manufacturing operations (mom) infrastructure plant‐floor to enterprise systems such as sap, or...

Manufacturing Operations Management (MOM) infrastructure ‐

it’s all about the meta‐data

Copyright 2011 MESA North American Conference

Please view this PowerPoint in full screen presentation mode to see the built‐in animations

Dan Zajac, Janssen Biotech

Marc Gallant, OSIsoft

Gopal GopalKrishnan, P.E., OSIsoft.

.

We will talk about…• Enterprise Manufacturing Intelligence (EMI)

– Need for flexible, configurable data models– Naming convention and master model– Data, Event and Visual integration

• Visual integration (aka composites) requires no data exchange

• S95, MOM infrastructure and data models– Flow models, Component models, Transaction models…

• Use cases – not just “What”, but “How”– Production, Analysis and Reporting @ J&J – Maintenance @ Longview Power– Unit and system wide mass balance and meter reconciliation (Chevron refinery)– Visual integration (Quality & LIMS)

• Q & A


Enterprise Manufacturing Intelligence definition

“Enterprise manufacturing intelligence (EMI) software is a suite of software applications that integrates a company's manufacturing data from multiple sources to aid in reporting, analysis, visual summaries and passing data between enterprise‐level and plant‐floor systems. The combined data can be given a

new structure to make locating information easier. ”http://searchmanufacturingerp.techtarget.com/definition/manufacturing‐intelligence‐software


EMI infrastructure ‐ Core Functions

• Aggregation– data from many sources, including databases.

• Contextualization– flexible data model, including ISA‐S95 standard and others– link data to specific batches, equipment, and events

• Analysis– analyze data across production sites– real‐time ad hoc analysis and reporting

• Visualization– configurable visual summaries, including dashboards– real‐time alerts and actionable insights

• Data, Event and Information Propagation– line‐of‐business integration – plant‐floor to enterprise systems such as SAP, or vice versa

Reference: AMR Research (Gartner)

PUMPS VALVES TRANSMITTERS ACTUATORS OTHERTRANSFORMERSMOTORS

LAB MANUAL DATA

CONTROL SYSTEMSOTHER DIAGNOSTICS DATABASES

HEx

BUSINESS SYSTEM

INFORMATION FLOW

MONEY FLOW

MATERIAL FLOW

Safety & Environmental Management

Quality Management

Energy Management

People Effectiveness

Supply Chain Management

Production & Operation

Management

Maintenance management

Availability & Reliability

Management

MOM InfrastructureProductionMaintenanceQualityInventory

MOM infrastructure

Introduction

• Dan Zajac: Director of Process Excellence– Janssen Biotech Inc., Malvern, PA

Pharmaceutical Segment ‐Manufacturing

Pharmaceutical Segment ‐ Research

Janssen Supply Chain Mfg. Sites


CHEMICALSAthens, GACork, Ireland Geel, Belguim*Schaffhausen,SwitzWilmington, DEWestbury, Tasmania

BIOLOGICSCork, IrelandLeiden, NetherlandsMalvern, PAManati, PR

Fill FinishBeerse, BelgiumFuji, JapanGurabo, PRHyang Nam, KoreaLatina, ItalyManati, PRPuebla, MexicoSao Jose dos Campos, BrazilSchaffhausen,SwitzerlandVacaville, CAXian, China

Manufacturing requirements at Pharma• Collect all data (GMP & non‐GMP)

– Process, Alarm & Events, Batch Events

• Deliver a consistent infrastructure globally– no differentiation from R&D to Pilot to Commercial

• Provide common visualisation– Consolidated data visualization for improved process monitoring

and historical batch analysis– Consolidated alarm reporting for process control, building

management, laboratory equipment, utility systems, warehouse equipment…

• Targeted compliance reporting– Autoclaves, washers (non‐MES related!)

GMP= Good Manufacturing Practice

Enterprise challenge – disparate systems

13

Mix of DCS Vendors

Vendor F

Vendor G

Vendor C

Mix of PLC Vendors

Vendor A

Vendor B

Vendor C

Vendor D

Vendor E

Process Control System

DCSPLCBMS

Level 4ERP

Level 3MES

Level 2

Level 1

Manufacturing Operations Management (MOM)

BatchControl

ContinuousControl

DiscreteControl

Business Planning & Logistics

Level 0Equipment

ISA S95

Enterprise data presentation and metadata

• Disparate systems– distributed process control systems with historian– stand‐alone instruments with paper printouts

• Capture and aggregate data– for visualisation, reporting & analysis

VariousBMS’s

EmersonDeltaV

GEUnicorn

SiemensPCS7

ABB800xA

Various PLC’s/Instruments

BMS – Building Management System

PI System = enterprise data presentation and metadata layer

PLC – Programmable Logic Controller

Production order statusMaterial consumptionsInventory update

Master material dataOverall inventory

Material lot statusProduction orders

Sample IDs

Act

ual

valu

es

Process ControlSystem(s)

ERP

MES

Material Master Overall InventoryOverall GenealogyMaster Batch RecordMaster Production ScheduleProduction Orders

Production Resource ManagementDetailed Master Batch RecordsProduction Plan Execution Electronic Batch RecordingWeigh & DispenseReview by ExceptionMaterial Management in Production

TrainingManagement

Building AutomationSystem(s)

DMSSOP’s / WI’sDrawingsInstructions

Scales&

Printers

LIMS

J&J Users (LDAP / AD)

CAPA

Set

val

ues

Tele

gram

s

Login verification

UserID & PW

Doc

ID

File

ManualInterface

Syn

chro

niza

tion

Proc

ess

data

Ala

rm &

Ev

ents

ManualInterface

Global

MES

Local

Proc

ess

data

,Bat

ch d

ata,

Ala

rm &

Ev

ents

Historian (OSIsoft PI System)

System Overview

IPC instruments

Res

ults

Results

Integration Benefits

16

Ben

efits

EBR / Paper on glass

Full paperless manufacturing

Weighing & dispense

Recipe integration across lifecycle / support DtV

Implement Lean mistake proofed Manufacturing Systems using standard architecture and applications to improve efficiency and achieve compliant Right First Time (RFT) manufacture & release of products. These systems will align with R&D and drive Design to Value (DtV) to achieve seamless transfer for New Product Introduction (NPI) and Life Cycle Management (LCM) projects.

Cost & Effort

ISA S95 View of MES/MOM

ProductionData

Collection

ProductionExecution

Management

ProductionResource

Management

ProductionDispatching

ProductionTracking

Productionperformance

DetailedProductionScheduling

Productionschedule

ProductDefinition

Management

Production level 1-2 functions

ProductionPerformance

Analysis

Productioncapability

Productdefinition

Equipment and processspecific production rules

Equipment and processspecific data

Operationalresponses

Operationalcommands

• Process data retrieval using flexible template based models

• Electronic Batch Record “Batch Context”

WerumPAS|X

OSIsoftPI System

Werum PAS‐X as MES – PI System as MOM

Our Approach•PAS|X Commands & setpoints to Process Control System•Process Data collected in OSIsoft PI System•OSIsoft PI System data referenced in the PAS|X EBR•PAS|X EBR batch execution data referenced in OSIsoft PI System

Process Control System(s)

MES

19

OSIsoft PI System

Using OSIsoft PI AF (Asset Framework)

• Developed an AF database to provide a generic production view on our data.

• Allows for class based PAS|X elements to be developed.

• Retrieve all process data for EBR’s from OSIsoft PI System using AF aliasing resolved at runtime.

Production Analysis using PAS|X EBR Framing• OSIsoft and Werum collaborated to

develop a batch event interface which will become a standard OSIsoft product.

• This enables us to perform batch analysis at the EBR level from a Basic Operation (BO) or Basic Function (BF) perspective.

• The capability to store basic function activities as batch events is similar to current DCS batch events.

Data model instance from an AF template• Model represents the virtualization of a process object – with dynamic data, static data, relationships, calculations etc.

From PI AF to EBR


PI AF Element Templates•Class based template

•Unit Elements created from template

Upload from PI AF•Class based PCS MBR elements

•Building blocks for MBR modellers

MBR Generation•Class based MBR

EBR Execution•Unit resolved at run‐time

•Data retrieved from PI AF Unit Element

PAS‐X – PI System• pH testing before (mostly paper-based)


Result in batch record, printout and copy are reviewed before batch release.

Batch Record instructs technician to take pH sample.

Technician takes sample and performs test and prints out result.

Technician copies result and attaches it to Batch Record and records result.

Technician manually checks pH meter logbooks.

Technician manually checks pH meter logbooks.

EBR prompts Technician to. Take pH sample.

Technician scans pH meter for status

Technician scans pH meter for status

Technician performs pH test and result is sent to the PI System

PAS|X requests result from PI AF

Result is evaluated, during EBR execution.

• pH testing after (replaced paper with electronic process)

Process Data & Equipment Data

Process, Equipment & Cleaning Data

Process, Equipment & Material Data

Complete Data Context

Production, Analysis & Reporting Summary


Key Takeaway:Use flexible, template based data models in your MOM infrastructure





• Use cases – not just “What”, but “How”– Production, Analysis and Reporting @ J&J – Maintenance @ Longview Power– Unit and system wide mass balance and meter reconciliation (Chevron refinery)– Visual integration (Quality & LIMS i.e. lab systems)

• Q & A


GenPower – Longview Power use case


• 695 net MW, supercritical pulverized coal (SCPC ) technology• Siemens Benson turbine, heat rate 8728 Btu/Kwh• Commissioning and start-up in Summer/Fall 2011• Coal available directly from the mine (mine-mouth operation)• PJM inter-connection

http://www.genpower.net/longview/


PI SystemManufacturing Operations Management (MOM)

(Production, Maintenance, Quality, Inventory)

Continuous Control

Discrete Control

Batch Control

Business Planning & Logistics(ERP Systems)

LEVEL 3

LEVEL 4

LEVEL 1, 2

Maintenance, Quality, Inventory

ERP Systems (SAP, Oracle…)

Production Ops to Level 3 and Level 4 activities

Longview Power

• Production Ops Maintenance Ops

– Data models for XML messaging

• IBM Maximo Meter object– Equipment run hours– Process and equipment alerts

• Template based transaction data models


IBM Maximo consultant - Can you generate this XML?

<TAG>TestTag5</…>

<LASTTIMESTAMP>2010-08-26T13:10:00Z<CURVALUE>9900</...>

<QUALITY>GOOD</...>

Can you generate this XML?

Template based transaction data model


PI to Maximo data flow

Maximo

EQUIPMENT

OPERATING PARAMETERSRunning or IdlePressureTemperatureRpmVibrationOil level

PI server

Portal

PI Maximo Integration(via Maximo Integration

Framework)

PI Alerts

WORK ORDER

Measurements

Process Analysis

Data

or

WorkOrder trigger

Meter Acknowledgment

GAUGE

CHARACTERISTICCONTINUOUS

Condition Assessment

Meter Acknowledgment

CRON TASK

XML Data Flow

PI Notifications

XML

jms queue ‐ IBM Websphere

Maximo

Video demoMaximo Meter integration via XML messaging


Hyperlink launches the demo only during a live session

Production to Maintenace_IBM_Maximo_Integration_PI_XML_Messaging_Demo





• Use cases – not just “What”, but “How”– Production, Analysis and Reporting @ J&J – Maintenance @ Longview Power– Unit and system wide mass balance and meter reconciliation (Chevron refinery)– Visual integration (Quality & LIMS i.e. lab systems)

• Q & A


Process flow model for mass balance


Process flow model – crude system


Visual integration – MOM data (Maintenance and Production)


Browser based display for MOM data –combined Maintenance Work Order and Process trend data in context – shared naming convention from master data

Visual integration – MOM data (Quality and Production)


Browser based display for MOM data –combined Quality (LIMS) and Process trend data via shared context

Recap• Enterprise Manufacturing Intelligence (EMI)

– Aggregation, Contextualization, Analysis, Visualization, Data/Event/Information Propagation

• MOM infrastructure– Role of flexible and configurable data models

• Process flow, Component, Transaction models…– Data, Event and Visual integration– Naming convention and master data

• Visual integration (Composites) requires no data exchange– Use cases from J&J, Longview Power, Chevron...


Questions?MOM infrastructure for Manufacturing Transformation

• Misconception: “We know what we want” – Reality: After MOM deployment, most end users only just start to understand

what they really want which leads to a much longer business process definition and requirement phases…

• Misconception: “Operations know what is really important” – Reality: Operations deploy point solutions for those that shout or complain the

hardest, not necessarily for those with the biggest inefficiencies or need. This focuses on the relationship between business goals, related performance, and perceived "pains" to establish implementation priorities.

• Misconception: “When we finish the project we’re done” • Misconception: “A MOM project is an IT project”Reference:https://services.mesa.org/Document/ResourceFile?resourceId=fb22c0e4‐321b‐4e28‐8314‐95a2919ad53b&documentId=88925d0c‐5fa2‐4313‐a448‐00faefd3ff11https://services.mesa.org/Document/ResourceFile?resourceId=df12bfae‐4787‐4396‐8255‐ff578bcb828d&documentId=dbe56e56‐9d6e‐478e‐ac1b‐2561338f89bc


Other resourceshttp://www.osisoft.com/templates/item‐abstract.aspx?id=4836&terms=ChavezChevron’s use case with a process flow model and Sigmafine


manufacturing operations (mom) infrastructure plant‐floor to enterprise systems such as sap, or...

Documents