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QUESTION : If a business used DSS, GDSS, and ESS more widely, would managers make better decisions? Why or why not? Research and discuss two examples of these types of systems.

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BOOK

Management Information Systems, 16th Edition

ISBN: 9780135192047

By: Kenneth C. Laudon; Jane P. La...

Kenneth C. Laudon; Jane P. Laudon

12-4 How do different decision-making constituencies in an organization use business intelligence, and what is the role of information systems in helping people working in a group make decisions more efficiently?

Earlier in this text and in this chapter, we described the different information constituencies in business firms—from senior managers to middle managers, analysts, and operational employees. This also holds true for BI and BA systems (see Figure 12.4). More than 80 percent of the audience for BI consists of casual users who rely largely on production reports. Senior executives tend to use BI to monitor firm activities using visual interfaces like dashboards and scorecards. Middle managers and analysts are much more likely to be immersed in the data and software, entering queries and slicing and dicing the data along different dimensions. Operational employees will, along with customers and suppliers, be looking mostly at prepackaged reports.

Figure 12.4 Business Intelligence Users

Casual users are consumers of BI output, while intense power users are the producers of reports, new analyses, models, and forecasts.

Figure 12.4 Full Alternative Text

Decision Support for Operational and Middle Management

Operational and middle management are generally charged with monitoring the performance of key aspects of the business, ranging from the downtime of machines on a factory floor to the daily or even hourly sales at franchise food stores to the daily traffic at a company’s website. Most of the decisions these managers make are fairly structured. Management information systems (MIS), which we introduced in Chapter 2, are typically used by middle managers to support this type of decision making. Increasingly, middle managers receive these reports online and are able to interactively query the data to find out why events are happening. Managers at this level often turn to exception reports, which highlight only exceptional conditions, such as when the sales quotas for a specific territory fall below an anticipated level or employees have exceeded their spending limits in a dental care plan. Table 12.6 provides some examples of MIS for business intelligence.

Table 12.6 Examples of Mis Applications

company

Mis Application

California Pizza Kitchen

Inventory Express application “remembers” each restaurant’s ordering patterns and compares the amount of ingredients used per menu item to predefined portion measurements established by management. The system identifies restaurants with out-of-line portions and notifies their managers so that corrective actions will be taken.

Black & Veatch

Intranet MIS tracks construction costs for various projects across the United States.

Taco Bell

Total Automation of Company Operations (TACO) system provides information on food, labor, and period-to-date costs for each restaurant.

Support for Semi-structured Decisions

Some managers are “super users” and keen business analysts who want to create their own reports and use more sophisticated analytics and models to find patterns in data, to model alternative business scenarios, or to test specific hypotheses. Decision-support systems (DSS) are the BI delivery platform for this category of users, with the ability to support semi-structured decision making.

DSS rely more heavily on modeling than MIS, using mathematical or analytical models to perform what-if or other kinds of analysis. “What-if” analysis, working forward from known or assumed conditions, allows the user to vary certain values in test results to predict outcomes if changes occur in those values. What happens if we raise product prices by 5 percent or increase the advertising budget by $1 million? Sensitivity analysis models ask what-if questions repeatedly to predict a range of outcomes when one or more variables are changed multiple times (see Figure 12.5). Backward sensitivity analysis helps decision makers with goal seeking: If I want to sell 1 million product units next year, how much must I reduce the price of the product?

Figure 12.5 Sensitivity Analysis

This figure displays the results of a sensitivity analysis of the effect of changing the sales price of a necktie and the cost per unit on the product’s break-even point. It answers the question: What happens to the break-even point if the sales price and the cost to make each unit increase or decrease?

Figure 12.5 Full Alternative Text

Chapter 6 described multidimensional data analysis and OLAP as key business intelligence technologies. Spreadsheets have a similar feature for multidimensional analysis called a pivot table, which manager “super users” and analysts employ to identify and understand patterns in business information that may be useful for semi-structured decision making.

Figure 12.6 illustrates a Microsoft Excel pivot table that examines a large list of order transactions for a company selling online management training videos and books. It shows the relationship between two dimensions: the sales region and the source of contact (web banner ad or e-mail) for each customer order. It answers the question: Does the source of the customer make a difference in addition to region? The pivot table in this figure shows that most customers come from the West and that banner advertising produces most of the customers in all the regions.

Figure 12.6 A Pivot Table That Examines Customer Regional Distribution And Advertising Source

In this pivot table, we are able to examine where an online training company’s customers come from in terms of region and advertising source.

Courtesy of Microsoft Corporation

Figure 12.6 Full Alternative Text

One of the Hands-On MIS projects for this chapter asks you to use a pivot table to find answers to a number of other questions using the same list of transactions for the online training company as we used in this discussion. The complete Excel file for these transactions is available in MyLab MIS. We also provide a Learning Track on creating pivot tables using Excel.

In the past, much of this modeling was done with spreadsheets and small stand-alone databases. Today these capabilities are incorporated into large enterprise BI systems where they are able to analyze data from large corporate databases. BI analytics include tools for intensive modeling, some of which we described earlier. Such capabilities help Progressive Insurance identify the best customers for its products. Using widely available insurance industry data, Progressive defines small groups of customers, or “cells,” such as motorcycle riders age 30 or above with college educations, credit scores over a certain level, and no accidents. For each “cell,” Progressive performs a regression analysis to identify factors most closely correlated with the insurance losses that are typical for this group. It then sets prices for each cell and uses simulation software to test whether this pricing arrangement will enable the company to make a profit. These analytic techniques make it possible for Progressive to profitably insure customers in traditionally high-risk categories that other insurers would have rejected.

Decision Support for Senior Management: Balanced Scorecard and Enterprise Performance Management Methods

The purpose of executive support systems (ESS), introduced in Chapter 2, is to help C-level executive managers focus on the really important performance information that affects the overall profitability and success of the firm. There are two parts to developing ESS. First, you will need a methodology for understanding exactly what is “the really important performance information” for a specific firm that executives need, and second, you will need to develop systems capable of delivering this information to the right people in a timely fashion.

Currently, the leading methodology for understanding the really important information needed by a firm’s executives is called the balanced scorecard method (Kaplan and Norton, 1992, 2004). The balanced scorecard is a framework for operationalizing a firm’s strategic plan by focusing on measurable outcomes on four dimensions of firm performance: financial, business process, customer, and learning and growth (Figure 12.7).

Figure 12.7 The Balanced Scorecard Framework

In the balanced scorecard framework, the firm’s strategic objectives are operationalized along four dimensions: financial, business process, customer, and learning and growth. Each dimension is measured using several KPIs.

Figure 12.7 Full Alternative Text

Performance on each dimension is measured using key performance indicators (KPIs), which are the measures proposed by senior management for understanding how well the firm is performing along any given dimension. For instance, one key indicator of how well an online retail firm is meeting its customer performance objectives is the average length of time required to deliver a package to a consumer. If your firm is a bank, one KPI of business process performance is the length of time required to perform a basic function like creating a new customer account.

The balanced scorecard framework is thought to be “balanced” because it causes managers to focus on more than just financial performance. In this view, financial performance is past history—the result of past actions—and managers should focus on the things they are able to influence today, such as business process efficiency, customer satisfaction, and employee training. Once a scorecard is developed by consultants and senior executives, the next step is automating a flow of information to executives and other managers for each of the key performance indicators (see the Interactive Session on Management). Once these systems are implemented, they are often referred to as ESS.

Another closely related popular management methodology is business performance management (BPM). Originally defined by an industry group in 2004 (led by the same companies that sell enterprise and database systems like Oracle, SAP, and IBM), BPM attempts to systematically translate a firm’s strategies (e.g., differentiation, low-cost producer, market share growth, and scope of operation) into operational targets. Once the strategies and targets are identified, a set of KPIs are developed that measure progress toward the targets. The firm’s performance is then measured with information drawn from the firm’s enterprise database systems. BPM uses the same ideas as the balanced scorecard but with a stronger strategy flavor.

Corporate data for contemporary ESS are supplied by the firm’s existing enterprise applications (enterprise resource planning, supply chain management, and customer relationship management). ESS also provide access to news services, financial market databases, economic information, and whatever other external data senior executives require. ESS also have significant drill-down capabilities if managers need more detailed views of data.

Interactive Session Management

Anthem Benefits from More Business Intelligence

Anthem Inc. is one of the largest health benefit companies in the United States. One in eight Americans receives coverage for their medical care through Anthem’s affiliated plans. Anthem also offers a broad range of medical and specialty products, such as life and disability insurance benefits; dental, vision, and behavioral health services; and long-term care insurance and flexible spending accounts. Anthem is headquartered in Indianapolis, Indiana and earned over $90 billion in revenue in 2017.

Anthem has been an industry leader in analyzing data to reduce fraud and waste, cultivate customers, fine-tune its products, and keep healthcare costs low. For example, the company analyzes the data it collects on benefit claims, clinical data, electronic health records, lab results, and call centers to determine each person’s risk for an emergency room visit or a stroke. This information helps the company identify opportunities for improvement and individuals who might benefit from additional services or wellness coaching.

Now Anthem is applying its analytical skills internally to sharpen decisions about how to deploy and develop its employees as a strategic resource. Anthem has 56,000 employees and would like better answers to these questions: What is our employee turnover rate for call center staff in Colorado Springs or across the United States? What is the cost of that turnover? Are we differentiating rewards for our top performers? How many of our nurses might retire within the next couple of years? What is the relationship between our time-to-hire metrics and national unemployment rates?

Anthem created a cloud-based People Data Central (PDC) portal, whose interactive “workforce intelligence” dashboard lets HR and other Anthem users access and ask questions such as these using internal and third-party data. The portal serves 56,000 employees and presents data in dashboards, graphs, reports, and other highly visual formats that are easy to comprehend and manipulate.

A high-level “executive scorecard” feature explores the relationship between Human Resources metrics and business outcomes to use in short- and long-term planning. One scorecard report showed the relationship between customer growth, Anthem’s net hire ratio, and total costs associated with internal and external labor. PDC provides a total of seven dashboards on “hire-to-retire” HR operations, 50 summary views from which users can drill down into detailed reports, and links to other relevant data and training sources.

The company also formed a Talent Insights team, whose members include MBAs, PhDs, and CPAs, to develop more sophisticated data analysis and help users work with the data. For example, the Talent Insights team worked with Anthem’s Wellness team to examine data on employees who utilize company wellness credits to determine whether this could be correlated with reduced absenteeism and employee turnover. Every month, the team looks at a different slice of data using various indicators such as company performance, employee participation in wellness programs, or reductions in absenteeism. For example, one month the team broke down Anthem’s workforce by generational age bands and examined the potential ramifications for company performance, highlighting its analysis in the portal’s “insight spotlight” section.

The PDC uses Oracle Human Capital Management (HCM) Cloud and Oracle Business Intelligence Cloud Service. Oracle HCM Cloud is the cloud-based version of Oracle tools for Human Capital Management (human resources management), including tools for talent management and workforce management. Oracle Business Intelligence Cloud Service provides powerful data analytics tools as a cloud service that is available to anyone in the enterprise. The service includes tools for ad hoc query and analysis, interactive dashboards, and reports. Anthem’s Oracle-based cloud platform took less than six months to deploy.

Anthem’s human resources and talent data used to be scattered among many different systems and spreadsheets, so they were difficult to aggregate and compare. Basic questions such as “What is our turnover rate?” or “How many open positions do we have right now?” produced different answers, depending on who was asked, even within the same location or work group. To improve the HR department’s recruiting, hiring, promotion, and employee development programs, Anthem needed better analytic tools and a single standardized enterprise-wide view of its data.

The PDC is able to perform sentiment analysis (see Chapter 6). The portal includes a channel where employees can voice their opinions about their work experiences confidentially, using emojis and limited text entries. The system analyzes these ongoing sentiments to create “team vitals” reports to help management identify and address potential productivity risks. The company often finds information on which it can immediately take action, such as ideas for improving processes or re-prioritizing resources.

The Anthem Talent Insights team is also helping other business groups outside of Human Resources make better use of data. These employees from other functional business areas typically have access only to the data for their business units. Talent Insights helps them combine these data with HR data so they can answer questions such as how their high-potential employees whose careers are advancing compare with those in other parts of the company. The team developed a model that accurately predicts first-year attrition and identifies the causes of employee turnover. Anthem management is using this information to increase employee retention and create better profiles for future hires.

Sources: Rob Preston, “People Data Central,” Profit Magazine, Winter 2018; Michael Singer, “Anthem Prescribes Oracle Analytics for Talent Lifecycle,” blogs.oracle.com, February 26, 2018; www.antheminc.com, accessed April 24, 2018; and Jennifer Bresnick, “Borrowed from Retail, Anthem’s Big Data Analytics Boost Member Engagement,” HealthIT Analytics, August 4, 2017.

Case Study Questions

Why did Anthem need better data and analytics tools for Human Resources? What management, organization, and technology factors contributed to Anthem’s need for better HR data and analytics?

Describe the business intelligence capabilities of the PDC portal.

What groups in the company benefited from Anthem’s new analytics tools? Explain your answer.

How did Anthem’s new data analytics capabilities change the Human Resources function at the company?

Well-designed ESS help senior executives monitor organizational performance, track activities of competitors, recognize changing market conditions, and identify problems and opportunities. Employees lower down in the corporate hierarchy also use these systems to monitor and measure business performance in their areas of responsibility. For these and other business intelligence systems to be truly useful, the information must be “actionable”—it must be readily available and also easy to use when making decisions. If users have difficulty identifying critical metrics within the reports they receive, employee productivity and business performance will suffer.

Group Decision-Support Systems (GDSS)

The systems we have just described focus primarily on helping you make a decision acting alone. However, what if you are part of a team and need to make a decision as a group? Group decision-support systems (GDSS) are available for this purpose. The collaboration environments described in Chapter 2 can be used to help a set of decision makers working together as a group, in the same location or different locations, to solve unstructured problems. Originally, GDSS required dedicated conference rooms with special hardware and software tools to facilitate group decision making. But today GDSS capabilities have evolved along with the power of desktop PCs, the explosion of mobile computing, and the rapid expansion of bandwidth on Wi-Fi and cellular networks. Dedicated rooms for collaboration can be replaced with much less expensive and flexible virtual collaboration rooms that can connect mobile employees with colleagues in the office sitting at desktops in a high-quality video and audio environment.

For example, Cisco’s Collaboration Meeting Rooms Hybrid (CMR) allows groups of employees to meet using any device via WebEx video software, which does not require any special network connections, special displays, or complex software. The software to run CMR can be hosted on company servers or in the cloud. The meetings can be scheduled by employees whenever needed. CMR can handle up to 500 participants in a meeting, but that is quite rare. Skype began deploying a similar cloud-based collaboration environment integrated with Microsoft Office called Skype for Business to support online meetings and sharing of documents, audio, and video.

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