## 1 Introduction  
Nowadays, there is a considerable number of public and private operators, who make extensive use of decision support systems (DSSs or DS systems) in a surprising large number of operational procedures and businesses. These operations include:  
* Vehicle or vessel fleet management [1; 2; 3];
* Marine services and policies (testing, inspection and certification services in the areas of quality, health and safety, as well as security, environmental considerations and relevant policy impacts, etc.) [4; 5; 6];
* Management of goods in road, rail, sea and intermodal transportation. These operations are very complex, involving public and/or private train, truck and ship owner companies, goods owners and buyers, transport operators, intermodal terminal operators, infrastructure providers, safety and security operators, etc. [7; 8; 9];
* Handling of emergencies and crisis [10; 11; 12; 13];
* Complex engineering tasks and, in general, large and complex sequences of operations and/or tasks covering very extended geographical areas (e.g., aircraft and sea vessel design and very large and/or complicated production lines in factories and many more) [14; 15; 16; 17];
* Transport planning (estimation of important parameters than cannot be measured, forecasts, policy making, etc.) [18; 19; 20; 21];
* Cost-benefit analyses [22; 23; 24];
* Healthcare, a domain that is fundamental for human society, especially nowadays (in 2020) due to the COVID-19 pandemic [25; 26; 27].  
These examples of operations constitute only a small number of those encountered in practice. In addition, in most cases, each associated DSS employs sets of very complex computational models and algorithms. In essence, a DSS is a special kind of a very complex information system (IS), where an information system is an integrated set of components for collecting, storing and processing data and for providing information, knowledge and digital products [28].  
The present manuscript is organized as follows:  
In Section 2, the authors deal with the recent tendency/necessity to build very large-scale decision support systems. In fact, it is argued that the advances in information and communication technology have given the opportunity to engineers to try to develop DSSs that tackle very big and/or particularly complicated practical problems.  
In Section 3, a number of serious difficulties and problems are stated, in connection to specific properties/characteristics of a very large-scale decision support system. These properties concern the following:  
* The ability to efficiently update a DS system;
* The flexible expansion of the DSS;
* The consistent reducibility of an information system;
* The ability to make changes to the information system;
* The upgrading of the DSS.  
In Section 4, crucial characteristics of contemporary, mainstream methodologies for creating large-scale decision support systems are reported. It is argued that any ad hoc development of a DS system is bound to suffer from serious intrinsic problems, which only a consistent fully methodological approach can resolve.  
In Section 5, the authors introduce a methodology that circumvents all the aforementioned problems.  
In Section 6, the authors state the reasons for which the introduced methodology resolves these problems associated with very large-scale DSS development.  
Finally, in Section 7, the conclusions of the present work are stated.