Periodic Reporting for period 1 - FlowForma BPM-CDL (NEXT GENERATION BPM TO MAKE AUTOMATIC DECISIONS)
Reporting period: 2018-05-01 to 2018-08-31
Nowadays, Digital Transformation is a must for organizations to stay in business. There are three principles guiding this transformation: agility, innovation and improved interactions. The ability to deliver them in an organizational context is what makes the newly established category of Digital Process Automation (DPA) tools a key enabler of this transformation. DPA tools are Low-Code/No-Code platforms focused on delivering intelligent business processes without need of IT personnel. With DPAs, such as FlowForma Process Automation(FF PA), business owners take control of their business processes and automate them quickly.
However, for everyday business it is important not only to automate processes but also take the right decisions when faced with a business problem. The cost of bad decisions can cost companies dearly (from an average €50,000 for a bad hiring to going out of business, such as with Blockbuster). As employees have to take decisions (individual or shared) in many steps of the business processes, lack of enough information makes them prone to irrationality and discretion, and, therefore, risky and costly for the business.
Our project FlowForma Learning (FF-Learning) proposes a DPA tool that enables an educated decision making in business processes based on advanced Machine-Learning (ML) technologies. This is a step forward in business automation as it will let businesses learn from their running processes and let decision-makers decide based on data-driven intelligence. FF-Learning will boost a company’s value by: 1) maximizing efficacy of decisions (40%); 2) reducing human error (33%); and 3) increase process productivity (50%).
We have set a list of future technical and commercial objectives that should positioned FF-Learning as a reference DPA. Initially targeting SMEs and departmental processes, FF-Learning is highly scalable and will allow the rapid implementation of digital transformation in companies of all sizes and business sectors.
However, for everyday business it is important not only to automate processes but also take the right decisions when faced with a business problem. The cost of bad decisions can cost companies dearly (from an average €50,000 for a bad hiring to going out of business, such as with Blockbuster). As employees have to take decisions (individual or shared) in many steps of the business processes, lack of enough information makes them prone to irrationality and discretion, and, therefore, risky and costly for the business.
Our project FlowForma Learning (FF-Learning) proposes a DPA tool that enables an educated decision making in business processes based on advanced Machine-Learning (ML) technologies. This is a step forward in business automation as it will let businesses learn from their running processes and let decision-makers decide based on data-driven intelligence. FF-Learning will boost a company’s value by: 1) maximizing efficacy of decisions (40%); 2) reducing human error (33%); and 3) increase process productivity (50%).
We have set a list of future technical and commercial objectives that should positioned FF-Learning as a reference DPA. Initially targeting SMEs and departmental processes, FF-Learning is highly scalable and will allow the rapid implementation of digital transformation in companies of all sizes and business sectors.
Over the course of the Phase I project, we were able to validate the technical feasibility of FF-Learning, by assessing the remaining technical requirements, as well as the corresponding risks and mitigation measures to be able to take FF-Learning from TRL6 to TRL9. We identified the main development areas and set milestones for each one, including calendar and personnel effort.
We also performed a complete Freedom to Operate Analysis through the examination of global patent application databases, which successfully confirmed the innovative and unique characteristics of FF-Learning.
The validation of the commercial feasibility involved the analysis of FF-Learning value chain, its end-user and clients and decision makers. This analysis included extensive research to identify global trends, market opportunities and risks, as well as identify the need for early adopters and business opportunities. The research allowed us to validate the complete commercial feasibility and identify the best approach towards full commercialization after Phase II development.
Finally, to validate the financial feasibility, we also developed an exhaustive review of future revenues and costs, leading to a conservative sales forecast and ROI analysis for the 5 years following the full commercialization of FF-Learning. Analysis consisted on a deep assessment of commercial risks associated with expanding our geographical markets.
We also performed a complete Freedom to Operate Analysis through the examination of global patent application databases, which successfully confirmed the innovative and unique characteristics of FF-Learning.
The validation of the commercial feasibility involved the analysis of FF-Learning value chain, its end-user and clients and decision makers. This analysis included extensive research to identify global trends, market opportunities and risks, as well as identify the need for early adopters and business opportunities. The research allowed us to validate the complete commercial feasibility and identify the best approach towards full commercialization after Phase II development.
Finally, to validate the financial feasibility, we also developed an exhaustive review of future revenues and costs, leading to a conservative sales forecast and ROI analysis for the 5 years following the full commercialization of FF-Learning. Analysis consisted on a deep assessment of commercial risks associated with expanding our geographical markets.
Even though most organizations understand the need for becoming digitally run organizations, implementation of the digital transformation culture is going rather erratic. This is due to several factors: lack of clear digital strategy, lack of perception of what digital transformation implies, not enough IT expertise and a natural resistance to change.
With FF-Learning we introduce an DPA that streamlines the digitalization of business process, that will be done by the business developers without need of IT personnel, but also will help them to do better decisions but introducing machine learning in the everyday business processes. FF-Learning will allow decision makers to maximizing decisions (40%), reducing human discretion errors (33%), increase productivity (50%), improving employees moral and company value.
Finally, FF-Learning, offered as a SaaS business model, will be a key product for the expansion of FlowForma both with direct and indirect sales and according to an ambitious commercial plan.
With FF-Learning we introduce an DPA that streamlines the digitalization of business process, that will be done by the business developers without need of IT personnel, but also will help them to do better decisions but introducing machine learning in the everyday business processes. FF-Learning will allow decision makers to maximizing decisions (40%), reducing human discretion errors (33%), increase productivity (50%), improving employees moral and company value.
Finally, FF-Learning, offered as a SaaS business model, will be a key product for the expansion of FlowForma both with direct and indirect sales and according to an ambitious commercial plan.