The most significant change introduced by SDGToolkit is the provision of easy access to an integrated project development environment that combines an easy-to-follow due diligence project design procedure accompanied by all of the analytical tools needed to complete each procedure.
The ability to evaluate design changes in terms of sustainability and returns before committing funds reduces the common risk of over- or under-ambitious project designs that are both destined to have very low development impacts.
SDGToolkit does away with paper-based guidelines and the lack of appropriate analytical tools. It provides a live interactive development system placing all of the necessary analytical tools directly into the hands of those who need them to design, implement and manage decision making and project evaluation.
The climate crisis is too important to continue with out-dated and inappropriate project design systems.
Besides a considerable support for the design process, SDGToolkit allow for the input of the specifications of the projects design identified as the best feasible options or Logical Project Options (LPO) which is used to establish benchmarks for evaluation of performance using the implementation phase.
The system is the first system to apply all of the Open Quality Standards Initiative recommendations OQSI:4 (2021)
1 for project cycle and portfolio management and the system will be regularly updated as new recommendations are released.
These recommendations are based on a stepwise Due Diligence Design Procedure (3DP) with each step being supported by appropriate Analytical Tools (ATs) used to complete each step on the basis of logical evidence. Guidelines and technical support help users select the appropriate AT for each procedural step. Each AT comes with instructions on the inputs required, how to manage the tool's data processing and a series of automated functions to generate appropriate formats to present the findings or data output in the form of narratives, tables, graphs and reports.
In the case of simulation models, any "run" considered to be of interest (a combination of input values and results) can be saved under a specific record tag to enable later re-runs based on the same datasets.
In addition, for a user to be able to communicate tool output (results) with a reporting utility to prepare reports concerning the analysis and its results. All tool output can be viewed online and is printable or can be exported as Word
TM PDF
TM and Excel
TM formats for download.
Operational package
- Guidelines
- Additional documentation in the case of more complex topics
- Data input specifications
- Description of the analysis process
- Explanation of how to interpret output
- Processor/Simulator
- Output as:
- Natural language narratives generated by ADI2
- Graphs
- Tables
- Support narrative editor with output as:
- Online presentation
- WP3 format
- Pdf format
- SS4 formats
- Secure DataBases, standard and binary files5 including:
- IDEA - Instant Data Element Access6
- OPEE - Object Profile Elements Extension7capabilities
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The functional package associated with SDGToolkit ATs is detailed in the box on the right.
The collection of analytical tools is a work in progress. Over 200 have been identified as useful and so far 65 have been implemented. ATs are under constant review and being added to in terms of prioritization according to stakeholders and user requests. In this context, users of SDGToolkit join an active international network where evolving requirements are taken into account in response to requirements for new or improved analytical tools for certain domain-related calculations. Our team will design and implement these as they are defined and add them to the AT resource library. All updates are made immediately available to users and there is no need to download since all additions are already "on board" when users next login in to the system.
The ATs have an important role in improving the effectiveness of due diligence design procedures
2 in the identification, analysis and management of information to create more focused and transparent designs. This i an continuous learning process.
ATs are based on practical decision analysis
3 knowledge-based models that have proven to be effective in completing technical and economic analyses, calculations and projections that meet high standards of data quality. This level of intelligent support raises human resources productivity in complex decision-making and enables a more transparent identification of risks. With higher quality and more reliable data, designs can be more effectively optimized with respect to feasibility, risks and costs. This avoids the creation of project proposals that are over- or under-ambitious, both conditions that are deemed to fail resulting in a reduced impact of development investment.
In practice the use of these tools can reveal previously less understood development factor relationships leading to a widening of perspectives and the identification of additional decision options leading to beneficial innovation. The more advanced tools, based on simulation models, are the most effective means of introducing team-based instructional simulation to enhance learning and a shared understanding of the relationships critical to project and business success. The tools have a useful role in supporting training and advanced study by enabling trainees to move from theoretical concepts to working with practical examples and to enhance their understanding and capabilities by exploring the full range of relationships that determine outcomes.
