Question

It’s 7 a.m. in San Antonio, Texas, and Rich Marcogliese, chief operating officer of Valero Energy, is holding his usual morning meeting with the plant managers of 16 major refineries throughout the United States and Canada. On the walls of the headquarters’ operations center are a series of monitors centered by a giant screen with a live display of the company’s Refining Dashboard. Whether the executives are in the room or connected remotely, all eyes are trained on the Web-accessible gauges and charts, which are refreshed with the latest data every five minutes. “They review how each plant and unit is performing compared to the plan,” says Valero CIO Hal Zesch, “and if there is any deviation, the manager explains what’s going on at their plant.” For Valero, surprisingly little-known for a Fortune 10 (that’s right, one-zero) company with more than $118 billion (with a “b”) in revenue, just one dashboard needle moving from green to red might signal millions of dollars at stake. The point of the dashboard isn’t to call managers out; it’s to give executives timely information so they can take corrective action.

Valero’s Refining Dashboard is just the sort of cutting edge decision support tool that thousands, if not tens of thousands, of companies are now attempting to create.

Those companies have embraced the idea that decisions based on fact will consistently beat those based on gut. Business bestsellers including “Competing on Analytics,” “Super Crunchers,” and “The Numerati” have documented that it’s an approach that works. Financial analysts, board members, and even the news media increasingly expect sound, data-backed analyses from top management. And when things go wrong, regulators and, in some cases, even district attorneys follow the numbers to trace bad decisions. Plenty of obstacles stand in the way of better decision

support, from backward-looking metrics and ill-advised goals to antiquated budgeting approaches and technophobic executives. For management teams that can make use of

the data—and these days there’s always plenty of data there are huge opportunities to improve efficiency, develop innovative products, get closer to customers, and outsell competitors. Valero rolled out its dashboard in early 2008 at the behest of COO Marcogliese. He had launched a Commitment to Excellence program aimed at improving performance, and he wanted to see real-time data related to plant and equipment reliability, inventory management, safety, and energy consumption. Real-time performance data are compared against daily and monthly targets, and there are executive level, refinery-level, and even individual system-operator level dashboard views. It’s rare among business intelligence deployments to get fresh data every five minutes, but Valero has tapped directly into “process historian” systems at each plant in a six-month deployment of SAP’s Manufacturing Integration and Intelligence application.

A major focus of Valero’s Commitment to Excellence program is reducing energy consumption, so the company is rolling out separate dashboards that show detailed statistics on power consumption by unit and plant. “Based on the data, managers can share best practices and make changes in operations to reduce energy consumption while maintaining production levels,” CIO Zesch explains. Estimated savings to date: $140 million per year for the seven plants where the dashboards are in use, with expected total savings of $230 million per year once the dashboards are rolled out at all 16 refineries.

The terms scorecard and dashboard are often used interchangeably, but there’s an important distinction. Scorecards are all about tracking against defined metrics, and most scorecards are attached to a methodology, such as the Balanced Scorecard or TQM, says Mychelle Mollot, vice

president of worldwide marketing, analytics, and performance management at IBM. “Top executives have actually laid out a map for where they want to drive the business, and they’ve created metrics that will drive the behavior that will get them there,” Mollot says.

Whether they call their decision support tools scorecards or dashboards, only a small percentage of leading companies have actually mapped out enterprise wide goals with a formal methodology. Some companies come up with their own methodologies, but the key question is whether it’s a comparative decision support interface: Does it track performance trends relative to predefined goals? A much larger chunk of companies use dashboard-style interfaces that simply monitor the health of the business. “These types of decision-support tools aren’t often attached to a grand methodology or linked down to the bottom of the organization,” Mollot says.

At Elkay Manufacturing, a $1 billion plumbing fixture and cabinetry maker, the chief financial officer has led the company to embrace new approaches toward evaluation and reporting. The conventional budgeting process, by contrast, often takes too long, it’s a fixed contract, and “compensation schemes tied to it tend to encourage all sorts of bad behavior, like people sandbagging or just budgeting amounts based on last year’s budget,” says Adam Bauer, corporate planning manager at Elkay.

Elkay’s stated strategy is to grow profitably, so its sales related scorecards and dashboards include profit metrics so that salespeople don’t just drive revenue at the expense of the bottom line. Controller John Hrudicka says the company’s decision support tools have identified initiatives that produced more than $13 million in hard-dollar profit improvements while “helping us transform our culture to a profit mind-set.” Elkay put most of its decision-support technologies in place over the last two years. It tapped Host Analytics’ Software as a Service financial performance management system, which it uses for budgeting, planning, reporting,

and end-of-quarter financial consolidation. The system also supported the move, completed in September, to 18-month budgeting and planning cycles. Elkay chose Acorn Performance Analyzer software for activity based costing—analyses that reveal the true cost of delivering

products (including manufacturing, distribution, sales and marketing, and warranty claims)—as well as the true cost of sustaining customers (including products purchased, discounts applied, and ongoing service and support costs.)

For decision support, Oracle Business Intelligence Enterprise Edition pulls information from multiple enterprise systems to deliver multilevel scorecards and dashboards. “It starts with the corporate scorecard and it rolls down from there to the divisions and all the way down to individual employee goals that affect bonuses at the end of the year,” Bauer says. Bottom-up feedback, he says, is gathered during quarterly strategy reviews.

Few companies have worked as hard or as long at data driven decision making as Johnson & Johnson. There is an iterative process of assessing opportunities, developing goals, implementing improvements, and then monitoring their success with the aid of decision support tools. Indeed,

fact-based decision making is now “part of the culture at J&J,” says Karl Schmidt, vice president of business improvement, who leads a 9-person internal management consulting group.

Johnson & Johnson is decentralized, so there’s no single, overarching corporate dashboard. There are separate dashboards—or in some cases, balanced scorecards within the pharmaceutical, consumer, and medical device and diagnostics product divisions, and the dozens of companies in each of those groups. The key performance indicators include a mix of financial metrics (revenue, net income, cash flow); customer metrics (satisfaction, loyalty, market share); internal process metrics (product development, manufacturing efficiency, fulfillment); and employee metrics (engagement, satisfaction).

“It comes down to fact-based decision making,” he says. “In tough economic times, you want the best available data and analysis to make better decisions.” Some of the most decision-support-savvy executives can be found in e-commerce. For example, Patrick Byrne, chief executive officer of Overstock.com, is said to use dashboards to help set his daily schedule. If the problem of the day is gross profit margins, that will drive who he calls in for a discussion. “If you get invited into a meeting with that kind of metrics-oriented CEO, you better have your hands on the data, including the detail at the next level down,” says David Schrader, director of strategy and marketing at Teradata, the vendor behind Overstock’s data warehousing environment.

Overstock can roll up its profit and loss statement every two hours, “which is absolutely world class,” Schrader says. That capability gives executives accurate, up-to-date insight into the financial results they can expect, and it also drives operational decisions such as spot

buys of TV advertising.

Whether a company is an e-commerce powerhouse or not, digital marketing channels like e-mail, social media, and online advertising networks are increasingly important. Thus, top executives should be watching forward looking, upstream measures such as Web site performance,

Web-driven lead generation, and sales pipeline information. Here, again, you must be careful to select the right metrics.

“A lot of people are measuring the wrong thing, like how many people came in the door,” Schrader says. “What you really want to measure is how many people came in the

door and became qualified leads.”

And once prospects become customers, you’ll want to know if they are good or bad customers. That’s where analyses such as activity-based costing and customer segmentation come in. Lessons learned should come full circle and be reapplied to lead-generation campaigns and

marketing offers.

Considering all the IT systems now in place, the growing dominance of Internet-based marketing, and the intensely digital nature of services-based industries, there’s no doubt that data-driven decision making is the way forward. But the key questions are: How prepared are these organizations to synthesize and share key performance indicators? and How prepared are executives to draw insight from information?

Case Study Questions

1. What is the difference between a “dashboard” and a “scorecard”? Why is it important that managers know the difference between the two? What can they learn from each of them?

2. In what ways have the companies mentioned in the case benefited from their adoption of “fact-based” decision making? Provide several examples from the case to illustrate your answer.

3. Information quality is central to the approach that these organizations have taken toward decision making. What other elements must be present for this approach to be successful (technology, people, culture, and so on)?

Answer 1

1.  Scorecards are all about tracking against defined metrics, and most scorecards are attached to a methodology, such as the Balanced Scorecard or TQM.

Dashboard-style interfaces that simply monitor the health of the business. “These types of decision-support tools aren’t often attached to a grand methodology or linked down to the bottom of the organization.

Top executives have actually laid out a map for where they want to drive the business, and they’ve created metrics that will drive the behavior that will get them there so, they should know the difference between two.

Managers can share best practices and make changes in operations to reduce energy consumption while maintaining production levels

2. Taking fact based decision helped in benefiting the companies.

Controller John Hrudicka says the company’s decision support tools have identified initiatives that produced more than $13 million in hard-dollar profit improvements while “helping us transform our culture to a profit mind-set.

At Valero Energy,CIO Zesch explains. Estimated savings to date: $140 million per year for the seven plants where the dashboards are in use, with expected total savings of $230 million per year once the dashboards are rolled out at all 16 refineries.

3.In tough economic times, you want the best available data and analysis to make better decisions.

The key performance indicators include a mix of financial metrics (revenue, net income, cash flow); customer metrics (satisfaction, loyalty, market share); internal process metrics (product development, manufacturing efficiency, fulfillment); and employee metrics (engagement, satisfaction).