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Chemical Industry Tackles Operational Performance with Better Execution

DRIVING PROFITABILITY WITH PLANT AND PROCESS DATA

Chemical Industry Tackles Operational Performance with Better Execution

DRIVING PROFITABILITY WITH PLANT AND PROCESS DATA

TABLE OF CONTENTS

SECTION 1: Executive Summary and Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

SECTION 2: State of Plant and Process Manufacturing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

SECTION 3: Applications of IIoT and Enterprise Operational Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

SECTION 4: Opportunities in the Chemical Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

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Executive Summary and Demographics

Section 1

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Three Challenges, Three Areas for Investment

Chemical companies are beginning to recognize the value of data

created within their plants, but for many, the use of that data does not

extend past manufacturing operations . The data is not visible to roles

outside the four walls, and companies can’t combine it with corporate

or financial data for value-added analysis and superior execution .

However, some organizations are beginning to provide this visibility

and integrate their data into wide-ranging business and operational

analytics, with the expectation that combining manufacturing, supply

chain, and financial data, and even data external to the company, can

drive higher profitability by uncovering “hidden” opportunities and

previously unrecognized synergies for improvement .

In this research, LNS explores:

� The relationship between operational performance and

the use of newer operational technology (OT), industrial

internet of things (IIoT), and advanced analytics platforms in

performance improvement; and

� The impact of these capabilities on corporate performance .

Our initial survey findings reveal that the top three challenges facing

chemicals companies are:

� Market forecasting and supply chain visibility

� Leveraging and deploying new technologies

� Difficulties in improving Operational Excellence

and the top three areas for future investment are:

� Operational systems

� New production technology

� Big Data and predictive analytics capabilities

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Three Challenges, Three Areas for Investment (Cont.)

Companies that use plant and process data combined with

operational performance analytics have significantly better

performance compared to companies that don’t . This research

explains what separates the top performers from the rest of the pack,

and why, with recommendations for closing the gap .

© LNS Research. All Rights Reserved.

Dimensions of Data and Analytics

Fast

Latent

Precise

Ambiguous

Structured

Semi-structured

Unstructured

SPEED AND QUALITY OF DATA

TYPES OF ANALYTICSTYPES OF DATA

Descriptive

Diagnostic

Predictive

Prescriptive

Visualization

Dimensions of Data and Analytics

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We examined the use of plant and process data in a survey

with 350 valid respondents: manufacturing executives

responsible for operational technology (OT), across the

process, batch and discrete industries, using the dataset

acquired in 2018 for our Analytics Really Do Matter

publication . In early 2019, we supplemented the initial survey

data with additional questions explicitly tailored for three

segments of the chemical industry: petrochemicals, basic

and bulk, and specialty . We separated those respondents into

two groups: companies that had implemented widespread

availability of plant and process data and implemented IIoT

and enterprise operational analytics, and companies that

had not yet implemented these capabilities . We surveyed

industry trends, challenges, and where they plan to invest

over the next 12-18 months .

Demographics

GEOGRAPHYCOLOR BY HQ LOCATION

REVENUECOLOR BY COMPANY REVENUE

North America

EuropeSmall: <$250M

Medium: $250M-$1B

>$1B

37%

29%

34%

50%

26%

11%

13%

JOB TITLE

Senior Executive

Director

Manager

12%

23%

27%

22%16%

INDUSTRY

Discrete Manufacturing

Process Manufacturing

Batch Manufacturing

44%

28%

28%

DISCIPLINE / ROLE

Operations

Engineering

Information Technology

Quality

Maintenance

Research and Development

All Others

26%

20%20%

6%

5%

4% 19%

Asia / Pacific

Rest of World

Consultant

All Others

State of Plant and Process Manufacturing Data

Section 2

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Data Access, Sources, and Roles

We asked who has access to plant information to make timely

decisions and what their role is in the organization . Not surprisingly,

plants own, control and are the primary consumers of their data

with only about one-quarter of business unit leads having control

and access . This indicates that the visibility into and analysis across

multiple plants may be lacking in many chemical companies .

Access to Plant Information

Control of Data That Leaves the Plant

Business Plant Corporate

60%

50%

40%

30%

20%

10%

0%

Basic and bulk Specialty Petrochemicals

QUESTION

Business

Plant

Corporate

BASIC AND BULK SPECIALTY PETROCHEMICALS

14% 40% 36%

55%

9%30%

30%

29%

57%

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Given the traditional OT architecture, the plant historian is the

primary source of production data, especially for petrochemicals, but

is not the dominant source of data for enterprise analytics . Analytics

sources data from several systems, including quality . It’s interesting to

note that half of all companies indicated that they have the analytics

people that they need, but nearly one in four said they need help

from large-scale consultancies with specialized industry experience

or intend to hire additional advanced analytics capabilities .

Data Access, Sources, and Roles (Cont.)

Data Sources for Operational Analytics

Access to Information for Decision Making

Structured(traditional databases and applications, e.g.

PLM, ERP, MES)

Semi-structured(data historians,

time series, others)

Unstructured(data lakes,

streaming, video, GIS, web, social media

Don't know

60%

50%

40%

30%

20%

10%

0%

Basic and bulk Specialty Petrochemicals Basic and bulk Specialty Petrochemicals

Shop floor operators

Machine operators

Line / area operations

Maintenance personnel

Supervisors

Operations managers

Quality personnel

Plant / control engineers

Process / improvement staff

Plant managers

IT / Business analysts

Supply chain planners

Business unit managers

C-level executives

Don't know

0% 10% 20% 30% 40% 50% 60% 70%

38%

38%

24%

29%

33%

5%

10%

10%10%

24%

24%

57%

52%

14%

14%

14%

36%

36%

36%

27%

27%

18%

18%

0%

9%

36%

36%

45%

9%

9%

18%

40%

65%

30%

35%

40%

20%

20%

30%

40%

55%

65%

10%

25%

25%

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Plant system architectures are evolving across all of manufacturing,

and the chemical industry is no exception . Long adopters of ISA-95

and ISA-88, operating companies are beginning to incorporate IIoT,

Big Data, analytics, Digital Twins, Edge, and Cloud into their OT

architectures .

So rather than only aggregate manufacturing data in corporate ERP

and supply chain applications, the introduction of Edge and Cloud

into the OT architecture now allows timely access to company-wide

plant data providing increased visibility to manufacturing operations

to support better decision-making at the plant and corporate levels .

Operational Architectures are Evolving

WHAT ARE ISA-95 AND ISA-88?ISA-95 and ISA-88 are international standards from the International Society of Automation for global manufacturers. ISA-95 relates to developing an automated interface between enterprise and control systems. It outlines the standard terminology, object models, and boundaries between the enterprise systems and the control systems, and the information exchanged between the two. ISA-88 provides a consistent set of standards and terminology for batch control and defines the physical model, procedures, and recipes.

Learn more about them at International Society of Automation (ISA).

CONNECTIVITY BUS

AdvancedComputingPlatform

Controllers

Wired I/OWired Edge

Devices WirelessSensors

I/F

Wireless EdgeDevices

Historian

I/F I/F

LIMS

Analyzers MachineryMonitoring

ConnectedWorker

Internal OTPlatform

FieldNetworks

ElectricalSystems

SafetySystems

I/F I/FI/F

External3rd Party

OT Platform

BusinessTransactional

ComputingPlatforms

CyberThreat

Monitor

= DIGITAL TWIN

© LNS Research. All Rights Reserved.

Applications in Plant System ArchitecturesApplications in Plant System Architectures

Applications of IIoT and Enterprise Operational Analytics

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Top Metrics Among Manufacturers

Using Metrics

Using Analytics

2

0

60 100

140

1204080

8

1600 MPH

MEASURE ANALYZE

REACT

END

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Metrics Versus Analytics

“Operational analytics” is not merely a new way to say “metrics .” The

difference is an important one: metrics tell you what is happening or

has happened in the plant and your business, while analytics uncover

why things happen and what could happen — and even more impor-

tantly, show you what to do about them . In other words, process and

production metrics have been the primary source of information to

measure performance and to show people how they’re doing . The

approach is straightforward and has been supported for years by

systems such as data historians, dashboards, and standard BI tools .

Operational analytics, on the other hand, close the loop among the

data, the analysis of the data, and the decision of which action to take .

The manufacturing industry has seen a wide range of new analyt-

ics applications over the past three to five years . The surge includes

specialized applications in asset performance management (APM) and

other maintenance-related processes, typically focused on high-cost

resources . In previous years, companies started with a dashboard of

simple metrics to show the up-to-the-second status of equipment,

processes, and operations, and focused on improving response time

and time-to-resolution when issues occurred . However, very little

“live data” from these systems move into data stores outside the plant .

Corporate systems have been primarily reactive “scorekeepers,” not

proactive “scorers .” That’s changing in a big way in process manufac-

turing . In fact, it has been the reason why LNS has shifted focus from

business metrics to enterprise operational analytics — it’s also why

companies without a plan to address it need to take action .

2

0

60 100

140

1204080

8

1600 MPH

MEASURE COMBINE ANALYZE DECIDE ACT

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In 2018, we asked companies to gauge their analytics maturity on a

four-step scale:

1 . Descriptive (similar to metrics)

2 . Diagnostic

3 . Predictive

4 . Prescriptive

The research uncovered two significant facts . First, there was a

tremendous drop-off of adoption from predictive to prescriptive

analytics; most companies have not yet progressed to this final

maturity level (and the finding that analytics provide a path to

prescriptive control was most significant) . Second, predictive

analytics are just as prevalent as diagnostic analytics, driven by

the rapid uptake of predictive analytics, primarily focused on

equipment reliability . Data silos typically challenge all companies,

restricting access to data (e .g ., financial data and operational data

exist in separate databases or systems with strict controls in place) .

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Metrics Versus Analytics (Cont.)

Descriptive Diagnostic Predictive Prescriptive None

50%

45%

40%

35%

30%

25%

20%

15%

10%

5%

0%

Industrial Analytics Sophistication: Plant Operations and Related

Production

Inventory / Logistics

Maintenance

Quality

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Looking at the data for the petrochemicals, basic and bulk chemicals,

and specialty chemicals segments of the process industry, we found

several differences . Not surprisingly, their discrete and OEM/

machine counterparts we surveyed in the original 2018 research

were different, but more notably each segment was distinct . Further,

the chemical industry, which is a significant portion of the overall

process industry, lags discrete manufacturing sectors considerably in

the adoption of IIoT and enterprise operational analytics .

Manufacturing Software Adoption

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ADOPTION: Specialty Chemicals Sector

ADOPTION: Basic and Bulk Chemicals Sector

Advanced analytics

Advanced analytics

Data historian

Data historian

IIoT platform

IIoT platform

MOM

MOM

IMPLEMENTED

IMPLEMENTED

PILOT STAGES

PILOT STAGES

NO PLANSBUDGET (3 YEARS)

BUDGET (3 YEARS)

BUDGET (1 YEAR)

BUDGET (1 YEAR)

14%

30%

21%

15%

36%7%

10%

21%

5%

7%

10%

7%

0%

7%

20%

7%

5%

21%

5%

0%

5%

14%

25%

14%

14%

15%

50%

7%

10%

43%

50%

45%

21%

36%

20%

10%

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For example, the uptake of manufacturing software in the basic/bulk

and specialty segments lags in IIoT and advanced analytics . Further,

30-50% of companies across all three segments do not have spending

plans for these manufacturing applications . Analysis tools range from

spreadsheets to historian-based interfaces, to third-party visualiza-

tion tools, but nowhere outside of the ERP system do all these come

together in a coherent way . IIoT applications have reached the plant,

but are not being used significantly for enterprise operational analytics

yet, and where no single vendor — be it automation, ERP, big tech or

independent software vendor (ISV) – dominates the IIoT sector .

Manufacturing Software Adoption (Cont.)

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Level of Applications on IIoT Platforms

Petrochemical Specialty Basic and bulk

20% 20%12%

44%

44%

60%Yes, all applications built on IIoT platform

Yes, some but not all built on IIoT platform

No, none built on IIoT platform

11%

33%

56%

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Companies are primarily using operational analytics in forecasting,

quality control, asset performance, and plant performance metrics .

Organizations that have adopted IIoT and enterprise operational

analytics dominate every use case category . Still, no one use case ex-

ceeds 50%, indicating that there is plenty of room for growth . When

we combine this observation with the earlier low adoption of manufac-

turing software and IIoT, it tells us that enterprise analytics technology

has been very slow to penetrate these chemical industry segments,

with the specialty chemical sector being the slowest to adopt .

Enterprise Operational Analytics Adoption

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Enterprise Analytics Adoption:Chemical Industries Use Cases

Implemented Not implemented

Forecast production in individual plants

Forecast production across multiple plants

Improve manufacturing quality

Manufacturing process improvement

Performance visibility across plants

Improve asset performance (APM)

Identify plant KPI parameters

Replicate configurations and processes across plants

Alert management across plants

Benchmark performance across plants

0% 10% 20% 30% 40% 50% 60%

40%

27%

47%

50%

23%

23%

27%

40%

27%

37%

23%

23%

38%

15%

15%

8%

8%

8%

15%

15%

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IIoT Platform/Analytics Adoption:Chemical Industries Manufacturing Capabilities

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Enterprise Operational Analytics Adoption (Cont.)

Manage safety and risk

Production management of product lines and plants enterprise-wide

Production management across line of business and associated plants

Production management within plants

Rapidly respond to supply and demand changes

Deliver relevant, real-time KPIs to workers

Product and supplier quality

Speed new product designs through to volume production

Real-time visibility of manufacturing cost components

End-to-end product tracking and product component genealogy

Don't know

0% 10% 20% 30% 40% 50% 60% 70%

62%

38%

38%

54%

38%

31%

15%

8%

15%

38%

31%

37%

30%

47%

40%

43%

17%

13%

13%

27%

23%

27%

Implemented Not implemented

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Top performers — those that have adopted IIoT and enterprise

operational analytics — show significant differences in results

across every category, particularly in forecasting across multiple

plants, multi-plant performance visibility, continuous process

improvement, and quality . Furthermore, of these solution

components, analytics are the principal contributor to results with

the ability to uncover previously unidentified trends and synergies

(i .e ., hidden opportunities, especially across the supply chain and

multiple plants) .

These results reinforce our initial survey findings in which

market forecasting, supply chain visibility, and difficulties improving

operational excellence were top challenges . In addition, they explain

why one of the three top areas for future investment is Big Data and

predictive analytics capabilities .

Analytics Adoption Signals (Significantly) Better Performance

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Analytics Adoption Signals (Significantly) Better Performance (Cont.)

Our research clearly shows that operational analytics

positively impact the bottom line in measurable ways . While

operational analytics improve plant throughput through better

coordination across plants, they don’t affect individual plant

performance that much, for example, in downtime and risk

metrics, which already attract significant attention at the plant

level . Instead, the most significant impact is on business unit

metrics and supply chain in terms of reduced working capital,

on-time delivery, and quality . Also, the differences in profit

margin and ROI have critical financial ramifications .

Top performers choose specific financial and supply chain

use cases to build the business case and ensure the plant data

model integration is effective — including supply chain inventory

levels, profitability, on-time delivery, capacity, and others .

IIoT Platform/Analytics Adoption: Chemical Industries Performance

Implemented Not implemented

Reduced working capital

Increased plant throughput

Improved manufacturing

quality

Reduced planned and unplanned

downtime

Improved on-time delivery

Improved profit margins

Increased return on investment

Better compliance

Reduced operating risk

0% 10% 20% 30% 40% 50% 60%

47%

52%

48%

57%

60%

42%

44%

45%

47%

26%

38%

39%

50%

47%

40%

32%

52%

52%

21%

15%

9%

6%

14%

-7%

-5%

2%

12%

Improvement

Improvement

Improvement

Improvement

Improvement

Improvement

Improvement

Improvement

Improvement

Opportunities in the Chemical Industry

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The current state of adoption of IIoT and enterprise operational

analytics capabilities means that there is a significant opportunity for

industrial organizations .

ADDRESS DATA BARRIERS. Every organization should assess its

data access capabilities . Petrochemicals is a big-player game

where manufacturing plants are most likely to have mature sys-

tems with rich sources of operational data . The range of ma-

turity varies across large and small basic/bulk and specialty

chemicals producers, so first work to fill gaps where access to

automation, historians, maintenance, planning and scheduling,

and other key systems is limited .

How? First, deploy an Edge and Cloud architecture that enables

structured, semi-structured, and unstructured plant data to

flow to enterprise level Big Data . Since most plant data exists

in various silos, move plant data to a data lake (a system or re-

pository of data stored in its original format) to allow enterprise

operational analytics to access the data .

Second, give data context by investing in a scalable and extensi-

ble data model that explicitly contextualizes plant data, includ-

ing asset, process, and worker elements .

Third, address data quality issues . Enterprises usually address

quality at two levels . They reconcile plant production data local-

ly using data reconciliation and material balance . And second,

they use extract, transform, load (ETL) integration and other

tools in the data lake to clean and consolidate data so that oper-

ational analytics can consume it .

Recommendations for Chemical Companies

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UNDERSTAND THE ANALYTICS LANDSCAPE. Survey the market

to understand which solution providers offer various oper-

ational analytics capabilities, and how solutions can fit into

your enterprise and plant architectures . Industrial companies

will generally find three categories of enterprise operational

analytics suppliers:

� ERP vendors

� Automation vendors with custom applications built on top

of an enterprise historian

� Independent software vendors (ISVs) that offer solutions

compatible with multiple production and supply chain

systems

The ISV sector includes solutions built on third-party historians,

integrated software suites for manufacturing operations, and

stand-alone solutions .

An even broader group of vendors will offer IIoT solutions, all

relying on big tech for the platform-as-a-service (PaaS) func-

tionality . However, IIoT comes in more than one flavor, vendors

that offer IIoT platforms generally fall into three categories:

� Platform plus configurable operational analytics

applications

� Don’t offer the platform by itself, only the application on

which it is built

� Analytics toolset, but no specific application — either the

customer or a third party uses the platform and tools to

build the application

The first two options work best for most chemical companies,

since using the application delivers business value and not the

underlying plumbing, and because most end users will not

have the time and staff to “DIY .”

DEMAND RICH FUNCTIONALITY. Industrial companies should

expect a full-featured set of functionality to avoid multiple

overlapping and competing solutions . Look for solutions with

these capabilities:

� Data aggregation from OT and IT sources, including IIoT

and Edge devices

� Complex process KPI monitoring and calculation

� Production forecasting across individual and multiple

plants

� What-if analysis, including predictive and prescriptive

analytics

� Optimization of performance: throughput, yield,

conversion, energy, quality, etc .

� Integration with supply chain planning, scheduling, and

demand forecasting

BECOME A TOP PERFORMER. Top performing chemical compa-

nies use operational analytics more than their less-performing

counterparts and they have a strategy and vision for manufac-

turing and supply chain excellence . They are far more likely to

know what they want: data, use cases, and justification, have

an enterprise and plant architecture and data model in place

(or are well on the road to establishing them), and know what

role they want enterprise operational analytics to play .

Recommendations for Chemical Companies (Cont.)

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Industrial Transformation Resource Guide

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Companies use digital technology to drive transformation across the

value chain . Use these resources to learn how to align the people,

processes, and technologies required to achieve Operational

Excellence in your organization .

INDUSTRIAL TRANSFORMATION

BLOG | Understanding Industrial Transformation: Definition and Framework for SuccessView Blog

RESEARCH | Industrial Transformation: Architecture and Analytics Just the BeginningView Research

RESEARCH | Industrial Control Systems and Edge Computing: Enabling an Operational Architecture for Applications and AnalyticsView Research

INDUSTRIAL ANALYTICS

RESEARCH | Build a Flexible Industrial Analytics Strategy for Today and Tomorrow: Why Business Leaders Should Adopt a Use Case ApproachView Research

BLOG | How the Right Operational Architecture Powers the Analytics That MatterView Blog

RESEARCH | Analytics Really Do Matter: Driving Digital Transformation and the Smart Manufacturing EnterpriseView Research

FACTORY OF THE FUTURE

RESEARCH | Improving Continuous Improvement: Reinvent Lean Today with Digital TechnologyView Research

RESEARCH | Forging the Digital Twin in Discrete Manufacturing: A Vision for Unity in the Virtual and Real WorldsView Research

RESEARCH | MOM and PLM in the IIoT Age: A Cross-Discipline Approach to Digital TransformationView Research

APM 4.0

Solution Selection Guide | Asset Performance Management (Platform Vendors), 2018 EditionView Solution Selection Guide

RESEARCH | APM 4.0: Prescription for Better Profitability in OperationsView Research

RESEARCH | The Road to Digital Transformation Success: A Methodology to Modernize Operational ExcellenceView Research

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Industrial Transformation Resource Guide (Cont.)

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24 INDUSTRY FOCUS

AUTOMOTIVE RESEARCH | IATF 16949-2016: A Pivotal Opportunity in Automotive Quality ManagementView Research

AUTOMOVTIVE AND A&D RESEARCH | Manufacturing Performance: Automotive and A&D Gaining Momentum with AnalyticsView Research

LIFE SCIENCES RESEARCH | Digitalized Quality in Life Sciences: Roadmap to Sustainable Growth and Speeding Profitable, High-Quality Products to MarketView Research

LIFE SCIENCE RESEARCH | Quality 4.0 in Pharmaceutical: Use Cases and Advantage in a Digitally Maturing MarketView Research

METALS AND MINING RESEARCH | Data for Balanced Scorecard: Driving Profits in Mining, Metals, and Materials IndustriesView Research

POWER GENERATION RESEARCH | Driving Better Decision Making with Big Data: A Roadmap for Digital Transformation in the Power Generation IndustryView Research

QUALITY, COMPLIANCE

RESEARCH | Quality 4.0 Impact and Strategy HandbookView Blog

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ENVIRONMENT, HEALTH AND SAFETY

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Presented by:

© LNS Research, 2019. All Rights Reserved.

Author:

Joe Perino

Research Analyst

lnsresearch.com

Chemical Industry Tackles Operational Performance with Better Execution

DRIVING PROFITABILITY WITH PLANT AND PROCESS DATA

ACRONYMQUICK REFERENCE

VIEW ON BLOG

joe .perino@lns-global .com

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