Business Information Systems Chapter 9

Decision making

Which aspect is one of the most important and challenging aspects of management

1.Managers need to analyze large amounts of information. 2. Managers must make decisions quickly. 3. Managers must apply sophisticated analysis techniques, such as Porter’s strategies or forecasting to make strategic decisions.

3 managerial decision-making challenges

Problem identification, data collection, solution generation, solution test, solution selection, and solution implementation

6 steps of decision making process

Problem Identification

Define the problem as clearly and precisely as possible. Ex: What are the key problems affecting the business? What are customers saying about the service and the product? What are the root causes of . . .

Data Collection

Gather problem-related data, including who, what, where, when, why, and how. Be sure to gather facts, not rumors or opinions about the problem. Ex: Why are certain processes falling short? What are the immediate steps the company can take to adjust the current processes to improve them? Who are you listening to? Are they actual or rumor complaints? What departments are struggling?

Solution Generation

Detail every solution possible, including ideas that seem farfetched. Ex: What are some of the solutions you have for improvement? What are some of the solutions your management team has? How will you go about collecting all the best solutions?

Solution Test

Evaluate solutions in terms of feasibility (can it be completed?), suitability (is it a permanent or a temporary fix?) and acceptability (can all participants form a consensus?) Ex: Are these solutions long-term or short-term? What are some of the cost factors associated with the solutions? Does your team like the solution, or are they going to sabotage it because they are unhappy with the decision made?

Solution Selection

Select the solution that best solves the problem and meets the needs of the business. Ex: As the executive leader of the company, are you comfortable with the decision you made? How are you going to take a strong lead on this decision without alienating yourself from others?

Solution Implementation

If the solution solves the problem, then the decisions made were correct. If not, then the decision were incorrect and the process begins again. Ex: Evaluate and track how the solution is working. Is it achieving the results that you wanted? If the results are poor, what steps do you need to take to adjust? As the leader of the company, how will you appropriately change the solution direction without upsetting the environment or flow of the employees and production.

Operational level

Employees develop, control, and maintain core business activities required to run the day-to-day operations.

Managerial level

Employees are continuously evaluating company operations to hone the firm’s abilities to identify, adapt to, and leverage change.

Strategic level

Managers develop overall business strategies, goals, and objectives as part of the company’s strategic plan. External focus across industry; long-term multi-year impact.

Operational decisions

Affect how the firm is run from day to day. They are the domain of operations managers, who are the closest to the customer. Internal functional focus. Decisions made by staff employees.

Structured decision

Arise in situations where established processes offer solutions. —> Operational level.

Managerial decisions

Concern how the organization should achieve the goals and objectives set by its strategy, and they are usually the responsibility of mid-level management. Ex: Medium-range plans, schedules, budgets, policies, procedures, and business objectives. Internal cross-functional focus and decisions made by managers.

Semi-structured decisions

These occur in situations in which a few established processes help to evaluate potential solutions, but not enough to lead to a definite recommended decision. —> Managerial level.

Strategic decision

Involve higher level issues concerned with the overall direction of the organization. These decisions define the organization’s overall goals and aspirations for the future. Ex: Political, economic, and competitive business environment.

Unstructured decisions

Occur in situations in which no procedures or rules exist to guide decision makers toward the correct choice.

Model

A simplified representation or abstraction of reality.

MIS

These support systems rely on models for computational and analytical routines that mathematically express relationships among variables. Have the capability and functionality to express far more complex modeling relationships that provide information, business intelligence, and knowledge.

Online transaction processing (OLTP)

The capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, and (3) update existing information to reflect the new information."

Transaction processing system (TPS)

The basic business system that serves the operational level (analysts) in an organization. Operational accounting system: Payroll system or order entry or cash register.

Source document

Describes the original transaction records. It includes the details such as date, purpose, and amount spent and includes cash receipts, canceled checks, invoices, customer refunds, employee time sheet, etc.

Decision support systems (DSSs)

Model information using online analytical processing, which provides assistance in evaluating and choosing among different courses of action. Example: Sales Data Analysis, Manufacturing data analysis.

Online analytical processing (OLAP)

The manipulation of information to create business intelligence in support of strategic decision-making.

Executive information system (EIS)

A specialized DSS that supports senior-level executives within the organization [and unstructured, long term, nonroutine decisions requiring judgment, evaluation, and insight. Ex: Marketing sales outlook and industry sales outlook

Granularity

Refers to the level of detail in the model or the decision-making process.

Infographic (information graphic)

A representation of information in a graphic format designed to make the data easily understandable at a glance.

Visualization

Produces graphical displays of patterns and complex relationships in large amounts of data.

Bar chart

A chart or graph that presents grouped data with rectangular bars with lengths proportional to values that they represent.

Histogram

A graphical display of data using bars of different heights.

Pie chart

A type of graph in which a circle is divided into sectors that each represents a proportion of the whole.

Sparkline

A small embedded line graph illustrates a single trend.

Time-series chart

A graphical representation showing change of a variable over time.

Digital dashboard

Tracks key performance indicators (KPIs) and critical success factors (CSFs) by compiling information from multiple sources and tailoring to meet user needs.

Consolidation

The aggregation of data from simple roll-ups to complex groupings of interrelated information.

Drill-down

Enables users to view details, and details or details, of information. This is the reverse of consolidation; a user can view regional sales data and then drill down all the way to each sales representative’s data at each office.

Slice-and-dice

The ability to look at information from different perspectives . . . Often performed along a time axis to analyze trends and find time-based patterns in the information.

Pivot

Rotates data to display alternative presentations of the data.

Artificial Intelligence

Simulates human thinking and behavior, such as the ability to reason and learn.

Expert systems

Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.

Algorithm

A mathematical formula placed in software that performs an analysis on a dataset.

Genetic Algorithm

An AI system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.

Machine learning

A type of artificial intelligence that enables computers to both understand concepts in the environment and also to learn.

Data augmentation

Occurs when additional training examples by transforming existing examples.

Overfitting

Occurs when a machine learning model matches the training data so closely that the model fails to make correct predictions on new data.

Underfitting

Occurs when a machine learning model has poor predictive abilities because it did not learn the complexity in the training data.

Affinity bias

A tendency to connect with, hire, and promote those with similar interests, experiences, or backgrounds.

Conformity bias

Acting similarly, or conforming to those around you, regardless of your own views.

Confirmation bias

Actively looking for evidence that backs up preconceived ideas about someone.

Name bias

The tendency to prefer certain types of names.

Measurement bias

Occurs when there is a problem with the data collected that skews the data in one direction.

Prejudice bias

A result of training data that is influenced by cultural or other stereotypes.

Sample bias

A problem with using incorrect data to train the machine.

Variance bias

A mathematical property of an algorithm.

Neural networks

A category of AI that attempts to emulate the way the human brain works.

Fuzzy logic

A mathematical method of handling imprecise or subjective information. Ex: Washing machines; Goodwill in accounting

Black box algorithms

Decision-making process that cannot be easily understood or explained by the computer or researcher. Ex: Google’s Deep Dream

Deep learning

A process that employs specialized algorithms to model and study complex datasets; the method is also used to establish relationships among data and datasets.

Reinforcement learning

The training of machine learning models to make a sequence of decisions.

Virtual reality

A computer-simulated environment that can be a simulation of the real world or an imaginary world.

Augmented reality

The viewing of the physical world with computer-generated layers of information added to it.

Input: source documents. Process: CRUD, Calculate, and Summarize. Output: Reports

Systems thinking of a TPS.

Input: TPS. Process: What-If Analysis, Sensitivity analysis, Goal-setting analysis, and optimization analysis. Output is forecasts, simulations and Ad hoc reports.

Systems thinking of a DSS

Supervised, Unsupervised, and Transfer.

What are the 3 types of machine learning?

Supervised Machine Learning

Training a model from input data and its corresponding labels.

Unsupervised Machine Learning

Training a model to find patterns in a dataset, typically an unlabeled dataset.

Transfer Machine Learning

Transferring information from one machine learning task to another.

Deciding to enter a new market, enter a new industry in 3 years, choosing to eliminate an entire product line.

Strategic Decision examples

Reordering inventory, creating employee weekly schedules, creating an employee weekly production schedule, determining payroll issues.

Operational Decision examples

Determining sales forecasts for next month, analyzing the impact of last month’s marketing campaign; comparing expected vs. actual sales for last month.

Managerial Decision examples

Machine learning, Neural Networks, and Virtual Reality.

3 primary areas of AI