Decision support systems (DSS)
Businesses face dozens of decisions every day. Some are relatively easy and have little impact on the overall operation of the organisation, while others can be highly demanding and at times disastrous in their consequences. Decision support systems will work well for the latter in particular.
Decision support systems (DSS) combine knowledge and data from various sources and then subject them to detailed analysis with a view to presenting the company with the best possible options. This in turn improves its decision-making ability and allows it to act in a fully informed manner.
What sets our DSS systems apart?
Comparative data analysis
Advanced analytical mechanisms allow you to compare large volumes of data in any time frame – daily, weekly, monthly, and yearly.
Forecasting
We provide access to predictive analytics e.g. to forecast revenue based on specific sales-related assumptions.
Ability to anticipate consequences
Every decision has consequences. With our DSS systems, you will be able to anticipate these and make the best business decisions for the future based on them.
Implementation benefits
DSS-type systems are used in virtually all types of businesses in every industry. They make business processes faster and more efficient while bringing multiple benefits to the organisation.
Better performance
Decision support systems increase productivity and reduce the risk of errors. They make it possible to shorten the decision-making process and thus enable more decisions to be made in shorter times.
More efficient planning
Detailed plans for implementing procedures based on the data at hand enable errors to be corrected efficiently and new processes to be launched, ultimately contributing to business success.
Identifying impact
Decision support systems make it easier to determine the potential future outcomes of decisions, based on past data and analysis of the current situation. Understanding future impacts will allow you to make the best business decisions.
AI and ML in innovative DDSs
It enables a better understanding of trends, patterns, and relationships in data. This, in turn, allows more accurate decisions to be made.
Predicting future performance based on historical data, forecasting market trends and product demand, or even predicting machine failures.
Development of personalised DDS solutions that adapt to individual users' needs, e.g. recommendation systems that suggest the most appropriate decisions for specific situations or preferences.
AI and ML can be used to automate many of the decision-making processes in the DDS, allowing it to react faster to changes and optimise operations.
Types of decision support systems we can implement for you
Data-driven DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Model-based DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Communication- and group-based DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Knowledge-based DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Document-based DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Data-driven DSS
Model-based DSS
Communication- and group-based DSS
Knowledge-based DSS
Document-based DSS
It facilitates decision-making based on information from internal or external databases.
By using data mining techniques to recognise trends and patterns, it enables the prediction of future events and therefore better business management.
Key business benefits:
- Decisions made on purchasing, sales, hiring, and many other business processes.
- Anticipation of future behaviour to avoid its potential negative benefits
Example of an implementation of a decision support system
AIDA
Project:
According to research, the blood availability crisis in Western countries will start as early as the beginning of 2025. Other statistics (e.g. related to injuries, planned surgeries, and births) show that half of us will need a blood transfusion at least once in our lives. At the same time, 2,340,000 litres of blood is wasted annually. Given that, it is high time that blood management was data-driven.
Key goals and benefits:
AIDA is a DDS system for blood banks and hospitals to help provide blood whenever it is needed. AIDA uses artificial intelligence to streamline blood management processes, achieve optimal processing of whole blood and manage blood component stocks at the Blood Centre. The final version of the product will be a system equipped with a module for classifying donors and conducting personalised communication with them.
The biggest challenge was to translate medical knowledge and practice into technological language and create a UX so user-friendly that it would be accepted by doctors of all ages around the world.
Based on laboratory results and diagnostic data from patients who received blood transfusions, AIDA Diagnostics’ Data Science team developed a classification model based on the Support Vector Machine that predicts the need for transfusions in patients over the coming three days. The model was taught based on data from 18,917 records and over 170,000 variables.
AIDA
Project:
According to research, the blood availability crisis in Western countries will start as early as the beginning of 2025. Other statistics (e.g. related to injuries, planned surgeries, and births) show that half of us will need a blood transfusion at least once in our lives. At the same time, 2,340,000 litres of blood is wasted annually. Given that, it is high time that blood management was data-driven.
Key goals and benefits:
AIDA is a DDS system for blood banks and hospitals to help provide blood whenever it is needed. AIDA uses artificial intelligence to streamline blood management processes, achieve optimal processing of whole blood and manage blood component stocks at the Blood Centre. The final version of the product will be a system equipped with a module for classifying donors and conducting personalised communication with them.
The biggest challenge was to translate medical knowledge and practice into technological language and create a UX so user-friendly that it would be accepted by doctors of all ages around the world.
Based on laboratory results and diagnostic data from patients who received blood transfusions, AIDA Diagnostics’ Data Science team developed a classification model based on the Support Vector Machine that predicts the need for transfusions in patients over the coming three days. The model was taught based on data from 18,917 records and over 170,000 variables.
