops DB, and the maximal distance and average distance between gas stations is calculated to inform the user on the risk of something braking down and not being near a repair shop.

[0084] For example, in accordance with an embodiment of the present disclosure, as shown in FIG. 8 the risk analysis software 800 accesses the vehicle DB and retrieves the fuel tank size and Average Miles per Gallon of the vehicle (operation 805). The risk analysis software 800 also includes calculating the range of vehicle based on fuel tank size and Average Miles per Gallon of vehicle. Save calculated range to Risk DB (operation 810). The risk analysis software 800 also includes accessing the gas station DB and determining the maximal distance and average distance between gas stations for each route of the routing DB (operation 815) and stores this route data to the routing DB (operation 820). The risk analysis software 800 also includes accessing the repair shop DB and determining the maximal distance and average distance between repair shops for each route of the routing DB (operation 825) and store this route data to the routing DB (operation 830). The risk analysis software 800 also includes taking back up energy fuel sensor reading and saving that route data to the risk DB (operation 835).

[0085] FIG. 9 depicts a flow diagram of product quality software 900 of routing software in accordance with one or more embodiments of the present disclosure. The product quality software 900 determines the estimated product quality rating of the transported product based on its current product quality rating, the deterioration rates of the deterioration DB (which are specific to the product type and the temperature at which the refrigerated cargo container is held), and the estimated trip time length. According to an embodiment, for example, the estimated product quality rating is equal to the current product quality rating minus a deterioration rate that is multiplied by an estimated trip time length.

[0086] For example, in accordance with one or more embodiments of the present disclosure, as shown in FIG. 9, the product quality software 900 includes accessing product DB and retrieving product quality rating (operation 905) and accessing the product deterioration DB on the network (operation 910). Further, the product quality software 900 includes, for each route of the routing DB, calculating an estimated product quality rating based on the retrieved product quality rating, the deterioration rates from the deterioration DB, and the estimated trip time length (operation 915). The product quality software 900 also includes storing all the collected and predicted route data into the routing DB (operation 920).

[0087] FIG. 10 depicts a route management tool graphical user interface (GUI) for the routing software in accordance with an exemplary embodiment. As show, the GUI may show information for two or more routes, for example, Routel and Route 2. As shown each route may include a number of different values that are shown to help a user compare the routes for selection. For example, the routes include precipitation, temperature, traffic delays, gallons of gas, refrigeration costs, distance, estimated time length of route, and predicted product quality out of 10. As well as a button to run the risk analysis, for example. According to one or more embodiments, the GUI can be displayed on a vehicle display by the routing software. The GUI can display data obtained/calculated by the various pieces of software. Further, according to an embodiment, the "Risk Analysis" button opens up the Route Management Tool - Risks GUI. The Route Management Tool GUI is used by the user (e.g. driver, distribution company) to help decide which route to use for a delivery.

[0088] FIG. 11 depicts a route management tool - risks GUI for the routing software in accordance with an exemplary embodiment. This risk GUI is opened up by selecting "Risk Analysis" button on the Route Management Tool GUI. The risk GUI displays data from the risk analysis software. The risk GUI can also display data from the spare parts DB. The "Close" button when selected closes the Route Management Tool - Risks GUI and returns the user to the Route Management Tools GUI. As shown, the risks GUI can provide risk information for one or more routes. As shown two routes are shown. Some examples of risk related route data that is shown includes, but is not limited to, range (on full tank), backup energy, maximal distance -gas station, average distance -gas station, maximal distance -repair shop, and average distance - repair shop. The GUI may also disclose a parts count table as shown.

[0089] FIG. 12 depicts a flow chart of a method 1200 of collecting and processing route information for a cold chain system in accordance with an embodiment of the present disclosure. The method 1200 includes determining, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination (operation 1205). The method 1200 also includes receiving, at the cold chain network, collected route data about the determined route (operation 1210). Further, the method 1200 includes calculating, using the cold chain network, predicted route data based on the collected route data (operation 1215). Additionally the method 1200 includes displaying, using a display of the cold transport vehicle, the collected route data and the predicated route data using a graphical user interface (GUI) (operation 1220) and receiving, from a user input device of the cold transport vehicle, a route selection based on the displayed collected route data and predicated route data (operation 1225).

[0090] One or more embodiments of the present disclosure may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of one or more embodiments of the present disclosure.

[0091] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non- exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0092] Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. [0093] Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0094] Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

[0095] These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

[0096] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

[0097] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

Claims

CLAIMS What is claimed is:

1. A method for analyzing and selecting a cold chain route of a cold chain system, the method comprises:

determining, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination;

receiving, at the cold chain network, collected route data about the determined route; calculating, using the cold chain network, predicted route data based on the collected route data;

displaying, using a display, the collected route data and the predicated route data using a graphical user interface (GUI); and

receiving, from a user input device, a route selection based on the displayed collected route data and predicated route data.

2. The method of claim 1, further comprising:

storing, in a first database, collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency; and

storing, in a second database, collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness,

wherein driver effectiveness is determined based on actual route data compared to predicted route data.

3. The method of claim 1, further comprising:

determining, using the cold chain network, a plurality of routes that each form a cold chain from the current location to the destination;

receiving, at the cold chain network, collected route data about the determined plurality of routes;

calculating, using the cold chain network, predicated route data based on the collected route data;

displaying, using the display of the cold transport vehicle, the collected route data and the predicated route data; and

receiving, from the user input device, a route selection based on the displayed collected route data and predicated route data.

4. The method of claim 1,

wherein the collected route data consists of weather data and refrigerator data, wherein the weather data includes one or more of temperature, barometric pressure, wind speed, wind direction, cloud cover, storm warnings, humidity, ozone levels, and pollen levels, and

wherein refrigerator data includes one or more of temperature values over time, energy usage data, and current temperature value.

5. The method of claim 4,

wherein the predicted route data is at least one selected from a group consisting of weather effects on the refrigerator, predicted weather of different routes, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

6. The method of claim 1,

wherein the collected route data consists of driver data,

wherein driver data includes one or more of previous drive time for route, speed average, speed values throughout, drive times, stop lengths, stop locations, and fueling habits.

7. The method of claim 6,

wherein the predicted route data is at least one selected from a group consisting of gas mileage range value, semi-trailer truck route gas mileage, energy efficiency, predicted freshness and quality, and risk of route.

8. The method of claim 1,

wherein the collected route data consists of route data, route delays, route length, and semi-trailer truck data,

wherein route data includes route speed limit data, route incline data, gas station mapping data, repair shop mapping data, general mapping data, and road type data, and

wherein semi-trailer truck data includes gas tank capacity, tire pressures, mileage, engine temperature, engine sensor data, cabin sensor data, battery data, and generator data.

9. The method of claim 1,

wherein the predicted route data is at least one selected from a group consisting of gas mileage range values, refrigerator efficiency, weather effects on the refrigerator, predicted weather of different routes, semi-trailer truck route gas mileage, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route.

10. A route management tool for a cold chain system, the route management tool comprising:

a cold transport vehicle comprising: a display configured to display collected route data and predicated route data using a graphical user interface (GUI); and

a user input device configured to receive a route selection based on the displayed collected route data and predicated route data; and

a cold chain network configured to determine a route that forms a cold chain from a current location of a cold transport vehicle to a destination, receive collected route data about the determined route, and calculate predicted route data based on the collected route data, the cold chain network comprising:

a first database configured to store collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency; and a second database configured to store collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness,

wherein driver effectiveness is determined based on actual route data compared to estimated route data.

11. The route management tool of claim 10, wherein the cold chain network is further configured to determine a plurality of routes that each form a cold chain from the current location to the destination and receive collected route data about the determined plurality of routes.

12. The route management tool of claim 10,

13. The route management tool of claim 12,

14. The route management tool of claim 10,

wherein the collected route data consists of driver data, and

15. The route management tool of claim 14,

wherein the predicted route data is at least one selected from a group consisting of gas mileage range value, semi-trailer truck route gas mileage usage, energy efficiency, predicted freshness and quality, and risk of route.

16. The route management tool of claim 10,

17. The route management tool of claim 10,

18. A system for cold chain route management, comprising:

one or more processors in communication with one or more types of computer readable storage mediums having program instructions embodied therewith, the program instructions executable by the one or more processors to cause the processors to:

determine, using a cold chain network, a route that forms a cold chain from a current location of a cold transport vehicle to a destination;

receive, at the cold chain network, collected route data about the determined route; calculate, using the cold chain network, predicted route data based on the collected route data;

display, using a display of the cold transport vehicle, the collected route data and the predicated route data using a graphical user interface (GUI); and

receive, from a user input device of the cold transport vehicle, a route selection based on the displayed collected route data and predicated route data.

19. The system of claim 18, further comprising additional program instructions executable by the one or more processors to cause the processors to:

store, in a first database, collected route data that includes refrigeration unit data that is used to calculate predicted route data that includes refrigeration efficiency; and store, in a second database, collected route data that includes driver data that is used to calculate predicated route data that includes driver effectiveness,

20. The system of claim 18, further comprising additional program instructions executable by the one or more processors to cause the processors to:

wherein the collected route data consists of route data, route delays, route length, semi-trailer truck data, driver data, weather data, and refrigerator data,

wherein route data includes route speed limit data, route incline data, gas station mapping data, repair shop mapping data, general mapping data, and road type data,

wherein semi-trailer truck data includes gas tank capacity, tire pressures, mileage, engine temperature, engine sensor data, cabin sensor data, battery data, and generator data, wherein driver data includes one or more of previous drive time for route, speed average, speed values throughout, drive times, stop lengths, stop locations, and fueling habits, wherein the predicted route data is at least one selected from a group consisting of gas mileage range values, refrigerator efficiency, weather effects on the refrigerator, predicted weather of different routes, calculation of effects of weather on refrigeration, semi-trailer truck route gas mileage, calculation of effects of weather on refrigeration, energy efficiency, predicted freshness and quality, and risk of route,

wherein the weather data includes one or more of temperature, barometric pressure, wind speed, wind direction, cloud cover, storm warnings, humidity, ozone levels, and pollen levels, and