The dimension of ________ describes whether data elements are consistently defined.

Daimler�s plant delivery optimization system is an example of a decision-support system (DSS). It helps managers make decisions about the most efficient way to stage deliveries to loading points given production requirements, routes, and time frames. Such systems have powerful analytical capabilities that support managers during the process of arriving at decisions.

           As a manager, you will want to know how you can use information systems to improve your decision making, whether you are working alone or in a group. Just as important, you will be responsible for the decisions made by the people who work under you, and you�ll want systems to help them make better decisions as well.

           This chapter focuses on the specialized systems that firms use to achieve better decision making: management information systems (MIS), decision-support systems (DSS), group decision-support systems (GDSS), and executive support systems (ESS). You�ll learn how these systems work, how they provide value for the business, and the challenges of building and using them wisely.

DECISION MAKING AND DECISION-SUPPORT SYSTEMS

Chapter 1 points out that information systems have business value when they enable more efficient business processes and improve management decision making. As organizations flatten and push decision making to lower levels to include non-management employees, information systems make significant contributions to the overall quality of decision making for the entire firm.

           For instance, a service firm such as EchoStar Communications, one of the world�s largest television satellite firms, receives millions of calls at the company�s call centers each year. If a customer service representative has the right information about a customer calling into the call center, that employee can decide to offer the customer special services that could encourage the customer to stay with the company or perhaps purchase additional services, thus contributing to the firm�s value.

           On a larger scale, there are 142 million workers in the U.S. labor force producing $12.2 trillion in gross domestic product (GDP). If the decision-making quality of these employees could be improved by just 1 percent in a year, then the GDP might expand by a substantial amount, perhaps equivalent to 1 percent, or $122 billion, annually, perhaps far more. For both small and large firms the ability of managers and employees to make the right decision at the right time with the right information can have extraordinary business value.

Because of the importance of high-quality decision making, firms are investing heavily in business intelligence systems, which consist of technologies and applications designed to help users make better business decisions. When we think of intelligence as applied to humans, we typically think of people�s ability to combine learned knowledge with new information and change their behavior in such a way that they succeed at their task or adapt to a new situation. Likewise, business intelligence provides firms with the capability to amass information, develop knowledge about operations, and change decision-making behavior to achieve profitability and other business goals.

           Figure 13-1 illustrates the major applications and technologies used for business intelligence. They include the supply chain management, customer relationship management, and enterprise systems; systems for knowledge management; and technologies such as data mining and online analytical processing (OLAP) for obtaining knowledge and insight from analyzing large quantities of data. These systems work with specialized systems for management decision making (MIS, DSS, ESS) that focus on the specific decision needs of managers and employees.

FIGURE 13-1 Systems and technologies for business intelligence
Each of the major enterprise applications provides support for decision making or �business intelligence� at all levels of the firm in addition to processing daily transactions. Specialized systems such as MIS, DSS, and ESS described in this chapter work with these systems and technologies for data mining and online analytical processing (OLAP) to focus on the specific decision needs of managers and employees.

Let�s try to measure the business benefits of improved decision making and link these improvements to firm profitability. Table 13-1 describes an example of a small manufacturing firm operating in the United States with $280 million annual revenue and 140 employees. The firm has identified a number of key decisions where new systems investments might improve the quality of decision making and produce value. Analysts have estimated the value to the firm of improving each decision. The table provides selected estimates of the annual value (either in cost savings or revenue enhancement) of improved decision making in selected areas of the firm.

TABLE 13-1 Business Value of Enhanced Decision Making

Before making improvements in corporate decision making with system investments, you must understand more about the nature of decisions and the decision-making process. There are different information requirements at different levels of responsibility in the organization that affect the types of decisions made at each level.

DECISION-MAKING LEVELS

Chapter 2 shows that there are different levels in an organization. Each of these levels has different information requirements for decision support and different constituencies or groups that information systems need to serve (see Figure 13-2). The four different decision-making constituencies in a firm are the following:

FIGURE 13-2 Information requirements of key decision-making groups
Various levels of management in the firm have differing information requirements for decision support because of their different job responsibilities and the nature of the decisions made at each level.

• Senior management. Senior management is concerned with general yet timely information on changes in the industry and society at large that may affect both the long-term and near-term future of the firm, the firm�s strategic goals, short-term and future performance, specific bottlenecks and trouble affecting operational capabilities, and the overall ability of the firm to achieve its objectives.

• Middle management and project teams. Middle management is concerned with specific, timely information about firm performance, including revenue and cost reduction targets, and with developing plans and budgets to meet strategic goals established by senior management. This group needs to make important decisions about allocating resources, developing short-range plans, and monitoring the performance of departments, task forces, teams, and special project groups. Often the work of middle managers is accomplished in teams or small groups of managers working on a task.

• Operational management and project teams. Operational management monitors the performance of each subunit of the firm and manages individual employees. Operational managers are in charge of specific projects and allocate resources within the project budget, establish schedules, and make personnel decisions. Operational work may also be accomplished through teams.

• Individual employees. Employees try to fulfill the objectives of managers above them, following established rules and procedures for their routine activities. Increasingly, however, employees are granted much broader responsibilities and decision-making authority based on their own best judgment and information in corporate systems. Employees may be making decisions about specific vendors, customers, and other employees. Because employees interact directly with the public, how well they make their decisions can directly impact the firm�s revenue streams.

The characteristics of decisions faced by managers at different levels are quite different. Decisions can be classified as structured, semistructured, and unstructured. Unstructured decisions are those in which the decision maker must provide judgment, evaluation, and insights into the problem definition. Each of these decisions is novel, important, and nonroutine, and there is no well-understood or agreed-on procedure for making them.

           Structured decisions, by contrast, are repetitive and routine, and decision makers can follow a definite procedure for handling them to be efficient. Many decisions have elements of both and are considered semistructured decisions, in which only part of the problem has a clear-cut answer provided by an accepted procedure. In general, structured decisions are made more prevalently at lower organizational levels, whereas unstructured decision making is more common at higher levels of the firm.

           Senior executives tend to be exposed to many unstructured decision situations that are open ended and evaluative and that require insight based on many sources of information and personal experience. For example, a CEO in today�s music industry might ask, �Whom should we choose as a distribution partner for our online music catalog�Apple, Microsoft, or Sony?� Answering this question would require access to news, government reports, and industry views as well as high-level summaries of firm performance. However, the answer would also require senior managers to use their own best judgment and poll other managers for their opinions.

           Middle management and operational management tend to face more structured decision scenarios, but their decisions may include unstructured components. A typical middlelevel management decision might be �Why is the order fulfillment report showing a decline over the last six months at a distribution center in Minneapolis?� This middle manager could obtain a report from the firm�s enterprise system or distribution management system on order activity and operational efficiency at the Minneapolis distribution center. This is the structured part of the decision. But before arriving at an answer, this middle manager will have to interview employees and gather more unstructured information from external sources about local economic conditions or sales trends.

           Rank-and-file employees tend to make more structured decisions. For example, a sales account representative often has to make decisions about extending credit to customers by consulting the firm�s customer database that contains credit information. In this case the decision is highly structured, it is a routine decision made thousands of times each day in most firms, and the answer has been preprogrammed into a corporate risk management or credit reporting system.

           The types of decisions faced by project teams cannot be classified neatly by organizational level. Teams are small groups of middle and operational managers and perhaps employees assigned specific tasks that may last a few months to a few years. Their tasks may involve unstructured or semistructured decisions such as designing new products, devising new ways to enter the marketplace, or reorganizing sales territories and compensation systems.

SYSTEMS FOR DECISION SUPPORT

There are four kinds of systems used to support the different levels and types of decisions just described (see Table 13-2). We introduced some of these systems in Chapter 2. Management information systems (MIS) provide routine reports and summaries of transaction- level data to middle and operational-level managers to provide answers to structured and semistructured decision problems. Decision-support systems (DSS) are targeted systems that combine analytical models with operational data and supportive interactive queries and analysis for middle managers who face semistructured decision situations. Executive support systems (ESS) are specialized systems that provide senior management making primarily unstructured decisions with a broad array of both external information (news, stock analyses, industry trends) and high-level summaries of firm performance. Group decision-support systems (GDSS) are specialized systems that provide a group electronic environment in which managers and teams can collectively make decisions and design solutions for unstructured and semistructured problems.

TABLE 13-2 Organizational Level and Systems for Decision Support

Making decisions consists of several different activities. Simon (1960) describes four different stages in decision making: intelligence, design, choice, and implementation (Figure 13-3).

FIGURE 13-3 Stages in decision making
The decision-making process can be described in four steps that follow one another in a logical order. In reality, decision makers frequently circle back to reconsider the previous stages and through a process of iteration eventually arrive at a solution that is workable.

           Design involves identifying and exploring various solutions to the problem. Decisionsupport systems (DSS) are ideal in this stage for exploring alternatives because they possess analytical tools for modeling data, enabling users to explore various options quickly.

           Choice consists of choosing among solution alternatives. Here, DSS with access extensive firm data can help managers choose the optimal solution. Also group decisionsupport systems can be used to bring groups of managers together in an electronic online environment to discuss different solutions and make a choice.

           Implementation involves making the chosen alternative work and continuing to monitor how well the solution is working. Here, traditional MIS come back into play by providing managers with routine reports on the progress of a specific solution. Support systems can range from full-blown MIS to much smaller systems, as well as project-planning software operating on personal computers.

           In the real world, the stages of decision making described here do not necessarily follow a linear path. You can be in the process of implementing a decision, only to discover that your solution is not working. In such cases, you will be forced to repeat the design, choice, or perhaps even the intelligence stage.

           For instance, in the face of declining sales, a sales management team may strongly support a new sales incentive system to spur the sales force on to greater effort. If paying the sales force a higher commission for making more sales does not produce sales increases, managers would need to investigate whether the problem stems from poor product design, inadequate customer support, or a host of other causes, none of which would be �solved� by a new incentive system.

DECISION MAKING IN THE REAL WORLD

The premise of this book and this chapter is that investments in systems to support decision making produce better decision making by managers and employees in the firm, above average returns on investment for the firm, and ultimately higher profitability. Unfortunately, in the real world, for some firms, investments in decision-support systems do not always work out this way for three reasons: data quality, management filters, and organizational culture. Review the discussion on organizations in Chapter 3 as well.

Information Quality High-quality decisions require high-quality information regardless of information systems. You should consider seven dimensions of information quality when designing decision-support systems:

Accuracy: Do the data represent reality?

Integrity: Are the structure of data and relationships among the entities and attributes consistent?

Consistency: Are data elements consistently defined?

Completeness: Are all the necessary data present?

Validity: Do data values fall within defined ranges?

Timeliness: Are data available when needed?

Accessibility: Are the data accessible, comprehensible, and usable?

Management Filters Even with timely, accurate information, some managers make bad decisions. Managers (like all human beings) absorb information through a series of filters to make sense of the world around them. Managers have selective attention, focus on certain kinds of problems and solutions, and have a variety of biases that isolate them from reality. They filter by turning off information they do not want to hear because it does not conform to their prior conceptions.

           For instance, Cisco Systems Corporation, one of the most advanced users of online decision-support systems, nevertheless was forced to write off as a loss $3.4 billion in excess inventory in 2001. Cisco�s managers built inventory in response to the output from the company�s online sales order entry system, which throughout 1999 and 2000 showed exceptionally strong orders. Unfortunately, Cisco managers did not pay attention to the quality of those orders. Customers, perceiving a shortage of routers and other networking equipment, were placing orders with multiple manufacturers, awarding the business to the first one who could deliver, and canceling other orders. Cisco�s systems were recording high levels of order cancellation, but management ignored this �bad news� and emphasized the �good news�: new orders were piling up (Laudon and Laudon, 2003).

Organizational Inertia Chapter 3 shows that organizations are bureaucracies with limited capabilities and competencies for acting decisively. When environments change and businesses need to adopt new business models to survive, strong forces within organizations resist making decisions calling for major change. Decisions taken by a firm often represent a balancing of the firm�s various interest groups rather than the best solution to the problem.

           Consider the record label industry. Despite declining sales since 2000 and the explosion of songs downloaded for free over the Internet, major recording companies did not move quickly into online music retailing. This required a much leaner business model. The recording companies resisted selling music over the Internet for years. Until very recently, nearly all executives and most employees at record label companies continued to believe that music should be distributed on physical devices (records, tapes, or CDs) and sold through retail record stores.

           Studies of business restructuring find that firms tend to ignore poor performance until threatened by outside takeovers, and they systematically blame poor performance on external forces beyond their control such as economic conditions (the economy), foreign competition, and rising prices, rather than blaming senior or middle management for poor business judgment (John, Lang, Netter, et. al., 1992).

Systems supporting management decision making originated in the early 1960s as early MIS that created fixed, inflexible paper-based reports and distributed them to managers on a routine schedule. In the 1970s, the first DSS emerged as standalone applications with limited data and a few analytic models. ESS emerged during the 1980s to give senior managers an overview of corporate operations. Early ESS were expensive, based on custom technology, and suffered from limited data and flexibility.

           The rise of client/server computing, the Internet, and Web technologies has made a major impact on systems that support decision making. Many decision-support applications are now delivered over corporate intranets. We see six major trends:

• Detailed enterprise-wide data. Enterprise systems create an explosion in firmwide, current, and relatively accurate information, supplying end users at their desktops with powerful analytic tools for analyzing and visualizing data.

• Broadening decision rights and responsibilities. As information becomes more widespread throughout the corporation, it is possible to reduce levels of hierarchy and grant more decision-making authority to lower-level employees.

• Intranets and portals. Intranet technologies create global, company-wide networks that ease the flow of information across divisions and regions and delivery of near real-time data to management and employee desktops.

• Personalization and customization of information. Web portal technologies provide great flexibility in determining what data each employee and manager sees on his or her desktop. Personalization of decision information can speed up decision making by enabling users to filter out irrelevant information.

• Extranets and collaborative commerce. Internet and Web technologies permit suppliers and logistics partners to access firm enterprise data and decision-support tools and work collaboratively with the firm.

• Team support tools. Web-based collaboration and meeting tools enable project teams, task forces, and small groups to meet online using corporate intranets or extranets. These new collaboration tools borrow from earlier GDSS and are used for both brainstorming and decision sessions.

Which quality dimension of information is concerned that the data values?

Which phase of decision making finds or recognizes a problem?

Which type of decision is deciding whether to produce a new product?

Which of the following managerial roles is not supported by information system?