Field of the Invention

[0001 ] The present invention relates to a web platform for preparing a project management document, such as a digital project management plan. The present invention also relates to a method for generating a project management plan using a dynamically updated database.

Background

[0002] Project management software is well known in the art. In order to assist those doing project management, often project planners will include templates and other pre prepared data, in order to start a project with as much information as possible. However, this information is not typically integrated into off-the-shelf software packages. Templates, while helpful, often need to be completely rebuilt for the specific project type at hand. For example, a generic building project may be available in a template form, however, the information is static and predefined and often out of date.

[0003] Years and often decades of project information is available. However, to date this information is not being made accessible. Thus, the value of planned versus actual data has not been harnessed to help those doing new project planning jobs.

[0004] Project data regarding planning and actual results are stored in disparate places by individual companies or organisations and rarely is made available for third parties to access. This information would be very valuable to project planners in creating realistic task milestone and project works.

[0005] Another problem often facing project planners is the requirement that the project output be compliant with standards that the industry accepts as best practice. These standards have been defined and have been available for the past two or three decades. However, existing project management software packages and systems do not intentionally define their plans in terms of these standards, or supply outputs that are compliant to these standards. For example, the ISO project management standard defines certain output documents, their content and the level of analysis needed to qualify for the ISO qualification.

[0006] It will be clearly understood that, if a prior art publication is referred to herein, this reference does not constitute an admission that the publication forms part of the common general knowledge in the art in Australia or in any other country.

l Summary

[0007] The present invention in one preferred aspect of the present disclosure sets forth a web platform for preparing a digital project management document, such as project management plan. The web platform includes a first database including project data from prior projects. The first database is dynamically updated each time a project management document is uploaded to the platform. The web platform includes a processor configured to receive inputs from a user to generate the project management document. The processor is configured to: identify a project category based on the user inputs; compare the user inputs with data in the first database based on the identified project category; and generate an accuracy score of the project management plan being prepared based on the comparison of the user inputs with the data in the first database.

[0008] The platform may further include a second database including data relating to best practice guidelines and/or industry standards for a given industry. The processor may be configured to access data from the second database based on the identified project category and merge the accessed data with the project management document being prepared.

[0009] The processor may be configured to generate a best practice score based on a comparison of the user inputs with the data accessed from the second database.

[0001 0] The first database may include project data relating to one or more prior projects by the same user. The platform may further include a second database including project data derived from third party users. The project data may include data sources such as industry data and benchmark information.

[0001 1 ] Data from the first and second databases may be merged with the project management document being prepared.

[0001 2] The processor may be configured to compare the user inputs to project data in the first database to suggest project data from the first database for inclusion in the project document, based on the comparison. The processor may be configured to compare user content added to the project document to project data in the second database to suggest project content from the second database for inclusion in the project document, based on the comparison.

[0001 3] Data from the first and second databases may be differently weighted when generating the accuracy score.

[00014] The processor may be configured to generate two or more project

management documents having one or more common fields populated by the user inputs. The processor may be configured to automatically effect an update across the two or more project documents when one of the common fields are changed by the user inputs.

[0001 5] In another preferred aspect, the present disclosure sets forth a web platform for preparing a digital project management document, such as a project management plan. The web platform includes a first database including project data from prior projects. The web platform includes a second database including data relating to best practice guidelines and/or industry standards for a given industry. The web platform includes a processor configured to receive inputs from a user to generate the project management document. The processor is configured to identify a project category based on the user inputs. The processor is configured to compare the user inputs with data in the first database based on the identified project category. The processor is configured to compare the user inputs with data in the second database based on the identified project category. The processor is configured to generate at least one of an accuracy score of the project management document being prepared based on the comparison of the user inputs with the data in the first database, and a best practice score based on the comparison of the user inputs with the data in the second database.

[0001 6] The processor may be configured to generate a composite score based on both of the comparisons of the user inputs with the respective first and second databases.

[0001 7] The processor may be configured to populate the project management document being prepared with data from the first database based on the identified project category. The processor may be configured to populate the project management document being prepared with data from the second database based on the identified project category.

[0001 8] In another preferred aspect, the present disclosure sets forth a method for generating a project management document using a dynamically updated database. The method includes receiving a user input into a graphic user interface, the user input including a project category. The method includes comparing, based on the project category, the user input against data in a first database containing project data from prior project management documents. The method includes generating an accuracy score based on the comparison. The method includes finalising the project management document. The method includes updating the first database with project data from the finalised project management document.

[0001 9] The project management plan may be finalised if the accuracy score is above a predetermined threshold. [00020] The method may include comparing, based on the project category, the user input against data in a second database containing data related to best practice guidelines and/or industry standards for a given industry.

[00021 ] The method may include generating a best practice score based on the comparison of the user input with the data in the second database.

[00022] The method may include populating the project management document with data from the second database based on the project category and additional user input.

[00023] The method may include populating the project management document with data from the first database based on the project category and additional user input.

[00024] The method may include updating the first database with project data from sources other than the user.

[00025] The method may include weighing sets of data in the first database and using the weights to generate the accuracy score.

[00026] In another preferred aspect, the present disclosure sets forth a system for preparing a digital project management document. The system includes a first database including project data from prior projects. The system includes a second database including data relating to best practice guidelines and/or industry standards for a given industry. The system includes a processor configured to receive inputs from a user. The system includes a non-transitory computer readable medium encoded with a computer program coupled to the processor to: identify a project category based on the user inputs; compare the user inputs with data in the first database based on the identified project category; compare the user inputs with data in the second database based on the identified project category; and generate at least one of an accuracy score of the project management document being prepared based on the comparison of the user inputs with the data in the first database, and a best practice or quality score based on the comparison of the user inputs with the data in the second database.

[00027] The computer program encoded on the non-transitory computer readable medium may include instructions that when executed by the processor cause the processor to generate a composite score based on both of the comparisons of the user inputs with the respective first and second databases.

[00028] The computer program encoded on the non-transitory computer readable medium may include instructions that when executed by the processor cause the processor to populate the project management document being prepared with data from the first database based on the identified project category. [00029] The computer program encoded on the non-transitory computer readable medium may include instructions that when executed by the processor cause the processor to populate the project management document being prepared with data from the second database based on the identified project category.

[00030] The claims as filed and attached with this specification are hereby

incorporated by reference into the text of the present description.

Brief Description of the Figures

[00031 ] Fig. 1 is a diagram of a system for preparing a project management plan in accordance with an embodiment of the present disclosure.

[00032] Fig. 2 is a flow diagram of method steps performed by the system of Fig. 1 .

[00033] Fig. 3 is a diagram of algorithm steps performed by the system of Fig. 1 .

[00034] Fig. 4 is a diagram of another embodiment of a system for preparing a project management plan.

[00035] Fig. 5 is a flow diagram of the steps performed by the system of Fig. 4.

Detailed Description of the Drawings

[00036] Reference will now be made in detail to the present preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings.

[00037] Fig. 1 shows an embodiment of a system 100 for preparing or generating project documents, such as a digital project management plan. System 100 is configured to receive inputs from a user 102 via a user terminal 104 to generate or prepare a project document. The preferred elements of system 100 and their interrelationship are described below.

[00038] System 100 includes a server machine or server 106 capable of

communicating with user terminal 104 over a network 108, in this case, the Internet.

[00039] Server 106 is accessed by user 102 over network 108. Server 106 would typically include one or more processors 107 and one or more high-capacity storage devices such as hard drives, solid-state drives, flash disks and the like. The hardware of server 106 may be any of a number of off-the-shelf components known to those skilled in the art of computing. Server 106 stores databases of information as discussed in more detail below. Server 106 includes software which is executed on processor 107 to manage queries and inputs from user 102 and to generate the digital project documents. It will readily be appreciated that server 106 can include one or more machines acting together to define server 106, such as a cloud server.

[00040] Server 106 stores, or is in network communication with, a first database 1 10 and a second database 1 12.

[00041 ] First database 1 10 is a database of quantitative data sources relevant to detailed project data for project documents. Data sources in first database 1 10 include data or data sets from a company knowledge database, propriety database of system 100, third party databases, industry databases and other data sets.

[00042] First database 1 10 is connected to third party databases, such as industry databases, indicated by reference numerals 84, 86 via network 108. Any updates in the third party databases 84, 86 are communicated to first database 1 10 to keep first database 1 10 up to date. The updates may be real-time, near real-time, periodically, or may be event driven.

[00043] Quantitative data in first database 1 10 will include, for example, industry benchmark information such as labour rates, quantity rates, company knowledge data such as historical project performance information, databases of aggregated project performance, data proprietary to the system, third party data sets such as MS Project and government historical project information, and other data sets incorporated either through manual uploading or learned data.

[00044] Second database 1 12 is a database of Best Practice Standards and

Guidelines of written content and structure for project documents, specifically digital project management plans. The Best Practice Standards and Guidelines data sources include: International Organization for Standardization documents, precedent project management documents, internal best practice documents, third party best practice documents, industry best practice documents and other best practice documents.

[00045] Second database 1 12 is connected to third party databases, such as industry databases or reference databases, indicated by reference numerals 80, 82 via network 108. Any updates in the third party databases 80, 82 are communicated to second database 1 12 to keep second database 1 12 up to date. The updates may be real-time, near real-time, periodically, or may be event driven.

[00046] Best Practice Standards and Guidelines data will include, for example, PMBoK, ISO:21500, ISO:3100, Prince2, and Scrum standards; industry best practices such as AIPM, PMI, IPMA, Engineering, Software, Change Management; Company internal data; and third party best practices as learned over time from past project data.

[00047] Data, content and document structure in first and second databases 1 10, 1 12 are anonymised by deleting entity or author specific information from the documents.

Anonymization ensures confidentiality of the source documents, which may be sensitive or commercial in confidence to the contributors of the source documents to the first and second databases 1 10, 1 12. Anonymization may be performed automatically on source documents so that the data, content and document structures are suitably anonymous. The

anonymization algorithm may, for example, replace reference to the name of a person or legal entity identified as sensitive information with the some generic term.

[00048] The anonymization algorithm may start with the definition of a table of sensitive data which can be identified within documents, such as names and addresses. The table of sensitive data is used to generate a dictionary of terms. The dictionary of terms is used in a step of semantic analysis of the text in the source documents for the databases 1 10, 1 12. The confidential information in the source documents is detected and replaced by generic terms drawn from a library of generic terms. The document can be manually reviewed by a person who approves or rejects modified text in the source document. Finally, the anonymized data, content and document structure is entered into the first and second databases 1 10, 1 12.

[00049] Server 106 is accessible by terminals 104 via the network 108 using conventional web browser software. In another embodiment, terminal 104 may have an applications program or App executed on the terminal which communicates with server 106.

[00050] Although only one user 102 is shown accessing system 100, it will be appreciated that many users will be able to access system 100 simultaneously via different terminals. Each user has an account which allows the user to set up various projects relating to each account.

[00051 ] Each terminal 104 can be in the form of a desktop computer, laptop, PDA, tablet, smart phone or any other device with a display and capable of accessing the Internet.

[00052] Turning to Fig. 2, system 100 executes steps via processor 106 to generate, validate and score project documents as shown.

[00053] User 102 may require a new project management plan to be created from a system provided template. Server 106 is configure to present a user interface to user 102 via terminal 104, which allows user 102 to select the project category and document type required at step 1 14. Server 106 is configured to generate a graphical user interface displayed on terminal 104 for selecting the project category and project document type. The graphical use interface may have search fields for searching the project categories and project document types available in system 100. Server 106 presents a list of matching documents in response to the search. The graphical user interface may include drop-down boxes populated with different project categories and project document types for the user to select. User 102 may select more than one type of document for a given project category in instances where multiple documents are to be generated for a specific project.

[00054] Project categories may include, for example:

• Administrative

• Construction

• Computer Software Development

• Procurement

• Equipment or System Installation

• Event

• New Product Development

• Research

• Other

[00055] Project management document types may include, for example:

• Project Proposal

• Feasibility Study

• Project Management Plan

• Project Charter

• Project Scope Statement

• Project Scope Management Plan

• Work Breakdown Structure

• Resource Breakdown Structure

• Project Schedule

• Quality Management Plan

• Process Improvement Plan

• Staffing Management Plan

• Risk Management Plan

• Risk Register

• Quantitative Risk Analysis

• Risk Response Plan • Procurement Management Plan

• Contract Statement of Work

• Project Status Report

• Project Closure Report

• Post Implementation Review

• Business Case

• Other

[00056] System 100 retrieves the relevant templates, precedents, and similar examples from second database 1 12 based on project category and document type selected. The retrieved documents are provided to user 102 at step 1 18 via the user interface displayed at terminal 104.

[00057] The templates and precedents presented to user 102 are a merge of the different documents and data retained in second database 1 12.

[00058] A merging function of system 100 to merge the different documents and data retained in second database 1 12 is indicated by reference numeral 1 16. The document templates or precedents are a merge of the written content and structure of documents from the different data sources in second database 1 12. The merging function may include an algorithm that merges document structure of documents of the same project category and document type from different data sources. For example, the document structure of a document of a particular document type and project category from one data source is merged with the document structure of a document of the same document type and project category from a different data source. The merge algorithm may merge the document structures to contain all of the document structure from both source documents, but without duplication in the merged document.

[00059] The merge algorithm may have a weighting function which assigns different weights to different source documents. Different source documents are given different weights and preference is given to the document structure from documents with a higher weight. More recent or more authoritative source documents may be given more weight than older or unattested source documents.

[00060] The merge algorithm may similarly, or alternatively, have a weighting function wherein a weight is assigned to different data sources within second database 1 12. Project documents sourced from the International Organization for Standardization document source may, for example, be given more weight when generating a merged document than documents sourced from the internal best practices document source. [00061 ] It will be apparent from the description of the merging function above that the source documents in second database 1 12 each have metadata associated with the particular source document. System 100 is configured for each source document to include associated metadata of at least the project category associated with the document and the project document type. The metadata may further include the data source associated with the document and the assigned document weight. The metadata may further include description of the document and its contents.

[00062] Merged document templates and precedents have the same metadata of "project category" and "project document type" associated with the document templates and precedents, which reflect the metadata of the source documents from which the merged document templates and precedents are generated.

[00063] Merging document content, to generate content for the document templates and precedents, is performed in the same manner as described above for merging document structure from different source documents.

[00064] The document structure and content of the merged document templates and precedents is dynamic in that the document structure and content is automatically updated as new documents are added to second database 1 12.

[00065] User 102 populates or edits the project document provided as a template or precedent, as indicated at step 122. User 102 edits the documents to suit his/her particular needs and particular project requirements. User 102 may also start from a blank document as indicated in step 120.

[00066] System 100 recommends content for the project plan document at step 124. The content recommendation can be real-time or near real-time as the user is editing or populating the document. User 102 may choose to accept, ignore or modify the content recommend by system 100. The content recommended to user 102 is sourced from second database 1 12.

[00067] Server 106 is configured to monitor text edits by user 102. Server 106 includes a recommender engine executed on processor 107. The recommender engine compares the document edits being made by user 102 to the document structure and document content available to server 106 from source documents in second database 1 12. The recommender engine displays recommended content to the user, which may then be accepted, ignored, declined or modified. The recommender engine may use a content- based filter which utilizes a series of discrete characteristics of a section of the document being edited in order to recommend additional content with similar properties. [00068] Server 106 is operable to dynamically update content and data second databases 1 12. If a user continues to decline recommended content, data in second database 112 is updated with the user's data. Future recommendations by recommender engine will then include the updated data.

[00069] Steps 122 and 124 are executed iteratively in a feedback loop editing process 126. Adding content and editing content updates document data stored in a document data database 132 of server 106.

[00070] Projects documents 130 are automatically generated using the data in document data database 132. Some of the data in document data database 132 may be included in fields across multiple project documents. Changing or editing a field in one document, or in the document data database 132, dynamically updates the field for all other project documents for a specific project. Processor 107 is configured for common fields across documents to be automatically updated if user 102 changes the field in one document or in the central repository 132. Processor 107 effects the update across the multiple documents 130.

[00071 ] The project data in document data database 132 includes detailed quantitative data for the project. Initial quantitative data is input by user 102 at step 140.

[00072] The quantitative data may be input after the content and structure of the project document has been finalized. This sequence of development of the project document is indicated by broken line arrow 128. Alternatively, the quantitative data may be input concurrently with the content and structure of the project document being developed.

[00073] Server 106 is configured to validate the quantitative project data input by the user, as shown at step 142. The project data input by user 102 is compared to aggregated data from first database 1 10.

[00074] Quantitative data from the different data sources in first database 1 10 gets merged, as indicated by step 144. Server 106 is configured to execute a merge algorithm to merge the quantitative data from the different database sources. The merged data is dynamic in that the quantitative data in first database 1 10 is dynamically updated as new source documents are added to first database 1 10.

[00075] The merge algorithm may have a weighting function wherein a weight is assigned to different database sources or datasets in first database 1 10. Different database sources are assigned different weightings. Preference is given to document sources with higher weighting. For example, datasets from a company knowledge database may carry more weight than datasets from third party databases. [00076] The user's qualitative data inputs are validated against the merged data from database 1 10. Validation at step 142 is performed by a comparison engine executed on processor 107 of server 106. The comparison engine compares the qualitative data inputs by user 102 against the merged data from database 1 10. If the comparison engine recognizes discrepancies between the user data and the merged data, then comparison engine may recommend changes in the qualitative data to user 102 at step 148. The comparison engine may have to pass a threshold of conformity in order for system 100 not to recommend data. The threshold of conformity may be user adjustable. An inexperienced project manager may elect to have a high threshold of conformity so that system 100 recommends data and information when there is only slight deviation from best practice and accuracy. An experienced project manager may elect to have a low threshold of conformity so that system 100 does not recommend data and information even when there is relatively high deviation from best practice and accuracy.

[00077] As an example of validation and recommendation, a project management plan may rely on a software development component allocating a 65% utilisation of internal resources for developing the software. System 100 provides feedback to user 102 at validation step 142 that, based on the merged database sources of first database 1 10, resource utilisation for the specific type of software development is usually around 87%. System 100 recommends to user at step 148 to use the resource utilisation of 87%. The validation information helps or guides user 102 to enter more realistic or accurate data for the project plan.

[00078] Validation of resource costs by server 106 is another example of qualitative data validation. User 102 may have made certain assumption as to the cost of a software programmer per hour, these assumptions are validated at step 142 by comparison to the merged data from first database 1 10.

[00079] User 102 edits the detailed project data at step 146. System 100

recommends data and information for the project plan at step 148. User 102 may choose to accept, ignore or modify the detailed project data recommend by system 100. The detailed project data recommended to user 102 is sourced from first database 1 10.

[00080] Steps 146 and 148 are iterative in a feedback loop editing process 150.

[00081 ] Editing the qualitative data at step 146 updates the document data stored in document data database 132.

[00082] Projects documents 130 generated by system 100 include the written content and structure data, and the qualitative data, stored in document data database 132. Project documents 130 are generated automatically and may include all types of project documents, including a digital project management plan.

[00083] Project documents 130 are scored against best practice and for accuracy at step 134. A composite score is assigned to the generated project document depending on its compliance or conformity to the data from first and second databases 1 10, 1 12. The composite score may comprise two components, a score for written content and structure, and a score for the quantitative data. The score for the written content and structure is also referred to as the best practice score or quality score. The score for the quantitative data is also referred to as the accuracy score.

[00084] The quality score is calculated or generated based on the compliance or conformity of the written content and structure of the generated project document to the merged data of second database 1 12. The accuracy score is calculated or generated based on the compliance or conformity of the quantitative data in the generated project document as compared to the merged data of first database 1 10.

[00085] The scores are calculated using a comparison engine executed on processor 107 of server 106. The comparison engine is operable to calculate the scores based on the degree of compliance or conformity as discussed.

[00086] The composite score is derived from a combination of the quality score and the accuracy score. The two scores may be combined and differently weighted to provide the composite score.

[00087] Server 106 is operable to display the composite score and/or the quality score and/or the accuracy score to user 102 via the graphical user interface at terminal 104. The scores may be displayed real-time or near real-time as the user edits document data in document data database 132, or may be displayed at a time when user chooses to finalize one or more documents 130.

[00088] Server 106 may be operable to compare the calculated scores against a threshold or target score and give user 102 feedback of the comparison. For example, a company or organization may require that all project documents meet a system score of seven out of ten. This allows a company to set up minimum standards for project documentation and provides goals for improvement.

[00089] The server may be operable to calculate an average project document score for all project documents across a project. The overall score is the average of all of the composite scores for the different project documents for a given project. The average project document score may be compared to a threshold in the same manner as described above for the composite score. The average project document score may comprise two components, the average quality score and the average accuracy score.

[00090] The average quality score is the average of all of the quality scores for the different project documents for a given project. The average accuracy score is the average of all of the accuracy scores for the different project documents for a given project.

[00091 ] The average quality score and the average accuracy score may both be displayed to the user separately from the average project document score.

[00092] Server 106 may visually indicate if a score is below the threshold or target. For example, a score below seven may be highlighted in red to indicate that it has not met the threshold score. Scores above seven may be highlighted in green to indicate that the threshold score has been met. The scores may be digitally imprinted on the generated project documents or form part of metadata of the generated project documents.

[00093] Based on the score(s), the user may elect to change or update the project document(s) as shown in step 136. Document data 132 is updated and amended project documents are generated so that a fresh score is assigned at step 134.

[00094] User 102 may continue to edit the document data until he/she is satisfied with the assigned score, or the score meets the threshold.

[00095] Finalised project documents and data generated by system 100 are saved into first and second database 1 10, 1 12 to dynamically update the databases.

[00096] Finalised project documents and data generated by system 100 may also be saved to a local drive of terminal 104.

[00097] Fig. 3 shows a flow diagram of algorithm steps to generate and display the composite score and whether the composite score is above or below the threshold value.

[00098] System 100 receives user inputs at step 202 of the user's selection of the project category and project document type to be prepared. The project category is identified based on the user inputs at step 204.

[00099] User inputs include content and quantitative data inputs. The user's content inputs are compared to the data in second database 1 12, at step 208, for processor 107 to generate a quality score at step 212. The user's quantitative data inputs are compared to the data in first database 1 10, at step 206, to generate an accuracy score at step 210.

[0001 00] The composite score is generated or derived from the combination of the quality score and the accuracy score, as shown at step 214. The composite score generated in step 214 is displayed to user 102 at step 216. The composite score is compared to a threshold score at step 218. If the score is above threshold at point 220, then the above threshold status is indicated to user 102 at step 222. If the score is below threshold at point 220, then the below threshold status is indicated to user 102 at step 224.

[0001 01 ] Referring to Fig. 4, another embodiment of a system for generating a project management plan is shown. A user at terminal 10 uses a project management software application 1 1 that connects over a public network, such as the Internet 13 to a project management server 12 that has accounts 14 that allow users to set up various projects 15, 16, 17 related to each account 14.

[0001 02] The server 12 stores project data from other sources 18 including data related to each project such as project metadata 19 and information about resources related to each project 20.

[0001 03] To maintain the privacy and limit abuse of the project data, data is aggregated and anonymised from multiple projects within the accounts 14 on the server 12 as well as third party data from third party project management services 21 where users 22 have volunteered to allow their project data 23, 24 and 25 to be used in assisting with the planning of future projects on the server 12 by users with accounts 14.

[0001 04] Additionally, industry data and metadata 26 can be obtained from industry and association services and amalgamated with the aggregated project data 18 to enable users 14 to obtain this industry wide information for use in their own projects.

[0001 05] The database of metadata 19 allows the users 14 to look for example guidelines and restraints that can be used in realistically planning new projects. This data can be used in every aspect of project planning, from resource to allocation management and its assumptions, through to quality guidelines and budget estimations.

[0001 06] The result is a system that enables lessons learned from previous projects and the many hours of work and expertise of other users to be brought to bear to make a project plan more effective and efficient.

[0001 07] Referring to Fig. 5, key process steps used by the system of Fig. 4 is shown. The example control process of the example embodiment uses standard steps such as milestone development 41 task and resourcing 42 and critical path analysis 43. These could be used as basic stepping stones of any project management package and they involve iteration between the steps and refinement as data is added to the project. This process is expanded 40 to include steps to make the project compliant to industry standards and to enable anyone using the project to have confidence that certain basic criteria of

professionalism have been reached. [0001 08] At the beginning of the process, ISO compliant resource and guidelines are queried of the project manager or person producing the project 44. Also, business constraints such as budget, time and quality constraints are queried of the user to encourage them to start the planning process with as many ISO criteria being in place before the main planning process starts 45.

[0001 09] To aid in this process, industry benchmarks for milestone tasks, resourcing and all other project criteria, are presented to the user to assist them 46. Industry data, metadata and analysis tools are used to assist the project plan at the beginning of the project development process and throughout the traditional process of adding milestones, tasks, resourcing and critical path.

[0001 1 0] This process of introducing industry and third party information 46 is interactive and supplied continuously as a resource to the project planner, as they work through the project development process.

[0001 1 1 ] The whole system 40 is designed to be ISO compliant and thus when the project is deemed to be complete, a series of ISO compliant documentation is supplied 47. ISO compliant documentation is valuable in that it integrates the best practices as accepted by industry.

[0001 1 2] This system also enables compliance across multiple organisations, as the standards are universally accepted. It also speeds up the process of integrating the project within an organisation due to the high level of trust in the quality of the project information being shared.

[0001 1 3] Alternative embodiments of the invention will be apparent to those of ordinary skill in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the claims which follow. It will be understood that the term "comprising" is intended to have a broad, open meaning and not limited to a particular embodiment.

[0001 14] Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.