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Chapter

Enhancing Management Decision-making For The Digital Firm

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Objectives

How can information systems help individual managers make better decisions when the problems are non-routine and constantly changing?

How can information systems help people working in a group make decisions more efficiently?

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Objectives

Are there any special systems that can facilitate decision-making among senior managers? Exactly what can these systems do to help high-level management?

What benefits can systems that support management decision-making provide for the organization as a whole?

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Management Challenges

Building information systems that can actually fulfill executive information requirements

Create meaningful reporting and management decision-making processes

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Decision-Support Systems (DSS)

Computer system at the management level of an organization

Combines data, analytical tools, and models

Supports semi-structured and unstructured decision-making

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Systems and Technologies for Business Intelligence

Figure 13-1

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Decision-Making Levels:

Senior management

Middle management and project teams

Operational management and project teams

Individual employees

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Types of Decisions

Unstructured decisions: Novel, non-routine decisions requiring judgment and insights

Examples: Approve capital budget; decide corporate objectives

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Structured decisions: Routine decisions with definite procedures

Examples: Restock inventory; determine special offers to customers

Semistructured decisions: Only part of decision has clear-cut answers provided by

accepted procedures

Examples: Allocate resources to managers; develop a marketing plan

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Information Requirements of Key Decision-Making Groups in a Firm

Figure 13-2

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Systems for Decision Support

There are four kinds of systems that support the different levels and types of decisions: Management Information Systems (MIS)

Decision-Support Systems (DSS)

Executive Support Systems (ESS)

Group Decision-Support Systems (GDSS)

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Decision Making in the Real World

In the real world, investments in decision-support systems do not always work because of

Information quality: Accuracy, integrity, consistency, completeness, validity, timeliness, accessibility

Management filters: Biases and bad decisions of managers

Organizational inertia: Strong forces within organization that resist change

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Trends in Decision Support and Business Intelligence

Detailed enterprise-wide data

Broadening decision rights and responsibilities

Intranets and portals

Personalization and customization of information

Extranets and collaborative commerce

Team support tools

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Stages in Decision Making

Figure 13-3

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Management Information Systems:

Primarily address structured problems

Provides typically fixed, scheduled reports based on routine flows of data and assists in the general control of the business

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DSS

Support semistructured and unstructured problems

Greater emphasis on models, assumptions, ad-hoc queries, display graphics

Emphasizes change, flexibility, and a rapid response

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Types of Decision-Support Systems

Model-driven DSS

Primarily stand-alone systems

Use a strong theory or model to perform “what-if” and similar analyses

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Types of Decision-Support Systems

Data-driven DSS Integrated with large pools of data in major enterprise

systems and Web sites

Support decision making by enabling user to extract useful information

Data mining: Can obtain types of information such as associations, sequences, classifications, clusters, and forecasts

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Overview of a Decision-Support System (DSS)

Figure 13-4

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Components of DSS

DSS database: A collection of current or historical data from a number of applications or groups

DSS software system: Contains the software tools for data analysis, with models, data mining, and other analytical tools

DSS user interface: Graphical, flexible interaction between users of the system and the DSS software tools

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Model:

An abstract representation that illustrates the components or relationships of a phenomenon Statistical models

Optimization models

Forecasting models

Sensitivity analysis: Models that ask “what-if” questions repeatedly to determine the impact of changes in one or more factors on the outcomes

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Sensitivity Analysis

Figure 13-5

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Decision-Support Systems (DSS)

Associations: Occurrences linked to a single event

Sequences: Events linked over time

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Decision-Support Systems (DSS)

Classification: Recognizing patterns that describe the group to which an item belongs

Clustering: Similar to classification when no groups have yet been defined. Discovers different groupings within data

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Business Value of DSS Providing fine-grained information for decisions that enable the

firm to coordinate both internal and external business processes much more precisely

Helping with decisions in Supply chain management Customer relationship management Pricing Decisions Asset Utilization

Data Visualization: Presentation of data in graphical forms, to help users see patterns and relationships

Geographic Information Systems (GIS): Special category of DSS that display geographically referenced data in digitized maps

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Decision-Support Systems (DSS)Cargo revenue optimization of Continental Airlines

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DSS for Pricing Decisions

By analyzing several years of sales data for similar items, the software estimates a “seasonal demand curve” for each item and predicts how many units would sell each week at various prices.

The software uses sales history to predict how sensitive customer demand will be to price changes

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DSS for Supply Chain Management

Can help firms model inventory stocking levels, production schedules, or transportation plans

Can provide firms with information on key performance indicators such as lead time, cycle time, inventory turns, or total supply chain costs

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DSS for Customer Relationship Management

Uses data mining to guide decisions

Consolidates customer information into massive data warehouses

Uses various analytical tools to slice information into small segments

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DSS for Customer Analysis and Segmentation

Figure 13-6

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Predictive Analysis

Use of datamining techniques, historical data, and assumptions about future conditions to predict outcomes of events

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Web-Based Customer Decision-Support Systems

DSS based on the Web and the Internet can support decision making by providing online access to various databases and information pools along with software for data analysis

Some of these DSS are targeted toward management, but many have been developed to attract customers.

Customer decision making has become increasingly information intensive, with Internet search engines, intelligent agents, online catalogs, Web directories, e-mail, and other tools used to help make purchasing decisions.

Customer decision-support systems (CDSS) support the decision-making process of an existing or potential customer.

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Group Decision-Support System (GDSS):

An interactive computer-based system used to facilitate the solution of unstructured problems by a set of decision makers working together as a group.

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Components of GDSS

Hardware conference facility, audiovisual equipment, etc.

Software tools Electronic questionnaires, brainstorming tools, voting tools, etc.

People Participants, trained facilitator, support staff

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Overview of a GDSS Meeting

In a GDSS electronic meeting, each attendee has a workstation.

The workstations are networked and are connected to the facilitator’s console, which serves as the facilitator’s workstation

and control panel, and to the meeting’s file server.

All data that the attendees forward from their workstations to the group are collected and saved on the file server.

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The facilitator is able to project computer images onto the projection screen at the front of the room.

Many electronic meeting rooms have seating arrangements in semicircles and are tiered in legislative style to accommodate a large number of attendees.

The facilitator controls the use of tools during the meeting.

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Group Interaction

Group System Tools

Figure 13-7

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How GDSS can Enhance Group Decision-Making

Traditional decision-making meetings support an optimal size of three to five attendees. GDSS allows a greater number of attendees.

Enable collaborative atmosphere by guaranteeing contributor’s anonymity.

Enable nonattendees to locate organized information after the meeting.

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How GDSS Can Enhance Group Decision Making

Can increase the number of ideas generated and the quality of decisions while producing the desired results in fewer meetings

Can lead to more participative and democratic decision making

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Organizational Memory

Store learning from an organization’s history that can be used for decision making and other purposes

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Executive Support Systems (ESS): ESS can bring together data from all parts of the firm and

enable managers to select, access, and tailor them as needed.

It tries to avoid the problem of data overload so common in paper reports.

The ability to drill down is useful not only to senior executives but also to employees at lower levels of the firm who need to analyze data.

Can integrate comprehensive firmwide information and external data in timely manner

Inclusion of modeling and analysis tools usable with a minimum of training

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Executive Support Systems (ESS):

Monitor organizational performance

Track activities of competitors

Spot problems

Identify opportunities

Forecast trends

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The Role of Executive Support Systems in the Organization

Brings together data from the entire organization

Allows managers to select, access, and tailor data

Enables executive and any subordinates to look at the same data in the same way

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Drill Down

The ability to move from summary data to lower and lower levels of detail

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Developing ESS:

Ease of use

Facility for environmental scanning

External and internal sources of information to be used for environmental scanning

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Benefits of Executive Support Systems

Analyzes, compares, and highlights trends

Provides greater clarity and insight into data

Speeds up decision-making

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Benefits of Executive Support Systems

Improves management performance

Increases management’s span of control

Better monitoring of activities

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ESS for Competitive Intelligence Identify changing market conditions

Formulate responses

Track implementation efforts

Learn from feedback

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Balanced Scorecard

Model for analyzing firm performance that supplements traditional financial measures with measurements from additional business perspectives, such as customers, internal business processes, and learning and growth

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Strategic performance management tools for enterprise systems

SAP: Web-enabled mySAP.com™, Management Cockpit

PeopleSoft: Web-enabled Enterprise Performance Management (EPM)

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Activity-Based Costing

Model for identifying all the company activities that cause costs to occur while producing a specific product or service so that managers can see which products or services are profitable or losing money and make changes to maximize firm profitability

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Management Challenges:

Building systems that can actually fulfill Executive Information Requirements

Changing management thinking to make better use of systems for decision support

Organizational resistance

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Management Opportunities:

Decision-support systems provide opportunities for increasing precision, accuracy, and rapidity of decisions and thereby contributing directly to profitability

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Solution Guidelines:

Flexible Design and Development: Users must work with IS specialists to identify a problem and

a specific set of capabilities that will help them arrive at decisions about the problem.

The system must be flexible, easy to use, and capable of supporting alternative decision options.

Training and Management Support: User training, involvement, and experience; top

management support; and length of use are the most important factors in the success of management support systems.