big data and big analytics for product and process quality · gather data to understand what was...

BigData

anD Big analytics

for Product and Process Quality

How an enterprise-wide quality platform can turn existing data into substantial and sustainable revenue growth

and cost savings for global manufacturers.

BigData

anD Big analytics

for Product and Process Quality

S2 www.sas.com I www.Industryweek.com

About IW Custom Research

IW Custom Research is an

operating unit of IndustryWeek

magazine that provides insight

into executives’ opinions

and manufacturing trends.

IndustryWeek connects decision-

makers within manufacturing

enterprises to share ideas and

tools that inspire action. In

print, online and in person,

the IndustryWeek community

is the leading resource for

manufacturing operations

knowledge. IndustryWeek is a

property of Penton Media Inc.

For more information, go to

www.industryweek.com.

About SAS®

SAS® is the leader in business

analytics software and services,

and the largest independent vendor

in the business intelligence market.

SAS’s market-leading business

analytics software and services

help customers make fact-based

decisions to improve performance,

from identifying the right product

to market to forecasting trends.

For more information, go to

www.sas.com.

About This Report

This is a report on the findings of the IW/SAS Enterprise Quality Survey*.

The objectives of the survey were to determine concerns about quality among

manufacturers; investigate the tools used to measure quality; and examine how

using enterprise-wide analysis on quality data could improve performance.

E-mail survey: On July 7, 2011, IndustryWeek e-mailed invitations to participate in

an online survey to a total 53,391 subscribers; 422 completed

surveys were returned, for an effective response rate

of .8 percent.

Title, job function, and industry: Seventy-four

percent of respondents hold executive-level or

manager positions while the rest are individual

contributors with no direct reports. Respondents

mostly work in general management (22 percent)

or operations (20 percent); 13 percent work in a

quality function; 12 percent in production; 8 percent in

supply-chain management; and the remaining in finance, IT

or “other” functions. The top industries represented are industrial machinery (21

percent), automotive (11 percent), consumer durables (9 percent), metals/mining

(8 percent) and aerospace/defense, consumer/industrial electronics, and plastics

and rubbers (7 percent each). Twenty-nine percent of represented companies

have annual revenues of $1 billion to $10 billion. The rest have annual revenues of

less than $100 million to $999 million.

*Percentages for each question do not always add up to 100 percent due to rounding or multiple responses allowed.

Contents

➤ About This Report 2

➤ Quality’s Untapped Potential 3

➤ Better Quality Through Better Data 5

➤ What is an Enterprise Analytical Quality Platform (EAQP)? 8

➤ Steelmaker Achieves $1.2 Million ROI with EAQP 8

➤ Key Benefits: Time-to-Market, Problem Resolution, Performance Improvement 9

➤ Conclusion 11

www.sas.com I www.Industryweek.com S3

Quality’s untapped Potential

Quality is not a difficult concept

for manufacturers or their

customers to understand. If the

customer is 100 percent satisfied, the

manufacturer has achieved the highest

level of quality. Of course, this isn’t

possible all of the time. Even Six Sigma

allows room for some error. Quality,

then, is something that manufacturers

constantly work at, and the most

commonly used tool for this work is data.

Hundreds of everyday manufacturing

metrics measure some aspect of quality:

➤What’s this week’s scrap

rate compared with last week’s?

➤Have our warranty costs changed

since we switched suppliers?

➤ If we shorten order-to-delivery cycle

time, will we increase market share?

Customers don’t see all of the

measuring, inputting and analysis that

goes into quality assessment — which

is good. But for a variety of reasons,

access to and analysis of quality-related

data tends to be limited internally as

well — and that’s not good. This research

report explains why and describes

how to turn this common shortcoming

into a competitive advantage with an

enterprise-wide analytical solution.

IndustryWeek and SAS partnered to

delve deeper into how manufacturers

are using and not using quality-related

data to improve performance because

we’ve recognized that companies need

to change their thinking on quality in

order to address the ongoing challenges

of competing in a global economy.

In the past, quality improvements

occurred at a very granular level.

Teams would uncover defects and then

gather data to understand what was

going on with either the product design

or the production process. At the base

level, companies got serious about

quality when they introduced statistical

process control, which enabled them to

have some warning of processes moving

out of control. This was the first step.

Acknowledging the interdependence of

variables that contribute to yield and

quality objectives, manufacturers

took the next step in developing

their information systems: process

engineering. Process engineering

recognizes relationships among multiple

inputs and makes progress toward

assembling and displaying data in

cross-functional context. It disseminates

information faster to those who will take

corrective or optimization actions.

But might there be a third step?

Could we move from process

engineering to process

understanding? A holistic view

across the entire organization? A

high-level view distilled from the

lowest-level details? What if a

company could have strategic,

predictive and optimal vision based

on one version of the truth, assembled

from all relevant process, product,

equipment and service data — an

information framework that recognizes

the fundamental interdependence of

functions and metrics that were once

viewed in isolation?

“The individual quality

project is still very much

a part of the process.

Global collaboration does

not preclude innovative

process improvement

through an individual

engineer’s efforts. But with

an enterprise platform,

engineers can share best

practices, successful

models and techniques,

as well as data, thereby

increasing the value of

quality engineering to the

company.” — michael newkirk, manufacturing and supply chain marketing manager for sas


This is possible when an organization takes an

enterprise-wide or global approach to collecting

and analyzing quality-related data, and begins

to see this data not only as a quality-control tool,

but also as a strategy to guide action in key areas

that feed fast and direct results to the top and

bottom lines.

“Culturally, looking at process improvement

on a global basis is a tough sell,” said Michael

Newkirk, Manufacturing and Supply Chain

Marketing Manager for SAS. “So much process

improvement has occurred by decentralizing

and de-bureaucratizing quality initiatives.

Leaders and managers think, ‘By the time

we got all that data together, our individual

engineers could find and solve dozens of profit-

draining problems.’ That might have been

the case a decade ago, but global process

improvement is very attainable today. ”

As the IW/SAS Enterprise Quality Survey and

this report show, enterprise-wide quality is an

untapped gold mine for the modern, global

manufacturing company. Using an Enterprise

Analytical Quality Platform (EAQP), companies

can unify the quality-related data that they

already collect and analyze it in multiple

ways to identify new opportunities to reduce

costs and improve profits; and to prioritize

improvement opportunities by determining

which will result in the fastest and greatest ROI.

“The most sophisticated manufacturers are

already doing this,” Newkirk said. “They have

created a living archive of collective knowledge,

where data from across the organization

is warehoused together and accessible to

everyone working on process and product

improvement. The results of individual efforts

are published and scored. Models that work

the best are distributed while those that don’t

are discarded.

“There is, in essence, a virtual workbench

created for collaboration across the entire

company. Best practices, best models, design-

of-experiment results — all can be archived

and shared.”

Most importantly, according to Newkirk, the

individual quality project is still very much a part

of the process. The enterprise platform, while

being extendable and scalable — and capable

of handling deep analysis on large amounts of

data, such as data mining, text mining and high-

performance forecasting — also gives individual

engineers and analysts desktop copies of

analytical tools and visualization capabilities.

With these they can design experiments, and

visualize data, scenarios and outcomes; and

then share their results and models with the rest

of the enterprise.

“Global collaboration does not preclude

innovative process improvement through

an individual engineer’s efforts. But with an

enterprise platform, engineers can share best

practices, successful models and techniques,

as well as data, thereby increasing the value of

quality engineering to the company. ”

In this report, we will explain how an EAQP

works and how using such a platform can

accelerate time-to-market for new products;

reduce the negative effects of existing problems

by resolving them faster; and enable long-term

sustainment of quality improvements. We will

also tie these benefits to the results of the

IW/SAS Enterprise Quality Survey, which reveal

a need for such a platform in all sectors and

sizes of manufacturing enterprises.

As the IW/SAS

Enterprise Quality

Survey and

this report show,

enterprise-wide

quality is an

untapped gold mine

for the modern,

global manufacturing

company.


Manufacturers put an enormous amount of work into improving quality. Yet, for all of the

effort, they still struggle. Thirty-eight percent of respondents to the IW/SAS Enterprise

Quality Survey have seen the cost of quality as a percentage of cost-of-goods-sold increase

over the past two years; and it stayed the same for 28 percent of respondents. (See Figure 1.)

Better Quality through Better data

0% 10% 20% 30% 40% 50%

Change in COGSHow has your company’s quality costs as a percentage of COGS changed over the past two years?

Increased more than 20%

Increased 11%-20%

Increased 6%-10%

Increased 1%-5%

Stayed the same

Decreased 1%-5%

Decreased 6%-10%

Decreased 11%-20%

Decreased more than 20%

None of these

No reply

1%

2%

11%

24%

28%

19%

5%

2%

1%

5%

1%

0% 20% 40% 60% 80% 100%

Quality ChallengesWhich of these are your company’s biggest quality challenges? (Select up to three.)

Reducing scrap and rework costs

Sustaining desirable quality and/or yield levels

Having a sufficient amount of quality- related data to provide meaningful insight

Reducing warranty-claims cost

Our quality-related data is inconsistent, outdated or otherwise flawed

Other

57%

49%

22%

20%

10%

7%

FIGuRe 1

FIGuRe 2

More than half of respondents

identified reducing scrap and

rework costs as a top quality

challenge. (See Figure 2.)

About half identified sustaining

desirable quality and yield levels

as a top challenge, as well. The

third-most-cited challenge was

having a sufficient amount of

quality-related data to provide

meaningful insight.

We believe that manufacturers

are being held back on excelling

at the first two challenges

by the third. Our research

shows that only a small slice

of manufacturing employees

review quality data. Not

surprisingly, it’s common for

operations managers and quality

engineers to review the data.

(See Figure 3, page S6.) But

only one in three continuous-

improvement managers look at

the data, and only one in four

customer-facing employees

such as salespeople do. Only

27 percent of interviewees said

everyone at their company

reviews quality data.


In general, manufacturers could do a better job

of data integration, analysis and accessibility,

a fact that is particularly true for quality-related

data that could provide strategy-level insight.

This is because vast amounts of quality-related

data are hidden by the increasing complexity

of IT assets and the confusion and frustration

that results. It’s common at most companies —

regardless of size — for leaders and non-IT staff

members to assume that the data reports they

are given by the IT staff are all they need to work

effectively and all that is available. Not true. But

to change this environment, company leaders

will need to see the true strategic value in the

data they already collect.

“[Leaders] simply outsource the work of strategic

technology design to contractors and IT staff and

hope that the chaos gets sorted out over the years.

As a leader, you cannot afford to do this anymore.

You must have a strong working knowledge of

your digital plumbing before you can begin to

optimize its performance, and the performance of

your organization.” Scott Klososky, The

Velocity Manifesto1

Looking more closely at the IT landscape in a

manufacturing environment, it’s not uncommon

to find a multitude of different systems from

which data is extracted:

➤ERP/MRP systems: Often there are

multiple installations from one vendor, but all

are on different release levels, and they have

differing customizations. Because of mergers

and acquisitions, it is common to find

multiple vendors compounding the problem

of data access.

➤MES: A company with multiple

manufacturing facilities most likely has a

range of different Manufacturing Execution

System data sources. Harmonizing the MES

to a corporate standard is typically too huge

of an effort and is likely to cause production

disruptions that cannot be afforded. Also,

MES implementations have the same problem

that ERP/MRP implementations have with

0% 20% 40% 60% 80% 100%

Review of Quality-Related InformationWho reviews quality-related information (i.e., scrap, rework, warranty claims)at your company/plant?

Operations managers and supervisors

Quality engineers

Continuous-improvement leaders

Product engineers

everyone

Customer-facing functions such as sales

Procurement/purchasing professionals

Plant and maintenance engineers

Other

None of these

71%

52%

33%

30%

27%

24%

23%

14%

5%

1%

FIGuRe 3

1 Klososky, Scott, The Velocity Manifesto, Greenleaf Book Group Press, Austin, Texas, 2011


customizations, even using standardized

platforms. That is, even using the same

platform from plant to plant, the data can

be very hard to extract.

➤Testing: Test systems typically come

with a proprietary data format and storage

applications. Having acquired testers from

multiple vendors for valid purposes adds to

this complexity. Test data often are collected

into Quality Management Systems (QMS)

and Laboratory Information Management

Systems (LIMS) that are plant-specific and

difficult to share system wide.

➤Equipment: With more sensors and

microprocessors being engineered into

production assets, process equipment

and machinery is collecting an increasing

amount of data that could be used

to improve equipment performance,

processes and yields. This data is under-

utilized. Plant and facility engineers usually

use the data only for historical evaluations,

although it could be used for predictive

analysis.

➤Defects: Manufacturers perform

inspections on incoming material, the

semi-finished and the finished products,

and they use some sort of tracking and

cataloging of symptoms, defects and actions,

such as equipment repairs, adjustments to

processes or product redesigns. This is key

quality data, and having access to this and

similar data and analyses across other lines

and factories could have enormous benefits.

➤Process: Data from manufacturing

operations is collected from a variety

of shop-floor data sources such as

Programmable Logic Controllers (PLC),

RFID tags, bar code scanners and manual

data input stations (HMI/MMI), but it is not

aggregated and assembled in a way that

facilitates quality exploration and analysis.

➤Supplier: When manufacturers

receive raw material, components or sub-

assemblies from suppliers, they often

receive quality data along with products.

This information gets stored and is valuable

for analysis but not always easy for quality

analysts and engineers to access.

In such a complex environment, the process

of getting at the data is very tedious and

time consuming, and probably not very

repeatable. This is true for repetitive and

necessary tasks, such as root-cause analysis

and standard reporting. And for strategic

insight that requires real-time to near-real-

time data, gathering enough data in a timely

enough fashion for it to be meaningful and

actionable is impossible.

Yet, such a need is obvious among

respondents to our survey. When we

asked participants what would improve

their company/plant’s quality performance

as a contributor to profitability, here is how

they responded:

➤Early-warning analytics that enable

operators to proactively address potential

quality and performance issues before they

become customer problems: 49 percent.

➤Collaborative problem-solving

capabilities across departmental functions

through enterprise-wide technology tools:

49 percent.

➤Easier and faster access to quality-

related data and other knowledge:

47 percent.

➤Predictive modeling that allows optimal

process set-up, leading to improved asset

utilization, optimized material consumption,

reduced rework rates, and reduced scrap

expenses: 37 percent.

An EAQP can provide all of these

capabilities and elevate the quality data you

already collect from granular and tactical to

insightful and strategic.

In such a complex

environment, the process

of getting at the data is

very tedious and time

consuming, and probably

not very repeatable. This

is true for repetitive and

necessary tasks, such as

root-cause analysis and

standard reporting. And

for strategic insight that

requires real-time to

near-time data, gathering

enough data in a timely

enough fashion for it to be

meaningful and actionable

is impossible.

STeelmAkeR ACHIeveS $1.2 mIllION ROI wITH eAQP

A korean steelmaker had made sound incremental process improvement using a cadre of highly trained six

sigma black belts. But they sensed they could make a larger impact with an enter-prise-wide, coordinated effort that utilized data integration and enterprise data mining and modeling.

there were still large profit variables among plants and items; and scrap losses were unacceptably high. traditional analysis on isolated processes wasn’t sufficient.

By pulling all of its data together across plants and processes, the company re-duced its scrap ratio from 15 percent to 1.5 percent, saving $150,000 on one process. the company was able to identify variations in profitability by plant and item for cold roll steel, delivering an annual $1.2 million return on its investment in the soft-ware solution that the six sigma experts used to identify high-value improvement targets that teams then analyzed for root-cause problems.

the steelmaker also achieved a 50 per-cent reduction in lead times for standard hot coil production (from 30 to 14 days) and reduced inventory by 60 percent. these efforts helped reduce the planning and sales cycles, and maximized production utilization. the improvements also didn’t take months to accomplish — the analyti-cal cycle times actually dropped. 2


Figure 4 shows a conceptual

architecture of an enterprise

solution. It shows:

The Information Store: The data

sources on the left are the most

important building blocks for the EAQP

data model. The rest of the data sources

are augmenting the value of the solution.

Quality Lifecycle Analysis: This layer

includes multiple interfaces suited for

different types of users.

The interfaces could include:

➤ Dashboard to surface KPIs.

➤ Reports to fulfill an organization’s

reporting requirements.

FIGuRe 4

2 “How This Giant is Light on Its Feet: Korean steel giant POSCO achieves Six Sigma performance with the help of SAS®,” SAS Institute Inc., www.sas.com

what is an enterprise analytical Quality Platform (eaQP)?

Enterprise Analytical Platform Overview

Parts movement

measurements

Testing

Inspection

Products & Hierarchies

Finance & Organizational

equipment

Repair & FA

Field Failures

Supplier Data

➤ Alerts to surface continuous-monitoring results.

➤ Analytical workbench for root-cause analysis.

➤ Predictive modeling capabilities to allow proactive quality management.

➤ Model management to support the processes around predictive

models, including performance monitoring.

Users gain these benefits:

➤ Access to and integration of key data from multiple sources, platforms, and operational systems.

➤ Optimal analytical use of data.

➤ Creation of a single version of the truth for the entire organization.

➤ Disbursement of best-practices-based business rules and processes. These are applied to all

reports and analyses.


While the benefits of using an EAQP are many, the platform’s potential in three areas is key to manufacturing competitiveness: time-to-market, problem solv-ing and performance improvement. Let’s take a look at each of these. In the

following charts, the curves depict the lifecycle of a product.

Finance & Organizational

equipment

Repair & FA

Field Failures

Supplier Data

key Benefits: time-to-market, Problem resolution, Performance Improvement

Figure 5 shows that during development, a company invests heavily to develop a new product/model, and the early proto-types typically show low yields and are not sellable product. Once the company completes development and is ready to bring the new product/model to the market, it must introduce new processes into production.

Initially yields tend to be quite low at this point, and the goal is to bring them up to optimal levels. At the same time, production volumes need to be in-creased. In many companies, the ramp up of volumes and yields is moderate.

During the early part of a product’s lifecycle, the market will tolerate a higher price, but as competition quickly catches up, the street price for an item declines. This means that the time span to recover the upfront R&D investment is quite limited. The faster a company can ramp up volumes and yields, the more recovery of R&D investment and profit can be made.

An EAQP enables companies to signifi-cantly reduce the time-to-mature-yields during the yield-ramp up phase through: ➤ Swift feedback. ➤ Rich analytical toolset. ➤ Increased transparency and insight into manufacturing processes.

Once products have reached high volumes and mature yields, a variety of unexpected occurrences can cause a yield or quality problem to creep up, as is depicted in Figure 6. In these cases, it’s essential to detect the issue as fast as possible, uncover the root cause(s) and resolve the problem.

FIGuRe 5Time-to-Market

YIE

LD

TIME

DevelopmentYield Ramp

Volume Production

FIGuRe 6Problem Resolution

YIE

LD

TIME


Volume Production


This is because during a period of low yields/bad quality, a producer could be: ➤ Losing production capacity. ➤ Missing shipments. ➤ Incurring excessive costs. ➤ Exposing customers to faulty products. ➤ Facing future financial liabilities because of warranties, recalls and/or loss of goodwill.

An EAQP allows you to significantly reduce the amount of time it takes to resolve problems through monitoring and early-warning capabilities, and ana-lytical root-cause analysis capabilities. This means that high levels of quality can more easily be sustained. Sustain-ing desirable quality and/or yield levels was the second-largest quality chal-lenge survey respondents named at 49%. (See Figure 2, Page 5.)

Once yields have reached mature lev-els, companies might want to push the limits to increase output and produc-tivity — without needing to invest in additional production capacity. (See Figure 7.) This is hard work, and to achieve successful and sustain-able results, an organization needs advanced statistical discovery capa-bilities to uncover where the greatest improvement opportunities lie.

FIGuRe 7Performance Improvement

YIE

LD

TIME


Volume Production

*A decrease in time-to-market is an improvement; an increase is a decline in quality.

Companies with higher levels of data integration and sharing had greater improvement in quality, customer satisfaction and time-to-market over the past two years.

Data integration of/accessibility to product-related data ➔

High Some None Data sharing between upstream/ downstream processes ➔

High Some None

How has this metric changed in past 2 years? (estimated mean shown)Quality as % of COGS -0.8% +0.6% +1.6% -0.1% +0.8% +3.6%Customer satisfaction +4.9% +3.9% +3.1% +5.1% +3.3% +0.8%Time-to-market for new products*

-3.7% -3% -2.6% -4.7% -1.9% -0.5%

FIGuRe 8

An EAQP allows engineers and analysts to find the opportunities that will return the greatest gains for the effort by using hard statistical facts through a rich port-folio of analytical discovery tools. Because they have the data available and can collaborate on a common platform, these opportunities come fast enough to be realistically actionable. For all the investments of time, labor and sometimes equipment that perfor-mance improvement requires, choos-ing where to make the improvements

should not be left to “gut feel.”

Findings from the IW/SAS Enterprise Quality Survey also make a connection between these three areas and having highly integrated, widely accessible enterprise data. In Figure 8, notice that respondents whose companies have highly accessible, highly integrated data across the enterprise have reduced time-to-market, decreased COGS, and improved customer satisfaction more during the past two years.


Despite rapid advances in technology in all areas of business, companies have focused on communications tools the most and have greatly underestimated data analysis as a competitive weapon. 3

Quality data, which abounds in manufacturing enterprises, is one of the largest caches of untapped potential in this area and can be elevated to a strategic enabler by imple-menting an Enterprise Analytical Quality Platform (EAQP).

3 “Gearing For Growth: Future drivers of corporate productivity,” Economist Intelligence Unit, wwweiu.com

*A decrease in time-to-market is an improvement; an increase is a decline in quality.

Companies with higher levels of data integration and sharing had greater improvement in quality, customer satisfaction and time-to-market over the past two years.

Data integration of/accessibility to product-related data ➔

High Some None Data sharing between upstream/ downstream processes ➔

High Some None

How has this metric changed in past 2 years? (estimated mean shown)Quality as % of COGS -0.8% +0.6% +1.6% -0.1% +0.8% +3.6%Customer satisfaction +4.9% +3.9% +3.1% +5.1% +3.3% +0.8%Time-to-market for new products*

-3.7% -3% -2.6% -4.7% -1.9% -0.5%

conclusion

As leading companies have demonstrated, an EAQP creates a globalized manu-facturing intelligence architecture that returns swift and significant benefits with actionable and sustainable results. (See Figure 9.)

Companies, particularly manufacturing companies, will face continued pressure to improve performance with existing resources in order to remain competitive. C-level executives want a greater return on the 30-plus years of investment into operational and transactional IT systems. Unlocking the potential of existing quali-ty-related data can do this while supporting ongoing process-improvement efforts.

For more information on EAQP or other quality-related solutions, contact www.support.sas.com.

FIGuRe 9Manufacturing Intelligence Architecture

S12 www.sas.com I www.Industryweek.comSAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. 105414_S80746.1011

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