Marcos referenciales sobre competencias TIC en el contexto internacional

Suriadi et al. [108] identify only two approaches that deal with risk awareness at run-time. Conforti et al. [26] propose process models annotated with risk conditions. The probabilities of the occurrence of risks can be determined on the basis of information about current running process instances and historical data. The approach allows for the run-time monitoring of risk conditions. When these conditions are fulfilled, alerts are triggered to request actions from users. Kang et al. [54] proposes a similar approach that offer a run-time risk detection mechanism that issues alerts to users when risks are identified. Although these two approaches do not implement any process adaptation mechanisms as other context awareness approaches, they provide information to the user, so the user can take adequate actions for each situation. These approaches are proactive, since alerts are triggered when probabilities of occurrence of failures are high, but before actual failures occur. Nevertheless, run-time adaptation is still a research gap in this area [108].

4.5

Management Support Systems

Management Support System (MSS) is a general term to describe all information systems

that are used to support management actions [111]. This includes Decision Support Systems (DSSs), Knowledge Management Systems (KMSs), and Business Intelligence (BI).

MSSs are the information systems that are closer to business managers. Generally, they offer features that aim to help managers take better decisions. For this reason, most MSSs can be classified as DSSs. The main tasks of MSSs are to enable managers to capture and analyze information about the company’s activities, to adequately share this information with relevant stakeholders, and to objectively evaluate decision alternatives on the basis of this in- formation [111].

4.5.1 Decision Support Systems

Decision Support Systems (DSS) are information systems designed to support the decision making process in organizations [90]. Research on DSS spreads over several disciplines, such as artificial intelligence, operations research, and management information systems [65]. Sev- eral different techniques for supporting decision making have been proposed and used in a large set of applications. As a result, what is called DSS take many different forms for different managers, vendors, and consultants [90].

In general, a DSS consists in three components: data management, model management, and interface (or dialogue management) [65]. The data management component is responsible for data and information retrieval. It employs data warehousing techniques to obtain information from several sources. The model management component is used to formulate problems and solutions. The interface component enables user interaction, produces reports, graphs, and display solutions.

The integration of DSS and other enterprise systems produces what is called Integrated Decision Support Systems (IDSS). An IDSS is able to capture business models from ERP, Customer Relationship Management (CRM), and Supply Chain Management (SCM) systems. For Liu et al. [65], IDSS improves the performance of isolated DSS, but still lack flexibility.

4.5 MANAGEMENT SUPPORT SYSTEMS 60

They argue that IDSSs should evolve into the so-called Integrated Decision Support Environ- ments (IDSE). IDSEs are described as service-oriented solutions that allow DSSs to be quickly reconfigured to respond to new decision requirements in dynamic business situations [65].

4.5.2 Business Intelligence & Analytics

An essential task of strategy management is the performance measurement and assessment. To track the progress of the company in the achievement of its goals, companies need to define

performance indicators. Such indicators also serve the purpose of enabling the diagnostics of

the current state of the company’s internal and external environment.

Business Intelligence (BI) and Business Analytics (BA) [59] are technologies that have as primary purpose to deliver valuable information to support decision making. To that end, they integrate several data processing and analysis techniques that are capable of extracting useful information from raw data from diverse sources. BI and BA provide access to business information in an understandable form that fits the needs of business stakeholders [53]. They compose an efficient approach to implement performance measurement systems.

The term BI became popular in the 90’s. In the 2000’s, BA was introduced as a complemen- tary component of BI [22]. Together, business intelligence and analytics (BI&A) are becoming increasingly important for organizations [22]. Information technologies provide companies with large volumes of data about its operations. Thousands of transaction records, performance indicators measures, and network traffic data are produced everyday. Making sense of all this information to the benefit of the organization is one of today’s biggest trends in information technology.

According to Gartner’s evaluation of the BI&A market [101], some of the essential features of BI&A platforms are:

• reporting: the ability to create formatted and interactive reports, possibly parameterized, with distribution and scheduling capabilities;

• dashboards: the ability to publish web-based (or mobile) reports that intuitively display performance metrics compared with a goal or target value;

• ad hoc queries: enables users to ask questions of the data without the necessity of medi- ation of the IT staff to create reports;

• online analytical processing (OLAP): enable a style of analysis known as ‘slicing and dicing’. Users are able to navigate the data through multimensional “drill paths”. They can observe the data from any dimension (e.g., product type, region) or combination of dimensions (e.g., product type by region) and calculate measures in real-time (e.g., number of sales, revenue);

• scorecards: take metrics in a dashboard and apply a strategy map to align performance indicators with strategic objectives;

• interactive visualization: the ability to display numerous aspects of the data efficiently through interactive charts or pictures.