Business Intelligence Blog from arcplan
13Mar/120

Analytics – Not Gut Feeling – Should Drive Business Decisions

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You know the gut feeling that leads you to take a different route to work or accept one job over another? Those gut feelings may have led you on the right path, but they’re personal decisions where you have only so much information (a traffic report on the radio or both companies’ financials) and you would expect to make your decision based on gut instinct. These personal choices affect only you and potentially your family in the case of a new job. But relying on gut instinct alone in your business life is a mistake – there’s simply too much supporting evidence to take into account when making business decisions (decisions that affect much more than just yourself). Why play Russian roulette with these decisions when you’re surrounded by analytics?

Sound business decisions are based on facts, data analysis, trend spotting, or other complex calculations, and yes – a bit of intuition. But your instinct should be used as an indicator, not the basis for your decisions. In every business there are variables and unique scenarios that make planning and analysis imperative; neglecting these factors could have serious implications. Consider this example: The 2010 Report of Anton R. Valukas examined the demise of Lehman Brothers, a formerly dominant global financial institution that went bankrupt during the recent financial crisis. It revealed that the company excluded some assets from routine stress performance calculations (meaning the company couldn’t know how much money it was in a position to lose because it was not performing what-if analysis) and valued some real estate investments on a combination of financial projections and “gut feeling” according to a Lehman Brothers vice president. In essence, the company’s business practices lacked analytic insight, or at least the will to get it. There is no doubt that Lehman Brothers had access to multitudes of data on its assets, on the market, and on its level of risk. Armed with this information, I’d hope executives would have made better choices, taken on less risk, and valued their assets more realistically.

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9Mar/120

Big Data for Manufacturers: Customer Feedback Should Influence R&D

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In 2001, The McKinsey Global Institute published a comprehensive report on big data, Big Data: The Next Frontier for Innovation, Competition, and Productivity, which explores the value that companies across various industries may yield as result of the big data explosion. So far we’ve explored the impact of big data on retail and healthcare companies, but today I’ll explore how big data analytics impact the manufacturing industry.

The manufacturing sector stores more data than any other sector, according to the McKinsey report. Manufacturers will likely get the most benefit from big data analytics since they have so much “raw material” to work with (from machinery metrics to sales systems). Manufacturing is a relatively efficient industry, with many advances made over the last few decades to streamline processes and improve quality through management practices like lean & six sigma (and lean six sigma!). But big data can be the impetus for the next wave of improvements in manufacturing, especially in R&D.

Research and Development
Streamlining the R&D process results in greater efficiency and reduced costs for US manufacturers and is important for products to be competitive in the global economy. But in 2012 and beyond, manufacturers should be going further, leveraging big data to influence design decisions. This means incorporating customer feedback into the process, designing products and adding features that customers actually want. McKinsey calls this “design to value” or “value-driven design.”

Surveys: I’ve taken consumer surveys that ask questions like “How much more would you be willing to pay for x feature?” and I now understand why companies are asking this. They are culling data from consumers about what features are desired and if they are included in the product/service, what is the value, i.e. how much are people willing to pay for it. Gathering concrete insights is one step toward big data analytics influencing R&D. Manufacturers should be listening to what consumers want and refining their designs accordingly. It’s just smart business.

Here’s a concrete example: Domino’s Pizza. You might not think of Domino’s as a manufacturer, but it is – the company is a serious dough manufacturer, producing and distributing dough to more than 5,000 US stores.

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14Feb/121

Business Intelligence at Hospitals: Real-World Examples of Hospital Efficiency & Quality Metrics

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As both the battalion chief of my local ambulance and rescue squad and a business intelligence consultant, you can imagine that healthcare analytics are near and dear to me. Plus, living in the Philadelphia region, it’s impossible to escape the news of numerous hospital closures every year. So at arcplan, I love working with hospitals and healthcare organizations to build reports and dashboards that track the metrics critical to their survival. All the way back in 2001, Paul Mango and Louis Shapiro of McKinsey & Company argued that hospitals are essentially a commodity business and therefore need to compete on the basis of operational efficiency. This sentiment rings true more than 10 years later, with skyrocketing medical costs, declining insurance reimbursements, and increased utilization by an aging population. Giving hospital executives (and physicians!) access to real-time data has never been more critical to hospital operations.

Hospital executives often report on financial, operational and clinical system metrics which are crucial to ongoing operations and management. The hospitals we’ve worked with often have an overarching goal to provide efficient, quality care to patients, and they need access to their existing data to make sure they are achieving that goal. Important metrics that roll up to the goal of “efficiency” include the average wait time for a hospital bed, physician productivity, nurse turnover rates and the cost per discharge. Metrics that roll up to a “quality” goal include average length of stay, re-admission rates and patient satisfaction. The only way to improve the quality and efficiency of care is to analyze current performance and identify areas for improvement.

One of arcplan’s customers, the largest private operator of healthcare facilities in the world, came to us when they were focusing on efficiency. For more than 5 years, they have used an arcplan-powered business intelligence system (with data from Oracle Essbase and Teradata) to track key metrics and make decisions that improve efficiency of care – specifically in emergency rooms. All of their ERs needed to reduce wait times, shorten lengths of stay, and avoid people leaving the ER without care and treatment. The goal became to have every ER patient seen by a doctor within 45 minutes of arrival.

So what metrics do they track to achieve this goal? Here are a few examples:

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30Aug/110

Look Ahead with Leading Indicators

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Just as you can’t drive to work with your eyes only on the rearview mirror, you can’t drive your business forward by focusing on the past. Yet that’s what you’re doing if you’re relying solely on lagging indicators such as revenue, profit, or Cost of Goods Sold (CoGS) to manage your organization’s performance. These factors are important, but once they’re calculated, it’s too late to impact them. What you need are good leading indicators that allow you to spot trends and see issues before they balloon into real problems.

Leading vs. Lagging Indicators
Leading indicators are early predictors of sales and profit, and in combination with lagging indicators, they give you a holistic view of your company’s performance. Lagging indicators such as revenue, sales, expenses, and inventory turnover help you understand whether or not certain objectives have been met. They can depict trends when periods are compared, but by then, you’re too late to profit from the early discovery of the trend. Lagging indicators are calculated at the end of a period (month, quarter, etc.), so you won’t know whether or not a goal has been met until nearly the end of the period. Even if you run some ad-hoc reports throughout the period, you likely can’t get to the root of a problem in time to impact the outcome. Chances are, things were going wrong long before the lagging indicator on your dashboard turned yellow.

On the other hand, leading indicators pinpoint the source of future problems and help you predict whether or not the target values for your lagging indicators will be met. Leading indicators enable your company to avoid problems and operate more cost-effectively. For example, rather than tracking product returns (a lagging indicator), reporting a 90-day customer complaint trend allows you to fix problems earlier and less expensively. Drilling down into the complaints themselves, you might discover that a particular product has a defect that your quality assurance team didn’t catch. Removing the product from your shelves may save you a lot of trouble in the long-run, reducing complaints (and the negative feelings your customers may be starting to harbor toward you) as well as the cost of returns (returns aren’t free – they cost retailers nearly $14 billion a year).

Tracking receivables turnover (a leading indicator) enables the company to better manage its cash (a lagging indicator).

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