U.S. patent application number 13/319066 was filed with the patent office on 2012-12-20 for business information and innovation management.
This patent application is currently assigned to STRATEGYN, INC.. Invention is credited to Mark Jaster, Anthony W. Ulwick.
Application Number | 20120323628 13/319066 |
Document ID | / |
Family ID | 42729077 |
Filed Date | 2012-12-20 |
United States Patent
Application |
20120323628 |
Kind Code |
A1 |
Jaster; Mark ; et
al. |
December 20, 2012 |
BUSINESS INFORMATION AND INNOVATION MANAGEMENT
Abstract
A system constructed using one or more of the techniques
described includes a job or outcome engine for storing a job or
outcome data structure in accordance with a coherent relational
model, a solution engine for storing a solution data structure in
accordance with a coherent relational model, and a capability
computation engine for matching the job or outcome to the solution
to determine the extent to which the solution meets the needs of
the job or outcome. The results can then be provided to a
commercial activity server for the purpose of acting on identified
solutions that meet needs better than current solutions.
Inventors: |
Jaster; Mark; (Rosemont,
PA) ; Ulwick; Anthony W.; (Aspen, CO) |
Assignee: |
STRATEGYN, INC.
Aspen
CO
|
Family ID: |
42729077 |
Appl. No.: |
13/319066 |
Filed: |
March 10, 2010 |
PCT Filed: |
March 10, 2010 |
PCT NO: |
PCT/US10/26858 |
371 Date: |
April 5, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61209764 |
Mar 10, 2009 |
|
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|
Current U.S.
Class: |
705/7.29 |
Current CPC
Class: |
G06F 16/367 20190101;
G06Q 30/0201 20130101; G06Q 30/02 20130101; G06Q 10/06
20130101 |
Class at
Publication: |
705/7.29 |
International
Class: |
G06Q 10/00 20120101
G06Q010/00 |
Claims
1. A system comprising: a needs-based job or outcome engine,
wherein, in operation, the needs-based job or outcome engine
creates a needs-based job or outcome record including at least one
constraint parameter associated with a needs-based job or outcome,
and stores the needs-based job or outcome record in accordance with
a coherent relational model; a solution engine, wherein, in
operation, the solution engine creates a solution record including
a capability parameter indicative of a degree of capability of a
solution in achieving the needs-based job or outcome using the
constraint parameter, and stores the solution record in accordance
with the coherent relational model; a capability computation engine
coupled to the needs-based job or outcome engine and the solution
engine, wherein, in operation, the capability computation engine
computes a difference between the capability parameter for the
solution and one or more constraints associated with the constraint
parameter of the needs-based job or outcome; a commercial activity
server coupled to the capability computation engine, wherein, in
operation, facilitates management of commercial actions taken in
association with the difference between the constraint parameter of
the needs-based job or outcome and the capability parameter of the
solution, wherein disparate marketing and product development
information solutions are stored in the coherent relational
model.
2. The system of claim 1, wherein the difference between the
capability parameter and the constraint parameter is indicative of
potential innovation to achieve a new solution to the needs-based
job or outcome more effectively bounded by relevant
constraints.
3. The system of claim 1, wherein inventory is collected in
formation, and stored in data tables that are integrated
relationally to job or outcome data.
4. The system of claim 1, wherein, in operation, the needs-based
job or outcome engine finds passages in documents that relate
semantically to the needs-based job or outcome that are
systematically selected and related to the needs-based job or
outcome record.
5. The system of claim 3, wherein, in operation, the commercial
activity server uses the systematized relationships to provide
information for work that can benefit from the information.
6. The system of claim 1, wherein, in operation, the solution
engine finds passages in documents that relate semantically to the
solution that are systematically selected and related to the
solution record.
7. The system of claim 6, wherein, in operation, the commercial
activity server uses the systematized relationships to provide
information for work that can benefit from the information.
8. The system of claim 1, further comprising a jobs and outcomes
repository coupled to the needs-based job or outcome engine, for
storing the needs-based job or outcome record.
9. The system of claim 1, further comprising a solutions repository
coupled to the solution engine, for storing the solution
record.
10. The system of claim 1, wherein the capability computation
engine creates a capability/constraint difference record, further
comprising a capability/constraint difference repository coupled to
the capability computation engine, for storing the
capability/constraint difference record.
11. The system of claim 1, wherein, in operation, the commercial
activity server identifies the needs-based job or outcome and
identifies the solution in association with the needs-based job or
outcome.
12. A method comprising: creating a job or outcome data structure
including at least one constraint parameter associated with a job
or outcome; storing the job or outcome data structure in accordance
with a coherent relational model; creating a solution data
structure including a capability parameter indicative of a
capability of a solution to meet needs of the job or outcome using
the constraint parameter; storing the solution data structure in
accordance with the coherent relational model; computing a
difference between the capability parameter for the solution and
one or more constraints associated with the constraint parameter of
the job or outcome; facilitating management of commercial actions
taken in association with the difference between the constraint
parameter of the job or outcome and the capability parameter of the
solution.
13. The method of claim 12, wherein the difference between the
capability parameter and the constraint parameter is indicative of
potential innovation to achieve a new solution to the needs-based
job or outcome more effectively bounded by relevant
constraints.
14. The method of claim 12, further comprising finding passages in
documents that relate semantically to the needs-based job or
outcome that are systematically selected and related to the
needs-based job or outcome record.
15. The method of claim 14, further comprising using the
systematized relationships in work that can benefit from the
information.
16. The method of claim 12, further comprising finding passages in
documents that relate semantically to the solution that are
systematically selected and related to the solution record.
17. The method of claim 12, further comprising linking disparate
marketing and product development information solutions into a
coherent relational model, including the capability parameter of
the solution.
18. The method of claim 12, further comprising identifying the job
or outcome and the solution in association with the job or
outcome.
19. A system comprising: a means for parameterizing a needs-based
job or outcome, including at least one constraint parameter
associated with the job or outcome, to create a needs-based job or
outcome data structure in accordance with a coherent relational
model; a means for identifying a capability associated with a
solution, wherein a capability parameter for the solution is
indicative of a degree of capability of the solution in achieving
the needs-based job or outcome; a means for parameterizing the
solution in association with the needs-based job or outcome and the
capability parameter of the solution, to create a solution data
structure in accordance with the coherent relational model; a means
for computing a difference between the capability parameter for the
solution and constraints associated with the at least one
constraint parameter of the needs-based job or outcome; a means for
providing data associated with the difference between the
capability and the constraints to a commercial activity engine,
wherein the commercial activity engine identifies the needs-based
job or outcome, identifies the solution in association with the
needs-based job or outcome, and facilitates management of
commercial actions taken in association with the difference between
the constraint parameter of the needs-based job or outcome and the
capability parameter of the solution.
20. The system of claim 16, further comprising a means for finding
passages in documents that relate semantically to the needs-based
job or outcome and systematically selecting and relating the
passages to the needs-based job or outcome record.
21. The method of claim 17, further comprising a means for using
the systematized relationships in work that can benefit from the
information.
22. The method of claim 16, further comprising a means for finding
passages in documents that relate semantically to the solution and
systematically selecting and relating the passages to the solution
record.
23. The method of claim 19, further comprising a means for using
the systematized relationships in work that can benefit from the
information.
24. A system comprising: a coherent relational model; a
collaboration and knowledge integration platform coupled to the
coherent relational model; a value added workflow engine coupled to
the collaboration and knowledge integration platform, wherein, in
operation: the value added workflow engine provides data to an
enterprise and receives enterprise-specific inputs from the
enterprise; the collaboration and knowledge integration platform
integrates the enterprise-specific inputs into the coherent
relational model; the coherent relational model provides augmented
data to the enterprise, including proposed solutions to jobs or
outcomes identified in the enterprise-specific inputs in accordance
with activities, assets, priorities, or constraints identified in
the enterprise-specific inputs; wherein the augmented data is
useful to the enterprise in generating ideas or determining how to
allocate resources to meet needs.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 61/209,764 filed Mar. 10, 2009, which is
incorporated by reference. This application is related to
co-pending U.S. patent application Ser. No. 12/563,969, filed Sep.
21, 2009, which is incorporated by reference.
BACKGROUND
[0002] Today's modern business enterprises require and make use of
sophisticated information systems to acquire vital insights into
the performance or prospective future performance of their business
relative to goals, market metrics, competitive information, and the
like. This class of information products is known in the field
today as Management Information Systems (MIS) or Business
Intelligence (BI) tools. In addition businesses seek better ways to
identify the right strategies and new ideas that can help them
grow, and information solutions supporting these objectives are
often referred to as Collaboration Technologies, and Innovation
Management Systems. Collectively these information systems fall
under the general category of Enterprise and Marketing Intelligence
Systems and represent a critical part of today's business software
and information systems marketplace.
[0003] While data management and reporting technologies have
advanced to become adept at efficiently retrieving information
stored in these systems and generating reports, the problem that
plagues all these systems is the lack of a unifying information
framework, or ontology, that provides a stable and fundamental
frame of reference that is absolute and consistently meaningful
across all domains for gleaning business insights or for
facilitating value creation. The lack of an ontology means that
evaluations on the information gathered are highly subjective and
dependent on interpretation, and that each information domain tends
to exist as an island where local rules prevail, rather than as a
part of an integrated whole. The problems this creates for business
are innumerable; consequently MIS and BI systems today, while
enabling better informed decisions, have failed to deliver on their
promise of transforming management decision making. For example,
these systems can easily track the sales results and underlying
demographics for a particular market, but utterly fail at providing
any empirically defensible prediction, save extrapolation of past
results, around whether these results are sustainable or what
impact a new idea will have. More generally, the lack of a valid
unifying and quantifiable frame of reference for business insight
and intelligence means that compromises are made in making
decisions and projections into future business impacts are largely
guesswork. This problem has always existed in business information
analysis and decision making, and it is a root cause of many
mistaken beliefs and failures in business information technology
initiatives.
SUMMARY
[0004] Presented herein are techniques for facilitating commercial
activity using a coherent relational model that includes jobs and
outcomes, and solutions to one or more of the jobs and outcomes.
Using one of the techniques, an entity can, for example, identify
new product opportunities, assess the threat from market changes,
quantify future economic value and development investment
uncertainty, and provide information to capital markets related to
asset value compared to others in its sectors.
[0005] A system constructed using one or more of the techniques can
include a collective set of data structures, uniquely designed
entities, information tools, and/or computational and machine
methods useful to store, append, interact with, retrieve, process,
and present data and information in a fashion that enables
associations to be made between the entities and the jobs and
outcomes that pertain to actual or potential markets of an
enterprise, which have been identified using a methodology that
facilitates the creation of a coherent relational model between
jobs and outcomes and actual or potential solutions to those jobs
and outcomes. Through the associations, users can attain insights
and explore innovations and new business strategies that are
virtually unworkable without the system.
[0006] A system constructed using one or more of the techniques
described includes a job or outcome engine for storing a job or
outcome data structure in accordance with a coherent relational
model, a solution engine for storing a solution data structure in
accordance with a coherent relational model, and a capability
computation engine for matching the job or outcome to the solution
to determine the extent to which the solution meets the needs of
the job or outcome. The results can then be provided to a
commercial activity server for the purpose of acting on identified
solutions that meet needs better than current solutions.
[0007] Processes/decisions that can potentially be improved using a
technique described in the detailed description can include, for
example, Primary Market Research, Use of Secondary Market Research,
Product Management and Marketing Strategy, Marketing
Communications, R&D, New Product Development, General Business
Strategy, Innovation Strategy, Innovation Collaboration, Ideation,
Business Case Analysis, IP Strategy, and Mergers & Acquisition
Strategy and Due Diligence. Business insights that can potentially
be improved using a technique described in the detailed description
can include, for example, Competitive Intelligence and Industry
Benchmarking, Unmet Market Demand, Modeling of underlying market
trends, Cause and Effect of Marketing Communications Results, New
Technology Assessments and Scouting, and New Product/Platform or
other Growth Investment Risk/Return. These improvements are
intended to be examples, not limitations, and some of them may not
be achieved in certain implementations of the techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 depicts an example of a system including a universal
strategy and innovation management system (USIMS) server.
[0009] FIG. 2 depicts an example of a USIMS system.
[0010] FIG. 3 depicts a flowchart of an example of a method for
external data integration.
[0011] FIG. 4 depicts a flowchart of an example of a competitive
assessment method.
[0012] FIG. 5 depicts a flowchart of an example of a needs delivery
of current products method.
[0013] FIG. 6 depicts a flowchart of an example of a needs delivery
enhancement strategy method.
[0014] FIG. 7 depicts a flowchart of an example of a needs based IP
strategy method.
[0015] FIG. 8 depicts a flowchart of an example of a consumption
chain needs delivery method.
[0016] FIG. 9 depicts a flowchart of an example of a method for
computationally enabling and enhancing an ODI process.
[0017] FIG. 10 depicts a flowchart of an example of a method for
creating an innovation strategy.
[0018] FIGS. 11-15 depict flowcharts of examples of market growth
strategy methods.
[0019] FIG. 16 depicts a flowchart of an example of a method for
facilitating the creation of an overall growth blueprint.
[0020] FIG. 17 depicts a flowchart of an example of a method for
facilitating the development of a consumption chain improvement
strategy.
[0021] FIG. 18 depicts a flowchart of an example of a method for
facilitating qualitative research.
[0022] FIG. 19 depicts a flowchart of an example of a method for
facilitating quantitative research.
[0023] FIG. 20 depicts a flowchart of an example of a method for
identifying opportunities.
[0024] FIG. 21 depicts a flowchart of an example of a method for
segmenting the market.
[0025] FIG. 22 depicts a flowchart of an example of a method for
defining the targeting strategy.
[0026] FIG. 23 depicts a flowchart of an example of a method for
conceptualizing breakthroughs.
[0027] FIG. 24 depicts a flowchart of an example of a method for
innovation management.
[0028] FIG. 25 depicts an example of an integrated innovation
platform.
DETAILED DESCRIPTION
[0029] FIG. 1 depicts an example of a system 100 including a
universal strategy and innovation management system (USIMS) server.
In the example of FIG. 1, the system 100 includes a network 102, a
USIMS server 104, clients 106-1 to 106-N (referred to collectively
as the clients 106), an Outcome Driven Innovation (ODI) data
repository 108, and optional components including: a mail server
110, a mail data repository 112, a document management applications
(DMA) server 114, and a document data repository 116.
[0030] In the example of FIG. 1, the network 102 can include a
networked system that includes several computer systems coupled
together, such as the Internet. The term "Internet" as used herein
refers to a network of networks that uses certain protocols, such
as the TCP/IP protocol, and possibly other protocols such as the
hypertext transfer protocol (HTTP) for hypertext markup language
(HTML) documents that make up the World Wide Web (the web). Content
is often provided by content servers, which are referred to as
being "on" the Internet. A web server, which is one type of content
server, is typically at least one computer system which operates as
a server computer system and is configured to operate with the
protocols of the World Wide Web and is coupled to the Internet. The
physical connections of the Internet and the protocols and
communication procedures of the Internet and the web are well known
to those of skill in the relevant art. For illustrative purposes,
it is assumed the network 102 broadly includes, as understood from
relevant context, anything from a minimalist coupling of the
components, or a subset of the components, illustrated in the
example of FIG. 1, to every component of the Internet and networks
coupled to the Internet.
[0031] A computer system, as used in this paper, is intended to be
construed broadly. In general, a computer system will include a
processor, memory, non-volatile storage, and an interface. A
typical computer system will usually include at least a processor,
memory, and a device (e.g., a bus) coupling the memory to the
processor.
[0032] The processor can be, for example, a general-purpose central
processing unit (CPU), such as a microprocessor, or a
special-purpose processor, such as a microcontroller.
[0033] The memory can include, by way of example but not
limitation, random access memory (RAM), such as dynamic RAM (DRAM)
and static RAM (SRAM). The memory can be local, remote, or
distributed. As used in this paper, the term "computer-readable
storage medium" is intended to include only physical media, such as
memory. As used in this paper, a computer-readable medium is
intended to include all mediums that are statutory (e.g., in the
United States, under 35 U.S.C. 101), and to specifically exclude
all mediums that are non-statutory in nature to the extent that the
exclusion is necessary for a claim that includes the
computer-readable medium to be valid. Known statutory
computer-readable mediums include hardware (e.g., registers, random
access memory (RAM), non-volatile (NV) storage, to name a few), but
may or may not be limited to hardware.
[0034] The bus can also couple the processor to the non-volatile
storage. The non-volatile storage is often a magnetic floppy or
hard disk, a magnetic-optical disk, an optical disk, a read-only
memory (ROM), such as a CD-ROM, EPROM, or EEPROM, a magnetic or
optical card, or another form of storage for large amounts of data.
Some of this data is often written, by a direct memory access
process, into memory during execution of software on the computer
system. The non-volatile storage can be local, remote, or
distributed. The non-volatile storage is optional because systems
can be created with all applicable data available in memory.
[0035] Software is typically stored in the non-volatile storage.
Indeed, for large programs, it may not even be possible to store
the entire program in the memory. Nevertheless, it should be
understood that for software to run, if necessary, it is moved to a
computer-readable location appropriate for processing, and for
illustrative purposes, that location is referred to as the memory
in this paper. Even when software is moved to the memory for
execution, the processor will typically make use of hardware
registers to store values associated with the software, and local
cache that, ideally, serves to speed up execution. As used herein,
a software program is assumed to be stored at any known or
convenient location (from non-volatile storage to hardware
registers) when the software program is referred to as "implemented
in a computer-readable storage medium." A processor is considered
to be "configured to execute a program" when at least one value
associated with the program is stored in a register readable by the
processor.
[0036] The bus can also couple the processor to the interface. The
interface can include one or more of a modem or network interface.
It will be appreciated that a modem or network interface can be
considered to be part of the computer system. The interface can
include an analog modem, isdn modem, cable modem, token ring
interface, satellite transmission interface (e.g. "direct PC"), or
other interfaces for coupling a computer system to other computer
systems. The interface can include one or more input and/or output
(I/O) devices. The I/O devices can include, by way of example but
not limitation, a keyboard, a mouse or other pointing device, disk
drives, printers, a scanner, and other I/O devices, including a
display device. The display device can include, by way of example
but not limitation, a cathode ray tube (CRT), liquid crystal
display (LCD), or some other applicable known or convenient display
device.
[0037] In one example of operation, the computer system can be
controlled by operating system software that includes a file
management system, such as a disk operating system. One example of
operating system software with associated file management system
software is the family of operating systems known as Windows.RTM.
from Microsoft Corporation of Redmond, Wash., and their associated
file management systems. Another example of operating system
software with its associated file management system software is the
Linux operating system and its associated file management system.
The file management system is typically stored in the non-volatile
storage and causes the processor to execute the various acts
required by the operating system to input and output data and to
store data in the memory, including storing files on the
non-volatile storage.
[0038] Some portions of the detailed description may be presented
in terms of algorithms and symbolic representations of operations
on data bits within a computer memory. These algorithmic
descriptions and representations are the means used by those
skilled in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of operations leading to a desired result. The operations are those
requiring physical manipulations of physical quantities. Usually,
though not necessarily, these quantities take the form of
electrical or magnetic signals capable of being stored,
transferred, combined, compared, and otherwise manipulated. The
signals take on physical form when stored in a computer readable
storage medium, such as memory or non-volatile storage, and can
therefore, in operation, be referred to as physical quantities. It
has proven convenient at times, principally for reasons of common
usage, to refer to these signals as bits, values, elements,
symbols, characters, terms, numbers, or the like.
[0039] It should be borne in mind, however, that all of these and
similar terms are to be associated with the appropriate physical
quantities and are merely convenient labels applied to these
quantities. Unless specifically stated otherwise as apparent from
the following discussion, it should be appreciated that throughout
the description, discussions utilizing terms such as "processing"
or "computing" or "calculating" or "determining" or "displaying" or
the like, refer to the action and processes of a computer system,
or similar electronic computing device, that manipulates and
transforms data represented as physical quantities within the
computer system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices.
[0040] The algorithms and displays presented herein are not
necessarily inherently related to any particular computer or other
apparatus. Various general purpose systems may be used with
programs to configure the general purpose systems in a specific
manner in accordance with the teachings herein, or it may prove
convenient to construct specialized apparatus to perform the
methods of some embodiments. The required structure for a variety
of these systems will appear from the description below. Thus, a
general purpose system can be specifically purposed by implementing
appropriate programs. In addition, the techniques are not described
with reference to any particular programming language, and various
embodiments may thus be implemented using a variety of programming
languages.
[0041] Referring once again to the example of FIG. 1, in the
example of FIG. 1, the USIMS server 104 is coupled to the network
102. The USIMS server 104 can be implemented on a known or
convenient computer system, specially purposed to provide USIMS
functionality. The USIMS server 104 is intended to illustrate one
server that has the novel functionality, but there could be
practically any number of USIMS servers coupled to the network 102
that meet this criteria. Moreover, partial functionality might be
provided by a first server and partial functionality might be
provided by a second server, where together the first and second
server provide the full functionality.
[0042] Functionality of the USIMS server 104 can be carried out by
one or more engines. As used in this paper, an engine includes a
dedicated or shared processor and, hardware, firmware, or software
modules that are executed by the processor. Depending upon
implementation-specific or other considerations, an engine can be
centralized or its functionality distributed. An engine can include
special purpose hardware, firmware, or software embodied in a
computer-readable medium for execution by the processor. Examples
of USIMS functionality are described with reference to FIGS.
4-24.
[0043] In the example of FIG. 1, the clients 106 are coupled to the
network 102. The clients 106 can be implemented on one or more
known or convenient computer systems. The clients 106 use the USIMS
functionality provided by the USIMS server 104. Depending upon the
implementation and/or preferences, the clients 106 can also carry
out USIMS functionality. Depending upon the implementation and/or
preferences, in addition to or instead of using the USIMS
functionality provided by the USIMS server 104, the clients 106 can
provide ODI or other useful data to the USIMS server 104. The
clients 106 can also be USIMS-agnostic, and take advantage of USIMS
functionality without implementing any novel functionality on their
own.
[0044] In the example of FIG. 1, the ODI data repository 108 is
coupled to the USIMS server 104. The ODI data repository 108 has
data that is useful to the USIMS server 104 for providing the USIMS
functionality. The ODI data repository 108 can store data entities,
such as those described later with reference to FIG. 24. The ODI
data repository 108, and other repositories described in this
paper, can be implemented, for example, as software embodied in a
physical computer-readable medium on a general- or specific-purpose
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. This and other
repositories described in this paper are intended, if applicable,
to include any organization of data, including tables,
comma-separated values (CSV) files, traditional databases (e.g.,
SQL), or other known or convenient organizational formats.
[0045] In an example of a system where the ODI data repository 108
is implemented as a database, a database management system (DBMS)
can be used to manage the ODI data repository 108. In such a case,
the DBMS may be thought of as part of the ODI data repository 108
or as part of the USIMS server 104, or as a separate functional
unit (not shown). A DBMS is typically implemented as an engine that
controls organization, storage, management, and retrieval of data
in a database. DBMSs frequently provide the ability to query,
backup and replicate, enforce rules, provide security, do
computation, perform change and access logging, and automate
optimization. Examples of DBMSs include Alpha Five, DataEase,
Oracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,
Firebird, Ingres, Informix, Mark Logic, Microsoft Access,
InterSystems Cache, Microsoft SQL Server, Microsoft Visual FoxPro,
MonetDB, MySQL, PostgreSQL, Progress, SQLite, Teradata, CSQL,
OpenLink Virtuoso, Daffodil DB, and OpenOffice.org Base, to name
several.
[0046] Database servers can store databases, as well as the DBMS
and related engines. Any of the repositories described in this
paper could presumably be implemented as database servers. It
should be noted that there are two logical views of data in a
database, the logical (external) view and the physical (internal)
view. In this paper, the logical view is generally assumed to be
data found in a report, while the physical view is the data stored
in a physical storage medium and available to, typically, a
specifically programmed processor. With most DBMS implementations,
there is one physical view and a huge number of logical views for
the same data.
[0047] A DBMS typically includes a modeling language, data
structure, database query language, and transaction mechanism. The
modeling language is used to define the schema of each database in
the DBMS, according to the database model, which may include a
hierarchical model, network model, relational model, object model,
or some other applicable known or convenient organization. An
optimal structure may vary depending upon application requirements
(e.g., speed, reliability, maintainability, scalability, and cost).
One of the more common models in use today is the ad hoc model
embedded in SQL. Data structures can include fields, records,
files, objects, and any other applicable known or convenient
structures for storing data. A database query language can enable
users to query databases, and can include report writers and
security mechanisms to prevent unauthorized access. A database
transaction mechanism ideally ensures data integrity, even during
concurrent user accesses, with fault tolerance. DBMSs can also
include a metadata repository; metadata is data that describes
other data.
[0048] In the example of FIG. 1, the optional mail server 110 is
coupled to the network 102, to the USIMS server 104, and to the
mail data repository 112. The mail data repository 112 stores data
in a format that is useful to the mail server 110. In this example,
the mail server 110 is considered an "external application" in the
sense that the format of data in the mail data repository 112 is
not necessarily in the same format as in the ODI data repository
108. To the extent mail data is used by the USIMS server 104 in
this example, it is assumed that the mail data has been translated
into a format that is useful to the USIMS server 104, which may or
may not be necessary depending upon the implementation. For
example, in another implementation, the mail server 110 could be
implemented as an integrated application in the sense that the
format of data in the mail data repository 112 is in the same
format as in the ODI data repository 108. In this implementation,
it is possible that no translation of the data stored in the mail
data repository 112 into another format would be necessary.
[0049] Particularly where the USIMS server 104 functions as a
business process management (BPM) server, it may be desirable to
enable the USIMS server 104 to have access to mail data. BPM, as
used in this paper, is a technique intended to align organizations
with the needs of clients by continuously improving processes. BPM
is an advantageous implementation because it tends to promote
business efficacy while striving for innovation and integration
with technology.
[0050] It should be noted that business process modeling and
business process management are not the same, and, confusingly,
share the same acronym. In this paper, business process management
is given the acronym BPM, but business process modeling is not
given an acronym. Business process modeling is often, though not
necessarily, used in BPM. Business process modeling is a way of
representing processes in systems or software. The models are
typically used as tools to improve process efficiency and quality,
and can use Business Process Modeling Notation (BPMN) or some other
notation to model business processes.
[0051] A business process, as used in this paper, is a collection
of related, structured activities or tasks that produce a service
or product for a particular client. Business processes can be
categorized as management processes, operational processes, and
supporting processes. Management processes govern the operation of
a system, and include by way of example but not limitation
corporate governance, strategic management, etc. Operational
processes comprise the core business processes for a company, and
include by way of example but not limitation, purchasing,
manufacturing, marketing, and sales. Supporting processes support
the core processes and include, by way of example but not
limitation, accounting, recruiting, technical support, etc.
[0052] A business process can include multiple sub-processes, which
have their own attributes, but also contribute to achieving the
goal of the super-process. The analysis of business processes
typically includes the mapping of processes and sub-processes down
to activity level. A business process is sometimes intended to mean
integrating application software tasks, but this is narrower than
the broader meaning that is frequently ascribed to the term in the
relevant art, and as intended in this paper. Although the initial
focus of BPM may have been on the automation of mechanistic
business processes, it has since been extended to integrate
human-driven processes in which human interaction takes place in
series or parallel with the mechanistic processes.
[0053] Referring once again to the example of FIG. 1, the optional
DMA server 114 is coupled to the network 102, to the USIMS server
104, and to the document data repository 116. The document data
repository 116 stores data in a format that is useful to the DMA
server 114. In this example, the DMA server 114 is considered an
"external application" in the sense that the format of data in the
document data repository 116 is not necessarily in the same format
as in the ODI data repository 108. To the extent document data is
used by the USIMS server 104 in this example, it is assumed that
the document data has been translated into a format that is useful
to the USIMS server 104, which may or may not be necessary
depending upon the implementation. For example, in another
implementation, the DMA server 114 could be implemented as an
integrated application in the sense that the format of data in the
document data repository 116 is in the same format as in the ODI
data repository 108. In this implementation, it is possible that no
translation of the data in the document data repository 116 into
another format would be necessary.
[0054] The USIMS server 104 can, of course, be coupled to other
external applications (not shown) either locally or through the
network 102 in a known or convenient manner. The USIMS server 104
can also be coupled to other external data repositories.
[0055] The USIMS system 100 is but one example of systems with
which techniques described in this paper can be used. For example,
the ODI database 108 could be replaced with some other database
that enables storage of a coherent relational model that includes
jobs and outcomes, solutions, and other data.
[0056] FIG. 2 depicts an example of a USIMS system 200. In the
example of FIG. 2, the system 200 includes a job or outcome engine
202, a jobs and outcomes repository 204, a solution engine 206, a
solutions repository 208, a capability computation engine 210, a
capability/constraint difference repository 211, and a commercial
activity server 212. The system 200 can also include clients 214-1
to 214-N (collectively, clients 214) that are coupled to the
commercial activity server.
[0057] In the example of FIG. 2, the job or outcome engine 202 can
make use of various engines to obtain data that can be used to
parameterize jobs and outcomes. For example, a search engine that
includes one or more communications protocols could be used to find
data. An example of one such protocol is the financial information
exchange (FIX) protocol for electronic communication of
trade-related messages. It is a self-describing protocol in many
ways similar to other self-describing protocols such as XML. (XML
representation of business content of FIX messages is known as
FIXML.) FIX Protocol, Ltd. was established for the purpose of
ownership and maintenance of the specification and owns the
specification, while keeping it in the public domain.
[0058] FIX is provided as an example in this paper because FIX is a
standard electronic protocol for pre-trade communications and trade
execution. Another example of a protocol is Society for Worldwide
Interbank Financial Telecommunication (SWIFT).
[0059] Yet another example is FIX adapted for streaming (FAST)
protocol, which is used for sending multicast market data. FAST was
developed by FIX Protocol, Ltd. to optimize data representation on
a network, and supports high-throughput, low latency data
communications. In particular, it is a technology standard that
offers significant compression capabilities for the transport of
high-volume market data feeds and ultra low latency applications.
Exchanges and market centers offering data feeds using the FAST
protocol include: New York Stock Exchange (NYSE) Archipelago,
Chicago Mercantile Exchange (CME), International Securities
Exchange (ISE), to name a few.
[0060] The job or outcome engine 202, making use of a search
engine, can search data streams for relevant data for tagging;
identifying competitors; and populating product, market
communications, service programs, NPD tables, etc. When the various
products, competitors, and the like are found, they can be
integrated into the core ODI model by storing relevant data
entities in the relevant repositories in a coherent relational
manner.
[0061] As another example, the job or outcome engine 202 could use
a process engine implemented, for example, as a BPM engine or a BPM
suite (BPMS). An example of a BPMS is Bluespring's BPM Suite 4.5.
However, any applicable known or convenient BPM engine could be
used. Of course, the BPM engine must meet the needs of the system
for which it is used, and may or may not work "off the shelf" with
techniques described in this paper.
[0062] As another example, the job or outcome engine 202 could use
a segmentation engine that facilitates segmenting a market. This
can involve providing data manipulation tools to facilitate
compiling and loading data sets into external statistical analysis
packages, providing tools to interact with statistical analysis and
modeling packages and import additional metadata tags into a
job/outcome data schema, and/or providing utilities to enhance the
visual representation and tabular reporting of the statistical data
properties.
[0063] As another example, the job or outcome engine 202 could use
a metadata engine implemented as a data analysis engine that tags
data records algorithmically, appends meta-data associated with
business information to data records, facilitates pipeline
prioritization, facilitates calculation, ranking and reporting of
opportunity scores, facilitates interaction with data, and performs
other functionality that makes data more useful in a BI
context.
[0064] As another example, the job or outcome engine 202 could use
a strategy engine implemented as a business intelligence (BI) tool.
An example of a BI tool is Microsoft Office PERFORMANCEPOINT.RTM.
Server, or Microsoft's SQL Server Reporting Services (SSRS), which
can be used to create analytical cubes for querying. An advantage
of PerformancePoint.RTM. and SRS is that they are integrated with
other Microsoft Office products, such as Excel, Visio, SQL Server,
SHAREPOINT.RTM. Server, and the like, and have monitoring and
analytic capabilities (e.g., Dashboards, Scorecards, Key
Performance Indicators (KPI), Reports, Filters, and Strategy Maps),
and planning and budgeting capabilities. Using the toolset, one can
create data source connections, create views that use the data
source connections, assemble the views in a dashboard, and deploy
the dashboard to Microsoft Office SHAREPOINT.RTM. Server 2007
(MOSS) or Windows SHAREPOINT.RTM. Services, and can save content
and security information to a SQL sever database. Using the
toolset, one can also define, modify, and maintain logical business
models integrated with business rules, workflows, and enterprise
data. It should be noted that the scope of the
PERFORMANCEPOINT.RTM. product has grown with time, and some of the
capabilities may seem to overlap with some of the other engines in
the Application Layer 202.
[0065] In general, a strategy engine can include tools that are
useful for pulling in data from various sources so as to facilitate
strategic planning, such as needs delivery enhancement strategy,
needs-based IP strategy, innovation strategy, market growth
strategy, consumption chain improvement strategy, etc. It is
probably desirable to ensure that the tools in the strategy engine
are user-friendly, since human input is often desirable for certain
strategic planning.
[0066] As another example, the job or outcome engine 202 could
include a reporting engine implemented as SSRS to prepare and
deliver interactive and printed reports. Crystal Reports is another
implementation, and any applicable known or convenient BI tool
could be used. It is frequently seen as an advantage to have
reports that can be generated in a variety of formats including
Excel, PDF, CSV, XML, TIFF (and other image formats), and HTML Web
Archive, which SSRS can do. Other report generators can offer
additional output formats, and may include useful features such as
geographical maps in reports.
[0067] In the example of FIG. 2, the job or outcome engine 202
could include a collaboration engine implemented as a MOSS. It
should be noted that Windows SHAREPOINT.RTM. Services (WSS) might
actually provide adequate functionality to serve as a collaboration
engine, but the MOSS can bolted on top to provide additional
services and functionality. MOSS and similar technologies can
include browser-based collaboration and document management, plus
the ability to host web sites that access shared workspaces and
documents, as well as specialized applications like wikis and
blogs; and tools can enable the MOSS to serve as a social
networking platform. There are many conventional collaboration
tools that could be used as or as part of the collaboration engine
implementation, including, by way of example but not limitation,
adenine IntelliEnterprise, Alfresco, Nuxeo, Cisco WebEx Connect,
Liferay portal, Drupal, eXo Platform, IBM Lotus Notes, O3 spaces,
OnBase, Novell--Teaming and Conferencing link, Open Text
Corporation's Livelink ECM--Extended Collaboration, Oracle
Collaboration Suite, MediaWiki, and Atlassian Confluence.
[0068] In general, an applicable known or convenient tool that acts
as a collaborative workspace and/or tool for the management or
automation of business processes could be implemented.
Collaborative technologies are tools that enable people to interact
with other people within a group more efficiently and effectively.
So even email discussion lists and teleconferencing tools could
function as a collaboration engine; though sophisticated tools are
likely to encompass much more. For example, it is probably
desirable to enable users to have greater control in finding,
creating, organizing, and collaborating in a browser-based
environment. It may also be desirable to allow organization of
users in accordance with their access, capabilities, role, and/or
interests.
[0069] As another example, the job or outcome engine 202 could
include a transaction engine that provides interaction between
engines capable of writing to or reading from the jobs and outcomes
repository 204. If a data stream is being provided, a
transformation rules engine may or may not transform the data into
an appropriate format. Similarly, if data is being provided from
the jobs and outcomes repository 204 to an engine that can make no,
or limited, use of the data, the transformation rules engine can
transform the data to some other format. In a specific
implementation, the transformation rules engine is only needed when
interfacing with external devices because all internal devices can
use data in a standard format.
[0070] As another example, the job or outcome engine 202 could
include an ETL engine that extracts data from outside sources,
transforms the data to fit operational requirements, and loads the
transformed data into the jobs and outcomes repository 204. The ETL
engine can store an audit trail, which may or may not have a level
of granularity that would allow reproduction of the ETL's result in
the absence of the ETL raw data. A typical ETL cycle can include
the following steps: initialize, build reference data, extract,
validate, transform, stage, audit report, publish, archive, clean
up.
[0071] In operation, the ETL engine can extract data from one or
more source systems, which may have different data organizations or
formats. Common data source formats are relational databases and
flat files, but can include any applicable known or convenient
structure, such as, by way of example but not limitation,
Information Management System (MIS), Virtual Storage Access Method
(VSAM), Indexed Sequential Access Method (ISAM), web spidering,
screen scraping, etc. Extraction can include parsing the extracted
data, resulting in a check if the data meets an expected pattern or
structure.
[0072] In operation, the ETL engine transforms the extracted data
by applying rules or functions to the extracted data to derive data
for loading into a target repository. Different data sources may
require different amounts of manipulation of the data.
Transformation types can include, by way of example but not
limitation, selecting only certain columns to load, translating
coded values, encoding free-form values, deriving a new calculated
value, filtering, sorting, joining data from multiple sources,
aggregation, generating surrogate-key values, transposing,
splitting a column into multiple columns, applying any form of
simple or complex data validation, etc.
[0073] In operation, the ETL engine loads the data into the target
repository. In a particular implementation, the data must be loaded
in a format that is usable to the system 200, perhaps using a
transformation rules engine. Loading data can include overwriting
existing information or adding new data in historized form. The
timing and scope to replace or append are implementation- or
configuration-specific.
[0074] The ETL engine can make use of an established ETL framework.
Some open-source ETL frameworks include Clover ETL, Enhydra
Octopus, Mortgage Connectivity Hub, Pentaho Data Integration,
Talend Open Studio, Scriptella, Apatar, Jitterbit 2.0. A freeware
ETL framework is Benetl. Some commercial ETL frameworks include
Djuggler Enterprise, Embarcadero Technologies DT/Studio, ETL
Solutions Transformation Manager, Group 1 Software DataFlow, IBM
Information Server, IBM DB2 Warehouse Edition, IBM Cognos Data
Manager, IKAN--ETL4ALL, Informatica PowerCenter, Information
Builders--Data Migrator, Microsoft SQL Server Integration Services
(SSIS), Oracle Data Integrator, Oracle Warehouse Builder, Pervasive
Business Integrator, SAP Business Objects--Data Integrator, SAS
Data Integration Studio, to name several.
[0075] A business process management (BPM) server, such as
Microsoft BizTalk Server, can also be used to exchange documents
between disparate applications, within or across organizational
boundaries. BizTalk provides business process automation, business
process modeling, business-to-business communication, enterprise
application integration, and message broker.
[0076] An enterprise resource planning (ERP) system used to
coordinate resources, information, and activities needed to
complete business processes, can also be accessed. Data derived
from an ERP system is typically that which supports manufacturing,
supply chain management, financials, projects, human resources, and
customer relationship management from a shared data repository.
[0077] Derived data can also be Open Innovation (OI) data, which is
an outside source of innovation concepts. This can include
transactional data (send a network of outside problem solvers
Opportunities for new ideas and receive the ideas back) and
unstructured data (repository of ideas) for searching.
[0078] In the example of FIG. 2, the job or outcome engine 202 can
have access to various data, such as data associated with
customers, including customer profile, customer jobs region, and
customer outcomes region. In a specific implementation, a customer
profile region can include customer profile records that include
customer identifier (ID) and profile attributes. The customer ID
can be in accordance with a public key infrastructure (PKI). The
profile attributes can include fields associated with, for example,
demographics, customer of . . . , products used, job role, customer
chain role, consumption chain role, outcome-driven segments, and
attitudinal segments. The customer jobs region can include a
customer type code (note that customer ID and customer type code
can be dual PKIs), job map models, scoring tables, and raw data
tables. The customer outcomes region can include a customer type
code, job/outcome model tables, scoring tables, and raw data
tables. Other data can include price sensitivity data tables, which
can include jobs and outcomes (note that jobs and outcomes can be
implemented as dual PKIs) and fields that include customer IDs.
Other data can include a translation data region including customer
file translation tables, product/service offerings translation
tables, sales and marketing campaigns translation tables,
innovation concepts translation tables, business development
translation tables, and external data translation tables. In a
specific implementation, the customer file translation tables
include a customer ID to customer type code translation table. In a
specific implementation, the product/service offerings translation
tables can include job/outcomes as PKIs and cross-references
indicating relevance for product/service offerings, company
products (subsystems and parts, service programs), competitor
products (subsystems, service programs), and pipeline products. In
a specific implementation, the sales and marketing campaigns
translation tables can include job/outcome as PKIs and
cross-references indicative of relevance for sales campaigns and
marketing campaigns (company and competitor). In a specific
implementation, the innovation concepts translation tables can
include job/outcomes as PKIs and cross-references indicative of new
product development, R&D roadmap, sales and service concepts,
and new marketing positioning and branding concepts. In a specific
implementation, the business development translation tables can
include job/outcome PKIs and cross references indicative of new
M&A targets and new strategic partners. In a specific
implementation, the external data translation tables can include
job/outcome PKIs and cross-references indicative of patent records,
open innovation database records, and trade publication
records.
[0079] The various data entities can be integrated with the core
coherent relational model around, by way of example but not
limitation, products, platforms, projects, competitors,
technologies/IP, campaigns, organization, resources, and
performance. At least in part because the model is coherent and
relational, by way of example but not limitation, opportunity data,
context information, prompts for sparking creativity, and
management decisions can be implemented within a systematized idea
generation process. For example, in a certain context, it may be
the case that a limited number of parameters become relevant, and
therefore prompts associated with such a context can be used to
spark creativity by addressing one or more of the limited number of
parameters.
[0080] Advantageously, customer needs can be captured as the needs
related to a market, goods, and services. A core functional job can
have emotional jobs (e.g., personal jobs and social jobs) and other
functional jobs (e.g., jobs indirectly related to core job and jobs
directly related to core job), each of which can be analyzed using
a uniform metric. During a concept innovation phase, a job can be
broken down into multiple steps, each step potentially having
multiple outcomes associated with it. Desired outcomes are the
metrics customers use to measure the successful execution of a job.
When the outcomes are known or predicted, the concept innovation
stage passes into the devise solution stage, and then a design
innovation stage where consumption chain jobs are identified, such
as purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, obtain support, upgrade, replace,
dispose, to name several. Then it is time to design/support a
solution.
[0081] Using the various data, which is represented as input 216 in
the example of FIG. 2, to understand the needs, the job or outcome
engine 202 can create a needs-based job or outcome record including
at least one constraint parameter associated with a needs-based job
or outcome. The constraint parameter is associated with the
parameters that solutions must have to meet the need, and do the
job or accomplish the outcome. A given solution must normally fall
within the bounds of the constraint parameter to meet the need,
though it might be possible to come close to meeting the need if a
solution falls outside of the bounds of the constraint parameter. A
solution that falls within the bounds of the constraint parameter
will not necessarily be ideal. For example, a first solution might
have a relatively higher cost (in time or resources) to meet a need
than a second solution. Indeed, in many cases, a "perfect" solution
is more of an ideal than a reality.
[0082] In the example of FIG. 2, the job or outcome engine 202
stores a job or outcome record in the jobs and outcomes repository
204. It is important for the records stored in the jobs and
outcomes repository 204 to be compatible with a coherent relational
model. Thus, the records will tend to have a uniform data structure
that can be matched to solutions, each of which also have a uniform
data structure. While different jobs and outcomes may have
different relevant fields, the data structure as a whole should
meet the requirement of enabling matching of relevant solutions to
a job or outcome record.
[0083] In the example of FIG. 2, the solution compatibility engine
206 can make use of various engines, such as those described with
reference to the job or outcome engine 202, to obtain data that can
be used to parameterize solutions. It may be desirable for the
solution compatibility engine 206 to have access to the jobs and
outcomes repository 204 for the purpose of determining the jobs and
outcomes that are known, though this is not required.
Advantageously, since records are stored in accordance with a
coherent relational model, it should be possible to match solutions
to jobs and outcomes after the solutions have been stored, even
without knowledge of a job or outcome to which it will later be
matched.
[0084] Using the various data, which is represented as input 218,
which may or may not include data from the job or outcome engine
202, in the example of FIG. 2, to understand solutions for various
real or potential needs, the solution engine 206 can create a
solutions record including a capability parameter indicative of a
degree of capability of a solution in achieving a needs-based job
or outcome using the constraint parameter (note that the actual
comparison with the constraint parameter may not take place until
later, when determining the degree of compatibility between
solutions and jobs or outcomes). The capability parameter is
associated with the parameters that identify the capabilities,
costs, and other factors of solutions to meet needs.
[0085] In the example of FIG. 2, the solution engine 206 stores a
solution record in the solutions repository 208. It is important
for the records stored in the solutions repository 208 to be
compatible with a coherent relational model. Thus, the records will
tend to have a uniform data structure that can be matched to jobs
and outcomes, each of which also have a uniform data structure.
While different solutions may have different relevant fields, the
data structure as a whole should meet the requirement of enabling
matching of relevant jobs or outcomes to a solution record.
[0086] In the example of FIG. 2, the job or outcome engine 202 and
the solution engine 206 are coupled to the capability computation
engine 210. It may be noted that all three, or a subset of the
three, could be combined into a single engine and/or the job or
outcome engine 202 and the solution engine 206 could be used to
populate the jobs and outcomes repository 204 and solutions
repository 208, but not act as an interface for the capability
computation engine 210, which could have direct access to the
repositories; the actual layout of the engines and repositories is
intended to be conceptual in nature.
[0087] The capability computation engine 210 can make use of
various engines, such as those described with reference to the job
or outcome engine 202, to obtain data (not shown) that can be used
to identify how effectively a solution meets a need for a job or
outcome. The capability computation engine 210 can create a
capability/constraint difference record including a difference
between the capability parameter for a solution and one or more
constraints associated with a constraint parameter of a job or
outcome. This could be triggered by a specific command, or the
capability computation engine 210 could crunch through several jobs
or outcomes and solutions to see what needs are unmet or are
inadequately met. Regardless of when the computation occurs, the
capability computation engine 210 compares a capability parameter
indicative of a degree of capability of a solution in achieving a
job or outcome using a constraint parameter of a job or outcome
record to obtain a capability/constraint difference record, which
can be stored in the capability/constraint difference repository
211.
[0088] In the example of FIG. 2, the capability computation engine
210 is coupled to the commercial activity server 212. The
commercial activity server 212 can make use of various engines,
such as those described with reference to the job or outcome engine
202, to obtain data that can be used to make recommendations,
provide useful data, or the like.
[0089] In the example of FIG. 2, in operation, the commercial
activity server 212 identifies a job or outcome, identifies a
solution in association with the job or outcome, and facilitates
management of commercial actions taken in association with the
difference between the constraint parameter of the job or outcome
and the capability parameter of the solution. Since the commercial
activity server 212 includes processing functionality, it can be
referred to as an "engine." The capability/constraint difference
record is useful for the purpose of determining which solutions
will yield the highest relative improvements in meeting the needs
of a job or outcome. For example, if a first solution to a job
costs twice as much as a second solution to the job, then it may be
desirable to look into whether the second solution to the job can
be provided. In this way, it is possible to, for example, target
areas in which innovation can provide the greatest rewards.
[0090] In the example of FIG. 2, the clients 214 are coupled to the
commercial activity server 212. Here, the term "client" is used
because servers are typically referred to as serving clients. The
term is intended to be construed broadly. The clients 214 make use
of the recommendations, data, etc. that is provided to them by the
commercial activity server 212.
[0091] FIGS. 3-23 are provided to illustrate various optional
functions of a USIMS system making use of an ODI reference
model.
[0092] FIG. 3 depicts a flowchart 300 of an example of a method for
external data integration. The benefit of external integration is
to normalize disparate enterprise market data from exogenous
sources into a job/outcome reference model of ODI. Advantageously,
enterprises can cull information from these sources into a
consistent and searchable model of the marketplace. This method and
other methods are depicted as serially arranged modules. However,
modules of the methods may be reordered, or arranged for parallel
execution as appropriate.
[0093] In the example of FIG. 3, the flowchart 300 starts at module
302 with tagging external data records with job/outcome identifiers
using new algorithms that transform data into new data structures,
such as the data entities described with reference to FIG. 2. The
new algorithms begin with identifying whether a job or outcome of
interest relates to a specific field-of-use or solution context, or
to a general purpose solution such as a technology used by many
systems.
[0094] Depending on whether the answer is specific or general, the
system assigns an appropriate search strategy embodied within a
string of external data sources that can include appropriate
solutions. For outcomes associated with specific fields-of-use the
search strategy includes specific and highly qualified external
data sources which can include, for example, particular patent
classification sub classes, trade or academic publications, or
other applicable data.
[0095] For outcomes associated with general purpose needs the
system determines systematically the best sources for new enabling
technologies or solutions by automatically identifying and
weighting these through a routine like modern textual search. The
process continues by searching records found through this method
for text strings that include synonymous terms for the objects of
control or action from the particular outcome or job of interest.
The process completes by recording the existence of this match as a
data tag appended to the external data record identifying the
outcome/job that was matched and a score value is assigned
representing the closeness of this match.
[0096] In the example of FIG. 3, the flowchart 300 continues to
module 304 with estimating a level to which the solution described
in the external record satisfies the job/outcome of interest. The
solution can satisfy the job/outcome of interest either objectively
or subjectively by manual expert scoring or through available crowd
sourcing scores and recording this as coefficients. Crowd sourcing
scores might, for example, be derived from patent citations, web
page visits, records of the success of the inventor/author in the
field of use in general, novel real options based scores of large
communities of connected users, or other methods to capture group
opinion on the value of solutions to increase satisfaction of the
job/outcome of interest.
[0097] In the example of FIG. 3, the flowchart 300 continues to
module 306 with productionalizing in translation tables.
[0098] In the example of FIG. 3, the flowchart 300 continues to
module 308 with incorporating into query code. It is likely that
queries and reports will be desirable in a system implemented in
accordance with one or more of the techniques described in this
paper. Such reports may be ad hoc or pre-formed. Ad hoc reports may
include solution value added assessments, business case extracts,
marketing and sales campaigns needs extracts, or other queries as
necessary to provide functionality required or desired to perform
uses of a system implemented in accordance with one or more of the
techniques described in this paper. Some examples of ad hoc reports
are given below:
[0099] Solution value added assessment is an ad hoc use having the
same general purpose as a needs delivery enhancement strategy
report (see, e.g., FIG. 6), but constructed specifically to assess
the marketability of a particular solution concept during ideation.
It may incorporate a process such as that described with reference
to the example of FIG. 3, flowchart 300, module 304.
[0100] Business case extracts of the database is an ad hoc use to
assess the return on investment (ROI) of particular solutions. The
extracts can be used by other reports, or separate business case
models to facilitate or improve enterprise investment decision
making. The data values extracted include, for example, job/outcome
importance, satisfaction, and opportunity scores (both raw and in
processed forms), customer data, satisfaction improvement estimates
of solutions (see, e.g., FIG. 3), cost and pricing data, and other
applicable information.
[0101] Marketing and sales campaigns needs extracts are ad hoc
reports to assess the market effect of new marketing and sales
campaigns based on positioning a product to address unmet needs or
otherwise using similar insights to design and assess new marketing
and sales campaigns. Like other data in the coherent relational
model, data entities can be integrated around the products and
campaigns.
[0102] FIGS. 4-8 depict examples of pre-formed query methods. FIG.
4 depicts a flowchart 400 of an example of a competitive assessment
method. Advantageously, a competitive assessment will enable an
enterprise to quantitatively analyze a probable marketplace
effectiveness of known customer-facing activities of its
competitors, and forecast impacts to its own business plans.
[0103] In the example of FIG. 4, the flowchart 400 starts at module
402 with identifying competitors. Competitors can be identified
explicitly, found through search, ETL, or the like, or a
combination of these. Competitors can also be identified later in
the process, for example after a market becomes more defined.
[0104] In the example of FIG. 4, the flowchart 400 continues to
module 404 with populating product, market communications, service
programs, NPD tables, etc.
[0105] In the example of FIG. 4, the flowchart 400 continues to
module 406 with estimating satisfaction coefficients through
customer data analysis, manual estimation, or crowd sourcing and
may incorporate a process such as that described with reference to
the example of FIG. 3, flowchart 300, module 304.
[0106] In the example of FIG. 4, the flowchart 400 continues to
module 408 with productionalizing in translation tables.
[0107] In the example of FIG. 4, the flowchart 400 continues to
module 410 with incorporating into query code.
[0108] FIG. 5 depicts a flowchart 500 of an example of a needs
delivery of current products method. Advantageously, a needs
delivery of current products will provide an enterprise with a
flexible reporting engine to assess how well the current state of
products are fulfilling the needs of customers across many
different referential dimensions (e.g., functional needs, emotional
needs, consumption chain needs, platforms, market segments,
etc.).
[0109] In the example of FIG. 5, the flowchart 500 starts at module
502 with determining assessment and reporting criteria.
[0110] In the example of FIG. 5, the flowchart 500 continues to
module 504 with selecting a needs and product set based on the
criteria.
[0111] In the example of FIG. 5, the flowchart 500 continues to
module 506 with selecting meta-data for a report based on the
criteria.
[0112] In the example of FIG. 5, the flowchart 500 continues to
module 508 with analyzing and displaying importance, satisfaction,
and opportunity data.
[0113] In the example of FIG. 5, the flowchart 500 continues to
module 510 with preparing and displaying meta-data reports (e.g.,
un-penetrated economic opportunity).
[0114] FIG. 6 depicts a flowchart 600 of an example of a needs
delivery enhancement strategy method. Advantageously, a needs
delivery enhancement strategy builds upon the prior use to assess
the level of enhancement that pipeline innovations are likely to
deliver to the current business portfolio. This may include a
product roadmap and R&D.
[0115] In the example of FIG. 6, the flowchart 600 starts at module
602 with determining assessment and reporting criteria.
[0116] In the example of FIG. 6, the flowchart 600 continues to
module 604 with selecting needs and markets based on the
criteria.
[0117] In the example of FIG. 6, the flowchart 600 continues to
module 606 with querying current products, NPD projects, and/or
R&D initiatives for needs enhancements.
[0118] In the example of FIG. 6, the flowchart 600 continues to
module 608 with analyzing and displaying importance, satisfaction,
and opportunity data and can incorporate a process such as that
described with reference to FIG. 3, flowchart 300, module 304.
[0119] In the example of FIG. 6, the flowchart 600 continues to
module 610 with preparing and displaying a needs-gaps report.
[0120] In the example of FIG. 6, the flowchart 600 continues to
module 612 with preparing and displaying meta-data strategy reports
(e.g., un-penetrated economic opportunity).
[0121] FIG. 7 depicts a flowchart 700 of an example of a needs
based IP strategy method. Advantageously, a needs-based IP strategy
can enable an enterprise to efficiently scout internal and external
sources of IP, technologies, and other innovation solutions to
secure advantages in pursuing strategies to satisfy unmet market
needs.
[0122] In the example of FIG. 7, the flowchart 700 starts at module
702 with determining assessment and reporting criteria.
[0123] In the example of FIG. 7, the flowchart 700 continues to
module 704 with selecting a product or technology set based on the
criteria.
[0124] In the example of FIG. 7, the flowchart 700 continues to
module 706 with identifying matching enterprise IP.
[0125] In the example of FIG. 7, the flowchart 700 continues to
module 708 with displaying needs addressed by the IP.
[0126] In the example of FIG. 7, the flowchart 700 continues to
module 710 with analyzing and displaying needs un-addressed by the
IP with opportunity assessment data.
[0127] In the example of FIG. 7, the flowchart 700 continues to
module 712 with importing needs tagged external patent records and
outside innovation records.
[0128] In the example of FIG. 7, the flowchart 700 continues to
module 714 with preparing and displaying reports on IP acquisition,
defense, and development priorities.
[0129] FIG. 8 depicts a flowchart 800 of an example of a
consumption chain needs delivery method. Advantageously, a
consumption chain needs delivery can enable an enterprise to assess
disparate needs and associated importance levels of participants in
consumption chains of the enterprise's products in order to
optimize investments for sales impact.
[0130] In the example of FIG. 8, the flowchart 800 starts at module
802 with constructing a consumption chain job map with mapping
tools.
[0131] In the example of FIG. 8, the flowchart 800 continues to
module 804 with querying ODI needs data tables for matching
job/outcome data.
[0132] In the example of FIG. 8, the flowchart 800 continues to
module 806 with appending meta-data on importance level on
participant in consumption chain in purchasing decisions.
[0133] In the example of FIG. 8, the flowchart 800 continues to
module 808 with appending economic business case data quantifying
the particular consumption cases.
[0134] In the example of FIG. 8, the flowchart 800 continues to
module 810 with generating reports for price sensitivity data
collection.
[0135] In the example of FIG. 8, the flowchart 800 continues to
module 804 with generating lever reports to isolate economic
opportunities in satisfying consumption chain needs.
[0136] FIG. 9 depicts a flowchart 900 of an example of a method for
computationally enabling and enhancing an ODI process. In the
example of FIG. 9, the flowchart 900 starts at module 902 with
creating an innovation strategy.
[0137] FIG. 10 depicts a flowchart 1000 of an example of a method
for creating an innovation strategy. In the example of FIG. 10, the
flowchart 1000 starts at module 1002 with facilitating gathering of
baseline data on strategy variables. This may include, for example,
conducting an inventory of a current strategic roadmap for
qualitative impact assessment and/or conducting an inventory of
anecdotal data and hypotheses on unmet and over-served needs.
[0138] In the example of FIG. 10, the flowchart 1000 continues to
module 1004 with generating reports that facilitate the decision of
prioritizing projects to pursue viable objectives in a market
growth strategy. Five market growth strategies are provided as
examples herein, and it should be recognized that at module 1004,
reports could be generated to facilitate the decision of
prioritizing projects to pursue viable objectives in one or more
market growth strategies, with the number depending upon
implementation and/or configuration. The examples of market growth
strategies are: 1) grow or protect a high-share market, 2)
aggressively grow a low-share market the enterprise is already in,
3) enter an attractive market that others are already in, 4) enter
a new or emerging high growth market, 5) find a market for a new or
emerging technology.
[0139] FIGS. 11-15 depict flowcharts of examples of market growth
strategy methods that advantageously use ODI data to assess
economic valuation and risks of different market growth strategies.
FIG. 11 depicts a flowchart 1100 of an example of a method for
growing or protecting a high-share market. In the example of FIG.
11, the flowchart 1100 starts at module 1102 with reporting on key
trends and competitive position in core markets. For example, the
report can include share, position, response to key trends,
strengths, weaknesses, or other applicable information.
[0140] In the example of FIG. 11, the flowchart 1100 continues to
module 1104 with facilitating qualitative impact assessment of
developing core market innovations to a strategic roadmap. For
example, the assessment can include how many pipeline products are
touched, whether ODI projects will enhance or detract from the
pipeline product (and how much), the dollar value of pipeline
products touched, the revenue value of pipeline products touched,
and other applicable information.
[0141] In the example of FIG. 11, the flowchart 1100 continues to
module 1106 with facilitating inventory of value delivery platforms
within each core market. In at least one implementation, it is
considered advantageous to collect inventory in formation, and
store the information in data tables that are being integrated
relationally to job/outcome data.
[0142] In the example of FIG. 11, the flowchart 110 continues to
module 1108 with facilitating a qualitative risk, cost, and benefit
assessment of the different innovation strategies on the core
market platform. For example, the assessment can include platform
innovation, business model innovation, features, and other
applicable information.
[0143] FIG. 12 depicts a flowchart 1200 of an example of a method
for aggressively growing a low-share market the enterprise is
already in. In the example of FIG. 12, the flowchart 1200 starts at
module 1202 with facilitating inventory of attractive low share
markets.
[0144] In the example of FIG. 12, the flowchart 1200 continues to
module 1204 with reporting on key trends and competitive position
in underperforming markets. For example, the report can include
share, position, response to key trends, strengths, weaknesses, and
other applicable information.
[0145] In the example of FIG. 12, the flowchart 1200 continues to
module 1206 with facilitating inventory of value delivery platforms
within underperforming markets. As mentioned previously, in at
least one implementation, it is considered advantageous to collect
inventory in formation, and store the information in data tables
that are being integrated relationally to job/outcome data.
[0146] In the example of FIG. 12, the flowchart 1200 continues to
module 1208 with facilitating a qualitative risk, cost, and benefit
assessment of applying different innovation strategies to
underperforming market platforms. For example, the assessment can
include platform innovation, business model innovation, feature
development, and other applicable information.
[0147] FIG. 13 depicts a flowchart 1300 of an example of a method
for entering an attractive market that others are already in. In
the example of FIG. 13, the flowchart 1300 starts at module 1302
with facilitating inventory of attractive new but proven markets.
As mentioned previously, in at least one implementation, it is
considered advantageous to collect inventory in formation, and
store the information in data tables that are being integrated
relationally to job/outcome data.
[0148] In the example of FIG. 13, the flowchart 1300 continues to
module 1304 with reporting on key trends and competitive position
in new markets. For example, the report can include share,
position, response to key trends, strengths, weaknesses, and other
applicable information. Moreover, the reports can be made part of
the relational data model as well.
[0149] In the example of FIG. 13, the flowchart 1300 continues to
module 1306 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms for the new
market. The assessment information can be made part of the
relational data model and inventory capture functionality.
[0150] FIG. 14 depicts a flowchart 1400 of an example of a method
for entering a new or emerging high growth market. In the example
of FIG. 14, the flowchart 1400 starts at module 1402 with
facilitating inventory of new and emerging high growth markets. As
mentioned previously, in at least one implementation, it is
considered advantageous to collect inventory in formation, and
store the information in data tables that are being integrated
relationally to job/outcome data.
[0151] In the example of FIG. 14, the flowchart 1400 continues to
module 1404 with reporting on key trends and competitive position
in emerging high growth markets. For example, the report can
include share, position, response to key trends, strengths,
weaknesses, and other applicable information. The information can
be made part of the relational data model and inventory capture
functionality.
[0152] In the example of FIG. 14, the flowchart 1400 continues to
module 1406 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms for the new
market.
[0153] FIG. 15 depicts a flowchart 1500 of an example of a method
for finding a market for a new or emerging technology. In the
example of FIG. 15, the flowchart 1500 starts at module 1502 with
facilitating inventory of new and emerging technologies. As
mentioned previously, in at least one implementation, it is
considered advantageous to collect inventory in formation, and
store the information in data tables that are being integrated
relationally to job/outcome data.
[0154] In the example of FIG. 15, the flowchart 1500 continues to
module 1504 with facilitating a qualitative risk, cost, and benefit
assessment of developing new value delivery platforms incorporating
the new technology.
[0155] Referring once again to the example of FIG. 10, the
flowchart 1000 continues to module 1006 with facilitating the
creation of an overall growth blueprint. Advantageously, the growth
blueprint can enable the business to orchestrate and prioritize the
market growth strategy through the use of the ODI data. If multiple
market growth strategies are pursued, multiple growth blueprints
may be created. The overall growth blueprint can, in addition,
identify or facilitate the identification of particular market
growth initiatives in implementing the market growth strategy
through the use of the ODI data.
[0156] FIG. 16 depicts a flowchart 1600 of an example of a method
for facilitating the creation of an overall growth blueprint. In
the example of FIG. 16, the flowchart 1600 starts at module 1602
with consolidating the inventory of jobs and demographics (the
markets) in which the company competes or seeks to compete. The
consolidation can include, for example, an evaluation of possible
market growth strategies. As mentioned previously, in at least one
implementation, it is considered advantageous to collect inventory
in formation, and store the information in data tables that are
being integrated relationally to job/outcome data.
[0157] In the example of FIG. 16, the flowchart 1600 continues to
module 1604 with facilitating support to pursue a market growth
initiative in a growth path model deemed available for the market
of interest. The support may include, for example, using a growth
paths model to facilitate a subjective assessment of the growth
blueprint for one or more markets of interest. It may be desirable
to evaluate the likelihood of desired outcomes, company-executable
actions, and costs to satisfy assumptions, conditions precedent,
and management decision criteria that must be present to support
pursuing the market growth initiative. This capability can be
controlled by user privileges.
[0158] In the example of FIG. 16, the flowchart 1600 continues to
module 1606 with generating a growth blueprint. The growth
blueprint can represent, for example, management's selection of
markets of interest, associated market growth initiatives with the
presumptive growth paths game plan, and other applicable data.
Criteria and strategies associated with this can be made part of
the relational data model. To generate the growth blueprint, it may
be desirable to compile actions on plan dependencies that must be
taken to satisfy conditions deemed necessary for the success of the
game plan. This capability can be controlled by user
privileges.
[0159] Referring once again to the example of FIG. 10, the
flowchart 1000 continues to module 1008 with facilitating the
development of a consumption chain improvement strategy.
[0160] FIG. 17 depicts a flowchart 1700 of an example of a method
for facilitating the development of a consumption chain improvement
strategy. In the example of FIG. 17, the flowchart 1700 starts at
module 1702 with facilitating an estimation of a quantitative
business impact from known or suspected consumption chain
bottlenecks. Facilitating the estimation can include facilitating
an inventory of known and suspected consumption chain bottlenecks
to aid in the estimation. It may also be desirable to sort priority
bottlenecks into the consumption chain jobs of, for example:
purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, dispose, or some other applicable
category.
[0161] In the example of FIG. 17, the flowchart 1700 continues to
module 1704 with facilitating compilation of consumption chain
growth plan dependencies. The consumption chain growth plan
dependencies can be compiled automatically or by an expert user,
depending upon implementation and/or preference. Automated features
can search growth plan dependencies for terms that match or are
associated with the consumption chain jobs of, for example:
purchase, receive, install, set-up, learn to use, interface,
transport, store, maintain, dispose, or some other applicable
category.
[0162] In the example of FIG. 17, the flowchart 1700 continues to
module 1706 with aggregating the consumption chain jobs requiring
opportunity research and prioritizing into a game plan.
[0163] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 904 with aggregating outcomes. Aggregating
outcomes can include facilitating qualitative research (FIG. 18) or
quantitative research (FIG. 19).
[0164] FIG. 18 depicts a flowchart 1800 of an example of a method
for facilitating qualitative research. In the example of FIG. 18,
the flowchart 1800 starts at module 1802 with providing a generic
hierarchy of jobs job map template and note taking tools to map the
job of interest and important related jobs.
[0165] In the example of FIG. 18, the flowchart 1800 continues to
module 1804 with providing outcome gathering common questions to
ask.
[0166] In the example of FIG. 18, the flowchart 1800 continues to
module 1806 with providing a shared environment for users to net
outcomes down to the critical set. This capability can be
controlled by user privileges.
[0167] In the example of FIG. 18, the flowchart 1800 continues to
module 1808 with providing an automated tool to translate other
primary and secondary market research into outcome statements.
[0168] In the example of FIG. 18, the flowchart 1800 continues to
module 1810 with relating the primary and secondary research that
has been translated and/or indexed into ODI terms back to core ODI
data records. It may be the case that some of these records are
created anew and some are pre-existing; so the relationships can be
the means to cross-reference the exogenous primary/secondary
research with the core ODI data.
[0169] FIG. 19 depicts a flowchart 1900 of an example of a method
for facilitating quantitative research. In the example of FIG. 19,
the flowchart 1900 starts at module 1902 with extracting job and
outcome statements directly into web survey tools.
[0170] In the example of FIG. 19, the flowchart 1900 continues to
module 1904 with facilitating procurement of customer lists for
direct quantitative research. This capability can be controlled by
user privileges.
[0171] In the example of FIG. 19, the flowchart 1900 continues to
module 1906 with providing rule-based utilities to assign survey
participants to segments or groups for later analytical purposes.
It may be desirable to provide tools and utilities to tag survey
participants with screening and segmentation factors. This
capability can be controlled by user privileges.
[0172] In the example of FIG. 19, the flowchart 1900 continues to
module 1908 with providing tools to deploy surveys directly to
customers, screen out known survey abusers, and randomize data
collection for reliability. This capability can be controlled by
user privileges.
[0173] In the example of FIG. 19, the flowchart 1900 continues to
module 1910 with assessing customer response data validity through
co-variance assessments on like outcomes.
[0174] In the example of FIG. 19, the flowchart 1900 continues to
module 1912 with automating collection of price sensitivity input
during initial quantitative research.
[0175] In the example of FIG. 19, the flowchart 1900 continues to
module 1914 with providing tools to import survey response data
directly into the data model. This capability can be controlled by
user privileges.
[0176] In the example of FIG. 19, the flowchart 1900 continues to
module 1916 with providing tools to optionally distribute data with
population averages back to survey participants to encourage
customer engagement. These tools, while useful, are optional.
[0177] In the example of FIG. 19, the flowchart 1900 continues to
module 1918 with providing search tools to find other jobs and
outcomes from other company or benchmark research reports and a
utility for affinity-tagging. The search tools can be automated.
The benchmark research reports can be Strategyn.TM. benchmark
research reports. The affinity-tagging can be used to record
associations and facilitate insights and inferences between related
factors of separate studies. This capability can be controlled by
user privileges.
[0178] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 906 with identifying opportunities.
[0179] FIG. 20 depicts a flowchart 2000 of an example of a method
for identifying opportunities. In the example of FIG. 20, the
flowchart 2000 starts at module 2002 with automating calculation,
ranking, and reporting of opportunity scores with associated
metadata. It may be desirable to provide report design tools to
customize query logic used to build reports.
[0180] In the example of FIG. 20, the flowchart 2000 continues to
module 2004 with building and displaying opportunity landscape
diagrams.
[0181] In the example of FIG. 20, the flowchart 2000 continues to
module 2006 with providing graphic based utilities to enhance and
interact with data depicted in the landscapes. This can facilitate
identifying, for example, affinities and correlation factors with
other data points in the landscape, particular solution concepts,
market growth strategies, market growth paths,
dependencies/insights on assumptions, conditions precedent,
decision criteria related to management investment decisions, and
other applicable information.
[0182] In the example of FIG. 20, the flowchart 2000 continues to
module 2008 with providing graphic based utilities to enhance and
modify visual representations of data within landscape diagrams.
This can enable a user to accentuate, for example, relationships,
properties of data points, or other insights. Other capabilities of
the utilities can include enabling visualization of economic
opportunity from satisfying unmet needs and integration of
statistical modeling methods to a project. This capability can be
controlled by user privileges.
[0183] In the example of FIG. 20, the flowchart 2000 continues to
module 2010 with providing tools to drill into metadata and analyze
it qualitatively. These tools can be used, for example, to
determine market strategies and prioritizing commercial activities
in the work flow engine.
[0184] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 908 with segmenting the market.
[0185] FIG. 21 depicts a flowchart 2100 of an example of a method
for segmenting the market. In the example of FIG. 21, the flowchart
2100 starts at module 2102 with providing data manipulation tools
to facilitate compiling and loading of datasets into external
statistical analysis packages.
[0186] In the example of FIG. 21, the flowchart 2100 continues to
module 2104 with providing tools to interact with statistical
analysis and modeling packages and import additional metadata tags
into a job/outcome data schema. The metadata tags may include, for
example, cluster affinity scores. This capability can be controlled
by user privileges.
[0187] In the example of FIG. 21, the flowchart 2100 continues to
module 2106 with providing utilities to enhance the visual
representation and tabular reporting of the statistical data
properties.
[0188] Referring once again to the example of FIG. 9, the flowchart
900 continues to module 910 with defining the targeting
strategy.
[0189] FIG. 22 depicts a flowchart 2200 of an example of a method
for defining the targeting strategy. In the example of FIG. 22, the
flowchart 2200 starts at module 2202 with providing a tool to
meta-tag jobs and outcome statements in respective data entities
with correlation values. The correlation values are useful to
assess alignment of current and future solutions with opportunities
of interest. This capability can be controlled by user
privileges.
[0190] In the example of FIG. 22, the flowchart 2200 continues to
module 2204 with providing a tool for end users to meta-tag jobs
and outcome statements in respective data entities with other
thematic tags. The thematic tags are useful to facilitate
collaborative ideation and business case development. This
capability can be controlled by user privileges.
[0191] In the example of FIG. 22, the flowchart 2200 continues to
module 2206 with delivering real-time tabular and visual
representations of the tagged jobs and outcomes to facilitate
innovation collaboration.
[0192] In the example of FIG. 22, the flowchart 2200 continues to
module 2208 with providing exports of jobs and outcomes. The
exports can be useful to facilitate external solution sourcing and
imports of respondent solutions into the data entities supporting
reporting. This capability can be controlled by user
privileges.
[0193] In the example of FIG. 22, the flowchart 2200 continues to
module 2210 with providing a utility to scout, cull, assess the
value of, and organize external sources of pre-made solutions
against jobs and outcomes. The jobs and outcomes can include, for
example, new technologies and inventions.
[0194] In the example of FIG. 9, the flowchart 900 continues to
module 912 with positioning current offerings. This can be
automated using capabilities described above with reference to one
or more of modules 902-910.
[0195] In the example of FIG. 9, the flowchart 900 continues to
module 914 with prioritizing the pipeline. Prioritizing the
pipeline can include providing a tool set to automate
prioritization of a business' new product development, R&D, and
business development by leveraging the capability. This may involve
a process similar to that described above with reference to FIG.
6.
[0196] In the example of FIG. 9, the flowchart 900 continues to
module 916 with conceptualizing breakthroughs.
[0197] FIG. 23 depicts a flowchart 2300 of an example of a method
for conceptualizing breakthroughs. In the example of FIG. 23, the
flowchart 2300 starts at module 2302 with synthesizing the
preceding functionality of FIGS. 20-22 to facilitate collaborative
discovery of innovation breakthroughs addressing considerable unmet
market needs. The information and inputs it operates on can include
scored ODI jobs/outcomes (see, e.g., FIG. 9, module 910),
information on reasons need-gaps exist in terms of customer,
technical, and competitive contexts, management criteria, value
platforms, products, and knowledge of emerging technologies. The
context information can include textual, quantitative, and
multimedia information.
[0198] In the example of FIG. 23, the flowchart 2300 continues to
module 2304 with isolating particularly attractive opportunities.
Opportunities can be attractive, for example, to disrupt current
platforms with new platforms or technologies having advantages in
cost over current platforms yet delivering satisfaction along
outcomes and jobs that are balanced with importance.
[0199] In the example of FIG. 23, the flowchart 2300 continues to
module 2306 with providing tools that compare similar jobs in
markets having similar outcomes, and sharing related
platform-enabling-technology-paradigms. The tools can look across
similar jobs in markets using either internal or external sources.
The same or related platform-enabling-technology-paradigms might
include, for example, technologies that are associated with
electronic storage media. This tool is useful to postulate
technology redeployment strategies and chart potential pathways of
technology-based disruption and new platform breakthroughs.
[0200] FIG. 24 depicts a flowchart 2400 of an example of a method
for innovation management. In the example of FIG. 24, the flowchart
2400 starts at module 2402 with creating a job or outcome record
including at least one constraint parameter associated with a job
or outcome. The record can be any applicable data structure that
includes constraint parameters that bound characteristics of a
solution that will meet a need of the job or outcome. The purpose
of the constraint parameter is to facilitate the identification of
needs-gaps, and any applicable parameter or plurality of parameters
that serves this purpose can be used. The parameters need not be
specifically associated with a constraint, and could be derived
from information stored in association with a job or outcome. For
the purposes of this example, the "constraint parameter" exists
even where various information is used to derive it, regardless of
whether computation or evaluation or reorganization of data is
desirable to determine the needs-gap. Moreover, the particular
constraint parameter may not be known until the job or outcome
record is compared to, for example, a solution. For example,
comparing two different solutions to the same job or outcome record
could result in two different constraint parameters derived from
different data associated with the job or outcome record.
[0201] In the example of FIG. 24, the flowchart 2400 continues to
module 2404 with storing the job or outcome record in accordance
with a coherent relational model. In order to have a coherent
relational model, the record must have a format that is similar to
other job or outcome records such that the various job or outcome
records can be matched to solutions that also fit into the coherent
relational model.
[0202] In the example of FIG. 24, the flowchart 2400 continues to
module 2406 with creating a solution record including a capability
parameter indicative of a capability of a solution to meet the
needs of the job or outcome using the constraint parameters. The
record can be any applicable data structure that includes
capability parameters that can be matched to a constraint parameter
of a job or outcome record such that the constraint parameter
bounds the capability parameter. The purpose of the capability
parameter is to facilitate the identification of needs-gaps, and
any applicable parameter or plurality of parameters that serves
this purpose can be used. The parameters need not be specifically
associated with a capability, and could be derived from information
stored in association with a solution. For the purposes of this
example, the "capability parameter" exists even where various
information is used to derive it, regardless of whether computation
or evaluation or reorganization of data is desirable to determine
the needs-gap. Moreover, the particular capability parameter may
not be known until the solution record is compared to, for example,
a job or outcome. For example, comparing two different jobs or
outcomes to the same solution record could result in two different
capability parameters derived from different data associated with
the solution record.
[0203] In the example of FIG. 24, the flowchart 2400 continues to
module 2408 with storing the solution record in accordance with a
coherent relational model. In order to have a coherent relational
model, the record must have a format that is similar to other
solution records such that the various solution records can be
matched to job or outcome records that also fit into the coherent
relational model.
[0204] In the example of FIG. 24, the flowchart 2400 continues to
module 2410 with computing a difference between the capability
parameter for the solution and one or more constraints associated
with the constraint parameter for the job or outcome. In practice,
it is unusual for a solution to perfectly meet the needs associated
with a job or solution. However, it is possible to identify a first
solution for a job or outcome that is currently being used and
identify a second solution for the job or outcome that is being
used in a difference context, or has not been implemented in
practice, that better meets the needs. For example, the second
solution might require less expertise on the part of an engineer to
implement, require less time to implement, require fewer resources
to implement, or may enable concurrent implementation of the
solution during a bottleneck of a production process, to name a few
examples. The difference can includes multiple different
characteristic areas (e.g., cost and time), some of which might be
better in one characteristic area and worse in others, but are
better for some reason (e.g., enabling concurrent operation with
another bottlenecked process). Therefore, although it might be
useful to refer to some solutions as having a bigger difference
(i.e., the solutions are not as effective at meeting the needs) it
should be noted that a superior solution might still be inferior in
certain respects, but is better in the aggregate for a specific job
or outcome.
[0205] In the example of FIG. 24, the flowchart 2400 continues to
module 2412 with identifying the job or outcome and the solution in
association with the job or outcome. Typically, multiple solutions,
if they are known to the system, will be applied to a job or
outcome to produce multiple different options. For illustrative
simplicity in this example, it is assumed that the best match of
solution to job or outcome is found for use in module 2414.
[0206] In the example of FIG. 24, the flowchart 2400 ends at module
2414 with facilitating management of commercial actions taken in
association with the difference between the constraint parameter
and the capability parameter. Commercial actions might include
taking no action because the new solution is not sufficiently
superior to an old solution, attempting to further identify reasons
why the identified solution is superior in certain contexts,
attempting to obtain patent protection for an idea that is fleshed
out in observation of the potential improved solution, to name a
few examples.
[0207] The flowchart 2400 can, of course, be repeated at various
stages, including finding another solution that appears to be
superior in some context (e.g., an originally identified first
solution might have higher cost than a later identified second
solution, and although the higher cost might be "worth it" in one
context, the higher cost might not be "worth it" in another
context; or it may be the case that a human can identify reasons
why the identification failed to find the superior solution on the
first attempt due to inadequate intelligence on the part of the
system), or attempting to match a different job or outcome to the
identified solution, or attempting to find solutions to jobs or
outcomes that are part of a larger process, to name a few
examples.
[0208] The flowchart 2400 can also be used in the context of
selecting a growth strategy that includes organizing data around a
market and optionally storing research results to improve the data
(modules 2402-2408), determining under/overserved jobs or outcomes
in the market and optionally determining how many under/overserved
needs exist in outcome-based and job-based market segments if
segment data exists (modules 2410-2412), and selecting and
prioritizing which growth paths to pursue for the market and for
specific outcome-based and job-based segments (module 2414).
Additional actions that can be taken in association with module
2414 include gaining management agreement on pursuit of growth
strategies (priority, timing, etc.), obtaining cost, timing, and
boundary inputs from management for each targeted growth path,
obtaining prioritized evaluation criteria from management for each
targeted growth path, defining a pool of potential participants for
idea generation, concept convergence, evaluation, concept testing,
etc., collecting analogies/examples of creativity triggers, and
signing up to get data pushed to an employee. Some of these
additional activities could include refining the data and
reexecuting the flowchart 2400. Similar techniques can be employed
for business model idea generation and for feature idea
generation.
[0209] FIG. 25 depicts an example of an integrated innovation
platform 2500. Advantageously, the platform 2500 enables an
enterprise to incorporate operational information from the
enterprise and relate that to the ODI data for the purposes of, for
example, enterprise performance management, resource allocation,
and idea creation. The platform 2500 includes a coherent relational
model 2502, multidimensional data analysis and metadata engines
2504, a collaboration and knowledge integration platform 2506, and
value added workflow engines 2508.
[0210] The coherent relational model 2502 includes systems, such as
described earlier in this paper, that store jobs and outcomes,
solutions, and other data in a relational database. The model can
include, for example, a relational ODI data environment. The model
will likely include various features and engines that facilitate
input, output, reorganization, and association of data.
[0211] The multidimensional data analysis and metadata engines 2504
are take advantage of the organization of the coherent relational
model 2502. Conceptually, the engines are "built on top of" the
coherent relational model 2502. Alternatively, the engines could be
considered part of or an extension of the coherent relational model
2502. Multidimensional data analysis, as used in this paper, is
essentially impossible to accomplish in a practical, useful manner
without an underlying methodology that supports association of
disparate solutions to jobs and outcomes, and comparisons between
other disparate records (e.g., jobs and outcomes to jobs and
outcomes, solutions to solutions, and other data to other
conceptually, contextually, or otherwise dissimilar data). Metadata
engines facilitate the association of various records on a metadata
level, possibly without higher level "data" analysis, or can be
used in conjunction with multidimensional data analysis.
[0212] The collaboration and knowledge integration platform 2506
provides the underlying data in a useful format to facilitate
collaboration between humans or business entities, and to integrate
new data into the existing relational model. The data derived by
the collaboration and knowledge integration platform 2506 can
"trickle down" to the multidimensional data analysis and metadata
engines 2504 to further enhance or "tweak" the coherent relational
model 2502.
[0213] The value added workflow engines 2508 are the "top level" of
the platform 2500, and, in operation, provide insights, in the form
of, for example, related insights data and media 2510 to an
enterprise 2512. It may be noted that the related insights data and
media 2510 could be connected to the collaboration and knowledge
integration platform 2506 and passed through to the value added
workflow engines 2508 and, as always, data from the coherent
relational model 2502 can be passed up through the layers of the
platform 2500, and other data (such as the related insights data
and media 2510) passed down for integration into the coherent
relations model 2502. The more the value added workflow engines
2508 learn about various aspects of the enterprise 2512, the better
the insights will be related to what the enterprise 2512 does. This
is because any data received about the enterprise 2512 is itself
integrated into the coherent relational model 2502 (in this
example, through the higher layers of the platform 2500). To this
end, the enterprise 2512 can provide inputs, in the form of, for
example, activities, assets, priorities, and constraints 2514. It
may be noted that the activities, assets, priorities, and
constraints 2514 can be recycled back to the enterprise 2512 with
the aid of value added workflow engines 2508, and could, as always,
be passed down to the coherent relational model 2502 for
integration. In the example of FIG. 25, the inputs from the
enterprise 2512 are provided back into the platform 2500 in the
form of innovation inputs, and outputs from the value added
workflows can be provided in the form of innovation results. This
is conceptually illustrated in the example of FIG. 25 by the box
2516, which shows innovation inputs directed toward the value added
workflow engines 2508 and innovation results directed away from the
platform 2500. It may be noted that the related insights may or may
not include "innovation results."
[0214] It is assumed that the coherent relational model 2502 will
also be updated from time to time by extracting new data 2518 from
markets and solvers 2520 in an automated fashion, though this would
not include extracting new data in a manual fashion. The new data
2518 can be provided to the platform 2500 as innovation inputs
and/or as raw data. Although the automated acquisition of the new
data 2518 is believed to be desirable, it is, strictly speaking,
optional, since a system could function without it after being
built, at least for a time, in a "demo" build, or for some other
reason.
[0215] Advantageously, by teaching the platform 2500 activities,
assets, priorities, and constraints of the enterprise 2512, the
value added workflow engines 2508 can enable the enterprise 2512 to
create new ideas and allocate resources (assets) toward researching
and/or implementing the new ideas, as well as other ideas that
might be gleaned from the coherent relational model 2502 during a
innovation cycle. Since the coherent relational model 2502 provides
contextualized jobs and outcomes and solutions data, the enterprise
2512 is more likely to match solutions to needs, and to allocate
resources to the jobs or outcomes that will benefit the most from
the allocation. That is, the enterprise 2512 can allocate resources
to the jobs or outcomes that have the largest needs-gap, or
identify needs that are entirely unmet. It may be noted that an
unmet need is for practical purposes no different than a poorly met
need in the sense that the needs-gap is still determined, and it
may be the case that the needs-gap is greater for a poorly met need
than an unmet need. Or, stated differently, an unmet need is a job
or outcome that has the solution "do nothing," which may or may not
have an explicit representation as a solution in the coherent
relational model 2502.
[0216] Using the platform 2500, the methods illustrated in FIGS.
3-24, a USIMS server, such as is illustrated in FIG. 1 or 2, is
capable of providing integration of data entities around products,
platforms, projects, competitors, technologies/IP, campaigns,
organization, resources, and performance with the core data model
(e.g., an ODI data model), and integration of opportunity data,
context information, prompts for sparking creativity, and
management decision criteria within a systematized idea generation
process.
[0217] Engines, as used in this paper, refer to computer-readable
media coupled to a processor. The computer-readable media have
data, including executable files, which the processor can use to
transform the data and create new data. The engines transform data
and create new data using implemented data structures, such as is
described with reference to FIG. 2, and implemented methods, such
as are described with reference to the various flowcharts.
[0218] The detailed description discloses examples and techniques,
but it will be appreciated by those skilled in the relevant art
that modifications, permutations, and equivalents thereof are
within the scope of the teachings. It is therefore intended that
the following appended claims include all such modifications,
permutations, and equivalents. While certain aspects of the
invention are presented below in certain claim forms, the applicant
contemplates the various aspects of the invention in any number of
claim forms.
[0219] For example, where this is an application in the United
States, while only one aspect of the invention is recited as a
means-plus-function claim under 35 U.S.C sec. 112, sixth paragraph,
other aspects may likewise be embodied as a means-plus-function
claim, or in other forms, such as being embodied in a
computer-readable medium. (Any claims intended to be treated under
35 U.S.C. .sctn.112, 6 will begin with the words "means for", but
use of the term "for" in any other context is not intended to
invoke treatment under 35 U.S.C. .sctn.112, 6.) Accordingly, the
applicant reserves the right to add additional claims after filing
the application to pursue such additional claim forms for other
aspects of the invention.
* * * * *