PROACTIVELY TAP MES DATA FOR OPERATIONAL EXCELLENCE
Tips and tools for creating and presenting wide format slides
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
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
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
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
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
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
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
•Cycle Time•Changeover Time•Maintenance Costs•Throughput•Product Quality•Yield•Right First Time•Equipment & Material Utilization
•Energy Efficiency•Agility
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
•Cycle Time•Changeover Time•Maintenance Costs•Throughput•Product Quality•Yield•Right First Time•Equipment & Material Utilization
•Energy Efficiency•Agility
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
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
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
20 Manufacturing sites
5 Development centers
30 Distribution Hubs
LATIN AMERICA12
Manufacturing sites3
Development centers30
Distribution Hubs
AFRICA-MIDDLE-EAST
8 Manufacturing sites
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?