Measurement Issues in Decision Support Systems

Measurement Issues in Decision Support Systems

William K. Holstein (University at Albany, State University of New York, USA) and Jakov Crnkovic (University at Albany, State University of New York, USA)
DOI: 10.4018/978-1-60566-026-4.ch402
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The past decade has seen tremendous progress in systems for information support–flexible and adaptable systems to support decision makers and to accommodate individual needs and preferences. These model- or data-driven or hybrid decision support systems (DSS), now often called business intelligence (BI) systems, incorporate diverse data drawn from many different internal and external sources. Increasingly, these sources include sophisticated enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, data warehouses and other enterprise-wide systems that contain vast amounts of data and permit relatively easy access to that data by a wide variety of users at many different levels of the organization. Decision support, DSS and BI have entered our lexicon and are now common topics of discussion and development in large, and even in medium-sized, enterprises. Now that DSS is well established, attention is turning to measurement and the metrics that populate such systems.
Chapter Preview
Top

Background

Decision-making as we know it today, supported by computers and vast information systems, is a relatively recent phenomenon. But the concept has been around long enough to permit the methods and theories of decision-making to blossom into “a plethora of paradigms, research schools, and competing theories and methods actively argued by thousands of scientists and decision makers worldwide” (Robins, 2003).

Early computer systems focused primarily on accounting and financial data. It is said that information systems are about transforming data. We could say that early systems transformed data into aggregated or summarized data – for example, wage rates, hours worked, benefits and tax data, and so forth transformed into departmental or corporate payroll reports.

In the mid-1960’s, the development of the IBM System 360 and rapidly proliferating competitive systems from other vendors ushered in the era of Management Information Systems (MIS). Applications quickly moved beyond finance and accounting data and into operations. Transaction processing systems began to generate order, usage, and customer data that could be analyzed with (what quickly became quite sophisticated) models. The transformation of data into information became commonplace. For example, data on sales and usage, costs, supplier lead times and associated uncertainties were transformed into reorder points, safety stocks, and comprehensive inventory management and production scheduling systems.

Despite the broader reach of MIS, such systems are characterized by highly structured, infrequent reports, often with standard formatting. Frequently, because it was “easier” (for the IT staff), each manager in a given function, for example, marketing, received the same voluminous report – even though a manager of activities in Japan could not careless about data relating to New Jersey. Despite the tremendous advance of MIS over previous-generation systems, contemporary MIS systems draw most of their data from enterprise resource planning (ERP) systems that contain mostly internal data on transactions, and therefore suffer from many of the same problems as older systems (an internal, historical, and financial focus).

Decision support systems “evolved from the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early ‘60s and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s” (Keen & Scott Morton, 1978; Power, 2003). By the end of the 1970’s, it was clear that model-based decision support had become a practical, useful tool for managers.

A 1970 article by John Little of MIT clarified the concept of decision support. In a 1979 paper he provided a definition that is paraphrased here:

A coordinated collection of data, systems, tools, and techniques along with requisite software and hardware, by which an organization gathers and interprets relevant information from the business and environment and turns it into a basis for action.

Another useful definition of a DSS is:

Interactive computer-based systems designed to couple the intellectual resources of individuals with the capabilities of the computer to utilize data and models to identify and solve semi-structured (or unstructured) problems and improve the quality of decisions (paraphrased fromGorry & Scott Morton, 1989)

Key Terms in this Chapter

Decision Support System (DSS): Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. [They comprise] a computer-based system for management decision makers who deal with semi-structured problems ( Gorry & Scott Morton, 1989 ).

Business Intelligence (BI): The process of gathering information in the field of business. Information is typically obtained about customer needs, customer decision making processes, the competition, conditions in the industry, and general economic, technological, and cultural trends. Business intelligence is carried out to gain sustainable competitive advantage, and is a valuable core competence in some instances. The term was first used by Gartner and popularized by analyst Howard Dresner. It describes the process of turning data into information and then into knowledge. The intelligence is claimed to be more useful to the user as it passes through each step (http://explanation-guide.info/meaning/Business-intelligence.html)

Benchmark: A standard, usually from outside sources and usually representing the best, or better than average, performance against which an activity’s metric is compared. For example, world-class competitors have 35 defects per unit within the first 6 months; we have 85.

Measurement: The process of determining values representing performance, or the results of the process. For example, the measurement process is now started, or the measurement was 35 days.

Metric: A predetermined measure that will be used as the basis for a measurement process. For example, percentage of customer calls answered within one minute.

Balanced Scorecard: An approach for measuring business and management results that goes well beyond financial metrics. Several “perspectives” are suggested, for example financial, customer, internal processes and learning and innovation ( Kaplan & Norton, 1992 ).

Complete Chapter List

Search this Book:
Reset