U.S. patent application number 14/147180 was filed with the patent office on 2014-07-03 for tracking industrial vehicle operator quality.
The applicant listed for this patent is Sergio Schulte de Oliveira, Robert J. Kelley, Benjamin J. Purrenhage, Philip W. Swift. Invention is credited to Sergio Schulte de Oliveira, Robert J. Kelley, Benjamin J. Purrenhage, Philip W. Swift.
Application Number | 20140188576 14/147180 |
Document ID | / |
Family ID | 50031544 |
Filed Date | 2014-07-03 |
United States Patent
Application |
20140188576 |
Kind Code |
A1 |
de Oliveira; Sergio Schulte ;
et al. |
July 3, 2014 |
TRACKING INDUSTRIAL VEHICLE OPERATOR QUALITY
Abstract
The overall quality of a workforce is analyzed, scored and
presented using an analysis engine that performs a multi-domain
analysis on enterprise data. The analysis engine presents key
information about the performance of a workforce across a range of
hardware devices so as to inform different users in their unique
contexts and roles within a business organization as to workforce
performance. The analysis engine associates a customizable
performance profile with each workforce member. Each performance
profile is comprised of a plurality of performance measures. Each
performance measure in turn, represents a performance metric that
measures some aspect of the job duties performed by the associated
workforce member, e.g., an industrial vehicle operator. The scores
are aggregated into an overall performance profile score. To
compute the scores, data is considered across multiple domains,
e.g., by collecting and analyzing data from industrial vehicle data
systems, warehouse management systems, labor management systems,
etc.
Inventors: |
de Oliveira; Sergio Schulte;
(Troy, OH) ; Kelley; Robert J.; (New Bremen,
OH) ; Purrenhage; Benjamin J.; (Kalamazoo, MI)
; Swift; Philip W.; (Dayton, OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
de Oliveira; Sergio Schulte
Kelley; Robert J.
Purrenhage; Benjamin J.
Swift; Philip W. |
Troy
New Bremen
Kalamazoo
Dayton |
OH
OH
MI
OH |
US
US
US
US |
|
|
Family ID: |
50031544 |
Appl. No.: |
14/147180 |
Filed: |
January 3, 2014 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
61748620 |
Jan 3, 2013 |
|
|
|
Current U.S.
Class: |
705/7.39 |
Current CPC
Class: |
B60W 40/09 20130101;
G06Q 10/06395 20130101 |
Class at
Publication: |
705/7.39 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A method of aggregating measures of industrial vehicle operator
performance, comprising: coupling an analysis engine executing on a
server computer to at least two independent and distinct electronic
data sources including a first data source that collects
information about industrial vehicles, and a second data source
that collects information about a workforce, wherein the first data
source receives electronic vehicle information including industrial
vehicle usage data collected from industrial vehicles during
operation thereof, which is wirelessly transmitted from the
industrial vehicles; storing a performance profile having a
plurality of performance measures, each performance measure
characterizing a measure of performance of an industrial vehicle
operator; assigning a specific industrial vehicle operator
identification to a copy of the performance profile to define an
operator-specific performance profile instance; and evaluating a
current state of the operator-specific performance profile instance
by: processing each performance measure based upon the assigned
industrial vehicle operator identification, using information from
the first data source and the second data source, such that both
the first data source and the second data source are queried to
obtain information necessary to evaluate at least one performance
measure of the performance profile instance; computing at least one
score for the operator identification based upon the evaluation of
the performance profile instance; and outputting a representation
of the current state of the operator-specific performance profile
instance.
2. The method of claim 1, wherein: computing at least one score for
the operator identification based upon the evaluation of the
performance profile instance, comprises: computing a performance
measure score for each performance measure; and assigning an
associated performance measure threshold target to each performance
measure; outputting the current state of the operator-specific
performance profile instance comprises displaying a representation
of each computed performance measure score relative to the
corresponding assigned performance measure threshold target.
3. The method according to claim 1, further comprising: assigning a
weight to each of the plurality of performance measures of the
operator-specific performance profile instance; and computing a
total score across the current state of the operator-specific
performance profile instance based upon the weighted scores of each
of the performance measures.
4. The method according to claim 1, further comprising: assigning a
group of industrial vehicle operator identifications to a team such
that at least one unique team is defined; assigning each industrial
vehicle operator identification to a copy of the performance
profile to define an operator-specific performance profile
instance; and performing an evaluation for each team by: evaluating
a current state of each operator-specific performance profile
instance assigned to the team, by: correlating information from at
least one of the first data source and the second data source to
evaluate each performance measure, based upon the assigned
industrial vehicle operator identification, such that both the
first data source and the second data source are queried to
correlate information necessary to evaluate at least one
performance measure of the performance profile instance; and
computing at least one score for the operator identification based
upon the evaluation of the performance profile instance; and
computing at least one team score that represents an overall score
for the group of industrial vehicle operator identifications
assigned to the team based upon the evaluations of the
corresponding performance profile instances; and outputting a
representation of the at least one team score.
5. The method according to claim 4, wherein: performing an
evaluation for each team comprises performing an evaluation for a
plurality of teams; further comprising: outputting a representation
of each team score in a manner that allows direct comparison of
each computed team score.
6. The method according to claim 5, further comprising: customizing
a threshold target for at least one performance measure of each
operator-specific performance profile instance to normalize the
scores computed for each team.
7. The method according to claim 1, further comprising: analyzing
each computed score against an associated threshold target;
selecting at least one computed score based upon the analysis of
each computed score; analyzing underlying data evaluated to derive
each selected score; and generating automatically, an indication of
attribution that identifies a key indicator of the reason for the
computed score.
8. The method according to claim 5, wherein: selecting at least one
computed score based upon the analysis of each computed score,
comprises: automatically selecting at least one computed score that
falls below a corresponding threshold; and automatically selecting
at least one computed score that falls above a corresponding
threshold; and generating automatically, an indication of
attribution that identifies a key indicator of the reason for the
computed score, comprises: generating automatically, an indication
of a key indicator of a contributing factor for failing to meet the
corresponding threshold; and generating automatically, an
affirmation identifying a contributing factor for meeting or
exceeding the corresponding threshold.
9. The method according to claim 1, further comprising: customizing
at least one performance measure of the operator-specific
performance profile instance according to the assigned industrial
vehicle operator identification.
10. The method of claim 9, further comprising: displaying a list of
the plurality of performance measures in the performance profile;
and providing a visual display configured to enable setting a
weighting to each of the plurality of performance measures.
11. The method of claim 1, further comprising: providing a user
interface configured to enable a user to drill down into the
underlying data used to evaluate the plurality of performance
measures of the operator-specific performance profile instance.
12. The method of claim 1, wherein: computing at least one score
for the operator identification based upon the evaluation of the
performance profile instance, comprises: computing a performance
measure score for each performance measure; and assigning an
associated performance measure threshold target to each performance
measure; further comprising: defining a window that limits the
scope of data from the first data source and second data source
that can contribute to evaluating the current state of the
operator-specific performance profile instance; defining an overall
target based upon each performance measure threshold target and the
defined window; comparing the computed performance measure score
for each of the plurality of performance measures against its
defined performance measure threshold target; aggregating each
computed performance measure score into an overall score; and
outputting a representation of the overall score relative to the
overall target. outputting a dashboard view characterizing the
current state of the operator-specific performance profile instance
by displaying a representation of the overall score relative to the
overall target.
13. The method of claim 12, further comprising: selecting at least
one computed score; analyzing underlying data evaluated to derive
the selected score; and generating automatically, an indication of
attribution that identifies a key indicator of the reason for the
computed score; outputting in the dashboard view, each computed
performance measure score relative to the corresponding assigned
performance measure threshold target, and the automatically
generated indication of attribution.
14. The method of claim 12, further comprising: providing a user
interface configured to enable a user to drill down into the
underlying data used to compute the scores of the plurality of
performance measures of the operator-specific performance profile
instance.
15. The method of claim 1, further comprising: configuring teams of
industrial vehicle operators by industrial vehicle operator
identification; receiving hypothetical fleet upgrade data;
calculating hypothetical average threshold values based at least in
part on hypothetical fleet upgrade data and the data received from
the fleet of industrial vehicles; performing a comparison of team
performance measures and hypothetical average threshold values; and
displaying the team performance measures based at least in part on
the comparison so as to recommend whether it is better to upgrade
to a new fleet vehicle or maintain the operator scores presently
attained using current vehicles.
16. The method of claim 1, wherein: outputting a representation of
the current state of the operator-specific performance profile
instance comprises: outputting to a vehicle operator display, a
graphical representation of the current state of the
operator-specific performance profile instance as a progress meter
that identifies the progress of the operator in view of tasks to be
completed, where the tasks are defined in the performance measures
of the operator-specific performance profile instance.
17. The method of claim 16, further comprising: providing an
interface view on the vehicle operator display that allows the
operator to zoom into a specific task; and the interface view
further displays a second progress meter that graphically
represents the progress of the operator relative to the specific
task selected by the operator.
18. The method of claim 16, further comprising: providing an
interface view on the vehicle operator display that: displays
information in a first window that is generated by a component of
the an industrial vehicle to which the vehicle operator display is
mounted; and displays information in a second window that is
obtained from the second data source.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application Ser. No. 61/748,620, filed Jan. 3, 2013,
entitled TRACKING INDUSTRIAL VEHICLE OPERATOR QUALITY, the
disclosure of which is hereby incorporated by reference.
BACKGROUND
[0002] The present disclosure relates in general to methods and
computer implemented systems for collecting workforce data, for
scoring workforce quality and for presenting actionable workforce
information.
[0003] Wireless strategies are being deployed by business
operations, including distributors, retail stores, manufacturers,
etc., to improve the efficiency and accuracy of business
operations. Wireless strategies may also be deployed by such
business operations to avoid the insidious effects of constantly
increasing labor and logistics costs.
[0004] In a typical wireless implementation, workers are linked to
a management system executing on a corresponding computer
enterprise via a mobile wireless transceiver. For instance, in
order to move items about the operator's facility, workers often
utilize industrial vehicles, including for example, forklift
trucks, hand and motor driven pallet trucks, etc. The wireless
transceiver is used as an interface to the management system to
direct workers operating the industrial vehicles in their tasks,
e.g., by instructing workers where and/or how to pick, pack, put
away, move, stage, process or otherwise manipulate the items within
the operator's facility. The wireless transceiver may also be used
in conjunction with a suitable input device to scan, sense or
otherwise read tags, labels or other identifiers to track the
movement of designated items within the facility.
BRIEF SUMMARY
[0005] According to aspects herein, a method of aggregating
measures of industrial vehicle operator performance is disclosed.
The method comprises coupling an analysis engine executing on a
server computer to at least two independent and distinct electronic
data sources including a first data source that collects
information about industrial vehicles, and a second data source
that collects information about a workforce. The first data source
receives electronic vehicle information including industrial
vehicle usage data collected from industrial vehicles during
operation thereof, which is wirelessly transmitted from the
industrial vehicles. The second data source may comprise for
instance, a warehouse management system, a human resources
management system, a labor management system, an enterprise
resources planning system, etc. The method also comprises storing a
performance profile having a plurality of performance measures,
where each performance measure characterizes a measure of
performance of an industrial vehicle operator. The method still
further comprises assigning a specific industrial vehicle operator
identification to a copy of the performance profile to define an
operator-specific performance profile instance.
[0006] Moreover, the method comprises evaluating a current state of
the operator-specific performance profile instance. The current
state of the operator-specific performance profile instance is
evaluated by processing each performance measure based upon the
assigned industrial vehicle operator identification, using
information from the first data source and the second data source.
In this regard, the evaluation is carried out such that both the
first data source and the second data source are queried to obtain
information necessary to evaluate at least one performance measure
of the performance profile instance. The method yet further
comprises computing at least one score for the operator
identification based upon the evaluation of the performance profile
instance and outputting a representation of the current state of
the operator-specific performance profile instance.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0007] FIG. 1 is a block diagram of a system that can be utilized
as an infrastructure to implement one or more of the methods,
processes, or features as set out in the flow charts, block
diagrams, screen shots and other views of FIGS. 2-17, individually,
or in combinations thereof, according to aspects of the disclosure
herein;
[0008] FIG. 2 is a block diagram of an association of industrial
vehicle operators to performance profiles, which may be utilized by
the analysis engine of FIG. 1, according to aspects of the present
disclosure;
[0009] FIG. 3 is a block diagram illustrating an organization of a
performance profile into a plurality of performance measures, which
may be utilized by the analysis engine of FIG. 1, according to
aspects of the present disclosure;
[0010] FIG. 4 is a block diagram illustrating an organization of a
performance measure into one or more criteria, thresholds and
algorithms, which may be utilized by the analysis engine of FIG. 1,
according to aspects of the present disclosure herein;
[0011] FIG. 5 is a flow chart of a method of associating
performance profile instances with industrial vehicle operators,
which may be utilized by the analysis engine of FIG. 1, according
to various aspects of the present disclosure;
[0012] FIG. 6 is an exemplary summary view, which can be displayed
on one or more processing devices of FIG. 1, according to various
aspects of the present disclosure;
[0013] FIG. 7 is an exemplary manager view illustrating a workforce
evaluation grouped by teams, which can be displayed on one or more
processing devices of FIG. 1, according to aspects of the present
disclosure;
[0014] FIG. 8 is an exemplary supervisor view illustrating a
workforce evaluation for a specific team, which can be displayed on
one or more processing devices of FIG. 1, according to further
aspects of the present disclosure;
[0015] FIG. 9 is the exemplary supervisor view of FIG. 8
illustrating drill down capability, according to aspects of the
present disclosure;
[0016] FIG. 10 is the exemplary supervisor view of FIG. 8
illustrating a supervisor assigning a priority and weight to
performance measures of a performance profile, according to aspects
of the present disclosure;
[0017] FIG. 11 is an exemplary supervisor view illustrating drill
down capabilities into the performance details of a specific
industrial vehicle operator, which can be displayed on one or more
processing devices of FIG. 1, according to aspects of the present
disclosure;
[0018] FIG. 12 is an exemplary operator view illustrating a pre-use
inspection checklist, which may be displayed on an industrial
vehicle as illustrated in FIG. 1, according to aspects of the
present disclosure;
[0019] FIG. 13 is an exemplary operator view illustrating a task
list, which may be displayed on an industrial vehicle as
illustrated in FIG. 1, according to aspects of the present
disclosure;
[0020] FIG. 14 is an exemplary operator view illustrating a drill
down of the task list of FIG. 13, illustrating details about the
current task, according to aspects of the present disclosure;
[0021] FIG. 15 is an exemplary operator view illustrating vehicle
state information, which may be displayed on an industrial vehicle
as illustrated in FIG. 1, according to aspects of the present
disclosure;
[0022] FIG. 16 is an exemplary operator view illustrating a summary
of an operator's performance score, which can be displayed on one
or more processing devices of FIG. 1, according to aspects of the
present disclosure; and
[0023] FIG. 17 is an exemplary operator view illustrating a summary
of a team performance score, which can be displayed on one or more
processing devices of FIG. 1, according to aspects of the present
disclosure.
DETAILED DESCRIPTION
[0024] According to various aspects of the present disclosure, the
overall quality of a workforce is analyzed, scored and presented
using a customizable analysis engine that performs a multi-domain
analysis on enterprise data. The analysis engine presents key
information about the performance of a workforce across a range of
hardware devices so as to inform different users (e.g., executives,
managers, supervisors and the scored operators themselves), in
their unique contexts and roles within the business organization,
as to the performance of work carried out within an operation.
[0025] The analysis engine associates a performance profile with an
associated workforce member. Each performance profile is comprised
of a plurality of performance measures. Each performance measure in
turn, represents a performance metric that measures some aspect of
job duties performed by the associated workforce member. The
various scores associated with the performance measures are
aggregated into an overall performance profile score. In order to
compute the various scores, data is considered across one or more
domains, e.g., by collecting and analyzing data from what are
normally separate and independent systems, such as industrial
vehicle data collection systems, warehouse management systems,
labor management systems, etc.
[0026] System Overview:
[0027] Referring now to the drawings and in particular to FIG. 1, a
general diagram of a computer system 100 is illustrated according
to various aspects of the present disclosure. The system 100 can be
utilized for collecting workforce data, for scoring workforce
quality, for presenting workforce information, and performing other
functions and features described in the subsequent figures, as will
be described in greater detail herein.
[0028] The computer system 100 comprises a plurality of hardware
and/or software processing devices, designated generally by the
reference 102 that are linked together by one or more network(s)
designated generally by the reference 104. Typical processing
devices 102 include for example, cellular mobile telephones and
smart telephones, tablet computers, personal data assistant (PDA)
processors, palm computers, and other portable computing devices.
The processing devices 102 can also comprise netbook computers,
notebook computers, personal computers and servers. Still further,
the processing devices 102 may comprise transactional systems,
purpose-driven appliances, special purpose computing devices and/or
other devices capable of communicating over the network 104,
examples of which are described in greater detail below.
[0029] The network 104 provides communications links between the
various processing devices 102, and may be supported by networking
components 106 that interconnect the processing devices 102,
including for example, routers, hubs, firewalls, network
interfaces, wired or wireless communications links and
corresponding interconnections, cellular stations and corresponding
cellular conversion technologies, e.g., to convert between cellular
and tcp/ip, etc. Moreover, the network(s) 104 may comprise
connections using one or more intranets, extranets, local area
networks (LAN), wide area networks (WAN), wireless networks (WIFI),
the Internet, including the world wide web, cellular and/or other
arrangements for enabling communication between the processing
devices 102, in either real time or otherwise, e.g., via time
shifting, batch processing, etc.
[0030] In certain contexts and roles, the processing device 102 is
intended to be mobile, e.g., a processing device 102 provided on an
industrial vehicle 108 such as a forklift truck, reach truck, stock
picker, tow tractor, rider pallet truck, walkie, etc. Under such
circumstances, an industrial vehicle 108 utilizes a corresponding
processing device 102 to wirelessly communicate through one or more
access points 110 to a corresponding networking component 106.
Alternatively, the processing device 102 on the industrial vehicles
108 can be equipped with, or otherwise access WIFI, cellular or
other suitable technology that allows the processing device 102 on
the industrial vehicle 108 to communicate directly with a remote
device, e.g., over the networks 104.
[0031] The illustrative system 100 also includes a server 112,
e.g., a web server, file server, and/or other processing device
that supports an analysis engine 114 and corresponding data sources
(collectively identified as data sources 116). The analysis engine
114 and data sources 116 provide the resources to analyze, score
and present information including the overall quality of a
workforce, as described in greater detail herein.
[0032] In an exemplary implementation, the data sources 116 are
implemented by a collection of databases that store various types
of information related to a business operation, e.g., a warehouse,
distribution center, retail store, manufacturer, etc. In the
illustrative example, the data sources 116 include databases from
multiple, different and independent domains, including an
industrial vehicle information database 118, a warehouse management
system (WMS) 120, a human resources management system (HRMS) 122, a
labor management system (LMS) 124, etc. The above list is not
exhaustive and is intended to be illustrative only. Other data,
such as from an enterprise resources planning (ERP) database,
content management (CM) database, location tracking database, voice
recognition, etc., may also and/or alternatively be present.
Moreover, data can come from sources that are not directly and/or
locally connected to the analysis engine 114. For instance, in
certain exemplary implementations, data may be obtained from remote
servers, e.g., manufacturer databases, etc.
[0033] Traditionally, the individual data sets that comprise the
data sources 116 are utilized in isolation resulting in under-use,
missed connection and unnecessary overhead. However, as will be
discussed in greater detail herein, the analysis engine 114
harvests, mines, queries, accesses, correlates, and otherwise
analyzes data across the various data sets/databases within the
data source 116 to present workforce information in the appropriate
context for a number of given roles.
[0034] In the present disclosure, the term "real-time" is used in
various contexts to describe aspects of the disclosed system. As
used herein, the term "real-time" includes near real time, such as
to account for delays caused by the nature of wireless
infrastructures, to address transmission delays with mobile
devices, computer systems and the inherent processing time required
to query data, perform computations generate results, deliver
results, etc.
[0035] Industrial Vehicle Operator Performance Profile:
[0036] Referring to FIG. 2, an extensible organizational structure
200 is provided that defines performance profiles where each
performance profile is associated with a workforce member. For sake
of clarity of discussion herein, the workforce members are
comprised of industrial vehicle operators. However, in practice,
the concepts herein can be applied to additional roles of workforce
members.
[0037] The organizational structure 200 may be utilized, for
instance, by the analysis engine 114 and may be stored within the
data source 116 of the system 100 (FIG. 1). The organizational
structure includes a plurality of industrial vehicle operator
identifications 202. Each industrial vehicle operator
identification 202, and hence each industrial vehicle operator, is
uniquely associated with a corresponding instance of a performance
profile 204 to define an operator-specific performance profile
instance.
[0038] A select vehicle operator identification 202 may comprise
any mechanism that uniquely associates an industrial vehicle
operator with data contained in the data source 116. In this
regard, the association between a vehicle operator identification
202 and corresponding information may be a direct association or an
indirect association that is derived, computed, implied, linked or
otherwise determined.
[0039] One or more industrial vehicle operator identifications 202
can be organized in any suitable manner. For instance, industrial
vehicle operator identifications 202 can be organized into groups,
such as teams, shifts, divisions or other logical organizations. In
the illustrative example, industrial vehicle operators are grouped
into teams 206. As such, each industrial vehicle operator is also
referred to as a team member herein.
[0040] Each group, e.g., team 206, may also be uniquely associated
with a corresponding instance of a performance profile 204. In this
regard, a group performance profile 204 may be the same as, or
different from the performance profiles 204 associated with
individual operator identifications.
[0041] Although illustrated with one grouping, the above approach
can be extended both vertically and horizontally. That is, an
individual can belong to zero or more groups, e.g., a team group
and a shift group (horizontal extension of the group concept).
Moreover, groups can be organized into further groups that have a
uniquely associated performance profile 204 associated therewith
(vertical extension of the group concept). Thus, three different
"shift" groups (such as first shift group, a second shift group and
a third shift group) can be organized into a "location" group,
etc.
[0042] Referring to FIG. 3, each performance profile 204 is
comprised of a plurality of performance measures 210. Each
performance measure 210 provides a metric that relates to an area
of interest. A few performance measures comprise in an exemplary
implementation, Productivity; Error Rate; Attendance; Skill;
Impacts; Truck Care; Energy Use; Semi-Automation Usage; Teamwork,
etc. Of course, the above list is not limiting to the various
aspects of the present disclosure herein. In general, each
performance measure 210 includes a definition that defines the
measure associated with the corresponding metric, e.g., defines how
Productivity is evaluated in one example. Each performance measure
210 may also have a threshold target, e.g., a baseline, goal,
requirement or other measure of operator performance that is set
generally or uniquely for a particular individual.
[0043] Referring to FIG. 4, in the example as illustrated, a
performance measure 210 comprises a definition 212, an optional
threshold 214 (also referred to herein as a performance measure
threshold target) and an optional algorithm 216 to evaluate or
assist in the evaluation of the corresponding definition 212 (e.g.,
which can be used to customize how an analysis engine evaluates the
definition 212).
[0044] The definition 212 includes at least one criterion (or set
of related criteria) that are used to evaluate the corresponding
metric. In this regard, each criterion may be expressed as a rule
that specifies conditions, requirements, or both, to evaluate the
metric or an aspect thereof. The ability to define a performance
measure 210 by a definition 212 that includes one or more criterion
allows the underlying metric to vary in complexity from a specific
area of interest (e.g., impacts while operating an industrial
vehicle) to a general area of interest (e.g., operator care while
operating an industrial vehicle).
[0045] Thus, a performance measure 210 models a corresponding
metric. For instance, a performance measure 210, which may relate
to productivity, operator error rate, operator attendance, operator
skill, number of impacts while operating an industrial vehicle,
industrial vehicle care, efficiency of energy use when operating an
industrial vehicle, use of industrial vehicle automation and
semi-automation features, teamwork, etc., may be characterized by
an appropriate number of criteria to model the desired metric given
the nature of the underlying available data.
[0046] The threshold 214 is optional, e.g., depending upon the
metric, and can be set for one or more criterion. Alternatively, a
threshold 214 may be applied across a set of criteria. Yet
alternatively, a threshold 214 can be optionally set for the
overall performance measure 210. The threshold 214 provides a
baseline of the performance of the associated vehicle operator
against the associated metric. Accordingly, in practice, each
threshold 214 can be set, e.g., by a manager or supervisor, to
represent a target achievement goal.
[0047] For instance, if a select performance measure 210 is
"Impacts", a criterion provided in a definition 212 may be "detect
impact while industrial vehicle is moving". Another criterion may
define a window, e.g., in time, events, etc., for which the
analysis is carried out. An exemplary corresponding algorithm 216
is "count each occurrence of a detected impact in the defined
window". Here, the threshold may be defined by a set number of
impacts that is customized for the corresponding operator. For
instance, a dock operator may trigger a relatively high (expected)
count of impacts that are caused by driving over uneven surfaces of
the loading dock, ramp and corresponding loading trucks. Thus, a
dock operator may have a custom threshold of X impacts. An
experienced industrial vehicle operator performing pick operations
on a smooth floor may be expected to produce less impacts. As such,
the same performance measure (Impacts) may have a relatively low
threshold 214, e.g., Y impacts for that operator. Thus, in this
example, two vehicle operators are associated with performance
profiles 204 that each include a performance measure 210 (Impacts)
with the same definition 212 and algorithm 216, but different
thresholds 214.
[0048] The threshold 214 can also be used as a baseline to
represent an attribute of operator performance, e.g., minimum
acceptable level of performance, average level of performance, etc.
Thus, each definition 212 can have an underlying algorithm 216 that
defines the manner in which the criterion/criteria of the
definition 212 is measured and optionally, how the
criterion/criteria is evaluated against the threshold 214. The
threshold 214 can represent above/below a target, pass/fail, or
other measure. Moreover, the threshold 214 may be complex, defining
one or more ranges, scores or other measures. For instance, the
threshold 214 can be utilized to define "grades" such poor, below
average, above average, and exceptional. Each of these "scores" can
be represented by a different visual metaphor and associated rules
that define the boundaries of the range, as will be described in
greater detail herein.
[0049] Thus, for any given performance measure 210, there can be
any number of definitions, thresholds, and algorithms, which can be
grouped and organized in any number of combinations to define the
desired performance measure. Where there are multiple rules,
criteria, thresholds, algorithms, etc., associated with a given
performance measure 210, the system can consolidate the various
calculations and comparisons into a single, overall aggregated
representation to report a single value and a single measure. This
approach can be used to create a hierarchical configuration of
definitions, thresholds and algorithms that an end user can
navigate through to see summary level or detail levels of
information pertaining to the given performance measure 210.
[0050] Each algorithm 216 can represent a simple measure or a
complex formula that extracts and analyzes data across multiple,
diverse and otherwise unrelated domains, such as the various
databases in the data source 116 (FIG. 1). The algorithms 216
themselves can be logically subdivided into a plurality of
parameters, conditions, classes, etc., which can be used by the
analysis engine 114 for attribution, e.g., to explain why a
particular set of data achieved the computed mark, as will be
explained in greater detail herein.
[0051] In this regard, the analysis engine 114 of FIG. 1 may
implement a particular algorithm 216 by extracting a data value
from a particular field in one of the data sources 116 according to
a corresponding definition 212, and use the extracted value
directly as a measure. For instance, a labor management system
(LMS) may provide a measure that can be read directly, e.g., number
of sick days. Here, no computation is necessary because the
relevant information can be read directly from a database.
[0052] However, in other exemplary applications, the data from a
data source may not provide directly meaningful information in the
context of a performance measure. Rather, other information must be
aggregated, inferred, computed, correlated, derived, etc.
[0053] For instance, an algorithm 216 directed to productivity can
use a Human Resources Management System (HRMS) to determine when a
vehicle operator clocks into work at the beginning of a work shift,
and when the vehicle operator clocks out of work at the end of the
work shift. However, the HRMS has no idea of what the worker does
in the period between when clocking in and clocking out. An
industrial vehicle management system (IVMS) (industrial vehicle
data 118 in FIG. 1) on the other hand, collects and logs truck data
based upon vehicle usage. The IVMS knows how the worker used the
industrial vehicle, but cannot account for operator time spent off
the vehicle. By "bookending" the industrial vehicle data with HRMS
data however, the algorithm 216 can make computations on
productivity of a worker throughout a working shift. Notably,
neither the HRMS nor the IVMS track productivity per se. However,
the intelligence of the system can compute a measure of
productivity based upon an analysis of the available data in the
HRMS and IVMS data sources by understanding complex data
relationships and correlations.
[0054] In yet another example, a scanning device that is used to
track the movement of products in a warehouse management system
(WMS) can provide a "split feed" to an IVMS to more strongly
correlate data between the WMS and IVMS to facilitate elaborate and
complex algorithms 216. Here, the scanner may not be part of the
industrial vehicle system at all. However, operator utilization of
the scanner can trigger an algorithm to draw a subset of vehicle
data collected by the IVMS.
[0055] Different systems can also be utilized to
confirm/verify/authorize/authenticate data from another system. For
instance, scan data from a WMS can be utilized to verify that
certain IVMS data belongs to a corresponding vehicle
task/performance measure.
[0056] As yet another example, a particular performance measure 210
may not have a corresponding record in any of the data sets.
Rather, the necessary data must be derived. For instance, a WMS may
dictate a specification for a performance item. The analysis engine
114 (FIG. 1) can utilize the IVMS to fill in the specification from
the WMS. Thus, the WMS may define a task, but leave it up to an
interpretation of vehicle data collected by the IVMS to determine a
measure of when a task begins and ends.
[0057] The above examples are not meant to be limiting, but rather
illustrative of various techniques to extract information either
directly or through associations, computations, etc. using
available data.
[0058] With reference generally to FIGS. 2-4, due to the
flexibility in vertical and horizontal scaling of the individual
and group contexts, each logical organization can have its own
unique performance profile 204. In this regard, each unique
instance can differ not only in data values, but in the design and
metrics addressed by each performance profile. That is, a
performance profile 204 set up for individual operator
identifications 202 can utilize the same or different performance
measures compared to a performance profile 204 set up for a group,
e.g., a team 206.
[0059] Measures of Industrial Vehicle Operator Performance:
[0060] Referring to FIG. 5, a flow chart illustrates a method 300
of computing operator scores by aggregating measures of industrial
vehicle operator performance. The method 300 may be implemented by
the analysis engine 114 of FIG. 1, using any combination of the
features disclosed with reference to FIGS. 2-4, according to
aspects of the present disclosure herein. In this regard, the
method 300 may be implemented by computer code stored in memory,
which is executed by a processor to perform the illustrated method
steps.
[0061] The method 300 optionally performs a set up at 302. The
setup at 302 includes coupling an analysis engine executing on a
server computer to one or more data sources. Here, "coupling"
includes direct connection, indirect connection, or otherwise
having the ability to exchange information, such as using
connectionless communication, e.g., by communicating over a network
as illustrated in FIG. 1.
[0062] As noted in greater detail herein, the analysis engine 114
can access a first data source, such as an industrial vehicle
management system that collects information about industrial
vehicles (e.g., as represented by the industrial vehicle data 118
described with reference to FIG. 1). The first data source receives
electronic vehicle information including industrial vehicle usage
data collected from industrial vehicles during operation thereof,
which is wirelessly transmitted from the industrial vehicles (e.g.,
to the first data source, as described more fully herein.
[0063] Moreover, for improved flexibility, the analysis engine 114
can access at least one additional, distinct data source, e.g., a
second data source that collects information about a workforce. For
instance, the second data source can collect data with regard to
the transactions of materials within a location that are handled
and moved by operators (e.g., the WMS data 120). Other examples of
the second data source are described in the discussion of FIG. 1
and can include HRMS data 122, LMS data 124, etc.
[0064] The set up at 302 can also comprise storing a performance
profile having a plurality of performance measures, each
performance measure characterizing a measure of performance of an
industrial vehicle operator (e.g., as described with reference to
FIGS. 2-4). The set up at 302 also assigns one or more associations
to the performance profile instance. In an example implementation,
the set up at 302 also includes assigning a specific industrial
vehicle operator identification to a copy of the performance
profile to define an operator-specific performance profile
instance, e.g., as described with reference to FIGS. 2-4.
[0065] In this manner, the setup 302 can also include setting up
other necessary information, which may apply uniquely to a
particular vehicle operator identification, or more generally
across multiple instances of a performance profile, such as by
setting up, creating, modifying or otherwise enabling definitions
212, thresholds 214 and algorithms 216 (FIG. 4). For instance, the
setup at 302 can comprise customizing at least one performance
measure of the operator-specific performance profile instance
according to the assigned industrial vehicle operator
identification.
[0066] The setup can also set weights to the various performance
measures. For instance, the method 300 can implement a graphical
user interface by displaying a list of the plurality of performance
measures in the performance profile, and by providing a visual
display configured to enable setting a weighting to each of the
plurality of performance measures (see for instance, the example
described with reference to FIG. 10).
[0067] In other examples, a group, e.g., a team, shift, location,
etc., further is assigned to a performance profile instance. For
instance, a group of industrial vehicle operator identifications
can be assigned to a team such that at least one unique team is
defined.
[0068] In practice, the method 300 can be used to compute operator
scores across a plurality of operators. As such, the method 300
iterates through for one user, a group of users, etc. For sake of
example, the method 300 is illustrated in a loop that computes
operator scores for an entire team of operators.
[0069] The remainder of the flow chart 300 is described with
reference to computing operator scores by aggregating measures of
industrial vehicle operator performance at the individual operator
identification level. However, the same flow can be applied to
other layers of granularity, e.g., by replacing "operator ID" with
"group ID", etc.
[0070] The method 300 evaluates a current state of the
operator-specific performance profile instance by processing each
performance measure based upon the assigned industrial vehicle
operator identification, using information from the first data
source and the second data source, such that both the first data
source and the second data source are queried to obtain information
necessary to evaluate at least one performance measure of the
performance profile instance. The evaluation further includes
computing at least one score for the operator identification based
upon the evaluation of the performance profile instance.
[0071] In an illustrative implementation, multiple scores can be
computed by computing a performance measure score for each
performance measure and by assigning an associated performance
measure threshold target to each performance measure. In this
manner, outputting the current state of the operator-specific
performance profile instance can include displaying a
representation of each computed performance measure score relative
to the corresponding assigned performance measure threshold
target.
[0072] As such, regardless of the embodiment, the method 300 can
further compute each score by (optionally) assigning a weight to
each of the plurality of performance measures of the
operator-specific performance profile instance (e.g., in the set up
302) and by computing a total score across the current state of the
operator-specific performance profile instance based upon the
weighted scores of each of the performance measures.
[0073] As described herein, the evaluation is based upon a "current
state" to account for the dynamic nature of the underlying data.
For instance, the first data source (e.g., the industrial vehicle
database 118 of FIG. 1) is typically frequently updated, based upon
the level of industrial vehicle usage in an environment. As such,
new data that can be correlated to the industrial vehicle operator
is continually generated simply by the operator performing assigned
tasks. Thus, a score computed by an operator evaluation can vary
over time (even during the course of a shift).
[0074] The method 300 obtains the next industrial vehicle operator
identification at 304 and obtains the performance profile instance
associated with the operator identification at 306
(operator-specific performance profile instance). Each performance
measure of the obtained performance profile instance is then
processed. For instance, the method 300 obtains the next
performance measure at 308 and implements the various computations
associated with the performance measure at 310. In an illustrative
implementation, for the current performance measure, each algorithm
is executed to process each associated definition. The results can
be compared against any assigned threshold (examples of which are
described with reference to FIGS. 2-4). If the performance measure
being evaluated includes multiple definitions, thresholds, etc., a
final overall performance measure result may also be computed. The
results are saved at 312.
[0075] At 314, a decision is made as to whether all of the
performance measures of the associated performance profile have
been considered. If there are still performance measures to be
computed, the method 300 loops back to 308.
[0076] Otherwise, the method 300 continues to 316 where a score is
computed for the operator associated with the performance profile
instance. The method 300 may further output a representation of the
current state of the operator-specific performance profile
instance.
[0077] In an illustrative example, the method 300 may define a
window that limits the scope of data from the first data source and
second data source that can contribute to evaluating the current
state of the operator-specific performance profile instance. Here,
the method defines an overall target based upon each performance
measure threshold target and the defined window, and compares the
computed performance measure score for each of the plurality of
performance measures against its defined performance measure
threshold target. The method further aggregates each computed
performance measure score into an overall score, and outputs a
representation of the overall score relative to the overall target.
For instance, the method can output a dashboard view characterizing
the current state of the operator-specific performance profile
instance by displaying a representation of the overall score
relative to the overall target.
[0078] If processing is performed at a group level, e.g., a team
level, at 318, a decision is made as to whether all members of a
corresponding team have been processed. If there are more members
of a corresponding team, the method loops back to 304 to process
the next operator. Otherwise, the method proceeds to 320 where an
overall score is computed for the entire team. Likewise, if
multiple teams are defined, the method 300 iterates until all teams
(or any other group) has been processed. In this regard, the method
300 may further output a representation of each team score in a
manner that allows direct comparison of each computed team score
(e.g., as will be described with reference to FIG. 7).
[0079] The analysis engine 114 of FIG. 1 can process the method 300
of FIG. 5. Moreover, the analysis engine 114 can communicate the
results of the scores to various users in various roles and
contexts, e.g., by delivering the scores to executives, managers,
supervisors, etc., operating a processing device 102 (as
illustrated in FIG. 1). The analysis engine 114 can also provide
the score for a given operator identification to the associated
operator.
[0080] Still further, the analysis engine 114 can be used for
customizing a threshold target for at least one performance measure
of each operator-specific performance profile instance to normalize
the scores computed for each team. For instance, in certain
contexts, comparisons can be made more uniformly using normalized
data. This allows for instance, an otherwise efficient worker to
not be scored low due to the equipment or tasks assigned to that
operator. By way of example, an operator on an older, slower
industrial vehicle may have a lower target threshold of
productivity compared to an operator of a newer, faster industrial
vehicle. As yet another example, a team with all experienced
workers may be held to a higher target threshold than a team of
new, less experienced workers, etc.
[0081] Still further, the method 300 can provide attributions and
detailed analysis information along with the computed scores. This
allows a user viewing the data to understand why a score is
computed. For instance, the method 300 may further perform
attribution by analyzing each computed score against an associated
threshold target, and by selecting at least one computed score
based upon the analysis of each computed score. Here, the method
300 further comprises analyzing underlying data evaluated to derive
each selected score and generating automatically, an indication of
attribution that identifies a key indicator of the reason for the
computed score.
[0082] The attributions can be in the format of affirmations, and
optionally, indicators of performance. For instance, the method 300
can select at least one computed score based upon the analysis of
each computed score, by automatically selecting at least one
computed score that falls below a corresponding threshold and by
automatically selecting at least one computed score that falls
above a corresponding threshold. Here, an indication of attribution
that identifies a key indicator of the reason for the computed
score, is automatically generated in the form of an indication of a
key indicator of a contributing factor for failing to meet the
corresponding threshold and an affirmation identifying a
contributing factor for meeting or exceeding the corresponding
threshold.
[0083] Examples of attribution are set out in the discussion of at
least FIGS. 7-9.
[0084] The above approaches herein can be extended with various
graphical user interface displays, examples of which are described
more fully herein. For instance, the method may provide a user
interface configured to enable a user to drill down into the
underlying data used to evaluate the plurality of performance
measures of the operator-specific performance profile instance.
Other exemplary interfaces are described below.
[0085] Performance Profile Data:
[0086] According to various aspects of the present disclosure, a
technical problem relates to how to compute and update the measures
of operator quality. For instance, in typical applications, it is
not unexpected for a particular operator to be able to operate more
than one (or one type) of industrial vehicle. Moreover, vehicle
operators are required to perform various tasks, which may be
assigned by a specific system, such as a warehouse management
system, which creates hurdles for third party software to normalize
various vehicle operator performance issues into an assessment of
workforce quality.
[0087] In this regard, aspects of the present disclosure provide a
technical approach that utilizes various combinations of data
capture, data integration and analysis to provide an automated and
continuously updated scoring and information presentation
application. Moreover, data capture and data integration are
achieved across multiple domains, as noted above.
[0088] One aspect of the technical solution to the above-problem is
to automatically collect vehicle usage information as a
corresponding vehicle is being used in daily operations (e.g., see
the industrial vehicle data 118 in FIG. 1. The vehicle usage data,
along with data from at least one other source, is automatically
correlated to a specific vehicle operator to capture an indication
of the work performed by the vehicle operator.
[0089] As a few illustrative examples, with specific reference to
FIG. 1, industrial vehicles 108 may each utilize an information
linking device (such as the information linking device 38 as
described in U.S. Pat. No. 8,060,400, entitled "FLEET MANAGEMENT
SYSTEM", the disclosure of which is incorporated by reference
herein) to collect data from the corresponding industrial vehicles
108. For instance, an information linking device on an associated
industrial vehicle 108 automates the collection of information,
such as the identity of the operator logged into the corresponding
industrial vehicle 108 (i.e., the industrial vehicle operator
identification 202), as well as operational parameter values of the
corresponding industrial vehicle 108 that may vary over time, such
as speed, temperature, battery state of charge, proprietary service
codes, fork height, weight of load, detected impacts and other
measurable and/or detectable parameters. For instance, the
information linking device can access data from across the vehicle
CAN bus, e.g., event codes, states of switches, temperature
readings, encoder and controller data, etc. The information linking
device can also collect data that relates to the actions of the
vehicle operator. For instance, if a seat switch is depressed, the
operator is sitting down. The information linking device can also
collect vehicle operator data such as the manner in which the
vehicle operator operates the industrial vehicle, e.g., how and
when traction controls are engaged, how and when hydraulics are
engaged, etc.
[0090] The information linking device on the industrial vehicle 108
may also further automatically track: when an operator is logged
onto an industrial vehicle 108; when the operator is on or off the
platform of the industrial vehicle 108; when the industrial vehicle
108 is moving; the industrial vehicle status while the vehicle is
in motion; etc.
[0091] The collected industrial vehicle data is wirelessly
communicated to the server 112 and is stored as the industrial
vehicle data 118. For instance, the server 112 of FIG. 1 may
include server software such as the mobile asset application server
14 as described in U.S. Pat. No. 8,060,400 and the industrial
vehicle data database 118 of FIG. 1 may store information related
to industrial vehicles in a data resource 16 as described in U.S.
Pat. No. 8,060,400.
[0092] According to further aspects of the present disclosure, a
further technical problem relates to how to manipulate data from
different and unrelated data sources into cohesive information that
can be utilized to assess operator quality, or some other measure
that is not inherent to any of the underlying data sources.
[0093] Some examples of data integration are discussed above.
However, as a few additional examples, with reference back to FIG.
1, the information linking device on an industrial vehicle 108
provides the ability to measure when an industrial vehicle 108 is
in use, but cannot always determine when the industrial vehicle 108
is active performing work. For instance, merely driving the
industrial vehicle 108 may not constitute "work". However,
knowledge of the information generated and stored in the industrial
vehicle data 118 can be correlated with task information stored in
the WMS data 120 to identify that travel of the industrial vehicle
108 was (or was not) in furtherance of the completion of a
work-based task, thus constituting work that generates a score.
Comparatively, driving the industrial vehicle 108 to a break room
may not constitute work that contributes to the score, despite the
fact that the data in the industrial vehicle data 118 indicates use
of the industrial vehicle 108.
[0094] As yet another example, a WMS system may instruct a worker
to perform a pick operation, e.g., pick up a pallet from a
designated rack position. The WMS data 120 knows the rack location
and the SKU of the pallet to be picked up. The WMS data 120 also
identifies when the pallet was scanned as picked up, and when the
pallet was scanned as being dropped off. However, the WMS data 120
may have no idea as to the energy used by the industrial vehicle to
pick up the pallet, or whether the vehicle operator traveled the
most efficient course, etc. Moreover, the WMS data 120 has no
information that characterizes the worker actions that were
executed to implement the pick operation. The industrial vehicle
data 118 however, knows the direction and travel of the industrial
vehicle 108 used for the pick operation. The industrial vehicle
data 118 knows how high the forks were raised, how fast the
operator was driving, the weight of the pallet, whether there was
an impact with the industrial vehicle, etc. The industrial vehicle
data 118 may also know the energy usage for the pick operation. As
such, domain knowledge of both these independent systems can
provide information used to compute a performance measure, despite
the performance measure being defined in a way that cannot be
measured directly by data from any one source.
[0095] As yet another example, one aspect of a productivity measure
210 may be a measure of how many controlled motions an industrial
vehicle operator performed to complete a given task. This may
demonstrate familiarity with vehicle controls, awareness of job
responsibility, confidence, misuse, etc.
[0096] Thus, the operator score may reflect not only successful
completion of the pick operation, but also the skill at which the
operator performed the operation.
[0097] Moreover, the industrial vehicle data 118 may know the
various industrial vehicle specifications. For instance, the
industrial vehicle data 118 may characterize maximum speed, load
capacity, fork raise height, etc. Thus, the capability of each
industrial vehicle 108 may also be known. This allows a supervisor
or manager to adjust the threshold (see 214 of FIG. 4) at the
vehicle or operator level, e.g., so that an operator score is not
adversely affected by using an older/slower vehicle, etc. Other
"normalizations" can also/alternatively be built into the system so
that vehicle operators, teams or other groups can be evaluated in
an appropriate context.
[0098] In summary, the WMS database 120 stores data related to the
movement and storage of materials (transactions) within an
operation, e.g., from a warehouse management system that knows
about the movement of materials within a facility. The movement of
materials can be carried out with the industrial vehicles 108 that
provide data to the industrial vehicle data database 118 of FIG. 1.
In the exemplary system, the WMS database 120 is linked either
directly or indirectly to the industrial vehicle operator
identification 202 so as to tie the associated WMS transactions to
the industrial vehicle operator identification 202. Similarly, the
HRMS database 122 and LMS database 124 store information that is
linked to specific industrial vehicle operator identifications
202.
[0099] According to aspects of the present disclosure, the
performance measures are conceptually broken down into "What" and
"Why" considerations.
[0100] Issues such as productivity, mispicks/mistakes and
attendance/compliance can be addressed by querying systems such as
the WMS 120, HRMS 122 and LMS 124. The "Why" of these questions can
be answered with industrial vehicle information collected and
stored in the industrial vehicle information database 118.
Moreover, industrial vehicle data can be used to answer both the
"What" and the "Why" as to performance measures such as skill,
impacts, truck care, energy/battery usage, semi-automated usage,
and teamwork. In this manner, a score can be computed for an
operator by considering the aggregate of values in the what
(criteria 212) and the why (algorithm 216) can provide explanations
for each performance measure 210.
[0101] According to aspects herein, key information provided across
a range of hardware is utilized to inform different users of the
system 100 in their unique contexts. For instance, supervisors,
managers and operators have different information needs driven by
their roles. Moreover, different types of information is provided
to satisfy different contexts, etc.
[0102] Referring to FIGS. 1-5 generally, in an illustrative
implementation of the disclosure herein, measures of industrial
vehicle operator performance are aggregated by facilitating
communication between the analysis engine 114 and a data source,
preferably two or more distinct data sources (see FIG. 1) and by
performing the method of FIG. 5 in accordance with the structures
described with reference to FIGS. 2-4.
[0103] In an illustrative implementation, a first dashboard view is
generated in response to a request from a first user. The first
dashboard view is generated by evaluating the performance profile
instance 204 of a vehicle operator, group of vehicle operators,
etc., as described more fully herein. More particularly, each
performance measure 210 is processed by causing the analysis engine
114 to query, based upon the assigned industrial vehicle operator
identification 202, the first data source (e.g., data source 118)
and the second data source (e.g., at least one of 120, 122, 124,
etc.) such that the first data source and the second data source
are each queried at least once in the evaluation of the performance
profile instance 204.
[0104] The first dashboard view is further generated by computing a
first score based upon the evaluation of the performance profile
instance 204, comparing the first score to a first predefined
threshold target 214 or other threshold as described in greater
detail herein, and outputting a representation of the first score
relative to the predefined threshold target, for viewing by the
first user.
[0105] In an illustrative implementation, a score is computed for
each industrial vehicle operator identification 202 within a group
(e.g., a team 206), as described more fully herein. Moreover, the
scores of individual team members is aggregated into an overall
team score, which is compared to a team threshold. The above is
extensible to groups of teams, shifts, facilities, etc. With the
computed scores, the system generates several views of the data.
Examples of various views and various roles presenting the data at
different granularities, are described in greater detail below.
[0106] Attribution:
[0107] According to yet further aspects of the present disclosure,
a technical problem relates to how to interpret and address
operator quality scores. As noted more fully herein, the system
herein can generate different views that each ultimately aggregates
a plurality of operator performance measures into one or more
scores. However, understanding the score may not be easy to for a
given manager.
[0108] As noted in greater detail herein, the methods herein can
analyze underlying data that was considered in deriving selected
scores and can automatically generate an indication of attribution
that identifies a key indicator of the reason for the computed
score.
[0109] Moreover, dashboard views are configured to tell the user
what matters, and what to do about it. The decision as to "what
matters" and "what to do about it" can be derived from machine
intelligence, through pre-programmed "mechanisms", etc.
[0110] For instance, in an exemplary implementation, a mechanism
chooser presents a plurality of options available to the user. The
user can then custom configure (or work from defaults) so that a
particular application can be customized to select information that
matters the most to a given circumstance. In an illustrative
implementation, the user also sets the various thresholds.
[0111] With reference to FIGS. 1-5 generally, the thresholds
determine what critical information is driven up the dashboard. For
instance, as noted in greater detail above, each performance
profile 204 can have an overall threshold. However, the performance
profile 204 is made up of performance measures 210, each of which
may have one or more thresholds. By evaluating how close to being
on target, what comparisons are over the threshold, under the
threshold, etc., the degree of being over/under each threshold, the
system can make intelligent decisions on which aspects of operator
performance should be percolated to the summary level of the
particular dashboard view. For instance, as an illustrative
example, the system can select the highest rated/scored and lowest
rated/scored measures for display, and tag lines can be generated
to describe these scores in the dashboard view using short but
meaningful statements.
[0112] Still further, the system can monitor historical performance
against thresholds and make recommendations to threshold levels.
Also, by mining underlying data, the system can recommend what the
threshold values should be, e.g., by looking for averages, trends,
etc. in the underlying historical data.
[0113] Also, when displaying results in a graphical user interface,
critical information can be presented in a short-term action
section and/or long-term action section, e.g., based upon
user-derived preferences that are (or even are not) based upon the
underlying data. For instance, the system can aggregate the data
and add something else of interest to the consideration. As an
example, a summary can be based upon an aggregation of a
performance profile, with a particular emphasis on impacts. As
another illustrative example, the "something else" may not be
native to the underlying data. Rather, information such as time of
day, operator role or interest, viewing habit, identity of the
particular user, etc., can be used to prioritize initial summary
level data to be displayed. Also, filters can be set up to prevent
or specifically require certain types of data to be considered for
presentation in the short-term action section and/or long-term
action section.
[0114] For instance, in the role of compliance checking, the system
can filter out non-compliance measures, etc. The user can then
navigate through the data drilling up and down through levels of
detail to understand the presented summary level information. Thus,
in illustrative implementations, (and for any of the dashboards
herein), the short-term action section and/or long-term action
section can be dynamically variable and user-modifiable.
[0115] Still further, the visual indicia can be accompanied by text
that provides additional support, information or other
descriptions. In an example implementation, a natural language
processor is used to facilitate text information and drill down
information presented to the user. The natural language processor
(e.g., within the analysis engine 114) can also select the verbiage
that is presented in the summary section based upon the state
(current, historical or predictive) of the data.
[0116] Upgrade Recommendation:
[0117] Referring to the FIGURES generally, in an exemplary
implementation of a view of the management or executive
information, the analysis engine 114 (FIG. 1) receives hypothetical
fleet upgrade data, e.g., from a remote manufacturer database
system. The analysis engine calculates hypothetical average
threshold values based at least in part on hypothetical fleet
upgrade data and the data received from fleet of industrial
vehicles and perform a comparison of team performance measures and
hypothetical average threshold values to determine if team or
operator performance could be increased through a vehicle upgrade.
The view allows communication to supervisors or executives to
recommend fleet adjustment recommendations.
[0118] Executive:
[0119] Referring to FIG. 6, an executive that interacts with
exemplary systems described herein, is provided with a graphical
executive interface 400 (dashboard view), such as via a
conventional web browser, client, etc. Because of the unique role
an executive plays in the daily operation of a facility, the
executive interface 400 includes information that is of summary
form and at levels that relate information for financial decisions.
For instance, the graphical executive interface 400 is used to
provide high level, location averaged information, such as by
identifying the highest ranked location and the lowest ranged
location of an operation. The graphical executive interface 400 can
also be utilized to show the executive trends, real-time
performance dashboards, messages, fleet statistics and fleet
utilization of the industrial vehicles operated by the
organization, operator training, etc. Here, the underlying data is
generated as described above with reference to FIGS. 1-5. However,
as the data for each performance profile is further grouped into
teams, locations, etc., different thresholds are applied to present
information to the executive in a format for making executive level
decisions. For instance, as noted above, different threshold and
scoring algorithms can be set up for different roles within an
organization. Thus, groups can be set up to allow executives to
evaluate managers and supervisors based upon the performance of
their team members.
[0120] Manager:
[0121] Referring to FIG. 7, an exemplary manager interface 500 is
illustrated, which presents information, e.g., generated by the
analysis engine 114 of FIG. 1, according to combinations of the
approaches set out with reference to FIGS. 2-5.
[0122] A manager interacts with the system described herein via a
graphical manager interface 500 (dashboard view). The graphical
manager interface includes four main sections including a
performance score status section 502, a menu section 504, a summary
section 506 and a details section 508. The performance score status
section 502 provides the manager with a visual representation of
the overall score of the supervisors/teams under the manager's
responsibility. In the illustrative example, the performance score
status section 502 illustrates an overall score of 87%. The menu
section 504 allows the manager to utilize the details section 508
to see various scores. For instance, multiple overall team scores
are graphically illustrated along with unique thresholds set for
each team. The scores can be computed as set out in FIG. 5. The
user can also drill down into various sub-categories of data, such
as an overview, team organization, industrial vehicle fleet
information, etc.
[0123] The summary section 506 provides glanceable, actionable
information. In the illustrative example, the summary information
is historically presented in chronological order. The information
that is selected to be displayed is based upon alerting the manager
to the most relevant aspect to be addressed, e.g., the attributions
described with reference to FIG. 5.
[0124] As illustrated within the details section 508, each team is
assigned a unique team target that represents the desired target
overall threshold (e.g., target score) for the team members. As
illustrated, the threshold is represented by the "tick". For
instance, a team with less members, e.g., a third shift team, may
have less total output than a team with more members and thus may
receive a lower team target. As another example, a team with
experienced operators may be held to higher productivity output
compared to a team of newer members and thus receive a relatively
higher team target. Still further, a team with access to older
industrial vehicles may not have the same output capability as a
team with access to newer industrial vehicles and may thus be held
to a relatively lower team target.
[0125] In the illustrated example, the details section indicates
that Team 1, Team 4 and Team 5 are all exceeding their assigned
target, as indicated by the associated bar graph (e.g., visually
presented in vertical cross hatch), and extending past the target
"tick" on the graph. Team 2 and Team 3 are each below their target,
as illustrated by a bar graph (e.g., visually presented in angled
cross hatch), stopping short of the associated target "tick" mark
on the graph. Team 3 is the furthest off target, so Team 3 is
identified in the summary section 506 as the call to attention. In
this example, since both Team 2 and Team 3 are near their
respective target, the visual indicia may use a color, such as
yellow. A red visual graph can be used for teams that are
significantly off from their assigned target, whereas a green
visual graph can be used for teams that exceed their target. Thus,
the attribution capabilities of the analysis engine herein
recognized that, in the context of the current view, team 3 was the
furthest off from meeting their unique target threshold, so an
attribution was raised to this point. Also, the teams were each
able to score high on a performance measure related to battery
changes, so a positive affirmation is provided, indicating to the
manager that the teams have completed their battery changes on
time. Also, the manager is warned that planned maintenance is due
on three trucks. This allows the manager to adjust the performance
profiles to account for the fact that trucks will be out of
commission during their ordinary maintenance.
[0126] Supervisor:
[0127] Referring to FIG. 8, an exemplary supervisor interface 600
is illustrated, which presents information, e.g., generated by the
analysis engine 114 of FIG. 1, according to combinations of the
approaches set out with reference to FIGS. 2-5.
[0128] The illustrative supervisor interface 600 is implemented as
a primary dashboard view that is logically organized into a menu
section 602, a performance score section 604, a short-term action
section 606 and a long-term action section 608.
[0129] The menu section 602 provides menu options to select various
teams managed by the supervisor and to select individual
performance measures to drill down into the details of specific
performance measures to uncover the reasons for the presented
scores.
[0130] The performance score section 604 provides a dashboard-style
view that presents the team and individual contributor level
performance score (as computed using combinations of the method set
out with reference to FIG. 5) across the performance measures. The
scores are displayed using any suitable manner, such as by
alpha-numeric or graphical/visual icon. Moreover, the supervisor
can drill down to specific performance measures for specific team
members by navigating this view. For instance, the team and
individual contributor level performance scores across the selected
performance measure can be highlighted, or contrarily, the
remaining non-selected performance measures can be muted, or
reduced in contrast, focus or other format.
[0131] Accordingly, the supervisor has access to hierarchically
generated scores that branch top down from the team to the
individual, from the individual to particular performance measures,
and from particular performance measures to individual criterion
that make up each performance measure.
[0132] The short-term action section 606 provides attributions
(e.g., as described with reference to FIG. 5). Here, the
attributions are implemented as summarized calls to action, which
may be positive reinforcement of team member performance, or a call
to action may be negative of team member performance. In the
illustrated example, bolded information can be used to drill down
into the specifics of what the issue is, and what corrective action
needs to be performed. As noted with reference to FIG. 4, the
specifics are derived based upon the particular algorithm 216
associated with the performance measure 210 of interest. Also, each
algorithm 216 itself can be comprised of multiple subparts, which
require data to be extracted from one or more of the databases,
e.g., 118, 120, 122, 124, etc., within the data source 116 (FIG.
1).
[0133] Because the analysis engine has domain level knowledge
across multiple different domains, the analysis engine provides the
necessary drill downs to the underlying information behind the
presented scores, and also serves as an instructional tool to
provide the supervisor with the necessary understanding of how to
implement corrective, supportive, reactive or other responsive
measures. For instance, as illustrated, the interface prompts the
supervisor to learn how to improve team and individual scores in
the areas of impacts and truck care using the bolded "see how"
links. The information displayed in the short-term action section
606 can comprise any combination of text, graphic displays, graphs,
charts and other visual metaphors for the underlying data and
content to be conveyed.
[0134] The long-term action section 608 provides longer term trend
information for performance measures that are of particular
interest to the supervisor. The long-term action section 608 can
also be utilized by the analysis engine 114 (FIG. 1) to prompt the
supervisor through the interface, to learn how to react to the
presented scores, such as by learning how to follow specific
performance measures as illustrated in this non-limiting example
using the "see how" links. The information displayed in the
long-term action section 608 can comprise any combination of text,
graphic displays, graphs, charts and other visual metaphors for the
underlying data and content to be conveyed.
[0135] Referring to FIG. 9, as noted above, the supervisor can use
the supervisor interface 600 to drill down through pages that
provide increasingly greater details of an area of interest to
explore the reason for various presented scores. For example, the
supervisor can drill down into a productivity performance measure.
In this manner, the top level navigation menu 602 indicates that
the Supervisor has navigated to the Productivity drill down from
the Primary Dashboard View. Also, the Productivity section of the
performance scores section 604 (shown as metric 1) is highlighted.
Moreover, the short-term action section 606 and the long-term
action section 608 provide information in graphical dashboard form
and in short form text that can be viewed and comprehended quickly
and easily. For instance, phrases summarize the reasons for the
performance measure score, with indicia (bolded Details prompt in
the illustrative example) that allows further drill down into the
underlying data.
[0136] The detailed drill down spells out key productivity metrics
that are below benchmark levels, and can be used to indicate the
root cause of the issues driving the Productivity scores. For
instance, the exemplary performance measure "Productivity" is
comprised of at least three different criteria. Based upon a
user-configured threshold, the system indicates that three members
of Team 1 are not meeting an established threshold. The long-term
action section 608 provides a graph highlighting the three
operators (OP 1, OP 3 and OP4 in this example) that have fallen
below the threshold for the first metric. This is also noted in the
performance scores section 604 by the visual indicator that OP1,
OP3 and OP4 have unfilled in circles in the metric 1 column. The
supervisor has the option to dig even deeper by clicking through
one or more levels of details, e.g., by clicking on the bolded
"Details" link.
[0137] Referring to FIG. 10, according to aspects of the disclosure
herein, the supervisor can decide which of the performance measures
are most important to a particular analysis. In an exemplary
implementation, the supervisor establishes a relative rank by
sorting the utilized performance measures, e.g., from most
important to least important. Moreover, the supervisor
assigns/adjusts a relative weight to each performance measure so
that the various performance measures contribute unevenly to the
overall performance score achieved by each team member and/or by
the associated team. As illustrated, the supervisor interface 600
allows the supervisor to edit the preference/order of the
performance measures by graphically re-ordering the performance
measure list. Individual measures can be added, deleted, modified,
etc. Still further, in certain illustrative implementations,
performance measures can be turned on and off. Here, a user may opt
to still view the resulting evaluation of a performance measure
that is turned off. However, that performance measure will not
contribute to the overall weighted score for a performance profile,
team, etc. Alternatively, the performance profile that has been
turned off can be removed from consideration and viewing.
[0138] Moreover, as described with reference to FIG. 4, each
performance measure 210 may itself comprise one or more individual
criteria specified in a corresponding definition 212. The display
can further be used to sort/prioritize each criterion in relative
order of importance and/or otherwise weighted, so that the various
criteria that make up a performance measure 210 do not contribute
equally to the overall performance measure score. Also, the
supervisor can drill down into the threshold settings to adjust the
trigger threshold levels via the thresholds 214. Still further, the
supervisor may have some ability to influence the algorithm 216,
e.g., to map plain English criterion to a corresponding query (or
queries) against the one or more data sources 116.
[0139] Referring to FIG. 11, the approaches herein can be combined
in any desired manner to provide glanceable and actionable
information for supervisors. For instance, a supervisor real-time
interface 620 is illustrated, which can be used to drill down to
the real-time information of a specific operator. In this example,
the supervisor real-time interface 620 includes four main sections
including a performance score status section 622, a menu section
624, a summary section 626 and a details section 628. The
performance score status section 622 provides the supervisor with a
visual representation of the overall score of the team under the
supervisor's responsibility. In the illustrative example, the
performance score status section illustrates a score of 12%. Since
the view is a real-time dashboard view, the low percentage could be
because the team just started a shift. The performance score status
section 622 thus tracks the team throughout the work shift and
updates the team score periodically, e.g., in real-time or near
real-time.
[0140] The menu section provides the supervisor with the ability to
select different team members to drill down into the performance of
each member of the team.
[0141] The summary section 626 provides real-time visibility of the
operator, indicating the industrial vehicle 108 that the operator
logged into (using the operator's assigned industrial vehicle
operator identification 204)--RR004 in this example, the location
of the operator within a facility (if location tracking is
utilized)--Building A in this example, and the performance level
(P-Tuning) of the operator--P2 in this example. The performance
level is an indicator of the skill of the operator, and can affect
the abilities/functions available by the corresponding industrial
vehicle, an example of which is set out in U.S. Pat. No. 8,060,400,
already incorporated by reference herein.
[0142] Here, the selected operator has an overall current score of
20% indicating that the selected operator is outperforming the
overall team (which is only at 12% in this example).
[0143] The details section 608 provides the various performance
measures 210 associated with the performance profile 202 associated
with the operator, as well as the score for each performance
measure 210. The detail section 608 also provides for each
performance measure 210, a short, glanceable summary that provides
a summary of the "Why" associated with each score.
[0144] The supervisor can also be reactive and scale information
accordingly. As an illustrative example, the system has the ability
to receive data from the fleet of industrial vehicles, including
receiving impact data and position-related data from the industrial
vehicles. The industrial vehicle operators report to the supervisor
that the impacts are caused by an environmental condition, e.g., a
crack in the floor. As a result of determining a presence of an
environmental hazard based on the impact data and position-related
data, the supervisor can quarantine the bad location (the crack in
the floor) and arrange to have the bad location addressed/fixed.
The supervisor can then weight impacts that occur in this area so
as to not carry the same weight as an actual impact (e.g., by
setting up a definition that includes criteria related to warehouse
position, impact measurement, time, etc.). Moreover, the supervisor
can provide warnings to the vehicle operators to watch out for the
crack, e.g., to slow down, avoid the area, etc.
[0145] Industrial Vehicle Operator:
[0146] Referring to FIG. 12, an industrial vehicle operator
interface 700 is provided as a display on an industrial vehicle 108
(FIG. 1) to provide information to the vehicle operator. The
industrial vehicle operator interface 700 includes a plurality of
views that each allow the operator to interact with the system to
see information that assists the operator in performing assigned
tasks. In a first view 702, the industrial vehicle operator
interface 700 can implement a pre-shift inspection checklist,
examples of which are described in U.S. Pat. No. 8,060,400, which
is already incorporated by reference herein.
[0147] Referring to FIG. 13, a second view 712 provides a view of
the work expected to be completed by the operator. With reference
briefly back to FIG. 5, the method 300 may output a representation
of the current state of the operator-specific performance profile
instance by outputting to a vehicle operator display, a graphical
representation of the current state of the operator-specific
performance profile instance as a progress meter that identifies
the progress of the operator in view of tasks to be completed,
where the tasks are defined in the performance measures of the
operator-specific performance profile instance. In the illustrative
example of FIG. 13, a progress meter 714 extends across the top of
a view illustrating to the operator, the overall progress of the
tasks queued up to be completed. The view highlights the current
task and displays a running list of one or more future jobs.
[0148] Referring to FIG. 14, an operator can drill down into the
detail of the second view 712 to display a detailed pick
information view 722. The detailed pick information view 722
provides information about the currently assigned task, including
information on where the operator is to go within the facility,
what SKU item to pick up and where to deliver the SKU.
[0149] With reference briefly back to FIG. 5, in addition to
displaying the current state of the operator-specific performance
profile instance as a progress meter (described with reference to
FIG. 13), the method may further provide an interface view on the
vehicle operator display that allows the operator to zoom into a
specific task. In this regard, the interface view further displays
a second progress meter that graphically represents the progress of
the operator relative to the specific task selected by the
operator.
[0150] For instance, referring back to FIG. 14, the top of the
detailed pick information view 722 includes a running progress
meter 724 illustrating to the operator, the overall progress of the
tasks queued up to be completed, as described above. However, a
second progress meter (seen at the bottom of the FIGURE) shows the
local progress of the individual task that is being displayed.
[0151] The progress meters can be determined using industrial
vehicle location tracking, e.g., as obtained by data sources such
as those described with reference to FIG. 1, e.g., by the
industrial vehicle information database 118, the tracking of
product information in the WMS 120, knowledge of the layout and
storage locations within a facility or combinations thereof.
[0152] Referring to FIG. 15, an output device can also display a
vehicle view 732, which displays several consolidated vehicle
measures in a single display.
[0153] With reference briefly back to FIG. 5, in addition to
displaying the current state of the operator-specific performance
profile instance as a progress meter (described with reference to
FIG. 13), the method may further provide an interface view on the
vehicle operator display that displays information in a first
window that is generated by a component of the an industrial
vehicle to which the vehicle operator display is mounted and
displays information in a second window that is obtained from the
second data source.
[0154] Referring back to FIG. 15, the top of the vehicle view 732
displays an overall progress meter 734 that tracks the operator
throughout the operator shift. The progress meter 734 displays the
operator's score in real-time as described more fully herein. The
view also displays a graphical representation of the fork height
along a vertical edge of the view. For instance, the exemplary
vehicle view 732 includes a graphical representation of the fork
height 736 (raised to 400 inches in the illustrated example). The
representation of the fork height 736 is a real-time gauge that
follows the actual height of the forks of the industrial vehicle
controlled by the operator.
[0155] The vehicle view 732 also includes a camera display 738 that
provides a camera view from the perspective of the forks. This
allows an operator to view the forks as a pallet is retrieved or
put away from a high storage location. The vehicle view 732 may
further comprise abbreviated task information in a task view 740.
Data displayed in the task view 740 may include data from the WMS
system, such as instructions on a SKU and location of the SKU. The
operator may be able to drill down into the details of FIG. 13 or
FIG. 14 from the task view 740.
[0156] The vehicle view 732 also provides a widget area 742. The
widget area 742 displays one or more gauges, such as a speed gauge,
battery life gauge, etc. The vehicle view 732 still further
provides a visual representation 744 of the industrial vehicle,
tracking and displaying the actions of the forks, traction control
and/or other vehicle parameters.
[0157] Regardless of whether a supervisor or manager provides
feedback to the industrial vehicle operators, the display provided
at the industrial vehicle 108 itself can be used to provide
feedback to the operator not only as to the specific operator's
performance, an example of which is illustrated in exemplary
operator summary view 752 FIG. 16, but also the performance of the
overall team, an example of which is illustrated in the exemplary
team summary view 762 FIG. 17. In this regard, the views
illustrated in FIGS. 16 and 17 are analogous to those set out in
greater detail herein. For instance, FIG. 16 illustrates an
operator view where the operator checks their personal score, e.g.,
as illustrated, a score of 20% (indicating that 20% of the
operator's tasks are complete. The score is computed using the
method set out in FIG. 5. Moreover, attributions are provided with
visual representations as noted in greater detail herein. FIG. 17
illustrates an operator view where an operator checks the status of
the operator's team. As illustrated, a team score is 70%
(indicating that 70% of the team's tasks are complete. The score is
computed using the method set out in FIG. 5. Moreover, attributions
are provided with visual representations as noted in greater detail
herein.
[0158] Moreover, because the underlying data is being measured
based upon real-time data being provided directly by the industrial
vehicles themselves (and by a WMS, ERP, HRMS, LMS, etc.)
intelligent performance measures can be determined and dynamically
updated, in real-time.
[0159] Exemplary Implementation:
[0160] The analysis engine 114 of FIG. 1, the structures of FIGS.
2-4, the method of FIG. 5 and the views of FIGS. 6-17 may all be
implemented by computer executable code, such as a computer program
product embodied on a non-transitory storage medium. For instance,
the server 112 may comprise a processor coupled to memory. The
memory includes computer instructions such that when the computer
instructions are read out and processed by the processor, the
computer performs the methods, implements the structures, and
generates the views of FIGS. 6-17 herein.
[0161] As an example, a method of scoring industrial vehicle
operators, comprises receiving data from an industrial vehicle 108,
e.g., via an industrial vehicle linking device described herein,
storing the data, e.g., in the industrial vehicle information
database 118 and receiving log in information from a user logging
into an industrial vehicle 108. The method also comprises
determining a classification of the user based at least in part on
the log in information (e.g., the user is in the role of industrial
vehicle operator), selecting, with a processor, a display format
based at least in part on the classification and displaying the
data based at least in part on the display format, e.g., by
providing a view (e.g., FIGS. 12-17). The system may further allow
a supervisor, manager, etc., to divide up the fleet of industrial
vehicles into teams of industrial vehicle operators and compute
individual and team scores based upon associated performance
profiles as described more fully herein. The results are displayed
in a role appropriate dashboard view.
[0162] Miscellaneous:
[0163] Various aspects of the present disclosure herein provide a
computational engine that produces data that is characterized in
simple, plain-English, resulting in glanceable, actionable
information. The information provides usable insight into an
operation, such as the quality of labor data, accountability
information, inspired operator confidence, continuous improvement,
automated management, battery management truck uptime/utilization,
glanceable actionable information, etc.
[0164] For instance, given the hierarchical nature of the
evaluation, an operator is associated with a single, overall score
based upon a performance profile. However, that overall score is
broken down into sub-scores based upon the evaluation of
performance measures. A user can drill up or down in the level of
detail for a given operator. Likewise, operators can be organized
into teams. By aggregating the scores of the individual members of
a team, an overall team score can be derived. This overall team
score can be one aspect of a measure of a supervisor. Likewise,
teams can be grouped into even further summarized divisions, e.g.,
shifts, location, etc. As the overall level of granularity changes,
the metric, thresholds and text based actionable information is
adjusted to be context appropriate.
[0165] Thus, an executive can look at data representing teams of
managers. Each manager is represented by data computed from an
aggregation of the supervisors under that manager. Each manager can
look at data representing teams of supervisors under the manager.
Each supervisor can be represented by an aggregation of the teams
of operators assigned to that supervisor. Likewise, each team of
operators can be represented by data computed from the individual
team member (e.g., by comparing individual performance profile
instances against thresholds, as described more fully herein).
Thus, despite the different contexts and roles, the underlying data
may be computed the same, with different aggregations and
thresholds applied thereto.
[0166] In exemplary implementations, the analysis engine 114
observes industrial vehicle activity in real time, using available
data, which may include location tracking information, industrial
vehicle operator feedback, and industrial vehicle data to determine
how the industrial vehicles are being used. The analysis engine 114
can also interact with other non-industrial vehicle specific
databases, including warehouse management systems, labor management
systems, etc., and uniquely associate information in these
different domains with specific industrial vehicle operator
metrics. The analysis engine 114 uses this data to provide
actionable data on how to improve asset productivity.
[0167] In illustrative implementations, the analysis engine 114
answers questions surrounding an industrial vehicle's productivity.
For instance, the analysis engine 114 can track a vehicle's
performance settings and the industrial vehicle's actual responses
in real time. Thus, with the industrial vehicle data provided by
the system, users can see which industrial vehicles are performing
as expected and which ones might need maintenance to improve
overall productivity.
[0168] Moreover, the system can monitor industrial vehicle activity
and measure it against preferred settings. The system can also
measure the use cycles of the industrial vehicle 108 to determine
aspects of vehicle use, such as whether the industrial vehicle 108
is in use regularly, whether the industrial vehicle 108 is
performing as expected, whether the industrial vehicle 108 is
consistently in use, etc. As such, operators have a direct view as
to when an industrial vehicle is safe to operate and within
compliance.
[0169] The analysis engine 114 can also enable users, such as
supervisors and managers to see the real-time total cost of an
industrial vehicle 108. For instance, the system can track the
operating cost of an industrial vehicle 108 based on use cycles,
battery health, age, maintenance costs, or combinations of the
above. As such, the system can forecast the overall cost of an
industrial vehicle 108 and relay that information to the user in
real time.
[0170] The analysis engine 114 further allows users to track what
tasks their industrial vehicles are doing and compare their
performance. For instance, the system can track which tasks a
vehicle performs, and can thus determine how an industrial vehicle
is being used, if it's being used correctly to the vehicle
potential, and if the operation is using the right kind of
industrial vehicle for their tasks.
[0171] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the disclosure. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0172] The description of the present disclosure has been presented
for purposes of illustration and description, but is not intended
to be exhaustive or limited to the disclosure in the form
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the disclosure.
[0173] Having thus described the invention of the present
application in detail and by reference to embodiments thereof, it
will be apparent that modifications and variations are possible
without departing from the scope of the invention defined in the
appended claims.
* * * * *