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8 WAYS TO BOOST BUSINESS WITH SMART DATA ANALYSIS

SUCCESSFUL EXAMPLES TO INSPIRE PROJECT TEAMS

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CONTENTS

Create a Data-Driven Culture

Increase Production

Meet Market Demand

Optimize Customer Service

Speed Regulatory Approval

Ensure Consistency

Save More Lives

Increase Sales

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Crayola

EvEryonE knows thE Crayola namE —they are the undisputed leaders in providing crayons and other colorful items children use to express their creativity. Far fewer recognize the company’s commitment to meeting the highest quality standards for the 2.2 billion crayons they make every year. “We start with the assumption that everything we do, we can do better,” explains Gary Wapinski, vice president of manufacturing.

Crayola’s data-driven approach began when Pete Ruggiero, executive vice president of global operations, went overseas in 2007. “I opened a box of our crayons in a shop, and some of the labels were coming off,” he re-calls. “I said ‘We’ve got to get this fixed.’”

Create a Data-Driven Culture

The company did an elementary Six Sigma project to resolve it, and its success prompted Crayola to go further. An initial wave of Six Sigma projects saved more than $1.5 million, and 7 additional waves of green belts and 3 waves of black belts project have followed.

Crayola’s leadership provides the support necessary to sustain a successful improvement program. Executives are actively involved in selecting and approving projects, and meet regularly with project leaders to review their progress. That top-level commitment helps project teams see the importance of their work.

Many Crayola projects help the company de-liver better crayons for kids. Crayola engineers used Design of Experiments (DOE) tools to

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study the breaking strength of cray-ons, leading to new standards and even the creation of a new crayon break-testing machine. In the past, “every time we tested a formula change, we needed to break tens of thousands of crayons to know if we’d made a difference or not,” says Bonnie Hall, vice president for global quality and continuous improvement. “Now it’s much faster and easier to evaluate how strong our crayons are.”

Sometimes data turns conventional wisdom on its head. One project improved production by reducing the line speed. “A capacity study showed that we got the same out-put at a lower speed with half the labor,” says Wapinski. “Running the machine faster just introduced more downtime and scrap.” And although peoples’ work has changed, no one at Crayola has lost a job because of a quality project.

Data-driven improvement trans-formed the way Crayola does business, and benefits the entire operation, Ruggiero says. “We’ve moved our culture from ‘When you have a problem, bury it,’ to ’We have a problem and we need to make it visible so we can solve it.’”

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Using Minitab to analyze data has helped Crayola develop new standards for assessing the strength of its crayons, and ensuring that new colors and creations meet strict quality requirements.

“Having an idea to make something better is great—but when you can prove it with charts and graphs and statistics, then you’ve got the foundation to make a change.”—James Collins, manager of continuous improvement and Six Sigma

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Bridgestone

BridgEstonE’s Costa riCa plant faCEd two intertwined challenges: meeting regulatory requirements, and overcoming an equipment issue that throttled production levels.

The plant had two types of machines for making tread: one that extruded tread in a single piece, and another that made tread by extruding many small ribbons. The rib-bon-tread machine required multiple steps and involved complicated variables such as feed rate and increments width. What’s more, tires created with it had failed to meet stan-dards. As a result, the ribbon-tread machine was under-used, which created a bottleneck.

A project team set out to identify factors critical to the quality of the ribbon-tread machine’s output, find optimal settings for those factors, and develop a model for using the machine to produce more high-quality tires with more speed. But testing tires is a

Increase Production

destructive and expensive process, so the team needed to evaluate the factors in as few experimental runs as possible.

After evaluating many potential variables, they prioritized four factors for further investiga-tion. Then they used the statistical method called Design of Experiments to quickly and efficiently gather enough data for reliable results without wasting time and resources. Their designed experiment involved produc-ing 16 tires using different factor settings, and putting them through the certification tests.

The results showed that all four factors, and the interactions between them, significantly affected the test results. The team used that data to redesign how they used the ribbon-tread machine. They forecast how different variable settings would affect the performance of a tire made with the resulting ribbon-tread, and then fine-tuned those

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settings to obtain the best possible results.

The new model was tried first on a tire size that had proven particular-ly challenging. The team used the model to determine optimal factor settings, then made some tires and tested them. To validate the results, they performed capability analysis to verify the production process could meet the required specifications. They also created a before-and-after control chart, which revealed how dramatically the new model improved the tire’s performance in certification tests. Use of the new model soon was expanded to other tire sizes.

Being able to make more tread means the plant can make more tires. The project increased pro-duction at the Costa Rica facility, saving hundreds of thousands of dollars and resulting in more than $2 million in opportunity costs. But the benefits go beyond the bot-tom line, points out project leader Kenneth Quiros. “Even projects with small savings can have a large impact on a company. Now, when issues come up, people around the plant ask, ‘How can we use statistics to solve these problems?’”

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“Now, when issues come up, people around the plant ask, ‘How can we use statistics to solve these problems?’”—Kenneth Quirós Acuña, Six Sigma Black Belt

The contour plot made it easy for the team to explore the relationship between their ribbon-tread variables and the diameter of the finished tires.

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AB InBev

ANHEUSER-BUSCH INBEV OFFERS MORE than 200 brands of beer in addition to their world-famous Budweiser beers.

But producing this variety brought chal-lenges, and planned downtime activi-ties—such as line start-ups and brand changes needed to produce all these beverages—affected the bottom line.

So a project team with members from every level of the business set out to reduce downtime by 30% and costs by $200,000.

To find the root causes, the team mapped the downtime process in great detail. They recorded the time it took workers to complete even the smallest actions, such as putting their gloves on and getting their tools.

Meet Market Demand

To make sense of all this data, they used Pareto charts to identify the big-gest causes of downtime, histograms to understand process behavior, and control charts and other graphs to compare multiple shifts and breweries.

Using hypothesis testing, they found significant differences in how different shifts handled the downtime process.

The team brainstormed ideas, mapped them to the root causes, and rated them for impact.

Their final solutions included opti-mizing the conversion process by streamlining and automating tasks, which reduced a 30-page downtime procedure to a 1-page checklist.

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They also delivered consistent training to line operators, and added an LED timer on the floor to provide accurate downtime feedback and create a sense of urgency.

Implementing the improved procedures on a trial basis sur-passed expectations, reducing planned downtime by 34% and saving more than $430,000.

Shorter downtimes also required less energy, providing additional savings. The team also saw a nearly 40% improvement in the time it took to resume production.

When the team created a con-version toolkit for other facilities to adopt, operators embraced the time-saving steps and gave the toolkit’s effectiveness an average rating of 4.85 out of 5.

The toolkit has since been applied to tens of thousands of processes at more than 180 facilities worldwide.

Analyzing data with Minitab made a lasting impact on ABInBev’s ability to affordably produce the wide variety of beers customers want.Based on ABInBev’s presentation in ASQ’s 2014 International Team Excellence Award competition.

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Analyzing data with Minitab made a lasting impact on ABInBev’s ability to affordably produce the wide variety of beers customers want.

The team used Minitab boxplots and other graphs to visualize differences in how various shifts performed brand changes.

Minitab control charts were used to establish limits and ensure results were sustained in the future.

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Edward Jones

EDWARD JONES KNOWS A THING OR two about high-quality customer service. Clients who call their service center receive timely, accurate, and professional respons-es that are friendly and customized to them. But a rapidly increasing volume of calls created a challenge: how could ser-vice associates meet these demands while maintaining, or even surpassing, their high service standards?

Rod Toro, Master Black Belt and business process improvement manager, used data analysis to meet that challenge. He and his team set out to improve all aspects of a call—from training new associates, to each phase of the call, and any after-call work. But considering the entire process meant many different factors needed to be considered.

Optimize Customer Service

Enter Minitab’s Design of Experiments (DOE) tools. A DOE is a series of experi-mental runs that provide insight into how multiple factors affect an outcome. Teams can change more than one factor at a time, and then use statistics to identify and set optimal levels of the important ones. DOE is common in manufacturing, but Toro realized he could also apply it to service processes.

The team selected four key factors—asso-ciate rating, after-call work, shift hours, and training hours—and designed an experi-ment using Minitab. After collecting their data, they used Minitab to analyze it and find the best mix of these factors.

At the start of the project, all associates were trained on multiple skills, and all skills received the same staffing priority.

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The results of the designed experiment indicated that cross-training associates on all skills was actually a detriment.

“Allowing them to focus on their best skills enabled them to perform better,” says Toro. “This was our ‘Aha’ moment. By assigning the right person to the right skills, we’re reducing average handle time and there-by improving average speed to answer, and the overall custom-er experience.”

Assigning associates with certain skills to the most ap-propriate areas increased the service center team’s capacity by over 10 percent, and also improved metrics including average handling time, average speed to answer, and how long employees take to complete after-call work.

The service associates are also happier—not only can they answer more calls in a workday, but their workdays flow much more smoothly. Many have even found time to become even more knowledgeable in their subject areas.

“Rather than basing our im-provements merely on intuition, we’ve been much more suc-cessful by using an approach that’s more fact-based and reliant on statistical analysis and Lean Six Sigma,” Toro says.

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“Rather than basing our improvements merely on intuition, we’ve been much more successful by using an approach that’s more fact-based and reliant on statistical analysis...”—Rod Toro, Master Black Belt, Edward Jones

Main effects and interaction plots for their designed experiment indicated that cross-training associates so they were well-versed on all skills was actually a detriment.

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Boston Scientific

BEforE transfErring produCtion of a diagnostic catheter to their facility in Costa Rica, Boston Scientific needed to prove that the catheters produced at the new location were just as strong as those from the original facility.

Although both facilities produced catheters that met the 5-pound specification for strength, inconsistencies in each facility’s test settings raised concerns.

Both facilities measured strength by applying increasing amounts of pressure to a catheter held between two grips on a universal testing machine. But the original facility’s catheter was approved in an era when validation rules were less stringent. The facility in Costa Rica, which aligned with modern American Society for Testing and Materials standards, used a different distance between the two grips that hold the catheter.

Speed Regulatory Approval

To get regulatory approval, the company needed to demonstrate that the differ-ent distance between the two grips did not compromise the tests. Otherwise, a time-consuming and costly test method validation process might be required.

The project team decided to compare the results of the original test settings with those produced under more current ASTM testing standards. Their goal was to prove conclusively that the altered testing process did not impact the demonstrated strength of the pull wire. If this could be shown to the regulators’ satisfaction, the Costa Rica facility would reduce both the amount of time and the number of test catheters needed to complete test method validation.

The statistical results demonstrated that both facilities’ test settings produced

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equally strong catheters, and the data they collected showed that both sets of catheters met the 5-pound regulatory minimum.

Based on the results, regulators agreed that Boston Scientific could adapt the catheter test method validation by leveraging its ASTM standard, with abso-lute confidence that the new pull-wire tension strength would continue to meet the standards set by the original manufacturer.

The reduced time spent on test method validation slashed costs, while allowing Boston Scientific to meet project deadlines and move on to the next phase of regulatory approvals.

Manufacturing engineer German Vindas estimates that using statistical tools reduced the timeline for transferring catheter production by about a month. “With Minitab, the pro-cess was very straightforward,” he says. “It saved us lots of time and money, and the project was able to continue on schedule.”

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The reduced time spent on test method validation slashed costs, and allowed Boston Scientific to meet project deadlines

The team’s DOE analysis found average pull strength was affected by two main factors—and an interaction between them.

A contour plot showed the ideal temperature settings needed to ensure the pouches had strong seals.

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Burley

BurlEy is known for BiCyClE trailErs that set the standard for safety, durability and design. So when disparities arose between inspectors at Burley’s U.S. headquarters and inspectors at a Chinese manufacturing partner, quality manager Mary Craighead needed to make sure all of Burley’s inspectors were flagging defects appropriately and consistently.

When different inspectors treat a potential defect differently, inspection results are un-predictable and can vary widely. Individual evaluators also may be inconsistent in their own judgments, passing a part today they might reject tomorrow. Both situations increase the risk of a defective product reaching a buyer.

The company needed to understand exactly where different inspectors weren’t in sync,

Ensure Consistency

and how far apart they were. Craighead performed an attribute agreement analysis, a statistical method that evaluates how con-sistently appraisers rate items. This analysis can confirm that appraisers judge items very consistently. Alternatively, it can reveal if some inspectors make very different judgments than others, or that individual evaluators do not always rate the same item the same way.

Craighead selected four sample trailer covers that met the company standard, and six that failed. Then four inspectors from each facility—two experienced, and two new—evaluated each cover twice, in two randomized sessions. The results verified that the evaluators were not judging items the same way. The overall accuracy percent-age was just under 60%, and parity between inspectors wasn’t what it needed to be.

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By revealing where the evalua-tions had great variation, the re-sults pinpointed where the team could make improvements. This enabled the company to develop clearer standards for those defects, and train inspec-tors to treat the same defects consistently.

Based on the analysis, Burley revised its inspection standards and added a color-coded risk and action matrix based on acceptable quality levels. The company also standardized training for inspectors at all facilities, creating a new visual guide that depicts defects and classifies their severity. A subsequent analysis confirmed the effectiveness of these improvements.

More consistent assessments help Burley’s bottom line by reducing scrap rates and unnecessary rework. Even more important, the improved inspection process helps ensure that Burley’s reputation for out-standing quality and durability remains strong.

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“We were able to identify where the evaluations had great variation, look closely at those types of defects, and determine how to make sure we were all treating the same defects consistently...”—Mary Craighead, quality manager

Attribute Agreement Analysis shows how well appraisers agree with each other and with established standards.

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Cathay General Hospital

you’ll find many lifEsaving tEChniquEs in hospitals, but one method experts are using might surprise you: data analysis. Consider the case of a project team at Cathay General Hospital in Taiwan, which sought to optimize its process for treating heart-attack victims.

The recommended treatment is angioplasty within 90 minutes of arrival. This involves inserting a small balloon inside the blocked blood vessel with a catheter. Inflating the balloon at the site of the blockage enables blood flow to resume.

Minimizing the time between a patient’s arrival and angioplasty—known as door-to-balloon (D2B) time—maximizes the chance for survival.

The team needed to evaluate each step of the treatment process, which included an

Save More Lives

electrocardiogram, the wait time before the operation, and the time required for balloon inflation.

After verifying the precision of their mea-surements and data collection methods, the team analyzed D2B data from 40 cases that occurred over a nine-month period.

To see how the D2B data fluctuated over time and identify any unusual sources of variation, they created an I-MR control chart. This chart shows how the mean and variation in the observations change, and flags un- usual observations for further investigation.

A capability analysis to determine whether their process could meet performance specifications confirmed that the hospital’s process needed improvement.

The team identified several steps where efficiency could be enhanced, including

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confirming the diagnosis, medi-cating the patient, preparing for the operation, transferring the patient to the catheterization laboratory, and inflating the balloon.

They implemented changes such as sending patients who arrive with chest pain directly to an electrocardiogram test, printing treatment sheets automatically, keeping heart-attack medication packs in the emergency depart-ment, contacting surgical staff upon diagnosis, and prepackaging necessary equipment in one box.

The team then collected data to reevaluate the improved process. They demonstrated that the average D2B time dropped from 139.2 to 57.9 minutes—a 58.4% improvement.

Patients now receive angioplasty more quickly, which saves lives. Moreover, the average hospital stay for heart attack patients has decreased by three days under the improved process, and the hospital has saved $4.4 million in medical resources.

Applying data analysis to health care doesn’t grab headlines like an experimental surgery might—but as more hospitals use data analysis to make pro-cedures better, faster, and safer, its benefits will be seen every day in the faces of patients whose lives are saved.

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Average time to treatment dropped from 139.2 to 57.9 minutes—a 58.4% improve-ment—enabling doctors to perform angioplasty more quickly and save lives.

The I-MR control chart was used to see how the D2B times fluctuated over a nine-month period and identify any unusual sources of variation.

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quality improvEmEnt rEaChEs far BEyond the walls of manufacturing plants. Buckman, a global leader in the chemical industry, trains their technical sales teams to use statistical tools. As the sales associates use their statistical knowledge to help pros-pects and customers understand the value of Buckman’s products and services, sales rise.

In a typical Lean Six Sigma deployment, statistical tools are taught in the context of working through a formal quality project. But many associates would not benefit from a full Lean Six Sigma course. “As we looked at this expanded view, we realized that a key group who should be taught these tools were our sales associates,” explains Drew Mohler, Lean Six Sigma black belt and se-nior organizational development consultant.

These sales associates, who have backgrounds in chemistry, biology, or

Increase SalesBuckman

engineering, work directly with customers to help them assess their processes and look for improvement opportunities. Mohler and his colleagues developed a course for the sales associates that linked appropriate statistical tools to each step of the selling process:• Gain knowledge of customer processes• Plan, run, evaluate and sell new chemical

programs• Manage ongoing chemical programs• Solve problems within the account

The associates learned to use tools like con-trol charts, hypothesis tests, and capability analysis with practical examples. “The end goal is to have our sales associates comfort-able using practical statistical tools, so they’ll be able to make better recommendations—data-driven recommendations—to help our customers,” notes Mohler.

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Now analyzing customer data is a key part the selling process. “Using Buckman’s chemistry solutions, our technical sales teams work to make our cus-tomers’ systems better,” Mohler says. “In essence, they function as process engineers for our customers.”

For example, an associate might run a trial to improve the brightness of paper pro-duced by a customer, collect before-and-after data to assess whether Buckman’s product has made a difference, then use a staged control chart to show the product’s impact on the process.

Sales associates at Buckman are improving conversion rates—but more important, they are delivering more value for customers. “Because they’ve done the statistical work behind the scenes, they are better prepared to explain the benefits resulting from our products and our customers see us as much more knowledgeable about their systems.”

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“Because they’ve done the statistical work behind the scenes, they are better prepared to explain the benefits resulting from our products, and our customers see us as much more knowledgeable about their systems.”—Drew Mohler, Lean Six Sigma black belt and senior organizational development consultant

Buckman uses control charts to facilitate conversations with customers and demonstrate the value their solutions provide.

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More than 90% of Fortune 100 companies use Minitab solutions to improve the quality of their products and processes.

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AccentureAdidasAdobeAllianzAmazon.comAmerican ExpressAnheuser-BuschAppleArcher-Daniels MidlandAvon ProductsBank of ChinaBASFBerkshire HathawayBHP BillitonBMWBPBridgestoneCanon, Inc.CartierChevronCisco SystemsCitigroupColgate-PalmoliveCredit SuisseDaimlerDanoneDellDeutsche TelekomDuPontE.On AGElectricité de France

Exxon MobilFannie MaeFord MotorGAPGeneral ElectricGilletteGoodyear Tire & RubberGoogleH.J. HeinzHitachiHonda MotorHSBC HoldingsHyundai MotorIBMIKEAING GroupJ.P. MorganJohn Deere & Co.Kellogg’sKia MotorsKimberly-ClarkKrogerMasterCardMcDonald’sMcKessonMedco Health SolutionsMercedes-BenzMetroMicrosoftMohawk IndustriesNestléNike

Nissan MotorOraclePanasonicPemexPenske Automotive GroupPepsiCoPetrobrasPeugeot S.A.PfizerPizza HutProcter & GambleR.R. Donnelley & SonsRoyal Dutch ShellSaint-GobainSamsung ElectronicsSAP AGSiemensSonyStarbucksTelefónicaThomas ReutersTiffany & Co.Toyota MotorUnited Parcel ServiceUnitedHealth GroupVeolia EnvironnementVerizon CommunicationsVisaVolkswagenWalt DisneyWeyerhaeuserXerox

The world trusts Minitab for quality.

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Get more out of Minitab! Minitab is bringing together some of the brightest minds in our field to help you master new skills, deep dive into process transformation and innova-tion, and enable you to connect with others who are passionate about process improvement.

Attending these two days of learning, networking and inspiration will provide you with greater confidence to deliver results, new strat egies to improve quality, and the understanding to get more out of Minitab and Companion by Minitab.

Who should attend? If you are involved in improving products or processes in your organization, the Minitab Insights Conference 2017 is for you.

You can tailor the conference to meet your specific goals by choosing from dozens of sessions on a wide range of topics—all designed to expand your ability to analyze and solve problems, whether you’re an expert practitioner, just getting started, or somewhere in between.

Hear from experts in quality improvement.Renowned quality improvement professionals will share their perspectives, insight, and experience.

Expand your analytical skills.Presenters will highlight effective ways to solve problems by providing tutorials, tips and tricks, and more.

Leverage the success of peers.Case studies from a range of industries will deliver valuable insight into how others are approaching and meeting their quality improve-ment challenges.

Experience the Minitab Lab.Minitab experts will be on hand at the Minitab Lab throughout the event. Test-drive our latest products, get personalized demos, ask questions and share your feedback.

Network, network, network!The program is packed, but you’ll also have plenty of time to meet informally with fellow attendees, discuss common issues, and build lasting relationships that will help sustain you long after the conference ends.

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Learn more at www.minitab.com or contact us at sales@minitab.com.

Insights 2017 SpeakersA stellar group of quality leaders will share their experience and insights with you.• Keynote Address: Peter Ruggerio, Executive

Vice President of Global Operations for Crayola Ruggerio initiated Crayola’s data-driven approach to solving problems, and the company has seen tremendous benefits as a result.

• The Five Y’s: Rethinking the Right Response Joel Smith, Dr Pepper Snapple Group

• Industry Examples of Binary Logistic Regression Scott Sterbenz, Ford Motor Company

• Testing for Differences using Stages: Exploiting Homogeneity in Time-Series Data Rip Stauffer, Management Science and Innovation, Inc.

• Mechanical Design Life and Capability from Weibull Failure Analysis Fred Eberle, Hi-Lex Controls

• Visualizing Your Analysis and the 7 Basic Quality Tools Thomas Rust, Autoliv

Presenters also include experts from Berry Global, Flowserve, Phillips Statistical Consult-ing, Rose-Hulman Institute of Technology, The Surgical Lab, LLC, Titus Consulting, and Minitab’s own experts in quality improvement and data analysis.

See full details at www.minitab.com/insights2017

Registration Registration is only $885 and early bird pricing is available. Discounts also are available for groups of three or more people from the same organization.

Venue and lodgingThe Omni Chicago Hotel offers the only all-suite luxury accommodations on Michigan Avenue, in the heart of the city. A block of rooms has been reserved at a special conference rate of $209 per night. Reserve yours at tinyurl.com/insights-hotel

Previous attendees say...“Minitab Insights delivered an incredible value for the registration price. It was the best conference I have attended in a number of years.”

“The event was well organized from beginning to end. I met some amazing people…it felt great to be surrounded by people working from a shared love for improving the world around us through process and product improvement.”

“Great conference—I got four key insights that will keep me busy for a long time!”

“I attended numerous presentations over the two days, learnt how to create Minitab macros, got some great new ideas for Minitab analysis, and met a lot of new people.”

“The greatest value of any conference I’ve attended. I walked away with a greater understanding of my challenges, some new approaches, a few solutions, and expanded my contacts for peer support.”