WO2001051895A1

WO2001051895A1 - Method for monitoring parcel shipping systems

Info

Publication number: WO2001051895A1
Application number: PCT/US2000/034886
Authority: WO
Inventors: Gregory D. Hoshal
Original assignee: SAFESHIP INTERNATIONAL Inc
Current assignee: SAFESHIP INTERNATIONAL Inc
Priority date: 2000-01-07
Filing date: 2000-12-20
Publication date: 2001-07-19
Anticipated expiration: 2002-07-07
Also published as: WO2001051895B1; AU2732701A

Abstract

A process for statistically sampling the handling quality of parcel shipping carriers. The process includes: associating a data recorder with a parcel (100); shipping the parcel with a parcel carrier (102); sampling physical conditions encountered by the parcel during shipment with the recorder (104); processing the data to summarize the handling quality during shipment (106). During shipping, the parcel is tracked to determine its location at particular times. The sampled physical conditions encountered may include impact, vibration, temperature change, humidity change, atmospheric pressure, compression, electromagnetic radiation, and magnetic fields. The physical parameters may be statistically analyzed to provide useful information concerning the carrier's handling quality. This information may be disseminated to consumers via passive or interactive Internet web sites so that the consumers may compare and select carriers that provide the most efficient handling quality.

Description

METHOD FOR MONITORING PARCEL SHIPPING SYSTEMS BACKGROUND OF THE INVENTION This application claims the benefit of U.S. Provisional Application No. 60/175,085, filed January 7, 2000 and entitled SMALL PARCEL HANDLING DATA WEB SITE; U.S. Provisional Application No. 60/176,381, filed January 14, 2000 and entitled SMALL PARCEL HANDLING DATA WEB SITE AND SHIPPING SELECTOR SYSTEM; U.S. Provisional Application No. 60/219,715, filed July 19, 2000 and entitled STATISTICAL PROCESS CONTROL FOR THE TRANSPORTATION INDUSTRY; and U.S. Provisional Application No. 60/222,246, filed August 1, 2000 and entitled ROUTING, SHIPPING AND HANDLING COLLECTION METHODOLOGY FOR SAMPLING THE SMALL PARCEL HANDLING ENVIRONMENT.

The present invention relates to shipping goods and more particularly to small parcel shipping systems.

Manufacturers, distributors, mail order retailers, e-commerce companies and other consumers increasingly recruit parcel shipping and transportation carriers to ship goods.

Conventionally, these consumers select carriers on three primary bases: delivery efficacy, cost, and reputation. The most efficacious, least expensive, and moderately reputable carrier typically is chosen over other carriers.

Unfortunately, selected carriers do not always provide a shipping environment conducive to the preservation of the parcel shipped. The parcel is sometimes damaged or otherwise altered in transport. This damage to the parcel or the goods therein may be the result of excessive cooling, heating, exposure to humidity, compression, atmospheric pressure, vibration, and/or impact to the parcel. For example, parcels may be dropped during loading and unloading from a carrier vehicle, or carelessly handled in a sorting machine. This may impart an excessive impact on the goods within the parcel. If the goods are fragile, for example, electronic devices, glass, or art items, they may be substantially damaged under such impact. Of course, when consumers receive goods damaged in shipping, they typically send those goods back to the retailer or manufacturer under warranty. The retailer or manufacturer in turn must replace the goods under warranty and resend the goods to the consumer. Currently, many goods are returned under warranty, due to damage or defects caused during shipping. This return is costly to retailers and manufacturers. Retailers and manufacturers attempt to reduce losses due to damage during shipping with conventional methods, such as packaging their goods in padded, shock resistant containers or insuring the goods during shipment. Although these solutions are somewhat successful, they still suffer shortcomings. First, these solutions add an extra cost to the goods. Second, they promote continued rough handling of the goods by careless carriers. Finally, these solutions are detrimental to the process of distinguishing careful carriers from careless carriers.

In another attempt to reduce losses due to damage during shipping, carriers are sampled. More specifically, test parcels are shipped multiple times from a first point to a second point. Upon arriving at each of the points, the parcel is opened and inspected for damage. Although this method provides a way to sample a carrier's handling quality, it is inefficient, costly, and time consuming.

SUMMARY OF THE INVENTION The aforementioned problems are overcome in the present invention wherein a process objectively samples the shipping and handling environment of parcels or other articles. More specifically, a preferred process of sampling and monitoring handling quality of parcel carriers includes the following steps: associating a data recorder with a parcel; shipping and/or handling the parcels; sampling physical variables experienced by the parcel with the data recorder; processing the data collected by the recorder, and evaluating the handling quality encountered by the parcel in shipping and/or handling. In a preferred process the following steps are implemented. First, field measurements for parcels are obtained by associating data recorders with those parcels when they are shipped by carriers. A parcel including a data recorder is referred to as a "data acquisition package." The data recorders are capable of sampling and recording physical variables experienced by the data acquisition package. Measured physical variables may be chosen from impacts (typically resulting from dropping the parcel), vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation, and magnetic fields. In a second step, the data acquisition package is routed through a plurality of pre-selected locations. Preferably, the locations are highly populated, highly industrial and have a high level of shipping activity. In a third step, the data acquisition package is shipped from a first location, a "source," to a second location, a "destination." This source-to- destination shipment is called a "leg." The data acquisition package may also be shipped from the second location to a third location, or on a second leg. A combination of legs form data acquisition package shipment "routes." In a fourth step, the "tracking" step, the exact date and time of shipping a data acquisition package from a source of each leg and the exact date and time of delivering a data acquisition package to a destination of each leg is recorded to separate, or "parse" data collected on different legs traversed by the data acquisition package. For example, when a data acquisition package is shipped from a source on a first leg of a route, the amount of time required to ship the data acquisition package from the source to the destination of the first leg is recorded by an accounting operator. The data acquisition package may be tracked multiple times for multiple legs in a route.

After shipping the data acquisition package over multiple legs, typically at the end of a route, the data recorder is removed from the data acquisition package. The physical variables sampled and recorded in the data recorder are then processed to create handling quality output, referred to as "Quality Measures," or "QMs." As an example, output representative of the impact-related QMs may include the number of impacts or energy transferred by impacts encountered by the data acquisition package during shipping or in material handling and sorting systems. The QMs may be statistically analyzed to provide further useful information concerning the shipping and handling quality encountered during shipment. This may include analyzing the QMs to calculate the minimum, maximum, and average number of impacts likely to be encountered by a parcel on a particular carrier leg. Of course, other physical variables such as vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation and magnetic fields encountered by the data acquisition package may be measured and statistically analyzed in a likewise manner.

In another step of the invention, the information created from the QMs may be summarized and disseminated to inform consumers of a carrier's handling quality. This dissemination may occur in any form, but in a preferred process, the information is distributed over the Internet to consumers who may view the information. With this information, consumers may compare and select carriers in an efficient and cost-effective manner.

The process of the present invention has a variety of benefits. Most notably, the statistical information concerning the handling quality of carriers may now be available to shippers, manufacturers, retailers, distributors, and any other consumers who ship goods, as well as the carriers themselves. With this information, anyone shipping goods may compare and select carriers that provide the most efficient handling quality for their goods.

Accordingly, the number of goods returned due to damage during shipping may be reduced.

Further, the statistical handling quality information may be used by carriers to monitor and analyze handling quality over time. With analysis, carriers may identify and correct unnecessarily rough handling conditions. For example, upon discovering an abnormal number of impacts experienced by parcels on certain legs or routes, carriers might examine those routes and take corrective action.

In another aspect of the invention, shippers and carriers may use handling quality information to optimize package design and prevent damage to shipped goods.

These and other objects, advantages, and features of the invention will be more readily understood and appreciated by reference to the detailed description of the preferred embodiment and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a sectional view of a preferred data acquisition package of the present invention;

Fig. 1A is a block diagram of a preferred process of sampling handling quality;

Fig. 2 is a routing scheme of the preferred process of the present invention; Fig. 3 is an example route file;

Figs. 4-6 are examples of individual parsed leg files collected from a data recorder;

Fig. 7 is an assimilation of parsed leg files collected from a data recorder; and Fig. 8 is a web site handling quality query schematic. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The preferred process of sampling the handling quality with which carriers ship, transport and handle parcels as depicted in Fig. 1A generally includes: associating a data recorder with a parcel 100; shipping the parcel from one location to another with a carrier 102; sampling with the recorder physical conditions experienced by the parcel during shipping by the carrier 104; and analyzing the data to survey the handling quality of the parcel during shipment 106. Here "handling quality" refers to the care with which a carrier ships goods and includes the relative degree to which the carrier subjects shipped articles to conditions such as impacts; changes in velocity; changes in acceleration; vibration, extreme, non-ambient, or variable temperature; extreme, non-ambient, or variable humidity; compression; atmospheric pressure, electromagnetic radiation, magnetic fields, or any other physical variable. As used herein, "carrier" means any entity capable of shipping an article. "Shipping" means transporting, disseminating, circulating, hauling, trucking, carrying, maneuvering, sorting, conveying, handling, or otherwise physically moving an article.

It will be appreciated that when sampling certain carriers and modes on certain legs or routes of travel, the data recorders may be shipped multiple times over multiple legs or routes. Here, "mode" refers to the way a parcel is shipped; "ground" transportation, "air" transportation, "one-day," "two-day," and "overnight," are examples of modes provided by carriers. A "leg" refers to a distance a parcel and data recorder is shipped from a first location, a "source," to a second location, a "destination." A "route" is a plurality of legs that when combined traverses a given distance. A particular type of route, a "round-robin" route includes a plurality of legs wherein an article is shipped from and then back to a single location.

Optionally, before the shipping step, the parcel with data recorder may be routed on a particular route to provide region or route-specific sampling data for a given carrier. The parcel with data recorder may also be routed to sample a carrier's primary shipping routes to substantially sample the handling quality of the carrier. Additionally, in the shipping step, the parcels may be tracked to determine when and where a parcel experiences physical variables, also referred to herein as events or conditions.

Further, the sampled data may be summarized and disseminated to inform consumers of the handling quality likely to be encountered when using a certain parcel carriers and modes. For example, this data may be assimilated in a form rating and/or differentiating carriers based on handling quality alone or in combination with other parameters such as price and shipping efficacy. The summarized data may also be used to inform consumers of the handling quality likely to be encountered when shipping over certain routes, or shipping during certain times of the day, week, month, or year.

Carriers may also use the data to alter their shipping and handling procedures to improve handling quality. Further, the data may be used to design packaging products to better protect goods in shipment.

As will be appreciated, the present invention may be used not only to sample a parcel carrier's handling quality, it may also be used to sample any shipping environment. For example, the present invention may be used to sample operations of moving companies, trucking companies, airline baggage handlers, factory assembly lines, and the like.

Data Recorder

In the preferred process, a data recorder is associated with a parcel or plurality of parcels. A "data recorder" is any device having the ability to sample or measure or otherwise perceive and record or transmit data relating to physical variables experienced by a parcel or parcels in shipping. "Physical conditions" or "physical variables" may include, but are not limited to, impact, change in velocity, change in acceleration, vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation and/or magnetic fields. Physical variables that are sampled and recorded or transmitted by the data recorder are referred to as "shipping-related data," or simply "data."

A single data recorder is preferably associated with a single parcel. The parcel may or may not include goods therein to be simultaneously shipped. A parcel including a data recorder is referred to as a "smart parcel" or "data acquisition package." Although the data recorder collects physical data for conditions encountered by that parcel, in some cases the recorder may also be used to ascertain physical experiences of nearby packages or parcels as well. For example, if a data acquisition package collects temperature information, the temperature data collected by the data acquisition package will be similar, if not identical to, temperature experienced by other parcels in proximity to the data acquisition package. Alternatively, a data recorder or multiple data recorders may be associated with a pallet or other similar parcel or article supporting device. Such a device combined with a data recorder is referred to as a "data acquisition pallet." With a data acquisition pallet, the physical conditions encountered by all parcels associated with the pallet may be collected. Data acquisition pallets are especially useful when shipping multiple parcels or large goods having identical or similar sensitivities to physical conditions.

The data acquisition package preferably includes no other items, like goods, that would impede accurate collection of data; however, data recorders may be shipped with goods to sample or monitor physical conditions experienced by those goods. The data recorders may then be used to determine when, where, and in whose possession those goods are damaged or encountered undesirable conditions.

The data recorder measures and records information relating to any impacts imparted on the data acquisition package such as those encountered when a parcel is dropped, or roughly handled during shipping. Impact data recorder, model EDR-3C-200, available from Instrumented Sensor Technology, Inc. of Okemos, Michigan is well suited to measure physical conditions such as impact. This model can measure impacts from about 0 to about 200 Gs with 0.1 G resolution. "G" represents the force of gravity. With this range and resolution, the data recorder covers most impacts encountered during conventional shipping processes. Of course, other data recorders may be used to measure impacts or various other physical parameters as the sampling application requires. Data acquisition packages shipped multiple times are preferably constructed of durable materials so that the parcels may be reused. Preferably, data acquisition packages are made of a wear-resistant plastic or plastic reinforced cardboard. Preferably, data recorders are packaged inside the data acquisition package itself, preferably in a padded encapsulation such as heavy foam padding. Of course, if data recorders are shipped within a parcel including goods, the packaging may be constructed of other materials as the goods require.

Fig. 1 generally depicts a data acquisition package 10. The data acquisition package includes a rigid or semi-rigid case 12. The case is depicted as a box; however, it may be of any suitable shape. The case 12 is preferably easily openable to access the data recorder 20 disposed therein. The case is made of a durable, wear resistant plastic; however, any durable material, such as plastic reinforced cardboard, may be used to construct the container 12.

The data recorder 20 is disposed in the case, and further seated in a cut out portion 28 in support material 30. The support material 30 is preferably a polyethylene foam, for example, available from Dow Chemical of Midland, Michigan, however, any suitable support material may be substituted therefor. The support material 30 substantially surrounds the data recorder 20 to keep it stationary in relation to the case 12 to provide consistent sampling along multiple axes of the data acquisition package. "Substantially surrounds" means that the support material is disposed adjacent to all surfaces of the data recorder to provide support to the surface on contact. As will be appreciated, the data acquisition package may be calibrated so that the data collected accounts for the impact absorptive or dampening properties, or any other property of the support material 30.

The data recorder may also include sensors 40 capable of sensing variables of the environment such as temperature, relative humidity, electromagnetic radiation, magnetic fields, compression and atmospheric pressure. Sensor 40 is disposed in the case 12 remote from the data recorder so that it may sense environment variables yet not be detected. As will be appreciated, it is preferable that a data acquisition package not be identified by a carrier so that it is not specially treated. Preferably, sensor 40 is covered with a concealing element 50 so that the sensor is not visible, but still sample physical variables external to the case in the shipping environment. For example, the concealing element may be configured to resemble a generic shipping order label. Preferably, the sealing element allows external physical variables to be sampled. For example, the element may be permeable or porous so that external temperature may be sampled by the sensor 40. Sensor 40 is also connected to the data recorder with conventional communication means 42, here a wire, to communicate data sensed by sensor 40 to the data recorder 20. Alternatively, the sensor may also be configured to transmit information to the data recorder.

As will be appreciated, the data acquisition package may include multiple sensors similar to sensor 40. The data acquisition package may also include other sensors, such as magnetic field sensor 60, and compression sensors 70 and 80.

Data acquisition packages may be of a variety of different sizes and or weights to replicate real-world parcel shipping. In the preferred process for measuring and testing carrier handling quality, data acquisition packages are grouped into three basic sizes: small, medium and large. Small data acquisition packages have dimensions of 8 inches x 8 inches x 8 inches and a weight of about 5 pounds. Medium data acquisition packages have dimensions of about 15 inches x 15 inches x 15 inches and a weight of about 25 pounds. Large data acquisition packages have dimensions of about 24 inches x 24 inches x 24 inches and a weight of about 50 pounds. Of course, these sizes and weights may be altered to any size or weight as the sampling application requires. Further, the sizing may be of greater or lesser gradation than small, medium and large. Moreover, when goods are shipped with data recorders, the dimensions and weight may vary greatly from the preferred parameters to accommodate the goods.

Routing and Shipping In another step of the preferred process, the data acquisition packages or data acquisition pallets are routed and shipped to sample the handling quality of carriers. As will be appreciated, during the shipping step, any of desired physical variables set forth above may be sampled with the data recorders in the data acquisition package. The routing and shipping steps are closely related and for the sake of efficiency will be described together here. In the preferred process, a routing scheme may first be selected to extensively ship multiple data acquisition packages through the distribution routes and/or legs of major parcel carriers to sample the handling quality provided by those carriers. Exemplary carriers include Federal Express®, United Parcel Services®, and Airborne®. Of course, other regional carriers and international carriers may also be sampled as desired. The routing scheme generally includes two optional sub-steps: region identification and route selection. In the region identification sub-step, regions are selected to sample shipping activity of carriers to and from those regions. The regions preferably have a large consumer and industrial population base, and accordingly a high level of parcel shipping activity. In one preferred sampling route identification sub-step, eight major industrial high population cities are selected as regions. These cities preferably: (1) provide geographical and regional variation; (2) have a significant level of high technology manufacturing, such as electronic or computer-based manufacturing; and (3) have a large consumer, industrial and retail population base having a significant level of parcel shipping activity. In one region identification example, cities selected include: Dearborn, Michigan; Portland, Oregon; San Francisco, California; Phoenix, Arizona; Austin, Texas; Miami, Florida; Piscataway, New Jersey and Denver, Colorado. Of course, other regions may be identified or added to these regions or cities as sampling requires. As will be appreciated, these regions may also include international regions or cities to offer broader sampling ofthe United States and overseas as well. With the regions of sampling identified, the routes of sample shipping may be selected.

In the route selection sub-step ofthe routing scheme, routes for shipping data acquisition packages are identified. An example of particular route selection is exemplified in Fig. 2. As can be seen in Fig. 2, the routes selected generally form a seven pointed star with Denver, Colorado near the center. As indicated in Fig. 2, DV represents Denver, Colorado; DB represents Dearborn, Michigan; PI represents Piscataway, New Jersey; MIA represents Miami, Florida; AU represents Austin, Texas; PH represents Phoenix, Arizona; SF represents San Francisco, California; and PO represents Portland, Oregon, respectively. Each ofthe lines, for example, YR1, BR3, GI, etc. represent a leg of a route.

The first letter of each of the symbols associated with an arrow on Fig. 2 indicates the four primary routes ofthe preferred embodiment: B, R, Y, and G. For example, RR1 from Dearborn, Michigan DB to Austin, Texas AU is a leg on the R route, YL2 from San Francisco, California to Austin, Texas is a leg on the Y route, etc. Additionally, for each route except for the G route, data acquisition packages may be shipped in one of two directions. For example, when shipping a data acquisition package between Portland, Oregon PO and Miami, Florida MIA, a data acquisition package may traverse between those two cities in either direction RR3, that is, from Portland, Oregon PO to Miami, Florida MIA or RL5, that is, Miami, Florida MIA to Portland, Oregon PO. With regard to the G route, the data acquisition package should be run in two directions rather than either of two directions to adequately sample the shipping conditions between Denver, Colorado DV and another city. Accordingly, the G route indicated does not include alternate directions indicated as R or L.

One sampling route of the preferred embodiment, for example, a complete round-robin route described above would entail shipping a data acquisition package from Dearborn, Michigan DB along leg YR1 to Miami, Florida MIA; then shipping the data acquisition package from Miami, Florida MIA along leg YR2 to Phoenix, Arizona PH; then shipping the data acquisition package from Phoenix, Arizona PH along leg YR3 to Portland, Oregon PO; then shipping the data acquisition package from Portland, Oregon PO along leg YR4 to Piscataway, New Jersey PI; then shipping the data acquisition package from Piscataway, New Jersey PI along leg YR5 to Austin, Texas AU; then shipping the data acquisition package from Austin, Texas AU along leg YR6 to San Francisco, California SF; then shipping the parcel from San Francisco, California SF along leg YR7 back to the originating point Dearborn, Michigan DB.

As will be appreciated, a round-robin route used in conjunction with the set of regions in Fig. 2 may be of any configuration. Another example of a simple round-robin route (not shown) is a route from Dearborn, Michigan DB along YR1 to Miami, Florida MIA; then from Miami, Florida MIA along G5 to Denver, Colorado DV; then from Denver, Colorado DV along G14 back to Dearborn, Michigan DB. Data acquisition packages may be shipped along any combination of legs or complete routes to form a round-robin route. Additionally, in some circumstances, it may be desirable to only ship a data acquisition package along one leg. For example, a data acquisition package may be shipped back and forth between Dearborn, Michigan DB and Miami, Florida MIA along YR1 and back to Dearborn, Michigan DB along YL7. As will be appreciated, the seven region, four primary route system of Fig. 2 may be expanded or condensed to include more, fewer or different regions, cities, legs, or routes as desired to provide an adequate sampling ofthe handling quality of selected carriers.

In the preferred process, once a routing scheme is devised, the data acquisition packages are shipped through the routes by selected carriers. To provide adequate sampling of physical variables, each of the legs in route should be sampled by data acquisition packages from source to destination. For example, a clockwise Y route comprised of legs

YR1-YR7 should be sampled along each of those legs from each source of the legs to each destination ofthe legs. Additionally, it is preferred to have multiple data acquisition packages sampling the same and multiple different routes simultaneously. Thus, two data acquisition packages may be sampling the Y route simultaneously. Two data acquisition packages may be sampling the R route simultaneously with one another and simultaneously to the Y route sampling and so on. As will be appreciated, it is preferable to employ as many data acquisition packages to sample as many routes and modes as many times as possible to ensure statistically accurate sampling of carrier handling^" quality.

Tracking

In another step of the preferred process, the data acquisition packages are

"tracked" while being shipped by a carrier, that is, the time of shipment from a source and the time of delivery to a destination are recorded and monitored to determine when and where the data acquisition package is when the data acquisition package experiences a physical condition.

Preferably, the data recorder of a data acquisition package includes a timing system. The timing system may be actuated or started when a data acquisition package is shipped from a first source to a first destination, along a first leg of a route. By accurately logging the shipping time from the source and the delivery time at the destination, it is possible to distinguish different legs along a route from one another. It is also possible to distinguish legs of shipping by a carrier from periods of time when a data acquisition package is handled at a location such as a source or destination. Consequently, the handling quality of a carrier along specific legs and the handling quality of an accounting operator at a source destination may be sampled by the data acquisition package. As used herein an accounting operator logs the shipping and delivery times of a data acquisition package at a source and destination. There may be multiple accounting operators at multiple sources and destinations along legs of routes. It will be appreciated that any packaging, shipping, mailing stores or field offices may be utilized as an accounting operator to accurately log as well as deliver and ship data acquisition packages along desired legs or routes. Of course, any company or person capable of receiving and resending a parcel may be utilized in the preferred process.

It is desirable to accurately record shipping and delivery times, that is, track the data acquisition package so it is later possible to correlate when, where, and how many physical conditions are experienced by the data acquisition package while being shipped by a carrier or handled by an accounting operator.

As an example, temporal accounting of a data acquisition package is conducted as follows: a data recorder is packaged in a data acquisition package in Dearborn, Michigan DB. The timer ofthe data acquisition package is activated. With reference to Fig. 2, the data acquisition package is prepared for shipment by a carrier from source, Dearborn, Michigan DB, along leg YR1 to destination, Miami, Florida MIA. When the data acquisition package is shipped from Dearborn, Michigan DB, a first accounting operator records the exact date and time the data acquisition package is taken for shipment by the carrier. Once the data acquisition package arrives at the destination, Miami, Florida MIA, it is delivered to a second operator present in the Miami, Florida MIA, who records the exact date and time of delivery to the second operator by the carrier.

Next, the second operator gives the data acquisition package to a carrier who ships the data acquisition package from Miami, Florida MIA along leg YR2 to Phoenix, Arizona PH to sample the handling quality ofthe carrier along that leg YR2. Upon shipping the data acquisition package from source, Miami, Florida MIA, along route YR2, the second operator records the exact date and time the data acquisition package is transferred to the carrier for shipping. When the data acquisition package is received at destination, Phoenix, Arizona PH, a third operator collects the data acquisition package from the carrier, and the third operator records the exact date and time of delivery. Of course, during all times after the timer ofthe data acquisition package is activated, the data acquisition package will record all selected physical conditions experienced by it.

In the preferred process, shipping and accurately logging shipping times from a source and delivery times at a destination is repeated for each leg along each of the four routes B, R, Y and G in Fig. 2 for a selected carrier and mode. In this manner, the handling quality of that selected carrier and mode between each ofthe cities, or that is, along each leg, may be statistically sampled with a data recorder. Because the exact date and time of delivery and shipment are accurately recorded, these times may be compared to the timer of the data recorder to separate, or "parse" legs of a route from one another in data files accumulated by the data acquisition package during shipping. Additionally, this method of accounting for delivery and shipping times from a location ensures that physical conditions experienced by a data acquisition package are attributed to the proper entity. For example, conditions sampled during the time the data acquisition package is in an operator's possession should not be attributed to the sampled handling quality of a carrier. Another, more specific example of a method for tracking a data acquisition package follows. First, each data acquisition package to be used in sampling a carrier's handling quality during shipping has associated with it a unique identification number. The identification number is labeled on the outside of the package, as well as appears in conventional bar code. Accounting operators are located at each source and destination along a route. The operator logs the shipment and delivery times of data acquisition packages at every source or destination. This operator is equipped with a computing processor and an associated bar code scanner. When a data acquisition package is received from a carrier after having traversed a leg from a source to the destination, the operator immediately scans the bar code. The bar code identifies the package along with the automatic local date and time. If the data acquisition package has associated with it a conventional carrier number, the operator bar code scans that carrier number also.

Preferably, the processor is interfaced with a network. The scanned data is then downloaded and e-mailed back to an information service provider. The data downloaded to the information service provider may also include the operator's location. A useful location identifier is the operator's zip code, as is preferably used in the present invention.

As used herein, an "information service provider" means an entity that collects or assimilates tracking data. Optionally, an information service provider may interface that data with physical conditions sampled by data acquisition packages and subsequently analyze the data to assess the handling quality of carriers shipping the data acquisition packages, or operators handling the data acquisition packages at given locations. Additionally, an information service provider may assess and analyze the tracking and shipping-related data and disseminate it to the public such as via an Internet web site, printed publication, or any other conventional means. After the scanned tracking data is downloaded and forwarded to the information service provider, it may be used to build a route file. This route file is used to track when and where the data acquisition package has been shipped. An exemplary route file from a data acquisition package is illustrated in Fig. 3. Of course, the route file of Fig. 3 may be in any format as desired and include any identifiers that may be helpful in tracking the shipment of a data acquisition package along routes.

As can be seen in Fig. 3, the data acquisition package has an identification number, T100. The route file also includes a route identification number, specifically R150. In this case, this route identification number represents a special code identifying a round- robin route, the particulars of which is not necessary to understanding the present invention. The route file also designates the carrier, UPS®, and mode of shipment, ground shipment.

In the table, a plurality of information columns are provided. In the first column, the "Source Zip Code" is identified to identify the source location. Of course, any other identification of location ofthe source of shipment may be used instead ofthe zip code. In the next column, the "Ship Date" is identified. In the next two columns are the "Ship Time" and the "Eastern Time." These columns indicate the time the data acquisition package is shipped from a source along a leg of a route. To standardize time calculations for routes typically traversing multiple time zones, the local ship time, designated the "Ship Time" is converted to "Eastern Time" as indicated. In the fifth column, the "Destination City and State" are identified. In the sixth column is the "Destination Zip Code" corresponding to the "Destination City, State." The seventh column identifies the "Arrival Date" of the data acquisition package at a destination. This arrival date may be recorded by the operator at the destination when the data acquisition package is delivered to the destination having traversed a leg, as described above. Columns eight and nine identify the "Arrival Time" of the data acquisition package at the destination, that is the time that the data acquisition package is delivered to the destination. Again, the "Arrival Time" in column 8 is standardized to "Eastern Time" as indicated in column 9. The tenth column identifies a "Tracking Number" typically used by the carrier to identify the package. Of course, this tracking number is optional in compiling the route file. This route file is used to compile all of the date/time, shipping/delivery, and location/zip code information. This information is used in a step described below to determine when and where, and more particularly, along which leg, the data acquisition package experienced physical conditions such as impacts, temperature variation, etc.

Data Acquisition Package Maintenance The data recorder of the data acquisition package includes a power source, such as a conventional battery, to run the data recorder. As will be appreciated, the recorder thus has a finite operational life and should be replaced or recharged at some point. Preferably, this replacement occurs when data is recovered from the data recorder ofthe data acquisition package. To facilitate maintenance and proper replacement of the power source to prevent unwanted loss of handling quality data, a preferred maintenance method is employed.

In the preferred maintenance method, the data acquisition package includes an external "expiration date," preferably affixed to the outside ofthe package with conventional means. The expiration date is a percentage of the estimated life of the power source of the data recorder used with the data acquisition package. In the preferred embodiment, the expiration date is computed by taking the date the data acquisition package is first shipped from a source to a destination and adding 80% of the life of the power source, that is, the battery life of the data recorder system. Using this computation, there is sufficient time to return the data recorder to an information service provider so data already collected is not lost. As will be appreciated, during the tracking step described above, instructions may be given to the accounting operator to "check the expiration date" on the data acquisition package. If upon examination ofthe expiration date, the operator determines that the expiration date is met or exceeded given the present date of examination, the operator is further instructed to forward the data acquisition package to the information service provider. When the data acquisition package is returned to the information service provider, that provider may replenish or replace the power source and reship the data acquisition package to continue sampling in accordance with the process of the present invention. Alternatively, if the expiration date on the package is not exceeded at the present date, the package may be forwarded to the next destination along its route as described above.

Data Processing In another step of the present invention, the data sampled during shipment of the data acquisition package is processed. As explained above, the data recorder records physical conditions experienced, or events, and indexes those events at a specific time increment on the data recorder's internal timer. After the sampling data is collected and indexed, that data is preferably integrated with the tracking data to determine when and where, that is, along what leg of a route the data acquisition package experienced an event. This integration makes it possible to properly attribute physical conditions experienced by the data acquisition package to the sampled carrier, an accounting operator at a location, or any other entity that may handle the data acquisition package during sampling. In this manner, handling entities, particularly carriers, may have their handling quality sampled accurately without contaminating that sampling with non-carrier handling. After integration, the data collected may be analyzed and put into a useful form. As preferred, this useful form incorporates the use of "Quality Measures" or "QMs," as described below. To integrate the sampling data collected by the data acquisition package and indexed by time with the tracking data relating to the data acquisition package, the sampling data from the data acquisition package and the tracking data is downloaded to a processor. The processor preferably includes an integration software program capable of assimilating and reducing data files. The preferred software for integrating data files, particularly impact recorder data files, is the DynaMax-Suite software package available from Instrumented Sensors Technologies, Inc. of Okemos, Michigan. This software can integrate tracking data and data acquisition package sampling data to separate or "parse" data files for specific legs to create independent "leg files." Accordingly, all the physical conditions or events encountered by a data acquisition package may be accurately assigned to the leg on which the event occurred during sample shipping.

An example of several parsed leg files are set forth in Figs. 4-6. These parsed leg files reflect the impact events encountered by a data acquisition package when on specific legs during shipping by a carrier. For example, Fig. 4 illustrates the impact events encountered by a data acquisition package during shipping by a carrier from Lansing, Michigan (Leg Source Zip Code: 48864) to Fremont, California (Leg Destination Zip Code: 94536). Similarly, Fig. 5 illustrates the impact events encountered by a data acquisition package during shipping by a carrier from Fremont, California (Leg Source Zip Code: 94536) to Austin, Texas (Leg Destination Zip Code: 78727). Fig. 6 depicts the impact events recorded by the data acquisition package during shipping by a carrier from Austin,

Texas (Leg Source Zip Code: 78727) back to Lansing, Michigan (Leg Destination Zip Code:

48911). As will be appreciated, this combination of legs forms a complete round-robin route.

In the preferred embodiment, in addition to integrating sampling data and tracking data to create parsed leg files, the preferred software is capable of combining parsed leg files in a summary format, along with all the other physical conditions experienced by the data acquisition package while it was activated and sampling.

An exemplary summary data file of all the impacts experienced by the data acquisition package, whose leg files are illustrated in Figs. 4-6 and described above, is set forth in the table of Fig. 7. In particular, this table includes all the impact events experienced during calibration ofthe data acquisition package, indicated as Cl Calibration, shipping by a carrier on LI Leg 1, L2 Leg 2 and L3 Leg 3, and handling by an accounting operator indicated as HI Holdover.

The Summary Data File, in table form, compiled by the integration software and depicted in Fig. 7 will now be briefly described. As will be appreciated, the format of this Summary Date File may be of any form helpful in summarizing the sampled data. With reference to Fig. 7, the "Time of Event" column indicates the exact date and time impact events, numbered in the adjacent "Event" column, were recorded by the data recorder. This "Time of Event" data may be used in conjunction with the parsed leg files Figs. 4-6 or route files of Fig. 3 to separate out impact events and attribute those impact events to the proper handling entity.

For example, from the first leg file, Fig. 4, it is known from the "Leg Source Date" the data acquisition package was transferred for shipping to the carrier at 16:25:00.00 on 05/12/2000. Thus, the LI Leg 1 shipping by the carrier began, that is, the data acquisition package was "shipped," between Event 1 :15 recorded on 5/12/00 at 11:52:40.56 and Event 1:16, recorded on 5/17/00 at 6:43:39,44. A line demarcating the shipping of the data acquisition package on LI Leg 1 is indicated. Similarly, it is known from "Leg Dest Date" of Fig. 4 that the data acquisition package was delivered on 5/17/00 at 13:35:00.00. Thus, the LI Leg 1 shipping by the carrier ended, that is, the data acquisition package was delivered between Event 1:19 recorded on 5/17/00 at 13:26:38.26 and Event 1 :20, recorded at 5/17/00 at 19:27:34.04. A line demarcating the delivery of the data acquisition package after traversing LI Leg 1 is indicated.

It will be appreciated that events 1:16, 1:17, 1:18, and 1 : 19 all occurred during shipping by the carrier on LI Leg 1 from Lansing, Michigan to Fremont, California. In a likewise manner of integrating the tracking data with the indexing data of the recorder, the other legs of shipping on the L2 Leg 2 and the L3 Leg 3, as well as the HI Holdover may be determined. As explained above, it is helpful to distinguish the periods of time a data acquisition package is being shipped by a carrier from the period of time a data acquisition package is in an accounting operator's control to properly attribute the cause of physical conditions experienced by the data acquisition package. As depicted in Fig. 7, the Holdover HI in Austin, Texas, between Leg 1 and Leg 2 illustrates an impact experienced by the data acquisition package while in the care of an accounting operator. This Holdover HI was not experienced on either Leg 1 or Leg 2, and thus does not reflect the handling quality of the carrier on those legs. In the preferred embodiment, this Holdover HI would not be included in analyzing the sampled carrier's handling quality. Of course, although not depicted in the example, multiple impacts may be experienced while the data acquisition package is in the care of an accounting operator or other entity during holdovers or calibration.

The Cl Calibration impact events are distinguished from the legs of the Summary Data File. These calibration events preferably are conducted before a data acquisition package is shipped with a carrier to ensure proper recordation of events during field sampling. Any number of calibration impact events may be tested, and the calibration may be tailored to the particular physical conditions sampled.

With further reference to Fig. 7, the columns of data will be briefly explained with reference to impact Event 1:17. Event 1 :17 was recorded on LI Leg 1 on 5/17/00 at 6:43:39.44. At this time, the recorder recorded maximum acceleration (Peak X, Peak Y and Peak Z in meters per second squared) and peak velocities (Velocity X, Velocity Y, and Velocity Z in meters per second). As will be appreciated "X, Y, and Z" represents arbitrary axes corresponding to horizontal, vertical and depth axes of a data acquisition package. Of course, the accelerations and velocities may be measured along an axis in any manner. Further, this impact data may be summarized in any manner desired.

During Event 1 : 17, it can be seen that the parcel was dropped and experienced a deceleration, indicated by the minus sign, of 300.329 meters per second squared on the Y axes. This means that the data acquisition package was dropped and experienced an impact (probably with the ground). The column indicated as "Peak R" is the root mean square ofthe Peak X, Peak

Y and Peak Z values. To calculate the Peak R value, the Peak X value is squared, the Peak Y value is squared, and the Peak Z value is squared. These three squared values are then added, and the square root is taken ofthe sum to calculate the Peak R. This Peak R may be used as a statistical indicia of the acceleration or forces impacted on a data acquisition package in an impact event. Multiple Peak Rs may also be statistically analyzed to calculate more general data concerning handling quality as desired.

Similarly, the Velocity R is calculated from the root mean square of the Velocity X, Velocity Y, and Velocity Z. This Velocity R indicates the change in velocity and thus the force of impact exerted on the data acquisition package during an impact event. With the preferred software, this Velocity R may be used to calculate statistical or mathematical information. Preferably, this Velocity R is used to calculate the equivalent drop height, indicated as "Max Drop Height," in meters in Fig. 7. This equivalent drop height represents the height from which the data acquisition package of given weight would have been dropped to experience the calculated Velocity R. As will be appreciated, the recorded data may be used to calculate other useful information such as the cumulative potential energy of an event, number of impacts, the total cumulative energy of all impact events, and the like.

Once the data acquisition package samples a carrier's shipping, and the data is processed, that data preferably is statistically analyzed to provide useful information about a carrier's handling quality. As an example, from Fig. 7, it is evident that the data acquisition package was dropped three times or experienced three "bumps" on LI Leg 1, was dropped fifteen times or experienced fifteen "bumps" on L2 Leg 2 and was dropped eleven times or experienced twelve "bumps" on L3 Leg 3. After multiple shipments over a particular route, statistical data covering the maximum, minimum, and average number of bumps likely to be encountered by a parcel may be assimilated and disseminated to consumers. Consumers may then make a carrier selection based on the likely number of times their parcel will be dropped.

In the present invention, the shipping-related data sampled and generated from physical variables encountered by the data acquisition package is preferably processed and grouped into a form of information useful to consumers of shipping services. This processed information is referred to as "Quality Measures" or "QMs."

QMs may serve several functions. First, QMs may be used to rate and differentiate carriers who transport data acquisition packages or data acquisition pallets. Second, QMs may be analyzed to affect a shipping consumer's carrier selection process. For example, based on the properties of a parcel, such as a sensitivity to physical variables, certain carriers may be more suited to ship certain goods or parcels than others. Additionally, the QMs may be analyzed and used by carriers to adjust physical parameters in the carrier's shipping environments. As will be appreciated, QMs may be further processed or statistically manipulated to give the relative quality of handling for a given shipment, a given carrier, a given handling mode, a given shipping leg or route, a given size or weight of parcel, or any other parameter as desired.

In the preferred embodiment, the collected QMs may be analyzed to generate statistical output such as minimum, maximum, averages, ranges, standard deviations and the like ofthe QMs to rate and compare carrier handling quality.

The data acquisition packages may be programmed to measure and record multiple physical variables alone or in combination. For example, the data acquisition packages may measure the number of impacts undergone by the smart pack during shipping, the vibration undergone during the shipping, as well as the magnetic fields the data acquisition package is subject to in shipping. Below are preferred QMs and methods of calculating QMs related statistical data. Of course, other QMs and methods of calculation than those described may be as the application requires.

A first QM, or "QM1" is related to the number of "bumps" a parcel encounters in shipping. A "bump" is any impact on an article such as a data acquisition package equivalent to the impact resulting from dropping that article from at least 3 inches above a structurally rigid surface, such as a concrete floor, onto that rigid surface. As used herein, an "impact" means a forceful collision between an article, such as a data acquisition package or other parcel, and a second body, such as a floor, another parcel, shipping machinery and the like. Impacts may result from drops, blows, or other mechanical maneuvers to articles during the shipping process. As an example, a bump would be recorded or "counted" when a parcel of a given weight is dropped from 3 inches or higher onto a concrete floor. Similarly, no bump would be counted when that parcel of given weight is dropped from less than 3 inches onto a concrete floor.

When multiple bumps occur in a shipping leg, the total number of these bumps may be recorded. This number of bumps is referred to as a "bump count." In the preferred embodiment, if no bumps are recorded, the bump count is zero. Likewise, if 3 bumps are recorded, there is a bump count of 3, and so on.

QM1 is preferably calculated for a selected carrier, mode, and leg of a route.

QM1 may be statistically analyzed to provide the following QM1 Bump Count Data: mean (in number of bumps); standard deviation (in number of bumps); standard deviation (in percentages); maximum (in number of bumps); minimum (in number of bumps); and range

(in number of bumps).

QM2 represents the single "biggest bump" encountered by the data acquisition package during shipping through a selected leg. This biggest bump is that impact on the data acquisition package imparting the single greatest energy on the data acquisition package. The biggest bump is preferably measured in linear units, for example, inches or centimeters, to represent the total potential energy of the parcel free-falling from a height to a surface. The total potential energy effectively represents the equivalent drop height experienced by a data acquisition package during shipping on a particular leg. A useful format for depicting QM2 after it has been collected and processed is in a histogram form, with the total potential energy of each biggest bump plotted on a vertical axis -and the number of biggest bumps displayed on a horizontal axis. As will be appreciated, the biggest bump may also be measured using the energy absorbed by the package or other practical measurements.

QM2 may be calculated as follows: for each leg file collected by the data recorder, arbitrarily called "j," data record entries are ranked based on the calculated drop heights so that the single largest drop height is "DH(j)" for that particular leg file. Thus, to calculate a mean QM2 for all leg files arbitrarily denoted as "N," the following equation is used:

QM2G) = Mean(DH(j)), j = 1 to n total leg files "N" (1) QM2 based on "j" samples may be further statistically analyzed for a selected carrier, mode and leg to provide the following QM2 statistics for those "j" number of samples: mean QM2 (in units of inches or centimeters); standard deviation QM2 (in units of inches or centimeters); standard deviation (in percentages); maximum QM2 (in units of inches or centimeters); minimum QM2 (in units of inches or centimeters); and range QM2 (in units of inches or centimeters).

QM3 indicates the cumulative measure of energy imparted on a data acquisition package for a given number (preferably 3) of the biggest bumps encountered during shipping. These bumps are preferably measured in linear units to represent the total cumulative potential energy imparted on a parcel.

QM3 may be determined as follows: for each leg file, arbitrarily denoted "j," collected by the data recorder, the data record entries in that leg file are ranked so that the drop height heights, arbitrarily denoted as DH(i,j), are in rank order, where DH(i,l) is the largest drop height, DH(i,2) is the second largest drop height, DH(i,3) is the third largest drop height, and so on. As noted above, QM3 is cumulative measure ofthe energy experienced by a package during shipment by the three largest bumps that it encounters. Thus, QM3 may be calculated with the following equation:

QM3 = Sqrt (SUM{i=l to 3 } [dH(i j)*dH(i j)]) (2)

QM3 may be further statistically analyzed for a selected carrier, mode and leg to provide the following QM3 statistics for the three biggest bumps: mean QM3 (in units of inches or centimeters); standard deviation QM3 (in units of inches or centimeters); standard deviation (in percentages); maximum QM3 (in units of inches or centimeters); minimum QM3 (in inches or centimeters); and range QM3 (in units of inches or centimeters).

QM4 represents the total cumulative energy of all bumps encountered by a data acquisition package during shipping. Each of the bumps and thus the total cumulative energy may be measured in linear units to represent the total cumulative potential energy imparted on a data acquisition package. QM4, expressed as drop height, may be calculated with the following equation for each "j" leg file data:

QM4G) = Sqrt (SUM{i=l to } [dH(i,j)*dH(ij)]) (3) QM4 may be further statistically analyzed for a selected carrier, mode, and leg to provide the following QM4 statistics for each "j" leg file: mean QM4 (in units of inches or centimeters); standard deviation QM4 (in units of inches or centimeters); standard deviation (in percentages); maximum QM4 (in units of inches or centimeters); minimum QM4 (in units of inches or centimeters); and range QM4 (in units of inches or centimeters). QM5 represents the sum of the total cumulative energy and force of impact for all bumps encountered by a data acquisition package during shipping. The total cumulative energy is the sum of all cumulative energies exerted on a data acquisition package during shipping. The force of impact is the greatest sampled energy exerted on the data acquisition package during shipping when the data acquisition package impacts a surface or another object. QM5 may be calculated with the following equation for each "j" leg file data:

QM5(j) = Sqrt (SUM{i=l to _nj} [R(i j)*dH(ij)]) (4)

QM5 may be further statistically analyzed for a selected carrier, mode, and leg to provide the following QM5 statistics: mean QM5 (in units of Package Bump Units (PBUs)); standard deviation QM5 (in units of PBUs); standard deviation (in percentages); maximum QM5 (in units of PBUs); minimum QM5 (in units of PBUs); and range QM5 (in units of PBUs).

It will be appreciated that it may be useful to measure and compare daily fluctuations in QMs throughout the week to determine "when" is the best time to ship parcels. Comparison of daily fluctuations is particularly useful for QM1-QM5. In the preferred embodiment, the statistical data computed in QM1-QM5 is separated as a function of the day of the week shipped. For example, with reference to QMl, the statistical data mean QMl, standard deviation QMl, etc., is computed separately for shipments made on Monday, Tuesday, Wednesday, Thursday, and Friday, respectively. As will be appreciated, once these statistical values are computed as a function of the day of the week of shipment, they may be graphically displayed in a user-friendly format. QM6 represents the orientation of the data acquisition package during a selected bump or other impact event. As will be appreciated, most parcels are designed to be dropped on a given "bottom side" down. QM6 indicates the frequency with which drops actually occur on the bottom only. In the preferred process, QM6 of zero "0" indicates that all drops occurred on the bottom only. QM6 is a measure of the number of bumps that occurred on the "bottom" of the package, relative to all faces. In the preferred process, QM6 for a leg may be indicated by a number between 0 and 100. If QM6 is zero, then all bumps were on the bottom. A large number indicates all bumps were other than bottom bumps. If QM6 is equal to 100, no bumps were bottom bumps. To generate QM6 bump orientations, the following calculation is utilized:

Let $OR(ij) be the "impact orientation" text descriptor for the ith leg file, event "j."

$OR(i,j) = "top," "bottom," "top-right-front," etc.

QM6 = 0

For i = 1 to N Forj = l to n_j

If $OR(i,j) < =,> "bottom" then QM6=QM6+1 (If not bottom bump)

Next i

QM6(i) = 100* QM6/ η (Normalize: 0 = all bottom, 100 = all not bottom)

Nextj (5) Where QM6(i) holds orientation data for all N leg files. QM6 may be further statistically analyzed for a selected carrier, mode and leg to provide the following QM6 statistics: mean QM6; standard deviation QM6; standard deviation (in percentages); maximum QM6; minimum QM6; and range QM6. In effect, QM6 is a way to ascertain whether a carrier or handler acknowledges "this side up" labels. Alternatively, QM6s may be recorded and indicated as top/bottom impact or left/right impact or front/back impact, or the like to indicate which side the data acquisition package was dropped on.

QM7 represents the total cumulative vibration, that is shaking, experienced by a data acquisition package during shipping. It will be appreciated that ambient vibrations, random or structured, experienced by a parcel in transit or handling maneuvers are of great significance, particularly if the goods shipped in such parcels have sensitivities or resonance responses to certain frequencies. It will further be appreciated that these vibrations depend on a plurality of interacting factors, such as the roads traveled, the suspension ofthe transport vehicle and the like. Moreover, parcels in transit are handled by sorting systems that are typically continuously vibrated. Thus, QM7 is useful in many sampling applications. In the preferred embodiment, the QM7 is processed and output as an integrated, total cumulative vibration for the data acquisition package during shipment. In an alternative embodiment, the QM7 may be processed and displayed in a histogram format with time on the horizontal axis and root mean square vibration on the vertical axis.

The cumulative vibration quality measure QM7, may be determined by summing up all of the time-sampled g-rms values from the "j" different leg shipments. To calculate QM7 for "j" leg files, the following calculation is used:

QM7(j) = Sqrt (SUM{i=l to } [SUM{k=l to 3 } [RMSj (i,k)*RMSj(i,k)]]) (6) Where RMS_j(i,k), i = 1 to nj rms level samples over "j" leg files, "k" is the measurement axis, that is, k=l, 2, 3 (x,y,z). QM7 may be further statistically analyzed for a selected carrier, mode and leg to provide the following QM7 statistics: mean QM7 (in units of total grms- seconds); standard deviation QM7 (in units of total grms-seconds); standard deviation (in percentages); maximum QM7 (in units of total grms-seconds); minimum QM7 (in units of total grms-seconds); and range QM7 (in units of total grms-seconds).

QM8 represents the temperature a parcel encounters during shipment. In a preferred process, the data acquisition package samples temperature variance over a period of time; usually during shipping. As depicted in Fig. 1, sensor 40 may sample the temperature of the environment in which data acquisition package 10 is shipped. The variance is measured with ambient room temperature, about 22° C, being a reference temperature. Of course, any reference temperature may be selected and the temperature may be measured and compared in any manner. The measured temperature is compared to this reference temperature to determine the variance. As will be appreciated, in the preferred process, the larger positive QM8s indicate the data acquisition package was shipped in an environment having temperatures above the reference temperature, that is, room temperature. Similarly, more negative QM8s indicate the data acquisition package was shipped in an environment having temperatures below the reference temperature. Finally, temperature variances near zero indicates that the data acquisition package was at or near the reference temperature throughout the shipment.

Preferably, two QM8s are calculated for temperature for each "j" leg file: a sample wise summed difference for ambient temperature and a sample wise summed difference for ambient temperature squared. In the preferred embodiment, there are N individual route files that match the temperature query, and each temperature array has n_j temperature measurements, each spaced 30 seconds apart. The following formulae are used to calculate the two QM8s, denoted as alpha and beta:

QMδ_Aip_haj = sum[i=l to n_j] ((Tj(i)-Ta)/1000) (Sample- wise summed difference from ambient temperature) (7) QM8β_etaj = sum[i=l to nj] ((Tj(i)-TaVlOOO)² (Sample-wise summed squared difference from ambient temperature) (8)

Where: T_j (i), i = 1 to n_j temperature readings in each of j=l to N leg files that match the query; Ta equals ambient room temperature, 22.2 degrees Celsius or 72 degrees Fahrenheit.

QM8 may be further statistically analyzed over all N leg files to provide the following for both the above alpha and beta QM8 calculations: mean QM8; standard deviation QM8; standard deviation QM8 (in percentages); maximum QM8; minimum QMS; and range QM8.

QM9 represents the relative humidity a data acquisition package experiences during shipping. In a preferred process, the data acquisition package samples the relative humidity variance over a period of time; usually during shipping. The variance is measured with ambient room relative humidity percentage, 50%, being a reference humidity. Of course, any reference relative humidity percentage may be selected and the humidity may be measured and compared in any manner. The relative humidity variance is measured by comparing a measured relative humidity to the reference relative humidity. As will be appreciated, larger relative humidity percentages indicate that the data acquisition package was shipped in an environment having relative humidities above the reference relative humidity, that is above ambient room relative humidity. Additionally, negative measured relative humidities indicate that the data acquisition package was shipped in an environment having relative humidities less than ambient relative humidity. Finally, a near zero QM9 indicates the data acquisition package was at or near the reference relative humidity. Preferably, two QM9s are calculated for relative humidity variance; a sample wise summed difference from ambient temperature relative humidity and a sample wise summed difference from ambient temperature squared. In the preferred embodiment, there are N individual route files that match the relative humidity percentage (%RH) query, and each %RH array has n_j %RH measurements, each spaced 30 seconds apart. The following formulae are used to calculate the two QM9s denoted as alpha and beta:

QM9_Ai_Phaj ⁼⁼ sum[i=l to nj] ((RHj(i)-RHa)/1000) (Sample-wise summed difference from ambient relative humidity) (9)

QM9_Beta_j = sum[i=l to nj] ((RHj(i)-RHa)/1000)² (Sample-wise summed squared difference from ambient relative humidity) (10)

Where: T_j (i), i = 1 to n_j %RH readings in each of j=l to N leg files that match the query; RHa equals ambient %RH which is 50%. QM9 may be further statistically analyzed over all N leg files to provide the following for both the above alpha and beta QM9 calculations: mean QM9; standard deviation QM9; standard deviation QM9 (in percentages); maximum QM9; minimum QM9; and range QM9.

QM10 represents the atmospheric pressure a data acquisition package experiences during shipping. QM10 is particularly useful because many products are pressure sensitive and subject to damage or losses due to changes in pressure from sea level atmospheric pressure. For example, ink-jet printers, biological products, live organisms, and the like are sensitive to variations in atmospheric pressure. In a preferred process, the data acquisition package samples atmospheric pressure that the data acquisition package experiences over a period of time, usually during shipping. The variance is measured with ambient sea level pressure, about 1 atmosphere, being the reference pressure. Of course, any reference pressure may be selected, and the pressure may be measured and compared in the manner. The measured pressure is compared to the reference temperature to determine the variance. As will be appreciated, positive numbers indicate that the data acquisition package was shipped in a environment having an atmospheric pressure above the reference atmospheric pressure, or atmospheric pressure at sea level. Additionally, negative QMlOs indicate that the data acquisition package was shipped in an environment having a pressure significantly below the reference atmospheric pressure. Finally, near zero QMlOs indicate that the data acquisition package was at or near the reference atmospheric pressure during shipping.

Preferably, two QMlOs are calculated for atmospheric pressure variance; a sample wise summed difference from ambient atmospheric pressure, and a sample wise summed difference from ambient atmospheric pressure squared. There are N individual route files that match the pressure query, and each pressure array has η pressure measurements, each spaced 30 seconds apart. The following formulae are used to calculate the two QMl 0s denoted as alpha and beta:

QM10_Ai_Phaj = sum[i=l to nj] ((Pj(i)-PaYlOOO) (Sample-wise summed difference from ambient atmospheric pressure) (11)

QMlOβetaj ⁼ sum[i=l to nj] ((Pj(i)-PaVlOOO) (Sample-wise summed squared difference from ambient atmospheric pressure) (12)

Where P_j (i), i = 1 to n_j pressure readings in each of j=l to N leg files that match the query; Pa equals ambient atmospheric pressure of 1.0 or atmospheres at 22.2 degrees Celsius. QM10 may be further statistically analyzed over all N leg files to provide the following for both the above Alpha and Beta QM10 calculations: mean; standard deviation; standard deviation (in percentages); maximum; minimum; and range for QMlOs.

QMl 1 measures the compressive force exerted on a package during shipping.

As will be appreciated, compressive forces during shipping usually result from stacking numerous packages on top of one another in trucks, distribution centers and/or warehouses. Compression can result in damage due to bursting of containers and subsequent loss of product or fear of mechanical damage to goods in a package.

QMl 2 relates to electromagnetic radiation encountered in the shipping environment. As will be appreciated, electromagnetic radiation may alter the physical, conductive, or electrical properties of materials. It is thus desirous to sample these fields in a carrier's shipping environment with the data acquisition package. QMl 2 may be sampled in a cumulative power density over time format, or any other format that makes comprehension ofthe electromagnetic fields encountered during shipping understandable.

QMl 3 relates to magnetic field strengths encountered in the shipping environment. As will be appreciated, magnetic fields, particularly static magnetic fields, may be harmful to electrical devices or software. It is thus desirous to sample these fields in a carrier's shipping environment with the data acquisition package. QMl 3 may be sampled in a power density over time format, or any other format that makes comprehension of the magnetic fields encountered during shipping understandable. It will be appreciated that other physical variables than those described herein may be process and used to generate QMs in addition to the QMs described above. It will further be appreciated that the above QMs may be measured, recorded and processed in any manner that facilitates interpretation of those QMs.

It will be appreciated that the software may also compute from the collected data other useful statistical information. For example, the software may statistically plot in a histogram the number of bumps or drops per leg versus the drop height for each drop. The software may also compute the total energy transfer measure representing the overall measure of handling quality. Where other physical conditions are sampled, other software also available from Instrumented Technology Corporation, may assimilate and statistically analyze the sampled data to give virtually any statistical summary required for the sampled physical conditions.

Dissemination

In another step of a preferred process, the handling quality information or sampling data is disseminated to carrier services consumers, that is, manufacturers, distributors, mailing companies, or retailers, or the carriers themselves or any party interested in carrier handling quality. The information may be disseminated by the entity sampling the handling quality or any other entity capable of distributing information. As will be appreciated, multiple schemes may be used to disseminate the information. For example, the information may be disseminated using one-time or periodic reports provided to consumers.

These reports may be consumer interactive or consumer passive. "Consumer interactive" reports provide specific information on carrier's handling quality as requested by consumers who specify the particulars ofthe parcel they desire to be shipped. Consumer passive reports are generic and non-specific; giving a broad range of handling quality information on a single carrier or multiple carriers without consideration to parcel-specific information requested from the consumer. All reports may be transferred to the consumer using any conventional means such as via mail, e-mail, fax, voice phone, or over the Internet.

In the preferred process, the handling quality information is provided by the entity collecting, assimilating, and analyzing the sampling and handling quality data via a consumer interactive Internet web site. In this format, consumers may log on the web site and request a recommendation for shipping a particular parcel based upon actual statistical data collected by the handling quality information service provider.

In the preferred embodiment of the present invention, the Internet web site will query perspective consumers and request that they enter specifications relating to physical parameters and sensitivities of the parcel the consumer wishes to ship. A typical web site query will request the following information: (1) parcel size and dimensions; (2) parcel weight; (3) shipping source, for example, City and State; (4) shipping destination, for example, City and State; and (5) shipping date, for example, day, month, year format. In addition to the above parameters, the web site will query a customer as to the particular sensitivities ofthe parcel to be shipped in a series of questions as outlined in the schematic of Fig. 8. As will be appreciated, the query fields requested on a web site may be altered to request any information. The parameters and input also may be of any units as an application requires.

In addition to the above parameters and sensitivities, the web site will also query a consumer as to the carriers and modes of shipment they desire. For example, a user may select between UPS®, UPS® Ground, UPS® 1-Day, UPS® 2-Day, Federal Express® 1- Day, Federal Express® 2-Day, Airborne® 1-Day, Airborne® 2-Day, and United States Postal Service Priority Mail®. The above carriers and modes are that of major United States carriers. It will be appreciated that the present invention is equally applicable to international and worldwide shipping carriers not listed above, as well as other United States carriers and other modes. These international and worldwide carriers may also be requested and selected as desired.

When all ofthe above query fields on the web site are entered by a consumer, the consumer will be asked in a conventional fashion to proceed with the Internet transaction, that is, particularly the shipping recommendation or advisory for a fee. The fee may either be paid online by credit card, or by any other conventional payment method to the proper information provider or web site owner.

Next, the queried information entered by the user will be processed and analyzed accounting for all the physical parameters, sensitivities, carriers, and modes input by the consumer. The web site will process the consumer's input to compare QMs for each carrier and mode, or any other physical parameter or sensitivity. The QMs will then be used to make a recommendation for the best carrier and mode that will statistically minimize the likelihood the ofthe consumer's parcel being damaged during shipping.

Preferably, the web site issues to the consumer a recommendation for a carrier and a mode, that is, 1-Day, 2-Day, etc. mailing modes. In addition, the web site may provide the consumer with summary statistical data including the following: expected number of impacts per shipment; highest expected drop height (in linear units); expected temperature range (a minimum to a maximum range in units of temperature); an average expected variation level (in cycles per second); maximum expected vibration level (in cycles per second or other time period); and the expected pressure range (a minimum to maximum range in atmospheres or pounds per square inch, or any other conventional pressure measurement unit).

A number of alternative information dissemination schemes exists in addition to the above described preferred process. Three specific web-based schemes are Safe Ship It!™, SafeStats™, and Safe Design Plus™. The Safe Ship It!™ scheme is generally directed to the average consumer, for example, a retailer, desiring to ship a parcel from a source to a destination. With Safe Ship It!™, the retailer may obtain a relative rating of handling quality based on QMs for a specific carrier and mode. In the Safe Ship It!™ process, a user, for example, the retailer, inputs a source, a destination, a parcel size, and a parcel weight into a web site query field. The user then specifies what carrier and mode the retailer desires to ship the item from the source to destination.

After submitting this data, the information service provider processes the input information and compares it to its existing database of handling quality information. The information service provider will then supply the user through the web site with a relative rating of handling quality for the carrier and mode selected by the retailer. For example, the web site may display to the user a rating from 1 to 5, with 1 being "good" relative handling quality, and 5 being "bad" relative handling quality. The user may then make a decision as to whether or not it wishes to ship its parcel with the given carrier and mode.

Additionally, the user may be provided with a confidence level of the rating accuracy. The confidence reflects the information service provider's certainty that the rating is accurate with respect to true handling quality provided by the carrier and mode selected by the user. For example, if a certain source to destination has not been heavily sampled in recent times, a lower confidence level may be provided to the user. A confidence level of 50% would thus indicate that the information service provider is only 50% confident that the given rating is accurate with respect to true handling quality. The Safe Ship It!™ scheme may be set up so that a user may request a relative handling quality rating for a given number of user input carrier and modes. For example, twenty relative handling quality ratings requested by a user may be provided for a fixed fee. As will be appreciated, any other payment method may be employed, such as a period subscription. The SafeStats™ scheme is directed to relatively sophisticated shipping consumers and carriers. The SafeStats™ scheme allows the user to access all the QMs, and the statistical data used to compile the QMs for all carriers and all modes. The user may then further process or use this information as it so desires. SafeStats™ may be provided to the consumer for a fee, payable on a periodic or transactional basis. The third scheme, Safe Design Plus™, allows a user to access all handling quality information used to compile QMs, all shipping related data, all the statistics for the data sampled, and optionally all other data collected in the field by data recorders including calibration data. The user may then process or use this information as it so desires. Safe Design Plus™ may be provided to the consumer for a fee, payable on a periodic or transactional basis as will be appreciated. This data of course may be free or at an additional charge. As will be appreciated, the above shipping recommendation provided by the Internet web site may include a plurality of other summary statistical data as the application requires.

Of course, the above web site query and recommendation may be supplied to consumers using other distribution means, such as by contacting the information provider directly using conventional means such as a telephone or fax; and the recommendation may be received from the information provider by similar conventional means.

It will also be appreciated that the recommendation is based on a database of statistical data compiled for carriers and modes that will be periodically updated and enlarged as more field data becomes available from ongoing field measurements ofthe carriers.

The above descriptions are those of the preferred embodiments of the invention. Various alterations and changes can be made without departing from the spirit and broader aspects of the invention as defined in the appended claims, which are to be interpreted in accordance with the principles of patent law including the doctrine of equivalents. Any references to claim elements in the singular, for example, using the articles "a," "an," "the," or "said," is not to be construed as limiting the element to the singular.

Claims

CLAIMS The embodiments ofthe invention in which an exclusive property or privilege is claimed are defined as follows:

1. A process for making available article-handling information comprising the steps of: shipping at least one article with a carrier; recording physical variables encountered by the article during said shipping step; processing the data to provide handling information; and making the handling quality information available to potential users of carrier services.

2. The process of claim 1 further comprising the step of repeating said shipping, recording, and processing steps for a plurality of carriers.

3. The process of claim 2 further comprising the step of comparing the handling quality information of a first carrier to that of at least one second carrier.

4. The process of claim 3 further comprising the step of selecting a carrier based on said comparing step.

5. The process of claim 1 further comprising the step of supplying consumers with the handling quality information.

6. The process of claim 5 wherein the consumers select a carrier based on the handling quality information.

7. The process of claim 1 wherein the handling quality information is used to alter the physical parameters a parcel is subjected to during shipping.

8. A process for providing information related to parcel carriers comprising the steps of: compiling a database related to the physical conditions experienced by a parcel when the parcel is shipped by a carrier; and making the database available to third parties.

9. The process of claim 8 wherein the physical conditions experienced by the parcel include at least one selected from impact, vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation, and magnetic fields.

10. The process of claim 9 further comprising the step of making the database available to consumers on a computer network.

11. The process of claim 10 wherein the consumers choose a parcel carrier based on the data.

12. The process of claim 8 further comprising the step of analyzing the database to rate a parcel carrier.

13. A process for supplying information related to conditions encountered by a parcel comprising the steps of: associating a recorder with the parcel; activating the recorder to collect data related to conditions encountered by the parcel; shipping the parcel and recorder; and making the data available on an information network.

14. The process of claim 13 wherein the article is shipped during said shipping step by a carrier.

15. The process of claim 14 further comprising the step of processing the data collected.

16. The process of claim 15 further comprising the step of evaluating the handling quality ofthe carrier during said shipping step.

17. The process of claim 13 further comprising the step of disseminating the data to consumers.

18. The process of claim 17 wherein said dissemination step is implemented via the Internet.

19. The process of claim 13 further comprising the step of summarizing the data for consumers.

20. The process of claim 13 further comprising the step of recommending a carrier on the basis of said data.

21. The process of claim 13 further comprising the step of selecting a carrier based on said data.

22. The process of claim 15 wherein said conditions are selected from impact, vibration, temperature, humidity, atmospheric pressure, compression, electromagnetic radiation and magnetic fields.

23. The process of claim 15 further comprising the step of altering the conditions based on the data.

24. The process of claim 23 wherein the conditions are altered by a carrier to improve the carrier's quality of handling parcels.

25. A process for monitoring conditions encountered by articles in transit comprising: shipping at least one article; sampling conditions encountered by at least one article during said shipping step; and making available to third parties data relating to the conditions encountered on an information network.

26. The process of claim 25 wherein the conditions sampled are chosen from impact, vibration, compression, temperature, humidity, atmospheric pressure, electromagnetic radiation and magnetic fields.

27. The process of claim 26 further comprising the step of associating a data recorder with the article to carry out said sampling step.

28. The process of claim 27 further comprising the step of recording data during said sampling step.

29. The process of claim 28 further comprising the step of making the data available to users of carrier services.

30. The process of claim 28 wherein said data is made available on the Internet.

31. The process of claim 26 further comprising the step of measuring the number of times the article is dropped during said shipping step.

32. The process of claim 31 further comprising the step of computing the equivalent height from which the article is dropped.

33. The process of claim 31 further comprising the step of determining the orientation ofthe articles when the article is impacted.

34. The process of claim 25 wherein said shipping step includes shipping the article over a route comprised of at least one leg.

35. The process of claim 34 wherein the conditions are sampled for at least one leg.

36. The process of claim 35 wherein the route is pre-selected so that the article is shipped through regions having a high level of shipping activity.

37. A process for disseminating shipping-related data comprising the steps of: providing consumers with shipping-related data sampled from at least one carrier with a data acquisition package; and charging the consumer a fee for said providing step.

38. The process of claim 37 wherein the sampled shipping-related data consists of conditions experienced by an article, selected from the group consisting of impact, vibration, temperature, humidity, atmospheric pressure, compression, electromagnetic radiation, and magnetic fields.

39. The process of claim 38 wherein the shipping-related data is collected by a data recorder associated with at least one article.

40. The process of claim 39 further comprising the step of summarizing the shipping related data.

41. The process of claim 40 wherein the consumers specify attributes of an article they desire to be shipped.

42. The process of claim 41 wherein the shipping related data is provided to consumers based on the consumer-specified attributes.

43. An apparatus for sampling handling quality of articles during shipping comprising: means for sampling and recording physical variables; a case enclosing said sampling means; and means for sensing physical variables external to said case.

44. The apparatus of claim 43 wherein said sampling means is a data recorder.

45. The apparatus of claim 44 wherein said data recorder is capable of recording physical variables chosen from impact, vibration, compression, temperature, humidity, atmospheric pressure, electromagnetic radiation, and magnetic fields.

46. The apparatus of claim 45 wherein said case is constructed of a durable plastic capable of being shipping a plurality of times.

47. The apparatus of claim 46 wherein said support material is foam.

48. The apparatus of claim 47 wherein said data recorder records impacts on said case and said data recorder accounts for the amount of impact absorbed by said padded foam.

49. The apparatus of claim 48 wherein said sensing means is in communication with said data recorder.

50. The apparatus of claim 49 wherein said external physical variables are chosen from temperature, humidity, atmospheric pressure, compression, and electromagnetic radiation.

51. The apparatus of claim 50 further comprising an element for concealing said external sampling means.

52. The apparatus of claim 51 further comprising second sensing means disposed inside said case for sampling magnetic fields.

53. The apparatus of claim 52 wherein said concealing element is a label disposed on said case, said label including a plurality of holes.

54. A data acquisition package for recording physical variables in a shipping environment comprising : a housing having an exterior; a data recorder supported within said housing; means for sensing physical variables, said sensing means connected to said data recorder, said sensing means being closer to said exterior than is said data recorder.

55. The data acquisition package of claim 54 wherein said support material is constructed of polyethylene foam.

56. The data acquisition package of claim 54 wherein said sensing means is capable of sensing physical variables chosen from temperature, humidity, electromagnetic radiation, compression, and atmospheric pressure.

57. The data acquisition package of claim 56 wherein said sensing means cannot be detected by one chosen from a person and a machine.

58. The data acquisition package of claim 57 wherein said case is constructed of a wear-resistant plastic.

59. An apparatus for sampling handling quality of articles during shipping comprising: means for supporting a plurality of articles during shipping; means for recording data representative of physical conditions encountered by the plurality of articles; and means for providing third parties with said data on an information network.

60. The apparatus of claim 59 wherein said supporting means is a pallet.

61. The apparatus of claim 60 wherein said recording means is capable of sampling physical variables chosen from impact, vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation, and magnetic fields.

62. A process for providing consumers with shipping-related data comprising the steps of: shipping at least one article; sampling conditions encountered by the at least one article during said shipping step; processing the conditions to create shipping-related data; and providing consumers with said shipping-related data.

63. The process of claim 62 wherein the shipping-related data is processed to provide consumers with a relative rating of a carrier's handling quality.

64. The process of claim 63 further comprising the step of providing a confidence level representative ofthe accuracy ofthe relative rating.

65. A process for collecting data related to conditions encountered by an article during shipping comprising the steps of: shipping the article from a source to a destination; recording the physical conditions encountered during said shipping step; repeating said shipping step and said recording step; and making available the recorded information over an information network.

66. The process of claim 65 further comprising the step of compiling a database from the statistical information.

67. The process of claim 66 wherein said statistical information is made available over the Internet.

68. The process of claim 67 wherein the physical conditions are chosen from impact, vibration, temperature, humidity, compression, atmospheric pressure, electromagnetic radiation, and atmospheric pressure.

69. The process of claim 68 wherein increased repetition of said shipping step increases the reliability of the statistical information.

70. A process of providing users of shipping services with shipping-related data comprising the steps of: requesting a user to input information related to a parcel to be shipped; and generating a display of shipping-related data for the user based on the input information.

71. The process of claim 70 wherein the input information is chosen from parcel weight, parcel size, parcel sensitivity to impact, parcel sensitivity to vibration, parcel sensitivity to compression, parcel sensitivity to temperature, parcel sensitivity to humidity, parcel sensitivity to atmospheric pressure, parcel sensitivity to electromagnetic radiation and parcel sensitivity to magnetic fields.

72. The process of claim 70 wherein said input information includes a shipping source, a shipping destination, and at least one from a selected carrier and a selected mode.

73. The process of claim 72 further comprising the step of providing a rating of at least one from a carrier and a mode based on the shipping-related data.

74. The process of claim 73 further comprising the step of providing a confidence level ofthe rating.

75. The process of providing comparative information for carriers comprising the steps of: storing shipping-related data for a plurality of carriers between the same source and the same destination; and displaying the shipping-related data in a manner that compares the shipping- related data ofthe different carriers between the same source and the same destination.

76. The process of claim 75 wherein the shipping-related data is compiled in a statistical format.

77. The process of claim 76 wherein a user may specify a given source and a given destination.

78. The process of claim 77 wherein different carriers are rated based on the shipping-related data between the given source and given destination.

79. The process of claim 77 wherein different modes are rated based on the shipping-related data between the given source and given destination.