MES, Operational Excellence, Data Analytics and Manufacturing Intelligence

PROACTIVELY TAP MES DATA FOR OPERATIONAL EXCELLENCE

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Bora SusmazPlatform Manager, Data [email protected]

Baha Korkmaz, PMP.Senior VP OperationsNorth [email protected]

MES 201611th Annual Forum on Manufacturing Execution Systems

Disclaimer: The views expressed in this presentation are those of the presenters and do not necessarily reflect the opinions of Sanofi.

This workshop will discuss the impact of MES and its related data to Operational Excellence

One of the many benefits of utilizing electronic systems is the increase in data availability and accessibility.

However, many companies record masses of data, but suffer from continuous improvement paralysis due to being overloaded by the amount of data at their fingertips.

Instead of being reactive, the key to maximizing your manufacturing data with MES comes from proactively leveraging the quality data to improve processes and efficiencies.

During this interactive workshop, we will examine Operational Excellence components, MES contribution to Operational Excellence, and manufacturing intelligence initiatives to transform into a high-performance and knowledge-driven organization.

Workshop Objectives

How can data be leveraged to improve processes? How can real-time use of data be applied to improve

performance? How can you utilize metrics and intelligence to improve shop

floor activities and relay that information to business operations?

How can Enterprise Manufacturing Intelligence increase productivity with minimal investments?

Key Questions to be Addressed

Part I: Introductions Part II: Operational Excellence Concepts Part III: MES Contribution to Operational Excellence Part IV: Data Analytics & Enterprise Manufacturing Intelligence Part V: Sanofi Journey

Agenda / Workshop Outline

Part I: Introductions


Introductions Your name and background Your function / role within the company

Do you have MES in your organization? If yes, what are the key uses of MES data? Do you have a Manufacturing Intelligence solution?

What are your expectations from this workshop?

Part II: Operational Excellence Concepts


Better Quality Higher Throughput Greater Availability Efficient Management of Assets Increased Productivity;

Operations & Maintenance Increased Agility Streamlined Compliance with

Regulatory Authorities

Operational Excellence Objectives Achieve Data Integrity Near Real Time Integration of

Manufacturing Systems to Business Systems

Reduce Operational Costs Reduce Waste Reduce Time to Market Prolong Product Life Maximize Profits

OE: Optimize Resources

Deliver the highest possible output of products with the highest possible

quality from a given volume of resources

Using the lowest possible amount of resources, deliver a

particular output with the highest possible

quality

Six Sigma Lean Manufacturing OEE – Overall Equipment Efficiency PAT – Process Analytical Technology

OE: Basic Tools & Techniques

Operational excellence impacts all phases of Product Life cycle by reducing time to market, maximizing yield/profit, and prolonging the product life span.

OE Impact on Product Life Cycle

Part III: MES Contribution to Operational Excellence


Typical MOM Functions

Production Systems(Source: ISA S95)

Typical MOM Functions

Typical MOM

Functions

MES Contribution to Operational Excellence

Reduce

Increase

• Inventory• Regulatory Costs• Waste• Time to market/volume• Cycle Time• Changeover Time• Maintenance Costs• Throughput• Product Quality• Yield• Right First Time• Equipment & Material

Utilization• Energy Efficiency• Agility

MES & OE: Efficiency Gains

RBE (Review by Exception) No duplicate data entry Minimized human error Consistency in operations Electronic batch review and release Minimize non-value added activities

Reduce

Increase

• Inventory•Regulatory Costs•Waste•Time to market/volume

•Cycle Time•Changeover Time•Maintenance Costs•Throughput•Product Quality•Yield•Right First Time•Equipment & Material Utilization

•Energy Efficiency•Agility

MES & OE: Collaborative Manufacturing ERP Integration PLM Integration PCS and Automation Integration LIMS Integration LMS Integration EDM Integration Asset Management Integration Data Historian Integration PDAT Integration Deviation / CAPA Integration

Reduce

Increase

•Inventory•Regulatory Costs•Waste•Time to market/volume



MES & OE: Better Decision Making

Real time monitoring Visibility to real time data & KPIs Context / role based dashboards Embedded analytics Golden batch comparison

Reduce

Increase

•Inventory•Regulatory Costs•Waste•Time to market/volume



Open Discussion How is your experience with your MES implementations? Do you typically implement MES in green field plants only? How about

established facilities? Are you achieving the intended benefits and ROI? How do you think you can get more benefits from your MES investment? What are the challenges in implementing these ideas?

Part IV: Data Analytics & Enterprise Manufacturing Intelligence


Do you have the right data to answer your burning questions?

Are you sure?

Process

Enterprise

Laboratory

Material

MES & OE: What Kind of Data? Maintenance schedules Equipment failures Machine downtime OEE Yield Test results Product genealogy

Cycle times Exceptions Changeover times Scrap Other process data (pH, temp, pressure,

duration, reactor,…)

MES & AnalyticsMES generates data in coordinating and managing manufacturing processes; but it is not designed to provide data analytics features.

MES typically is not a good fit for collecting all manufacturing data and providing powerful analytical capabilities, predictive tools and techniques.

In fact, MES is just another data source for your manufacturing analytics platform.

Data has a pace of its own…

Source: AMR Research

Sec Min Hr Day Week

Month

SensorsLogs

Batch ExecutionIn Process Controls

Lab Results EnvironmentalStabilityRaw Materials

"1 7 6 4 6 2 7 5 - 2 0 0 0 L B io re a cto r | IS | Ha rve st T a n k | A ct i v i ty H0 -H1 0 0 "E q u i p m e n t ID=V -2 5 0 1 D

V a l u e V a l u e _ L C L V a l u e _ UCL0 7 1 4 2 1 2 8 3 5 4 2 4 9 5 6 6 3 7 0 7 7 8 4 9 1 9 8

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

Challenges with Data…Drowning in

data but starving for knowledge

Decisions involving cross functional data

hard to formulate

Data buried in disparate systems

Getting business units and

departments to share across

organizational silos

Ability to handle the volume, velocity

and variety of data

Inclination to make decisions based on intuition rather than

data

ROI justifications for

improvements

Data quality and context

Data validationSecurity concerns

Lack of personnel / expertise to analyze

data

EMI - Enterprise Manufacturing IntelligenceEMI is a term which applies to software used to bring a corporation’s manufacturing related data together from many sources for the purposes of reporting, analysis, visual summaries, and passing data between enterprise level and plant floor systems.

Source: https://en.wikipedia.org/wiki/Enterprise_manufacturing_intelligence

EMI vs. BIEMI Expectations

Real time manufacturing data; including logistics, production, process, quality, resources

24x7 availability / reliability Context based KPIs and visualization Data quality supports regulatory

compliance

BI Challenges

BI operates in data collected in batch mode compared to real time data needed by EMI

Data volume / granularity is too much for BI systems to manage

Reliability of BI is generally not adequate for MI needs

Running your operations; where decisions are made in seconds, minutes or hours.

Running your business; where decisions are made in days, weeks or months.

EMI – Top 5 Drivers

Source: ARC Advisory Group

implementing best practices

Reducing costs / increasing profits

Getting value from data already collected

Faster decision making / avoid abnormal behavior

Improving process visibility

0% 2% 4% 6% 8% 10% 12% 14% 16%

EMI – Core CapabilitiesAggregation

Contextualization

AnalysisVisualization

Propagation

Aggregation: Making data available from many sources

Contextualization: Maintain functional/operational relationships between data elements from disparate sources

Analysis: Enabling users to analyze data across sources and especially across production sites.

Visualization: Providing the tools to create visual summaries of the data to alert decision makers and call attention to the most important information of the moment.

Propagation: Automating the transfer of data from the plant floor up to enterprise level systems or vice versa.

Source: AMR Research

EMI – Overview

DIKW Pyramid

Source: https://en.wikipedia.org/wiki/DIKW_Pyramid

Wisdom

Knowledge

Information

Data

Processing

Cognition

Judgment

Four Types of Data AnalyticsDescripti

ve Analytics

Diagnostic

Analytics

Predictive

Analytics

Prescriptive

AnalyticsWhat is happening now based on incoming data?

Past performance of what happened and why

Likely scenarios of what might happen

Identify the best course of action for any pre-specified outcome

Source: Gartner

Where data can make the difference…

Source: The Economist

Targeted capital spreadingThroughput improvement

Safety and facility managementPredictive maintenance / asset management

Supply chain management / sourcingProcess design and improvements

Operations managementProcess controls

Product quality management

0 10 20 30 40 50 60 70 80

10

12

20

30

30

36

42

44

72

Q: In which of the following areas do you see greater volumes of data yielding the biggest gains? Select top three. (% respondents)

Areas with mature data analytics…

Source: The Economist

Q: For which of the following functions and areas does your company have mature data analysis capabilities? (% respondents)

EMI – Visualization Key Aspects Accessible Simple, intuitive Contextualized / role based dashboard Allows interactivity to drill down Easy to implement and deploy

The Future: Industry 4.0

Source: https://en.wikipedia.org/wiki/Industry_4.0

Open Discussion What are your experiences dealing with data? What are the initiatives you have in your company to

make better use of your data? Do you have initiatives around predictive and

prescriptive analytics? Preparation for Industry 4.0?

Part V: Sanofi Journey

Part I: Introductions Part II: Operational Excellence Concepts Part III: MES Contribution to Operational Excellence Part IV: Data Analytics & Enterprise Manufacturing Intelligence Part V: The Future Part V: Sanofi Journey

Enabling process data analytics at sanofi

ELIMINATE THE BARRIERS THAT PREVENT HIGH VALUE ACTIVITES

Need to get

All the DataFor

All the Processes

To

All the Right PeopleWhen they need it!

GOAL:

MAKE PROCESS DATA ANALYTICS PART OF SANOFI’S CULTURE AND INCORPORATE IT INTO OUR DAY TO DAY ACTIVITIES

In more than 100 countries

107Industrial sitesin 40 countries

CLOSER TO OUR PATIENTS AND PARTNERS

EUROPE48

Manufacturing sites6

Development centers33

Distribution HubsNORTH AMERICA

19 Manufacturing sites

2 Development centers

8 Distribution Hubs ASIA-PACIFIC


5 Development centers

30 Distribution Hubs

LATIN AMERICA12

Manufacturing sites3

Development centers30

Distribution Hubs

AFRICA-MIDDLE-EAST


1 Development center

58 Distribution Hubs

Sanofi’s presence

WE ASPIRE TO DEPLOY PROCESS DATA ANALYTICS TO ALL OUR MANUFACTURING PROCESSES

OUR REALITY IS CONSTRAINED BY • BUDGET• EXPERTISE• TIME

CONVENTION ORIENTED ANALYTICS – MAKING IT POSSIBLE TO DEPLOY BASIC ANALYTICS TO A BROADER USER BASE.

Changing the Game to Achieve our Goals! Data

Prep

KPIEngine

ReportEngine

NotificationEngine

MasterData

Prepare DatasetsStandardAnalysesProcessKPIs

Reports

Alerts &Notifications

Data Sources

• Process Definition• Data Set Definition• KPI Definition

Setup and maintained by the users

• Centrally developed

• Harmonized work processes

• Master data driven

• Standard data prep and analytics

• Interactive user features

• Extensible for future needs

UNDERSTANDING HOW YOUR USERS WILL INTERACT WITH THE PLATFORM IS A KEY TO SUCCESSUSERS GRAVITATE TO DIFFERENT TOOLS BASED ON THEIR NEEDS

Casual Use Web PortalCompleted ResultsPublished ReportsPublished AnalyticsInteractive

UseDynamic DashboardPredefined datasets filters, analyses, and charts based off of master data definition

Exploratory Use

Interactive Web EnvironmentPredefined Datasets with Ad-Hoc capabilityPower Use Full Feature clientPrepare Data and Analysis. Publish Results to othersUnattended

UseAlerts & NotificationsEmail AlertsMobile AlertsReport Distribution

BRINGING ANALYTICS TO ALL THE PEOPLE

45 Apex Process Data Analytics Platform

Data

Captu

reDa

ta St

orag

e &

Acce

ssDa

ta An

alytic

s &

User

Too

ls

EnterpriseHistorian

ProcessData Warehouse

MESCAPA

LIMS

Data Access

Spreadsheet

Site 1 Site 2 Site 3

MDE

ERPSiteHist

SiteHist

SiteHist

History Aggregation Enterprise Integration (Tibco)

Web PortalInteractive Web

Statistics Tool

Enter

prise

/ Tra

nsac

tiona

lDa

ta

StatisticalAnalysis & Notifications

Time S

eries

Data

Manu

ally E

ntere

d Data

Web Form

Exploritory User

CasualUser

Dynamic Analysis and Charting

MasterDataManagement

InteractiveUser

OUR STATUSPLATFORM:Enterprise HistorianProcess Data WarehouseStatistica EnterpriseWeb PortalMDM and Dynamic Dashboard under construction

ADOPTION BY 2017:900+ Users300 Manufacturing processes20 Sites in all world areas

Questions?

MES, Operational Excellence, Data Analytics and Manufacturing Intelligence

Data & Analytics

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