arc advanced analytics manufacturing

By Tom Fiske and Simon Bragg

ARC STRATEGIES

JANUARY 2007

Emerging Practices and Strategies for Using Advanced Analytics in Manufacturing

Executive Overview .................................................................... 3

Eliminating the Data Rich/Information Poor Dilemma....................... 4

Compelling Business Drivers for Advanced Analytics ........................ 5

Adopt Pervasive Use of Analytics .................................................. 6

Choose the Right Tool for the Job ................................................14

How Users Derive Value from Advanced Analytics ..........................17

Some User Experiences with Advanced Analytics............................19

Recommendations .....................................................................26

THOUGHT LEADERS FOR MANUFACTURING & SUPPLY CHAIN

ARC Strategies • January 2007

2 • Copyright © ARC Advisory Group • ARCweb.com

On-line OptimizationClosed-Loop,

Open-Loop Advisory,APC

Quality Control

Decision Support

Off-line OptimizationPerformance Monitoring,

Analysis, Insights, Improvements

MES/CPM

MultivariateAnalysis

Neural Nets,Decision

Trees

Multivariate Visualization

ERP & Supply Chain ApplicationsERP & Supply Chain Applications

LIMSLIMS

Integration Layer

SCADA

HMI

SCADA

HMI

HistorianDCSPLC

Integration Layer

Virtual Sensors

Multivariate Statistical Process Control

Multivariate Modeling

Asset ManagementEquipment Monitoring, Condition Monitoring,

Fault Detection

Process, Performance and Equipment Monitoring & Control

Advanced Analytics Applications in Manufacturing

Prod Mgt. LIMS/QISERPHistorians DCS/PLC

Data Aggregation

SCADA/HMISCADA/HMI Prod Mgt. Historians DCS/PLC ERP/SCM LIMS/QIS

Plant Plant

Management Enterprise EnterpriseSupply Chain

QA/QC

Sales & Finance

OperationsEngineering

Role Based Dashboard or Workspace

Ad

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Analytics Provides Analysis and Intelligence to Manufacturing Data and Performance-Based Decision Support


Copyright © ARC Advisory Group • ARCweb.com • 3

Staying competitive means transforming all

the data in the plant into information and

making that information available in the

proper context to all personnel involved in

operations. Manufacturers must adopt a

comprehensive strategy to exploit Analytics

to gain a competitive advantage.

Executive Overview

For process manufacturing enterprises, the value-added in its manufactur-ing operations is the primary determinant of business performance. These companies use asset intensive operations to make their products. Extract-ing the greatest value from these assets is difficult in the global economy as market opportunities come and go quickly due to rapidly changing cus-tomer requirements and extreme global competition. Globalization is

forcing companies to improve their productiv-ity and quality by achieving greater flexibility, agility, safety, and flawless manufacturing exe-cution.

In a global marketplace, manufacturers are un-der incredible pressure to improve their profitability or they will not be able to compete

in the global economy. Historically, this meant that companies reduced their staffs to improve their cost position and competitiveness. Companies today continue to lose their most valuable asset – people – to retirement. The exodus of workers severely erodes a company’s knowledge base and compromises their productivity if not duly compensated by other means.

Competitive pressure is forcing enterprises to deploy and use their physical and human assets more effectively. The greater scrutiny of manufacturing operations leaves the smaller workforce with less time to collect data and make insightful analysis, and correspondingly good business decisions about operations. With less time for analysis, people must not only work harder, but also work smarter. Employees must have access to critical “on-demand” information to make better decisions to improve the bottom line.

The key to success is not to generate and distribute more data, but to im-prove understanding of the data and distribute timely information that companies already have in numerous disparate applications scattered throughout their organization. It also means using embedded “intelligent” applications that interpret the data and take action. Advanced Analytics is a key element in the distribution and interpretation of information. Staying competitive means transforming all the data in the plant into information and making that information available in the proper context to all person-nel involved in operations. Manufacturers must adopt a comprehensive strategy to exploit Analytics to gain a competitive advantage.



Advanced Analytics provides a major

opportunity for manufacturers to improve

their operations and competitiveness by

transforming data rich systems into

valuable contextual information that forms

the backbone of deeper understanding

and better decision making.

Eliminating the Data Rich/Information Poor Dilemma

Manufacturers must consistently meet or exceed performance targets and strive for continuous improvement to thrive. Data is collected to provide a measure of how well a company is doing compared to its key performance indicators (KPIs). Data is also collected on just about anything else that might be useful in identifying problems and highlighting opportunities for

improvement. Proliferation of IT and automation systems has furthered encouraged the buildup of data on all aspects of the process, equipment, and operations.

The growing popularity of Analytics software re-flects this buildup of data. The ability to graphically present information on dashboards and drill-downs

and to “slice and dice” this information across various dimensions has become an essential capability of plant floor and business applications. Statistical Qual-ity Control (SQC) charts are also becoming common for monitoring of performance throughout the manufacturing enterprise.

While current Analytics solutions are powerful, they lack certain capabili-ties that manufacturers need to achieve optimum performance. Most Analytics packages today only support “univariate” analysis. They allow users to examine individual variable in every possible way, but they offer little more than OLAP-style filtering to identify relationships between vari-ables. Manufacturers are finding that the ‘multivariate” relationships are the ones confounding their efforts to drive better performance.

Whether trying to optimize a production process or any other complex ac-tivity, relationships among process variables have a significant impact upon the best operating practices. Recipes that work in one plant do not work in another because of differences in environment, equipment, and other vari-ables. Demand for product varies across regions because of differences in consumer taste and competitor actions. Not understanding these relation-ships causes manufacturers to make decisions that lead to sub-optimal performance. In today’s world, this is no longer acceptable. Companies that ignore or “average out” influencing factors can find themselves losing market position to more innovative competitors who exploit their data to develop deeper process knowledge.



Companies with extensive banks of

data on their operations have the

opportunity to develop competitive

advantage and instill a culture of

continuous improvement, but only if

they can exploit this data to develop

new, more innovative processes.

On-line, conventional Analytics solutions may help manufacturers recog-nize that a problem has occurred, but they offer no insight as to what is needed to restore and improve performance. Root cause analysis, based solely upon ad hoc comparisons of individual variable traces is likewise a cumbersome approach to driving improvement. Even if relationships are detected, there is still nothing in conventional Analytics to guide develop-ment of acceptable operating procedures. Solutions that support true multivariate analysis of data are required and can bring substantial benefit.

Advanced Analytics, such as multivariate analysis, can be used to turn mounds of data into information to address manufacturing performance. ARC uses the term “Advanced” to describe a set of sophisticated visualiza-tion, data analysis, and modeling tools that extend the capabilities of conventional Analytics. How can end users apply these tools and associ-ated best practices to optimize performance in manufacturing operations?

Compelling Business Drivers for Advanced Analytics

Manufacturers in many industries make similar products, using similar equipment, with materials sourced from similar suppliers, in plants staffed by people with similar educational backgrounds. Superior manufacturing and business processes are among the last remaining points of differentia-tion.

As a collection of sophisticated tools and techniques, Advanced Analytics can help manufacturers gain additional, deeper insight into their operations. These solutions can help companies identify rela-tionships and acceptable, multivariate “operating envelopes”. In many cases, this information is use-ful for developing multivariate, causal models that support prediction of performance and develop-

ment of best practices for new scenarios. This predictive modeling capabil-ity is invaluable for such things as increasing production throughput and optimizing product mix around the most profitable products. In addition, such capabilities are applicable for use as off-line solutions or embedded on-line applications within plant automation systems.



Manufacturers have made considerable progress over the last decade using Ana-lytics to improve their operational management. Overall performance goals, like profitability, have been re-fined into constituent KPIs that help to focus attention on the real constraints and problem areas. Advanced Analytics can take this to the next level, by high-lighting the root causes for KPI variations. With this knowledge in hand, organizations can better focus their actions towards specific products, customers and resources and signifi-cantly reduce all variations. Moreover, operations that are more consistent open the door for more rapid improvement.

Advanced Analytics are more sophisticated than conventional Analytics and use of these practices in your organization requires a change in organ-izational culture. Operating personnel need training in the use of highly sophisticated visualization and monitoring tools. Power users will also be required to exploit the full power of these capabilities. The potential bene-fits are compelling in today’s global marketplace. New competitors are not reluctant to take advantage of advanced analytics and hesitation on your part will only empower your competition to take the lead.

Advanced analytics address a broad and diverse range of business proc-esses and manufacturing operations. Although the focus of this report is on manufacturing, Advanced Analytics are finding use in every aspect of a manufacturing organization: from finance and HR to marketing, R&D, and plant floor operations.

Adopt Pervasive Use of Analytics

Manufacturing offers a wealth of opportunities to exploit the power of Ad-vanced Analytics. Manufacturing operations use complex processes and practices along with devices, equipment, process units, and plants. Efforts to standardize practices and drive continuous improvement are frequently

Function Description

Finance Understand what drives financial performance.

HR Identify the most appropriate employee for each task, given their personality and the job requirements

Marketing Identify the characteristics of the most profitable customers.

Pricing Identify the optimal price for each segment, based on customer type, competitor actions, and company capabilities

R&D Identify profitable combinations of product and service features

Manufacturing Identify and correct production anomalies, improve control, and make continuous improvements

Advanced Analytics Enable Continuous Refinement of Business and Manufacturing Practices



Automation systems already

collect, process, and store

thousands of measurements.

Nevertheless, operators and

engineers need better tools to

assess, consolidate, reconcile,

and generally make sense of

what all this data is telling them.

frustrated by the need for exceptions to address different products, custom-ers, markets, geographic areas, etc. Understanding the real impact of these factors enables organizations to gain better control of their operations and predict the impact of new developments.

Many companies have already recognized the value of Advanced Analytics in the control of their production operations. Process and automation engi-neers frequently use these tools to develop more advanced operator interfaces and control strategies. The technical background of many man-agers in this arena also makes production a fertile area for expanding the use of Advanced Analytics to other production activities.

Operating and technical support personnel are tasked with gaining deeper insight into the behavior of produc-tion processes and providing means for monitoring, controlling, optimizing, innovating, and improving opera-tions. Their automation systems already collect, process, and store thousands of measurements that can be used as the source for advanced visualization and performance calculations. What they need are better tools to assess, consolidate, reconcile, and generally make sense of what

all this data is telling them. They understand that there is valuable infor-mation within their data that awaits detection. Moreover, transforming this data into meaningful, multivariate relationships will allow operators, engi-neers, and supervisors to reduce operating costs and improve product quality.

The approaching departure of retiring workers provides further impetus for adopting Advanced Analytics in the production area. Loss of this ex-perience will severely erode the company’s knowledge base of what they do to address the “exceptions”. It will also reduce the organizations’ ability to identify, preempt, and effectively manage production problems. Com-panies can use Advanced Analytics to help them avoid this critical situation by explicitly capturing knowledge and building models that incorporate important process relationships.

Some companies are already achieving significant benefits and gaining a competitive advantage by deploying Advanced Analytics throughout their entire organization. Other companies must do the same or risk falling be-hind their competition. Leading users are exploiting Advanced Analytics in both On-line and Off-line modes to drive benefits across the production



spectrum. These companies are using Advanced Analytics pervasively throughout all levels of their production environment in a variety of ways including:

• Embedded applications that perform device or equipment level analy-sis and diagnostics

• Visualization of data for process analysis, continuous improvements, and decision support

• Critical Condition Detection and Prevention • KPI monitoring • Predictive modeling • Reporting and analysis • Ad-hoc and “On-Demand” Analysis and Decision Support • Inferential property estimation

Following are some examples of how users are exploiting the power of Ad-vanced Analytics.

Multivariate Statistical Quality Control

Statistical Process Control (SPC), or univariate SPC, has been successfully applied in the discrete parts industry for decades. SPC is rooted in the use of statistical variation of measurements to determine how the process is performing and to detect any unusual events or faults that may occur.

In the process industries, there are typically many variables that together determine the final product results. In most situations, these variables are not independent of each other. Since the process is driven by the underly-ing phenomena, the relationship between variables must be considered. Multivariate Statistical Process Control (MSPC) provides a mechanism for gaining the powerful benefits of conventional SPC in these more complex environments. MSPC is applicable to any manufacturing environment, process, hybrid or discrete, where multiple, dependent variables must be simultaneously monitored and controlled.

A key feature of MSPC is its ability to model a process and quality data set with a smaller, simplified set of variables. Using available process data, a control region can be created representing the optimal run conditions. In addition, the decomposition of an MSPC model is useful for providing process insight and determining the cause of a process excursion.



Typical benefits of models

developed using Advanced

Analytics include production

increases of a few percent,

reduced emissions compliance

costs of up to 50 percent, and

improved product consistency.

Causal and Predictive Modeling

One of the most powerful uses of Advanced Analytics is in the develop-ment of causal models. Such models are being used extensively by Process

manufacturers and are at the heart of some of their most successful process control and monitoring activities. Other industries are adopting some of these developments in their own quest to improve their operations. The follow-ing categories provide a convenient structure for discussing the focus of some of the most popular applica-tions for Causal and Predictive Modeling:

• Virtual Sensors • Off-line optimization • On-Line Optimization & Advanced Process Control (APC) • Asset Management • Decision Support

Virtual Sensors

Variously called soft-sensors or inferential measurements, Virtual Sensors are causal models that use available or easy-to-access process variables into an estimate of an unavailable, or hard-to-measure, target variable. Various Advanced Analytic techniques are used in building virtual sensors from historical data.

Quality Control One important application for virtual sensors is in the estimation of quality parameters that are otherwise not available on-line, e.g., in the food or pharmaceutical industry, where fermentation occurs under sterile condi-tions. Without direct measurements of quality during production, plants can produce excessive off-spec product while waiting for off-line analysis results. To minimize this, manufacturers are often forced to run such proc-esses conservatively, wasting valuable production time and capability.

By using Advanced Analytic tools to explore their historical data, manufac-turers can identify combinations of process conditions, such as temperature, pressure, and material specs, which produce acceptable qual-ity. This information can then be used to develop a virtual sensor for product quality. Instead of waiting 20 minutes for a lab result, or 6 weeks for a thermal resistance measurement of an insulation product, operations incorporating a virtual sensor for these parameters will have immediate



feedback, albeit approximately, what quality the current process set-points are generating.

Advanced Analytics can also improve the efficiency of the quality control function by reducing the frequency of human quality checks and for early release of intermediates. In addition, virtual sensors should also be consid-ered for augmenting hardware analyzers or as possible alternatives.

Off-line Optimization

Improved fidelity of Process Models is one of the most valuable benefits manufacturers can get from the use of Advanced Analytics. These models can be used off-line to evaluate facility designs, to de-bottleneck existing facilities, to develop good operating practices for new products, etc. Mod-els developed using Advanced Analytics can also be used On-Line for operator guidance and process control.

Advanced Analytics applied to historical data provides a basis for building Off-Line models that reflect the importance and the impact of different process parameters. Once identified, these relationships can be used to pre-dict performance for a given set of conditions and to develop optimal practices in terms of quality, energy consumption, throughput, etc. Typical benefits of models developed using Advanced Analytics include produc-tion increases of a few percent, reduced emissions compliance costs of up to 50 percent, and improved product consistency.

FDA’s Process Analytical Technology (PAT) Initiative FDA compliance can be challenging for manufacturers. Advanced Ana-lytics provides a means to reduce the cost of compliance by facilitating analysis of practices relative to the “design space” or operating region where the process has been validated. Doing this requires a deep under-standing of the effect that each process variation and possible value of input variables has on the quality of the product.

Identifying the “Best Practices” for Identical/Similar Lines Often manufacturers produce the same product on similar or identical lines. Intel for instance, has a strategy of “copy exactly”. Most likely, how-ever, lines will produce different yields or qualities. The lines can be compared, and the most successful operating procedures and set points can be determined.



Recipe Optimization In the food industry, it is important to produce a given product with a con-sistent taste, look, and aroma, while achieving specific nutritional requirements. If the source of the ingredients or manufacturing environ-ment changes, then the quantities of ingredients need to change. There can be hundreds of ingredient options and experts all have rules of thumb as to how the ingredients affect the taste, look, and aroma of the final product. Advanced Analytics can help companies evaluate and capture these rules and optimize costs for available ingredients.

Reduced Grade Transition Times Models that incorporate all important variables and process dynamics can help manufacturers minimize both transition time and off-spec product during transitions. Advanced Analytics can be helpful in improving the quality of models for these complex situations.

Performance Management and Continuous Improvement Programs KPIs are critical for effective performance management. However, many KPIs today are derived using simplistic methods, such as the averages of data across many different situations, and have control limits based solely upon intuition and wishful thinking. Such KPIs often provide false alarms

and miss important trends and events. They also have limited capability in identifying problems or prediction outcomes.

Using analytics to build KPIs resolves many issues associated with univariate KPIs. KPIs constructed using analytic meth-ods inherently combine multiple factors. These model-based KPIs are useful for detecting trends, troubleshooting prob-lems, and predicting outcomes. Since they are a combination of many factors, they can also be used for root cause analysis. These types of KPIs can be used by all types of plant person-nel, at all levels, and with all types of process data.

Advanced Analytics supports continuous improvement programs such as six-sigma in a number of ways. Advanced Analytics provides the statistical capability and process intelligence to support the rigorous DMAIC model often used in six sigma to deliver breakthroughs in performance. The DMAIC model describes the sequential steps that should be taken in assess-ing and improving performance. It has five components: Define, Measure, Analyze, Improve, and Control. The first four helps companies to identify

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IMP

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IMP

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CONTROL

Elements of the DMAIC Six Sigma Model



problems and implement solutions. Control, which is the last part of DMAIC, makes sure that the process retains gains that have been made.

Within the Six-Sigma context, Advanced Analytics helps companies quan-tify performance expectations so that products, processes, equipment, and plants can be monitored against these expectations. The system can quickly identify events that have a detrimental impact on the process and automati-cally alert personnel as to what happened and assist in determining why.

On-line Optimization and Advanced Process Control

Leading manufacturers often move Off-line models to On-line once the model has been proven. By doing this, they can maintain optimal perform-ance despite changes in equipment performance and raw materials. On-line models can also reduce the number of spurious alarms. The quality of such models has gained recognition in recent years, and even the FDA states that with proper understanding of the process, companies can take corrective actions during process upsets.

Another benefit of On-Line process models is that production qualities and quantities can be dynamically predicted. This can provide early warning that tanks will overflow or that other constraints will be violated. If these warnings are integrated with production scheduling, there can be sufficient time to allow rescheduling. This can be particularly useful during a changeover, where abnormal quantities of off-spec material can be pro-duced.

Asset Management

Advanced Analytics models can also be used to predict the health of pro-duction equipment or a process. In particular, changes in the correlation among multiple variables can be used to predict the likely onset of a failure. Such information can be used to give operators sufficient time to ameliorate the potential problem and notify maintenance of the need to investigate the situation during the next planned shutdown. On-Line models developed for this purpose allow organizations to broaden their replacement of waste-ful preventive maintenance practices with more effective condition based asset management strategies. The analysis can also provide deeper under-standing of the conditions that merit maintenance, helping the company to reduce failures while still avoiding wasteful work.

The paper industry provides some good examples of the use of Advanced Analytics to develop on-line models for asset management. Prediction of



The characteristics of a machine

or process that is about to fail can

be identified using Advanced

Analytics, giving operators time to

ameliorate the potential problem.

paper breakages and failures of rotating machinery are popular applica-tions in this industry. One paper plant using Advanced Analytics reported that they were able to reduce paper breaks by 40 percent.

Decision Support and Knowledge Management

Market demands and customer requirements are creating a need for greater agility and flawless execution, yet the increasing number of retiring work-ers and the associated loss of knowledge is putting companies at risk. At the same time, companies are pushing more decisions down to the produc-tion floor where they have the greatest benefit on profitability. To combat the loss of knowledge and make the remaining worker more production, companies must adopt pervasive use of collaborate knowledge manage-ment tools, including Advanced Analytics.

Effective use of Advanced Analytics for manufacturing decision-support offers significant performance improvements for most manufacturers. The use of real-time and “On-Demand” decision support tools using current plant data is helping production workers to optimize asset effectiveness by reducing cost and raw material usage, increasing yields, improving reliabil-ity, and increasing agility. Offline or on-demand operations decision support tools allow users to adjust and improve plant operations on a daily basis to accommodate changing market conditions. Real-time operations decision support tools (open loop) calculate optimized process conditions and provide the results in an advisory fashion to the operator.

Tools that only report historical data are not equipped to guide pressing decisions. Since Advanced Analytics decision support tools are model-based and have predictive capability, users can perform what-if analysis to see how their actions affect plant performance. This capability is important when there are multiple objectives with complex interacting processes that

create unintended or unanticipated consequences to a decision.

The use of Advanced Analytics for strategic and opera-tional support can significantly improve the consistency and quality of decision making throughout the entire or-

ganization by enabling outcomes bases upon quantifiably criteria rather than instinct. Advanced Analytics also accelerate the decision making process, permitting production personnel to focus on other issues that are more important. In the end, companies that best utilize Advanced Ana-



Using Multivariate Visualization

techniques, operators and

engineers, who are not statisticians,

can rapidly see which combinations

of process parameters produce good

product, and which do not.

lytics to make better decisions will have achieved the greatest competitive advantage.

Choose the Right Tool for the Job

Advanced Analytics includes a broad range of multivariate technologies. The most basic are approaches to multivariate visualization that focus sim-ply on presenting information. Other multivariate technologies utilize multi-dimensional metrics and multivariate statistics to assign points to specific categories and/or provide “best” estimate models for predicting dependent variables from a set of related independent variables. Under-standing what practices and technologies are available along with the limitations and applicability of each is important to deriving value. Listed below are a few examples of popular techniques that manufacturers use.

Multivariate Visualization Methods

Multivariate Visualization goes far beyond the simple pie and bar charts beloved by the conventional Analytics suppliers. Good multivariate visu-alization tools enable users to cluster data points, spot correlations, and identify principle components without using statistic analysis tools. These tools exploit the inherent abilities of most humans and enable almost eve-ryone in an organization, to contribute to a continuous improvement initiative without extensive training.

N-dimensional geometry, supported by a few suppli-ers, is particularly appropriate for visualizing the effects of hundreds of process measurements from many batches, on overall quality and efficiency. Each input and output parameter is plotted on one of many parallel vertical lines. When all the batch parameters are plotted, that batch’s fingerprint appears as a zigzag

line. If multiple batches are run, there are multiple zigzag lines forming an operating envelope.

In principle, batches produced with identical settings should produce product of the same quality. A difference may indicate that the plant is de-grading. These tools can be used to support control by identifying the appropriate values for the set points given a target specification.



Multivariate Statistical Process Control (MSPC)

Typically, Statistical Process Control (SPC) methodologies are applied to systems or processes in which only one variable is measured and tracked on control charts. These charts are designed and calibrated such that if a value goes outside of established “control limits”, there is a high probability that the process is out of control, while variations within the limits are trusted as being normal process variations.

This univariate monitoring scheme is adequate when there is only one variable to monitor and control. When there is more than one variable to be simultaneously monitored, in-dividual charts become cumbersome, and more importantly misleading. Confidence that all factors are simultaneously “in control” drops rapidly and critical process excursions can be missed.

Multivariate Statistical Process Control (MSPC) overcomes this disadvantage by monitoring several variables simulta-

neously. Using multivariate quality control methods, engineers, and manu-facturers who monitor complex processes can therefore monitor the stability of their process. The idea is straightforward when one considers two variables. Rather than having two independent control bands, the safe control region becomes a two-dimensional ellipse whose axis and shape reflect any correlation between the two variables. Uncorrelated variables would have a circular envelope. This shifts to more of an elliptical shape as correlation increases. In all cases, the shape encompasses those combina-tions of points for which there is high confidence that the process is under control.

As MSPC is applied to more simultaneous variables, it becomes impossible to present the n-dimensional envelope graphically. In such cases, simple charts are used to track derived statistical functions and control limits on these charts reflect the safe region for the combined function.

Implementing an MSPC strategy requires specialized software that can ex-tract the needed information from historical data off-line and on-line software to track and present actual process performance. Once imple-mented, MSPC can be a powerful tool for control and continuous improvement.

Variable A

Vari

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Enables large numbers of interactive Process variables to be monitored simultaneously

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Multivariate Statistics and Analysis Tools

Multivariate statistics is a set of analysis techniques, which involves the analysis of more than one statistical variable at a time. There are many dif-ferent models, each with its own type of analysis. Regression analysis is one of the most popular methods of modeling processes. The main advan-tage of regression methods is its ability to extrapolate beyond the data used to build the model. However, the empirical approach requires selecting a model structure that does not provide a clear relationship to the underlying physical process. In addition, the method cannot handle input variables that are collinear, nor can it deal with noise in the data. An extension to multivariate regression called partial least squares overcomes these short-comings.

The partial least squares (PLS) method is often used to build inferential models. This technique is restricted to linear operating ranges and de-signed to handle data with collinear variables and noise. PLS is extensible to nonlinear regions by using other technologies such as fuzzy logic or neu-ral networks. Regression models generally require less data than other techniques, however, choosing the appropriate model structure, variables, and determining parameter values is challenging. PLS models can predict product quality during or at the end of a run, thus providing an early warn-ing of a process upset.

Principal component analysis (PCA) attempts to determine a smaller set of variables that summarizes the most important part of a larger data set while simultaneously filters out noise. PCA and its variations are often used to determine equipment and process faults.

Neural Networks

Neural networks represent a powerful tool for developing nonlinear rela-tion-ships between input and output variables. Building models with

neural networks can provide insight and greater un-derstanding of the process. Neural networks, with their ability to derive meaning from complicated or imprecise data, can be used to extract patterns and de-tect trends that are too complex to be noticed by either humans or other computer techniques. Neural net-work models can be incorporated into optimization schemes and advanced process control strategies.

Input layer Hidden layer Output layer

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True

Input layer Hidden layer Output layer

True

True

Neural Networks



Some companies derive enormous

value from Advanced Analytics,

while others do not. The difference

generally does not lie in the abilities

of the staff, but in the way in which

the initiative is managed.

Handling Real Data

Although Advanced Analytics are powerful, efforts to use them can be con-founded by reality. Some Advanced Analytics suppliers report that up to 80 percent of the effort in identifying a solution is spent in getting and pre-processing the data. The actual analysis is relatively quick. Thus, the abil-ity of the tool to shorten the time to identify a solution is important. Quite often, the following attribute differentiate software packages: data integra-tion, preprocessing of data, clustering by operating mode, variable length batches, and model diagnostics.

How Users Derive Value from Advanced Analytics

Some companies derive enormous value from Advanced Analytics, while others do not. The difference generally does not lie in the abilities of the staff, but in the way in which the initiative is managed. There are cases of an engineer who purchases a single license, who identifies a problem, solves it, and persuades the organization to implement the appropriate changes. However, such an engineer who can access the data, understand the meaning of the data, has the analytical skills, and the influence in the organization is rare. Moreover, after this success, that engineer is likely to be assigned to other tasks. The problem with this is that improvements,

like on-line models, require constant maintenance, since the characteristics of the situation change over time. Most likely, new problems will emerge, which will not be solved, and performance returns to previous levels. Five characteristics of companies that derive the great-est value from Advanced Analytics include:

Cross-Functional Teams

Applications that deliver the greatest value generally span multiple func-tions. Most teams comprise an IT expert who can access the data, engineers who understand the process and the data, and sometimes a statistician who builds and validates the model. In addition, other skills may be required, depending on the nature of the problem.



There is some debate about the local versus centralized analyst model. If there is local resentment towards head-office, then such organizational models tend to fail. The value of a centralized model is that it leverages the analytical skills across many plants to solve problems at the local level. Of-ten, the central analysts monitor the data, and occasionally advise local individuals to do something different. This works, so long as there are not too many false-positive nuisance alarms.

Enterprise Wide

Successful companies adopt a standard tool across their enterprise. Users share their experience and develop common procedures. When selecting a tool, many place just as much emphasis on the packages’ ability to visualize complex data and ease of use as it does on how well it supports advanced statistical methods.

Training

Most people have studied and forgotten the simple univariate statistics that they learned in college. Hoping that people can learn Advanced Analytics “on the job” is not practical. Some investment in training must be expected. For most packages, “power users” with technical degrees generally require about two weeks training, and “results” users around half a day.

Commitment to Model Maintenance

Situations change over time. Plants get new equipment, new material sup-pliers are selected, and distribution channels change. This requires periodic maintenance of all models to ensure that the data and analysis reflects cur-rent, rather than historical, operating conditions.

Management Commitment

To develop these four characteristics, management must demand rigorous data analysis as part of any justification for change. In addition, manage-ment must create the budgets and set aside people’s time to enable the creation of cross-functional teams, allow people to take training courses, and devote the time to maintain models. If a single department leads an Advanced Analytics initiative, such as quality control, IT, R&D, or Opera-tions, it will likely fail. A multi-functional team is needed and management commitment is required to ensure that these factions work together.



Some User Experiences with Advanced Analytics

Following are some case studies of manufacturers that are using Advanced Analytics to drive improvements in their businesses. Some have already demonstrated significant benefits. Others provide valuable information regarding the lessons they have learned in their efforts to embrace these emerging practices and technologies in their operations.

Dofasco Steel

Dofasco Steel in Canada was one of the pioneers in the use of on-line MSPC to monitor process performance. Back in the early 1990s, Dofasco identified several potential applications for MSPC within their production facilities. They saw a benefit of working with MSPC technology in that it provided a good balance between adequate model fidelity, process insight, ease of de-velopment, and robustness in the presence of noisy or missing data. At the time, however, the MSPC technology was immature, so they decided to de-velop and implement their own solutions.

One successful application was in their continuous casting operations. Initially their focus was on monitoring the operation to detect the onset of a caster strand breakout and to prevent it from hap-pening. Caster breakouts are catastrophic failures in the steel casting process. They occur when the shell of a strand does not form properly, causing molten steel to spill, damaging equipment, and causing production delays for all downstream op-erations. Since the introduction of MSPC, Dofasco has rapidly progressed from offline data analysis to on-line monitoring and control.

Since the on-line implementation in 1997, Dofasco has enjoyed its highest productivity levels and has seen a sharp decline in the number of break-outs. Prior to the implementation of the real-time process monitoring system the continuous casting process experienced about ten breakouts a year, which is about average for the industry average. Now they are down to about 2 or 3 breakouts a year. The cost of a typical breakout can range from $500K to several million dollars due to lost production, missed deliv-

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Liquid Pool

Bottom Mold

Strand

Continuous Casting Process



eries, safety issues, and equipment damage. With the confidence of fewer breakouts the average casting speed has increased.

The on-line prediction capability has also been applied at Dofasco to de-termine the optimal amount of reagent needed to remove sulfur accurately from pig iron in its Torpedo Car Desulphurization Station. The harsh envi-ronment of the facility presented challenges in providing reliable, high quality process data. Because of this, previous control methodologies achieved limited success. The new MSPC model has been on-line since 1995 and has required one retuning. The amount of sulfur reagent has been reduced by 8.5 percent, and the iron was increased by 0.25 percent.

NOVA Chemicals

NOVA Chemicals is a global manufacturer of plastics and chemicals with revenues of about $5.6 billion. The company produces several billion pounds of ethylene, polyethylene, styrene, and polystyrene each year. The market in which they compete is cyclical in nature, with profits inextricably linked to the delicate balance of supply and demand. Companies typically compete on price. Manufacturing efficiency and effectiveness often deter-mines the winners and losers in this market.

Path to Manufacturing Excellence

NOVA Chemicals understands its markets well and understands what it takes to be successful. Part of the company’s strategy is to be the low cost provider, build upon their sustainable competitive advantage, and invest only for high returns. The company has a history of implementing manu-facturing excellence initiatives around focus areas that encompass process automation, maintenance and reliability, engineering, quality, process con-trol, and operations. Its manufacturing excellence program has the goal to maximize total margin contribution of each product while ensuring that they operate in a safe and environmentally responsible manner. To accom-plish this goal, the company set out to create and maintain an infrastructure as well as business processes that supports excellence in all aspects of its manufacturing facilities.

NOVA Chemicals Allies with Suppliers to Develop RPM Solution

In its effort toward driving manufacturing excellence, NOVA Chemicals sought to enable real-time data visualization throughout the company to improve operational decision-making. The company worked with two of



Once the operators were acquainted with

the new system and had enough

confidence to trust the results, they

began to push the plant closer to the

theoretical limits and keeps it there, thus

generating significant value and benefit.

its technology providers, SAP and Pavilion Technologies, to develop a pilot application at its Joffre site in Alberta, Canada.

NOVA Chemicals Gets Real with the Production Rate Metric

Leveraging information to achieve manufacturing excellence at the poly-ethylene plant located in Joffre was not always easy. Collecting and analyzing the data from different information sources took an inordinate amount of time and only provided a historic perspective of what happened. Although not ideal, this information was used as a basis for troubleshooting and improving capacity utilization. As a result, NOVA Chemicals initiated a pilot project to deliver real-time performance information to operators, engineers, plant management and executives to drive operational decision-making by providing a window into what is currently happening on the plant floor and by providing insight as to what could happen on the plant floor.

The pilot project uses Pavilion’s Model Predictive Intelligence (MPI) technology to provide real-time visibility, predictive analytics for “what-if” sce-nario evaluation and theoretical versus plant capability performance insight. MPI technology complements the NOVA Chemicals implementa-tion of Advanced Process Control (APC) by

allowing the same models used in advanced process control to be leveraged for real-time performance management. Integration with financial data in their ERP system via SAP xMII application and the NetWeaver integration platform provides additional visualization and functional capability, in-cluding the ability to see contribution margin by product.

Operations Crank up Plant Utilization

The pilot application provides NOVA Chemicals’ operators, engineers, management, and executive team with a production rate capability metric that uses real-time data to predict the optimal utilization for the plant and compare it to the current performance. To augment the production rate metric, the system provides insight into the primary constraints that are inhibiting the plant from achieving optimal performance utilization. The combination of accurate performance data and an ability to identify and push constraints, provides operators with the information necessary to make better, faster, and more informed decisions to continuously maximize production.



Once the operators became acclimated to the metric and had confidence to trust the results, they began to leverage the tool to push the actual plant performance closer to the theoretical limits. To support the decision-making process, the system allows users to drill down into the causes of variability to determine which factors are contributing to bottlenecks and other problems. The system also provides the ability to create simulations to determine the implications of potential operating changes intended to generate incremental capacity.

A Strategic Tool for Better Business Decisions

Using SAP xMII, NOVA Chemicals is able to disseminate information through SAP’s Enterprise Portal to anyone in its organization that needs its. More importantly, the integration of real-time plant floor data with finan-cial information from the ERP systems provides a powerful tool for both tactical and strategic decision-making.

NOVA Chemicals uses this information in a number of ways. The com-pany continuously tracks the revenue contribution margin for every product, order, and customer. This information is put in the context of lost capacity utilization and delivered via the manufacturing portal to give management support for important business and financial decisions con-cerning its products and customers. The ability of the system to examine the effects of constraints and bottlenecks also provides plant management with valuable insight and guidance into plant capital investment decisions that reduce lost capacity utilization.

Significant Benefits Encourages Enterprise-wide Adoption

Although NOVA has not explicitly published or stated ROI for the project, they do claim they have achieved benefits. For instance, the insight pro-vided by MPI allows NOVA Chemicals to make operational adjustments that increase capacity. With the success of the initial project and benefits realized, NOVA Chemicals plans to roll-out the solution across its enter-prise. The company plans to bolster its capability to facilitate decision-making with other metrics that include quality management and produc-tion run consistency to name a few.

Lubrizol

Lubrizol is a global producer of advanced chemicals and specialty materials for the transportation, consumer, and industrial markets. The company is



organized into two businesses: Lubrizol Additives and Noveon, which produce lubricant additives and specialty chemicals, respectively.

Lubrizol Additives is a leading user of Advanced Analytics, and the com-pany’s journey to its current position is instructive. Lubrizol’s culture has always emphasized databased decision-making, in R&D and throughout their operations, which up to and through 1990s had generated many pock-ets of excellence. Nevertheless, senior management support was peripheral, and each project required a separate but rigorous justification, which slowed progress. Although each project initially demonstrated good results, many times they were unsustainable due to lack of corporate-wide support and an integrating infrastructure.

Around 2002, Lubrizol formalized what it knew: that a software package alone does not enable successful adoption of Advanced Analytics. Success-fully embedding analytics within an organization requires an infrastructure, which includes an appropriate organizational structure, cul-ture, hardware, systems, people, and processes.

Project Organization and Structure

To further address Lubrizol’s manufacturing systems, in 2002, they decided to heighten attention to progress by creating cross-functional Operations Management System (OMS) Teams consisting of key personnel from vari-ous groups including logistics, purchasing, statistics, IT, controls, executives, and suppliers to name several. An OMS steering team was also defined and above this, an OMS executive team was defined which in-cludes the VP operations.

The OMS’ goals are driven by clear business objectives and a written char-ter, vision and mission statement. The mission of the OMS is to reduce cost and improve quality, efficiency, and customer satisfaction by – providing a mechanism for capturing, storing, and exchanging relevant data; providing tools to access and analyze the data; efficiently using process automation systems to analyze, control, and optimize their manufacturing processes.

The vision was that analytics should cover all facets of their operations. Manufacturing applications include asset management applications, proc-ess optimization solutions, abnormal situation prevention, adaptive process control, and quality control. However, other facets include, for example demand forecasting, inventory modeling and management, and using de-sign of experiments not only in R&D but also for supplier selection.



Strategic Needs

Shared information management

Automation, analysis, and information systems

Data

EnterpriseResource

Planning Systems

Business Processes

Automation and Information Architecture

Operational Excellence

ManufacturingExecutionSystems

Process Analysis & Control Systems

(Equipment & Instrumentation)

Valves, instrumentation, meters, equipment,

sensors, controllers, etc.

Asset management, diagnostics, monitoring, performance, control, etc.

Integration between systems, data accessibility, on-line & off-line, etc.

Full data accessibility, data analytics and modeling, troubleshooting,

optimization, etc.

Full vertical & horizontal integration, leverage business and operational

processes, etc.

Operations Management System Focuses on Business Objectives

To measure progress, the steering team defined the following six KPIs, each with its own scorecard and stretch goals for each year:

1. Quantified Business Results: e.g., performing pre-project justification versus project postmortem. Lubrizol finds that it takes about 8 months to create an appropriate analysis methodology. They also found that this was time well spent.

2. Customer Satisfaction: as reported by internal operators, section super-visors, and strategic suppliers are getting the right information.

3. Meeting Commitments: such as milestones, project delivery, etc.

4. Generation of New Business Revenue

5. Migration Status: Lubrizol closely monitors the status of moving legacy systems to strategic suppliers and the status of upgrading legacy in-strumentation.

6. Dynamic Market Share: i.e., percentage of spend devoted to strategic suppliers like Emerson Process Management.

Initially, the team asked, “where are we now”, and “where do we want to be”, which led to a vision for three levels of analytics:

1. KPIs: The team sought to automate the generation of routing and re-petitive reports.



2. Off-line analysis: Lubrizol’s vision was to simplify offline analysis by making it easier for process engineers to assemble the data sets and per-form their own add hoc data analysis.

3. On-line analysis: Lubrizol’s intends to use many offline models in an online fashion. This means that every minute or so a system is queried about its current status and new data is input into the model. Impor-tantly, results were to be “deployable”, not only to other systems, e.g., the DCS, but also to people. Within the control system, both high and low level alarms were to be built around this control strategy, because the predicted quality being out of bounds is as important as a high temperature alarm.

The OMS team knew that its members would always change roles and posi-tions. Therefore, each OMS team member has a back-up person, who knows that someday he will be joining the team. To avoid the “I’m from head office, I’m here to help” problem, team members were drawn from operations in plants across the globe. In addition, the OMS teams sought relationships with similar groups in other companies to trade best practices in embedding analytics within the organization.

Project Phases

The integration aspects of the project split into two phases: the first focuses on vertical integration between ERP and plant floor systems while the sec-ond focuses on horizontal integration between applications and analysis tools. During the first phase, which is essentially complete, the OMS built an integrated automation and information infrastructure by, in part, stan-dardizing on an automation platform, software, and other information

technology. This included identifying and adopting standard analytic tools from SAS, Statgraphics, and Umetrics.

In addition, the OMS determined how to aggregate, consolidate, and access the necessary data for analysis. Lubri-zol realizes that to obtain the greatest benefits from Advanced Analytics it must integrate all its systems so that all the necessary data for analysis and re-porting is readily available.

Lubrizol’s Integration Strategy

.net Web servicesSAP Process Order& Recipe

Consumption Data

Firewall

ResourceOptimization andPlanning Application

Batch Exec & Campaign Mgr

Historian &Recipe Exchange

PRO+Operator Interface

Recipe Transfer via XML

Consumption from Batch Historian event file via XML

Control Network

LZ Domain

SAP Analysis server(s)Analysts

Embeddedanalytics

Device level analysis / diagnostics


Embedded analysis &

diagnostic apps.

Embedded analysis &

diagnostic apps.

Business & Process Analytics


.net Web servicesSAP Process Order& Recipe

Consumption Data

Firewall

ResourceOptimization andPlanning Application

Batch Exec & Campaign Mgr

Historian &Recipe Exchange

PRO+Operator Interface

Recipe Transfer via XML

Consumption from Batch Historian event file via XML

Control Network

LZ Domain

SAP Analysis server(s)Analysts

Embeddedanalytics



Embedded analysis &

diagnostic apps.

Embedded analysis &

diagnostic apps.



Data Transfer via XML

Pro+

.netWeb services

Batch Exec.

Consumption

ERP Recipe +Schedule

Historian

Operator interface

Data Transfer

Analysis serversAnalysts

EmbeddedAnalysis

XML



The second phase involves creating an infrastructure that uses Advanced Analytics for a broad range of applications – whether it is for routine re-porting, offline analysis and improvements or online monitoring and optimization.

Despite user’s adamant requests, the OMS teams intentionally delayed roll-ing out the tools until the data was clean, easily accessible, and the results deployable, e.g., capable of being fed back into a unit. A premature rollout would mean that not only would it take users longer to do multivariate analysis, but that the “analytics culture” would not take root. In two or three years’ times, Lubrizol believes that the whole company will improve its performance at a far faster rate, when the right infrastructure is in place.

To summarize, Lubrizol believes that best practices require a holistic ap-proach, support from top management, an integrated infrastructure, appropriate analytics tools, and the right culture. In addition, the OMS teams must include the right mix of capabilities during development and deployment. Such capabilities include an understanding of what to do and how to do it, the company’s business objectives, appropriate technologies, and backed by management to influence the use of analytics.

Recommendations

ARC offers the following recommendations for those involved in operating or improving the performance of manufacturing performance:

• Develop a strategic plan for the adoption and pervasive use of Ad-vanced Analytics. Your plan should include a benefit analysis with well-defined and measurable metrics to ensure business objectives are met. Recognize the need to develop and maintain new applications

• Create a cross functional team consisting of representatives from all stakeholders. Include executive staff on the team. Without commit-ment from top management, the project is not like to meet expectations. Training is an important ingredient to reap the full benefits of analytics.

• Support a culture that promotes the use of analytics. It is important to empower employees not only with information but also with responsi-bility and accountability to effect change and improve performance.



Analysts: Tom Fiske, Simon Bragg

Editors: Sid Snitkin, Larry O’Brien

Distribution: MAS-H and MAS-P Clients

Acronym Reference: For a complete list of industry acronyms, refer to our web page at www.arcweb.com/C13/IndustryTerms/

APC Advanced Process Control

CPM Collaborative Production

Management

DCS Distributed Control System

DOE Design of Experiments

ERP Enterprise Resource Planning

FDA Food & Drug Administration

HMI Human Machine Interface

HR Human Resources

IT Information Technology

KPI Key Performance Indicator

LIMS Laboratory Information Manage-

ment System

MES Manufacturing Execution System

MPI Model Predictive Intelligence

MSPC Multivariate Statistical Process

Control

OLAP On-Line Analytical Processing

OMS Operations Management System

PCA Principal Component Analysis

PLC Programmable Logic Controller

PLS Partial Least Squares

QIS Quality Information System

R&D Research & Development

SCADA Supervisory Control and Data

Acquisition

SPC Statistical Process Control

SQC Statistical Quality Control

Founded in 1986, ARC Advisory Group has grown to become the Thought Leader in Manufacturing and Supply Chain solutions. For even your most com-plex business issues, our analysts have the expert industry knowledge and firsthand experience to help you find the best answer. We focus on simple, yet critical goals: improving your return on assets, operational performance, total cost of ownership, project time-to-benefit, and shareholder value.

ARC Strategies is published monthly by ARC. All information in this report is proprietary to and copyrighted by ARC. No part of it may be reproduced with-out prior permission from ARC.

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