Chapter
Enhancing Management Decision-making
For The Digital Firm
1
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?
2
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?
3
Management Challenges

Building information systems that can actually fulfill
executive information requirements

Create meaningful reporting and management
decision-making processes
4
Decision-Support Systems (DSS)

Computer system at the management level of an
organization

Combines data, analytical tools, and models

Supports semi-structured and unstructured decisionmaking
5
Systems and Technologies for Business
Intelligence
Figure 13-1
6
Decision-Making Levels:

Senior management

Middle management and project teams

Operational management and project teams

Individual employees
7
Types of Decisions

Unstructured decisions:

Novel, non-routine decisions requiring judgment and insights

Examples: Approve capital budget; decide corporate
objectives
8


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
9
Information Requirements of Key
Decision-Making Groups in a Firm
Figure 13-2
10
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)
11
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
12
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
13
Stages in Decision Making
Figure 13-3
14
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
15
DSS

Support semistructured and unstructured problems

Greater emphasis on models, assumptions, ad-hoc
queries, display graphics

Emphasizes change, flexibility, and a rapid response
16
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
17
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
18
Overview of a Decision-Support System
(DSS)
Figure 13-4
19
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
20
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
21
Sensitivity Analysis
Figure 13-5
22
Decision-Support Systems (DSS)

Associations: Occurrences linked to a single event

Sequences: Events linked over time
23
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
24
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
25
Decision-Support Systems (DSS)
Cargo revenue optimization of Continental Airlines
26
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
27
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
28
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
29
DSS for Customer Analysis and
Segmentation
Figure 13-6
30
Predictive Analysis

Use of datamining techniques, historical data, and
assumptions about future conditions to predict
outcomes of events
31
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.
32
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.
33
Components of GDSS

Hardware



Software tools




conference facility,
audiovisual equipment, etc.
Electronic questionnaires,
brainstorming tools,
voting tools, etc.
People



Participants,
trained facilitator,
support staff
34
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.
35

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.
36
Group System Tools
Group Interaction
Figure 13-7
37
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.
38
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
39
Organizational Memory

Store learning from an organization’s history that can
be used for decision making and other purposes
40
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
41
Executive Support Systems (ESS):

Monitor organizational performance

Track activities of competitors

Spot problems

Identify opportunities

Forecast trends
42
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
43
Drill Down

The ability to move from summary data to lower and
lower levels of detail
44
Developing ESS:

Ease of use

Facility for environmental scanning

External and internal sources of information to be
used for environmental scanning
45
Benefits of Executive Support Systems

Analyzes, compares, and highlights trends

Provides greater clarity and insight into data

Speeds up decision-making
46
Benefits of Executive Support Systems

Improves management performance

Increases management’s span of control

Better monitoring of activities
47
ESS for Competitive Intelligence

Identify changing market conditions

Formulate responses

Track implementation efforts

Learn from feedback
48
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
49
Strategic performance management tools
for enterprise systems

SAP: Web-enabled mySAP.com™, Management
Cockpit

PeopleSoft: Web-enabled Enterprise Performance
Management (EPM)
50
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
51
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
52
Management Opportunities:

Decision-support systems provide opportunities for
increasing precision, accuracy, and rapidity of
decisions and thereby contributing directly to
profitability
53
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.
54