Enhancing Management Decision-making For The Digital Firm Chapter Enhancing Management Decision-making For The Digital Firm

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?

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?

Management Challenges Building information systems that can actually fulfill executive information requirements Create meaningful reporting and management decision-making processes

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

Systems and Technologies for Business Intelligence Figure 13-1

Decision-Making Levels: Senior management Middle management and project teams Operational management and project teams Individual employees

Types of Decisions Unstructured decisions: Novel, non-routine decisions requiring judgment and insights Examples: Approve capital budget; decide corporate objectives

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

Information Requirements of Key Decision-Making Groups in a Firm Figure 13-2

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)

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

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

Stages in Decision Making Figure 13-3

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

DSS Support semistructured and unstructured problems Greater emphasis on models, assumptions, ad-hoc queries, display graphics Emphasizes change, flexibility, and a rapid response

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

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

Overview of a Decision-Support System (DSS) Figure 13-4

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

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

Sensitivity Analysis Figure 13-5

Decision-Support Systems (DSS) Associations: Occurrences linked to a single event Sequences: Events linked over time

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

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

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

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

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

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

DSS for Customer Analysis and Segmentation Figure 13-6

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

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.

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.

Components of GDSS Hardware Software tools People conference facility, audiovisual equipment, etc. Software tools Electronic questionnaires, brainstorming tools, voting tools, etc. People Participants, trained facilitator, support staff

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.

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.

Group System Tools Group Interaction Figure 13-7

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.

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

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

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

Executive Support Systems (ESS): Monitor organizational performance Track activities of competitors Spot problems Identify opportunities Forecast trends

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

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

Developing ESS: Ease of use Facility for environmental scanning External and internal sources of information to be used for environmental scanning

Benefits of Executive Support Systems Analyzes, compares, and highlights trends Provides greater clarity and insight into data Speeds up decision-making

Benefits of Executive Support Systems Improves management performance Increases management’s span of control Better monitoring of activities

ESS for Competitive Intelligence Identify changing market conditions Formulate responses Track implementation efforts Learn from feedback

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

Strategic performance management tools for enterprise systems SAP: Web-enabled mySAP.com™, Management Cockpit PeopleSoft: Web-enabled Enterprise Performance Management (EPM)

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

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

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

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.